JP7651993B2 - Information processing system, information processing device, information processing method, program, and recording medium - Google Patents

Description

This disclosure relates to information processing systems, etc.

For example, there is known technology that provides a user with information about the effect on vehicle performance of replacing or adding parts when replacing or adding parts (see Patent Document 1).

In Patent Document 1, a vehicle performance simulation is carried out for both the current vehicle and after part replacement, and the results are displayed for comparison, providing the user with information on changes (improvements) in vehicle performance due to part replacement.

JP 2008-2907 A

However, for example, a user needs to consider the cost-effectiveness and decide whether or not to replace or add parts. Therefore, when the effect on vehicle performance of replacing or adding parts is provided to the user, it is desirable that the effect on vehicle performance of replacing or adding parts be predicted with high accuracy as a prerequisite.

In view of the above problems, the objective is to provide technology that can predict with greater accuracy the effect on vehicle performance of replacing or adding parts.

In order to achieve the above object, in one embodiment of the present disclosure,
A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed based on the virtual model generated by the generation unit, and predicts an effect of the treatment on the predetermined performance of the vehicle based on the result of the virtual simulation ;
the prediction unit selects, based on at least one of the content of the action and the content of the predetermined performance, whether to predict the effect by comparing the data acquired by the first acquisition unit with a result of a virtual simulation of the vehicle when the action is performed, or to predict the effect by comparing results of a virtual simulation of the vehicle regarding the predetermined performance before and after the action is performed .
An information processing system is provided.
In another embodiment of the present disclosure,
A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed based on the virtual model generated by the generation unit, and predicts an effect of the treatment on the predetermined performance of the vehicle based on the result of the virtual simulation;
The generation unit generates a virtual model corresponding to the vehicle by updating a general-purpose virtual model related to the vehicle based on the data acquired by the first acquisition unit.
An information processing system is provided.
In still another embodiment of the present disclosure,
A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed based on the virtual model generated by the generation unit, and predicts an effect of the treatment on the predetermined performance of the vehicle based on the result of the virtual simulation;
The generation unit generates a virtual model corresponding to the vehicle by updating a dedicated virtual model related to the vehicle based on the data acquired by the first acquisition unit.
An information processing system is provided.
In still another embodiment of the present disclosure,
A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed based on the virtual model generated by the generation unit, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation;
a reception unit that receives a request for improving the predetermined performance from a user,
The prediction unit predicts the effect of the treatment in response to the request.
An information processing system is provided.

In still another embodiment of the present disclosure,
A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed based on the virtual model generated by the generation unit, and predicts an effect of the treatment on the predetermined performance of the vehicle based on the result of the virtual simulation ;
the prediction unit selects, based on at least one of the content of the action and the content of the predetermined performance, whether to predict the effect by comparing the data acquired by the first acquisition unit with a result of a virtual simulation of the vehicle when the action is performed, or to predict the effect by comparing results of a virtual simulation of the vehicle regarding the predetermined performance before and after the action is performed .
An information processing device is provided.
In still another embodiment of the present disclosure,
A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed using the virtual model generated by the generation unit, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation;
The generation unit generates a virtual model corresponding to the vehicle by updating a general-purpose virtual model related to the vehicle based on the data acquired by the first acquisition unit.
An information processing device is provided.
In still another embodiment of the present disclosure,
A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed using the virtual model generated by the generation unit, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation;
The generation unit generates a virtual model corresponding to the vehicle by updating a dedicated virtual model related to the vehicle based on the data acquired by the first acquisition unit.
An information processing device is provided.
In still another embodiment of the present disclosure,
A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed using the virtual model generated by the generation unit, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation;
a reception unit that receives a request for improving the predetermined performance from a user,
The prediction unit predicts the effect of the treatment in response to the request.
An information processing device is provided.

In still another embodiment of the present disclosure,
A first acquisition step in which an information processing device acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation step in which the information processing device generates a virtual model corresponding to the vehicle based on the data acquired in the first acquisition step;
a prediction step of performing a virtual simulation of the vehicle regarding the predetermined performance when at least one of a treatment of replacing a part mounted on the vehicle and an treatment of adding a part to the vehicle is performed based on the virtual model generated in the generation step, and predicting an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the simulation,
In the prediction step, based on at least one of the content of the treatment and the content of the predetermined performance, a selection is made as to whether to predict the effect by comparing the data acquired in the first acquisition step with a result of a virtual simulation of the vehicle when the treatment is performed, or to compare results of a virtual simulation of the vehicle regarding the predetermined performance before and after the treatment is performed, and to predict the effect.
A method for processing information is provided.
In still another embodiment of the present disclosure,
A first acquisition step in which an information processing device acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation step in which the information processing device generates a virtual model corresponding to the vehicle based on the data acquired in the first acquisition step;
a prediction step in which the information processing device performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a treatment of replacing a part mounted on the vehicle and a treatment of adding a part to the vehicle is performed, using the virtual model generated in the generation step, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the simulation,
In the generating step, a general-purpose virtual model of the vehicle is updated based on the data acquired in the first acquiring step, thereby generating a virtual model corresponding to the vehicle.
A method for processing information is provided.
In still another embodiment of the present disclosure,
A first acquisition step in which an information processing device acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation step in which the information processing device generates a virtual model corresponding to the vehicle based on the data acquired in the first acquisition step;
a prediction step in which the information processing device performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a treatment of replacing a part mounted on the vehicle and a treatment of adding a part to the vehicle is performed, using the virtual model generated in the generation step, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the simulation,
In the generating step, a dedicated virtual model for the vehicle is updated based on the data acquired in the first acquiring step, thereby generating a virtual model corresponding to the vehicle.
A method for processing information is provided.
In still another embodiment of the present disclosure,
A first acquisition step in which an information processing device acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation step in which the information processing device generates a virtual model corresponding to the vehicle based on the data acquired in the first acquisition step;
a prediction step in which the information processing device performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a treatment of replacing a part mounted on the vehicle and a treatment of adding a part to the vehicle is performed, using the virtual model generated in the generation step, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the simulation;
a receiving step of receiving a request for improving the predetermined performance from a user by the information processing device;
In the prediction step, the effect of the treatment in response to the request is predicted.
A method for processing information is provided.

In still another embodiment of the present disclosure,
In the information processing device,
a first acquisition step of acquiring data relating to a state of the vehicle, the data including data relating to a predetermined performance of the vehicle;
a generating step of generating a virtual model corresponding to the vehicle based on the data acquired in the first acquiring step;
a prediction step of performing a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed using the virtual model generated in the generation step, and predicting an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation ;
In the prediction step, based on at least one of the content of the treatment and the content of the predetermined performance, a selection is made as to whether to predict the effect by comparing the data acquired in the first acquisition step with a result of a virtual simulation of the vehicle when the treatment is performed, or to compare results of a virtual simulation of the vehicle regarding the predetermined performance before and after the treatment is performed, and to predict the effect .
The program is provided.
In still another embodiment of the present disclosure,
In the information processing device,
a first acquisition step of acquiring data relating to a state of the vehicle, the data including data relating to a predetermined performance of the vehicle;
a generating step of generating a virtual model corresponding to the vehicle based on the data acquired in the first acquiring step;
a prediction step of performing a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed using the virtual model generated in the generation step, and predicting an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation;
In the generating step, a general-purpose virtual model of the vehicle is updated based on the data acquired in the first acquiring step, thereby generating a virtual model corresponding to the vehicle.
The program is provided.
In still another embodiment of the present disclosure,
In the information processing device,
a first acquisition step of acquiring data relating to a state of the vehicle, the data including data relating to a predetermined performance of the vehicle;
a generating step of generating a virtual model corresponding to the vehicle based on the data acquired in the first acquiring step;
a prediction step of performing a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed using the virtual model generated in the generation step, and predicting an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation;
In the generating step, a dedicated virtual model for the vehicle is updated based on the data acquired in the first acquiring step, thereby generating a virtual model corresponding to the vehicle.
The program is provided.
In still another embodiment of the present disclosure,
In the information processing device,
a first acquisition step of acquiring data relating to a state of the vehicle, the data including data relating to a predetermined performance of the vehicle;
a generating step of generating a virtual model corresponding to the vehicle based on the data acquired in the first acquiring step;
a prediction step of performing a virtual simulation of the vehicle regarding the predetermined performance when at least one of a treatment of replacing a part mounted on the vehicle and a treatment of adding a part to the vehicle is performed using the virtual model generated in the generation step, and predicting an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the simulation;
a receiving step of receiving a request for improving the predetermined performance from a user;
In the prediction step, the effect of the treatment in response to the request is predicted.
The program is provided.

In still another embodiment of the present disclosure,
A computer-readable recording medium having the above-mentioned program recorded thereon is provided.

According to the above-described embodiment, the effect of replacing or adding parts on vehicle performance can be predicted with greater accuracy.

FIG. 1 is a schematic diagram illustrating an example of a vehicle analysis system. FIG. 2 is a diagram illustrating an example of a hardware configuration of a vehicle. FIG. 2 illustrates an example of a hardware configuration of an analysis server. FIG. 2 is a functional block diagram showing an example of a functional configuration of the vehicle analysis system. FIG. 13 is a diagram showing an example of a predicted result of an effect on a specified vehicle performance by replacing or adding a vehicle part. FIG. 1 is a diagram showing an example of manufacturing variability in a transmission of the same type as the transmission mounted in a vehicle. FIG. 13 is a diagram showing an example of a result of reflecting vehicle-specific circumstances in a virtual vehicle model. 13A and 13B are diagrams illustrating another example of a result of reflecting vehicle-specific circumstances in a virtual vehicle model. 1 is a flowchart illustrating an example of a process for predicting an effect on a specific vehicle performance caused by replacing or adding a vehicle part. 10 is a flowchart illustrating an example of a second vehicle data acquisition process.

The following describes the embodiment with reference to the drawings.

[Vehicle analysis system overview]
An overview of a vehicle analysis system 1 according to this embodiment will be described with reference to FIG.

Figure 1 is a schematic diagram showing an example of a vehicle analysis system 1.

As shown in FIG. 1, the vehicle analysis system 1 (an example of an information processing system) includes a vehicle 10, an analysis server 20, a data server 30, and a user terminal 40.

The vehicle analysis system 1 performs an analysis on the state of the vehicle 10. Specifically, the vehicle analysis system 1 performs an analysis on the effects obtained by replacing and/or adding parts of the vehicle 10.

The vehicle 10 is the subject of analysis of its state by the vehicle analysis system 1, and runs on power transmitted from a specific prime mover such as an engine or electric motor to the drive wheels via a drive system including a transmission, differential, and drive shaft.

As described below, the vehicle 10 is equipped with a communication device 11 and is connected to an external device, such as an analysis server 20, via a specified communication line so that the vehicle 10 can communicate with the external device. This allows the vehicle 10 to transmit information signals to the analysis server 20 and receive control signals and information signals (e.g., software update data) from the analysis server 20.

The specified communication line includes, for example, a wide area network (WAN) such as a mobile communication network ending at a base station, a satellite communication network using a communication satellite, and the Internet. The specified communication line may also include, for example, a local area network (LAN) of the facility where the analysis server 20 and the data server 30 are installed. The specified communication line may also include, for example, a short-distance communication line based on a wireless communication standard such as WiFi or Bluetooth (registered trademark). The same may be true below for the communication line used for communication between the analysis server 20 and the data server 30, and the communication line used for communication between the analysis server 20 and the user terminal 40, etc.

The vehicle analysis system 1 may include one vehicle 10 or multiple vehicles 10. In the latter case, the multiple vehicles 10 included in the vehicle analysis system 1 may, for example, all be of the same model, or may include two or more models of vehicles 10.

The analysis server 20, for example, acquires various data (hereinafter, "vehicle data") indicating the state of the vehicle 10 from the vehicle 10, and performs analysis on the vehicle 10. Specifically, the analysis server 20 performs analysis on the effects obtained by replacing or adding parts to the vehicle 10.

The analysis server 20 is, for example, an on-premise server or a cloud server installed in a monitoring center that centrally monitors the status of multiple vehicles 10. The analysis server 20 may also be, for example, an edge server installed in an area where the vehicles 10 mainly travel (for example, an area where the vehicles 10 are legally registered). In this case, multiple analysis servers 20 may be provided for all the vehicles 10 included in the vehicle analysis system 1. The same may be true for the data server 30 below.

As described above, the analysis server 20 is communicatively connected to the vehicle 10 via a predetermined communication line. This allows the analysis server 20 to receive various information signals (e.g., vehicle data) from the vehicle 10 and to transmit various information signals and control signals to the vehicle 10.

The analysis server 20 is also communicatively connected to the data server 30 and the user terminal 40 via a specified communication line. This allows the analysis server 20 to transmit various information signals and control signals to the data server 30 and the user terminal 40, and to receive various information signals and control signals from the data server 30 and the user terminal 40.

The data server 30 stores data related to the vehicle 10.

The data related to the vehicle 10 includes, for example, data on the vehicle 10 and the parts mounted on the vehicle 10 at the time of manufacture (hereinafter, "manufacturing data"). The manufacturing data includes, for example, data indicating the serial numbers (lot numbers) of the vehicle 10 and the parts mounted on the vehicle 10. The manufacturing data also includes, for example, data on various inspection results in the final manufacturing process for each serial number of the vehicle 10 and the parts mounted on the vehicle 10. The manufacturing data also includes data on manufacturing variations of the vehicle 10 and the parts mounted on the vehicle 10.

The data relating to the vehicle 10 also includes, for example, data relating to parts to be replaced with parts installed in the vehicle 10 and parts to be added to the vehicle 10 (hereinafter, "replacement/additional parts data"). The replacement/additional parts data includes, for example, data relating to parts of the same type that can be shipped to replace the parts installed in the vehicle 10 and parts that can be shipped to be added to the vehicle 10. The replacement/additional parts data also includes data relating to the manufacturing variability of parts of the same type that can be shipped to replace the parts installed in the vehicle 10 and parts that can be shipped to be added to the vehicle 10.

The functions of the analysis server 20 and the data server 30 may be realized by consolidating them into one server, or by distributing them among three or more servers. For example, the functions of the data server 30 may be realized by distributing them among multiple data servers according to the type of data to be stored.

The user terminal 40 is a terminal device used (possessed) by the user of the vehicle 10. The user of the vehicle 10 is, for example, the owner of the vehicle 10 or a family member of the owner of the vehicle 10. The user terminal 40 is, for example, a portable terminal device such as a smartphone, a tablet terminal, or a laptop PC (Personal Computer). The user terminal 40 may also be, for example, a stationary terminal device such as a desktop PC.

As described above, the user terminal 40 is communicatively connected to the analysis server 20 via a specified communication line. This allows the user terminal 40 to receive control signals and information signals transmitted from the analysis server 20, and to allow the user of the vehicle 10 to understand the information contained in the signals through a display, speaker, or the like. In addition, in response to input from the user, the user terminal 40 can transmit control signals and information signals corresponding to the input content to the distribution server, and convey the user's request corresponding to the user's input content to the analysis server 20.

[Hardware configuration of vehicle analysis system]
Next, the hardware configuration of the vehicle analysis system 1 will be described with reference to FIG. 2 and FIG. 3 in addition to FIG.

<Vehicle hardware configuration>
FIG. 2 is a diagram illustrating an example of a hardware configuration of the vehicle 10.

As shown in FIG. 2, the vehicle 10 includes a communication device 11, a gateway ECU 12, an ECU 13, a sensor 15, a navigation device 16, an input device 17, a display device 18, and a central ECU 19.

The communication device 11 communicates with the outside of the vehicle 10 through a predetermined communication line. The communication device 11 is, for example, a mobile communication module that is connected to a mobile communication network with a base station as an end and can communicate with the outside, and may be, specifically, a DCM (Data Communication Module). The communication device 11 is communicatively connected to the gateway ECU 12, the ECU 13, the central ECU 19, etc. through a communication line inside the vehicle 10. The communication line inside the vehicle 10 is, for example, a one-to-one communication line or an in-vehicle network. The in-vehicle network is, for example, a CAN (Controller Area Network), a LIN (Local Interconnect Network), FlexRay, an in-vehicle Ethernet, etc. The communication device 11 may transmit a predetermined signal to the outside in response to, for example, a command (request) from the gateway ECU 12. In addition, a signal received from the outside by the communication device 11 is taken into the gateway ECU 12.

The gateway ECU 12 functions as a gateway between a communication line external to the vehicle 10 and a communication line internal to the vehicle 10 (e.g., an in-vehicle network).

The functions of the gateway ECU 12 may be realized by any hardware or any combination of hardware and software. For example, the gateway ECU 12 may have a hardware configuration similar to that of the central ECU 19 described below, and may realize various functions by loading a program installed in an auxiliary storage device into a memory device and executing it on a CPU (Central Processing Unit). The same may be true for the ECU 13 below.

For example, the gateway ECU 12 uses a known method to authenticate that a signal received from the outside by the communication device 11 is a signal sent from a legitimate source and has not been tampered with. A legitimate source refers to a source that is registered in advance as a source (e.g., the analysis server 20, etc.) or a source that has been authenticated in advance through mutual communication. The gateway ECU 12 transmits only the data of signals that have been successfully authenticated, out of the signals received by the communication device 11, to a specified in-vehicle device (e.g., the ECU 13 or the central ECU 19, etc.) through a communication line inside the vehicle 10.

In addition, for example, the gateway ECU 12 generates a signal including predetermined information, commands (requests), etc., in response to requests from various devices in the vehicle 10, and transmits the signal to a predetermined destination (for example, the analysis server 20) via the communication device 11.

One or more ECUs 13 are mounted on the vehicle 10. The ECUs 13 control various functions of the vehicle 10. For example, the multiple ECUs 13 include an ECU 13 that controls an engine serving as a power source for the vehicle 10, an ECU 13 that controls an electric motor (MG: Motor Generator) serving as a power source for the vehicle 10, an ECU 13 that controls the shift position of the transmission for the vehicle 10, and the like.

One or more sensors 15 are mounted on the vehicle 10. The sensor 15 outputs measurement data related to the state of the vehicle 10. The output (measurement data) of the sensor 15 is taken into the central ECU 19 through a communication line inside the vehicle 10. For example, the multiple sensors 15 include a sensor 15 (wheel speed sensor) that measures the wheel speed of each wheel of the vehicle 10. For example, the multiple sensors 15 include a sensor 15 (acceleration sensor) that measures the acceleration in the front-rear direction, left-right direction, and up-down direction of the vehicle 10. For example, the multiple sensors 15 include a sensor 15 (temperature sensor) that measures the temperature of a specific part of the vehicle 10. The temperature sensor includes, for example, a temperature sensor that measures the temperature of a driving battery or an auxiliary battery mounted on the vehicle 10, a water temperature sensor that measures the temperature of the cooling water of various devices, an oil temperature sensor that measures the oil temperature of the lubricating oil of various devices, and the like. For example, the multiple sensors 15 include a sensor 15 (GNSS (Global Navigation Satellite System) sensor) that measures the position of the vehicle 10.

The navigation device 16 guides the user (driver) of the vehicle 10 along the route that the vehicle 10 will take, via the display device 18. For example, the navigation device 16 guides the user of the vehicle 10 along the route that the vehicle 10 will take, under the control of one of the multiple ECUs 13.

The input device 17 is provided in the interior of the vehicle 10 and accepts various inputs from the passenger. The input device 17 includes an operation input device, such as a button, toggle, lever, touch panel, or touch pad, that accepts operation input from the passenger. The input device 17 also includes a voice input device, such as a microphone, that accepts voice input from the passenger of the vehicle 10. The input device 17 also includes a gesture input device, such as a camera that can capture an image of the passenger in the interior of the vehicle 10, that accepts gesture input from the passenger of the vehicle 10. An input signal representing the content of the input accepted by the input device 17 is taken into the central ECU 19 through a communication line inside the vehicle 10.

The display device 18 is provided in a location that is easily visible from the driver's seat inside the vehicle 10, and displays various information screens. The display device 18 is, for example, a liquid crystal display or an organic EL (Electroluminescence) display. The display device 18 is communicatively connected to the central ECU 19 through a communication line inside the vehicle 10, and the central ECU 19 can output a control signal to the display device 18 and cause the display device 18 to display a specified information screen.

The central ECU 19, for example, serves as a higher-level control device for various ECUs including the gateway ECU 12 and ECU 13 mounted on the vehicle 10, and controls the operation of the various ECUs and functions as a registration destination for various data used by the various ECUs.

The functions of the central ECU 19 are realized by any hardware or any combination of hardware and software. For example, as shown in FIG. 2, the central ECU 19 includes an auxiliary storage device 19A, a memory device 19B, a CPU 19C, and an interface device 19D, which are connected by a bus B1.

The auxiliary storage device 19A is a non-volatile storage means that stores the programs to be installed as well as necessary files, data, etc. The auxiliary storage device 19A is, for example, a flash memory.

For example, when an instruction to start a program is received, the memory device 19B loads the program from the auxiliary storage device 19A so that the program can be read by the CPU 19C. The memory device 19B is, for example, a static random access memory (SRAM).

The CPU 19C executes, for example, programs loaded into the memory device 19B and realizes various functions of the central ECU 19 according to the instructions of the programs.

The interface device 19D is used, for example, as an interface for connecting to a communication line inside the vehicle 10. The interface device 19D may include multiple different types of interface devices according to the type of communication line to be connected.

The programs that realize the various functions of the central ECU 19 are provided, for example, by a portable recording medium 19E. In this case, the interface device 19D functions as an interface for reading data from the recording medium 19E and writing data to the recording medium 19E. The recording medium 19E is a dedicated tool that is connected to an external connection connector such as a DLC (Data Link Coupler) via a detachable cable. The recording medium 19E may also be a general-purpose recording medium such as an SD memory card or a USB (Universal Serial Bus) memory. The programs may also be downloaded from another computer outside the vehicle 10 through a specified communication line and installed in the auxiliary storage device 19A.

<Hardware configuration of analysis server>
FIG. 3 is a diagram illustrating an example of a hardware configuration of the analysis server 20. As shown in FIG.

The functions of the analysis server 20 are realized by any hardware or any combination of hardware and software. For example, as shown in FIG. 3, the analysis server 20 includes an external interface 21, an auxiliary storage device 22, a memory device 23, a CPU 24, a communication interface 26, an input device 27, and a display device 28, which are connected by a bus B2.

The data server 30 and the user terminal 40 may have the same hardware configuration as the analysis server 20. Below, illustrations and explanations of the hardware configurations of the data server 30 and the user terminal 40 will be omitted.

The external interface 21 functions as an interface for reading data from and writing data to the recording medium 21A. Examples of the recording medium 21A include flexible disks, CDs (Compact Discs), DVDs (Digital Versatile Discs), BDs (Blu-ray (registered trademark) Discs), SD memory cards, and USB (Universal Serial Bus) memories. This allows the analysis server 20 to read various data used in processing through the recording medium 21A, store the data in the auxiliary storage device 22, and install programs that realize various functions.

In addition, the analysis server 20 may acquire various data and programs from external devices through the communication interface 26.

The auxiliary storage device 22 stores various installed programs as well as files and data necessary for various processes. The auxiliary storage device 22 includes, for example, a hard disk drive (HDD), a solid state drive (SSD), flash memory, etc.

When an instruction to start a program is received, the memory device 23 reads the program from the auxiliary storage device 22 and stores it. The memory device 23 includes, for example, a dynamic random access memory (DRAM) or a static random access memory (SRAM).

The CPU 24 executes various programs loaded from the auxiliary storage device 22 to the memory device 23, and realizes various functions related to the analysis server 20 according to the programs.

The high-speed calculation device 25 works in conjunction with the CPU 24 to perform calculation processing at a relatively high speed. The high-speed calculation device 25 includes, for example, a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), etc.

The high-speed calculation device 25 may be omitted depending on the required calculation processing speed.

The communication interface 26 is used as an interface for connecting to an external device so as to be able to communicate with the external device. This allows the analysis server 20 to communicate with external devices such as the vehicle 10, the data server 30, and the user terminal 40 through the communication interface 26. The communication interface 26 may have multiple types of communication interfaces depending on the communication method between the connected devices, etc.

The input device 27 accepts various inputs from the user.

The input device 27 includes, for example, an operation input device that accepts mechanical operation input from a user. The operation input device includes, for example, a button, a toggle, a lever, etc. The operation input device also includes, for example, a touch panel mounted on the display device 28, a touch pad provided separately from the display device 28, etc.

The input device 27 also includes, for example, a voice input device capable of receiving voice input from the user. The voice input device includes, for example, a microphone capable of collecting the user's voice.

The input device 27 also includes, for example, a gesture input device capable of accepting gesture input from a user. The gesture input device includes, for example, a camera capable of capturing an image of the user's gesture.

The input device 27 also includes, for example, a biometric input device capable of accepting biometric input from a user. The biometric input device includes, for example, a camera capable of acquiring image data containing information related to the user's fingerprint or iris.

The display device 28 displays an information screen and an operation screen to the user. The display device 28 includes, for example, a liquid crystal display or an organic EL (electroluminescence) display.

[Functional configuration of vehicle analysis system]
Next, the functional configuration of the vehicle analysis system 1 will be described with reference to FIG. 4 in addition to FIGS.

Figure 4 is a functional block diagram showing an example of the functional configuration of the vehicle analysis system 1.

<Vehicle functional configuration>
4, the gateway ECU 12 includes, as a functional unit, a communication unit 121. The function of the communication unit 121 may be realized, for example, by loading a program installed in an auxiliary storage device of the gateway ECU 12 into a memory device and executing the program on a CPU.

The communication unit 121 controls the communication device 11 to input signals received from outside the vehicle 10 into the gateway ECU 12 and to transmit signals to outside the vehicle 10.

The central ECU 19 includes, as functional units, a vehicle data acquisition unit 191, a storage unit 192, a vehicle data transmission unit 193, a performance improvement request unit 194, and a notification unit 195. The central ECU 19 also includes a vehicle data acquisition unit 196, a route guidance unit 197, a storage unit 198, and a vehicle data transmission unit 199. The functions of the vehicle data acquisition unit 191, the vehicle data transmission unit 193, the vehicle data acquisition unit 196, the route guidance unit 197, and the vehicle data transmission unit 199 may be realized, for example, by loading a program installed in the auxiliary storage device 19A into the memory device 19B and executing it on the CPU 19C. The functions of the storage units 192 and 198 may be realized, for example, by a storage area defined in the auxiliary storage device 19A.

The vehicle data acquisition unit 191 acquires vehicle data representing the state of the vehicle 10 at predetermined intervals. The vehicle data includes, for example, measurement data output from the sensor 15. The vehicle data also includes, for example, control data such as command values output from various ECUs such as the gateway ECU 12, ECU 13, and central ECU 19, and values of calculation results during control processing. Hereinafter, the vehicle data to be transmitted by the vehicle data acquisition unit 191 is distinguished from the vehicle data to be acquired by the vehicle data acquisition unit 196 (the vehicle data to be transmitted by the vehicle data transmission unit 199), and may be referred to as "first vehicle data" for convenience.

The storage unit 192 stores (registers) the vehicle data acquired by the vehicle data acquisition unit 191 in chronological order.

The vehicle data transmission unit 193 transmits (uploads) the first vehicle data acquired by the vehicle data acquisition unit 191 and stored in the memory unit 192 to the analysis server 20 via the gateway ECU 12 (communication device 11). For example, the vehicle data transmission unit 193 transmits the latest first vehicle data to the analysis server 20 in real time every time the latest first vehicle data is registered in the memory unit 192. In addition, the vehicle data transmission unit 193 may transmit the first vehicle data accumulated in chronological order in the memory unit 192 to the analysis server 20 all at once at a predetermined timing.

The first vehicle data that has already been transmitted to the analysis server 20 by the vehicle data transmission unit 193 may be deleted from the memory unit 192 or may remain registered.

The performance improvement request unit 194 transmits a signal representing a request for improving the performance of the vehicle 10 (hereinafter, the "performance improvement request signal") to the analysis server 20 in response to a request from a user, such as the driver of the vehicle 10, received through the input device 17.

For example, the user may input a request for improving the performance of the vehicle 10 to the vehicle 10 using a predetermined operation screen (hereinafter, "performance improvement request screen") displayed on the display device 18. Specifically, the performance improvement request screen displays a hierarchical list of multiple performance items related to the vehicle 10, ranging from major items to minor items, and the user may be able to select a performance item for which the user wishes to improve from among the multiple performance items (minor items) via the input device 17. In addition, the performance item screen may be configured so that the user can set and input the level of performance after improvement requested by the user for each of the multiple performance items (minor items) using numerical values, bar graphs, etc. The performance improvement request signal includes information indicating the content of the performance improvement request set by the user via the performance improvement request screen.

When a performance improvement request signal is sent to the analysis server 20 by the performance improvement request unit 194, the notification unit 195 notifies the user of the reply to the performance improvement request signal from the analysis server 20 via the display device 18 or the like. The reply from the analysis server 20 includes, for example, information on countermeasures for the user's request for performance improvement of the vehicle 10 and information indicating the performance improvement cost of the vehicle 10 when the countermeasure is selected. Countermeasures for the request for performance improvement of the vehicle 10 include, for example, at least one of replacing parts installed in the vehicle 10 and adding parts to the vehicle 10. The reply from the analysis server 20 also includes, for example, information on the cost of each countermeasure.

The vehicle data acquisition unit 196 (an example of a first acquisition unit) acquires predetermined vehicle data in response to a predetermined signal (hereinafter, "data acquisition request signal") requesting acquisition of data from the analysis server 20. The data acquisition request signal is transmitted from the analysis server 20 to the vehicle 10 when a performance improvement request signal is transmitted from the vehicle 10 (performance improvement request unit 194) or the user terminal 40 (performance improvement request unit 401 described later) to the analysis server 20. Hereinafter, the predetermined vehicle data to be acquired by the vehicle data acquisition unit 196 may be referred to as "second vehicle data" for convenience, in order to distinguish it from the vehicle data to be acquired by the vehicle data acquisition unit 191 described above (first vehicle data). The second vehicle data is a type of vehicle data necessary for the analysis server 20 to verify the effect of a countermeasure (e.g., replacement or addition of parts) for realizing performance improvement of the vehicle 10 on the target performance item of the vehicle 10. Specifically, the second vehicle data is vehicle data that includes vehicle data related to the performance item requested by the user and represents the current state of the target performance item in the vehicle 10. The second vehicle data corresponds to some or all of the types of vehicle data corresponding to the first vehicle data. Naturally, the second vehicle data may vary depending on the target performance item. For example, information regarding the type of second vehicle data to be acquired may be specified in advance for each of the multiple performance items and may be preregistered in the central ECU 19 (e.g., the auxiliary storage device 19A) or distributed to the vehicle 10 from the analysis server 20 or the like. This allows the vehicle data acquisition unit 196 to acquire appropriate second vehicle data in accordance with the target performance item.

For example, the vehicle data acquisition unit 196 records the type of vehicle data to be acquired as the second vehicle data in a chronological order in a ring buffer defined in the memory device 19B at each predetermined period. Then, when a predetermined condition (hereinafter, "acquisition condition") defined according to the target performance item is satisfied, the vehicle data acquisition unit 196 acquires vehicle data for a predetermined period at the time the acquisition condition is satisfied from the ring buffer as the second vehicle data, and stores it in the memory unit 198. The acquisition condition includes, for example, the driving conditions and environmental conditions of the vehicle 10 that are the premise for evaluating the effect of a countermeasure such as replacing or adding parts on the target performance item. Alternatively, the acquisition condition may be one or more.

The function of the vehicle data acquisition unit 196 may be transferred to the analysis server 20 (an example of an information processing device). In this case, the same functional unit of the analysis server 20 (an example of a first acquisition unit) acquires (extracts) second vehicle data that meets the acquisition conditions from the first vehicle data uploaded from the vehicle 10, for example.

The route guidance unit 197 controls the navigation device 16, and guides (directs) the user (driver) of the vehicle 10 along a route suitable for acquiring the second vehicle data while grasping the position of the vehicle 10 based on the output of the GNSS sensor (sensor 15). For example, the central ECU 19 (route guidance unit 197) may directly output a control command to the navigation device 16 to control the navigation device 16. Also, for example, the central ECU 19 (route guidance unit 197) may control the navigation device 16 through the ECU 13 that controls the navigation device 16 by outputting a control command to the ECU 13.

A route suitable for acquiring the second vehicle data is a route on which the effect of the target performance item in the vehicle 10 is relatively likely to occur. The conditions of a route (road) suitable for acquiring the second vehicle data are specified in advance by parameters such as the speed limit, the average speed of passing vehicles, the gradient, and the radius of curvature of the curve for each of a plurality of performance items. This allows the route guidance unit 197 to appropriately guide the user (driver) of the vehicle 10 to pass through a route suitable for acquiring the second vehicle data that matches the target performance item.

When the function of the vehicle data acquisition unit 196 is transferred to the analysis server 20, the function of the route guidance unit 197 may also be transferred to the analysis server 20. In this case, the functional unit of the analysis server 20 may control the navigation device 16 from outside the vehicle 10 while using a navigation server (not shown) to provide route guidance for the vehicle 10.

The memory unit 198 stores the second vehicle data acquired by the vehicle data acquisition unit 196.

The vehicle data transmission unit 199 transmits the second vehicle data stored in the memory unit 198 to the analysis server 20 via the gateway ECU 12 (communication device 11).

<Functional configuration of the analysis server>
4, the analysis server 20 includes, as functional units, a virtual vehicle model generation unit 201, a virtual vehicle model storage unit 202, a simulator unit 203, a request reception unit 204, and an effect prediction unit 205. The functions of the virtual vehicle model generation unit 201, the simulator unit 203, the request reception unit 204, and the effect prediction unit 205 are realized, for example, by loading a program installed in an auxiliary storage device of the analysis server 20 into a memory device and executing the program on a CPU. The function of the virtual vehicle model storage unit 202 is realized, for example, by a storage area defined in the auxiliary storage device of the analysis server 20.

The virtual vehicle model generating unit 201 (an example of a generating unit) generates a virtual model (hereinafter, "virtual vehicle model") corresponding to the vehicle 10 for performing digital analysis of the performance of the vehicle 10. Specifically, the virtual vehicle model generating unit 201 generates a virtual vehicle model (hereinafter, for convenience, "first virtual vehicle model") that reflects the current state of the vehicle 10 when the performance improvement request signal is received. In addition, the virtual vehicle model generating unit 201 updates the first virtual vehicle model to generate a virtual vehicle model (hereinafter, for convenience, "second virtual vehicle model") when it is assumed that a treatment such as replacement or addition of parts has been performed on the current vehicle 10. The virtual vehicle model is a model in which various characteristics of the vehicle 10 are reproduced in a virtual space. The virtual vehicle model may be, for example, a full vehicle model in which various overall characteristics of the vehicle 10 are reproduced, or a partial vehicle model in which only some characteristics of the vehicle 10 related to the performance items that are the subject of the effect prediction described later are reproduced. In addition, the virtual vehicle model may be a three-dimensional model in which a part or all of the shape of the vehicle 10 is reproduced.

The virtual vehicle model generation unit 201 generates a first virtual vehicle model by updating a predetermined base model in a manner that reflects the second vehicle data uploaded from the vehicle 10. This allows the virtual vehicle model generation unit 201 to reflect the current state of the vehicle 10 in the first virtual vehicle model and in the second virtual vehicle model generated based on the first virtual vehicle model.

The specified base model is, for example, a general-purpose virtual model (an example of a general-purpose model) for vehicle 10 of the same model or the same model and grade. The general-purpose virtual model may be, for example, a full vehicle model or partial vehicle model of the final specifications of the model or grade corresponding to vehicle 10, which is created during the development stage of a model corresponding to vehicle 10.

The predetermined base model may be a virtual model (an example of a dedicated model) dedicated to each vehicle 10. Specifically, the virtual vehicle model generation unit 201 may generate a virtual model dedicated to the vehicle 10 by appropriately updating the virtual model based on the first vehicle data uploaded from the vehicle 10 based on the above-mentioned general-purpose virtual model. This allows the virtual vehicle model generation unit 201 to generate and update the virtual model in accordance with at least one of the changes over time of the vehicle 10, the use conditions of the vehicle 10, and the changes in the components of the vehicle 10. Therefore, by using a virtual model dedicated to the vehicle 10 as a predetermined base model, the virtual vehicle model generation unit 201 can reflect the changes over time of the vehicle 10, the use conditions of the vehicle 10, and the changes in the components of the vehicle 10 in the first virtual vehicle model and the second virtual vehicle model. The use conditions of the vehicle 10 may include, for example, conditions related to the environment in which the vehicle 10 is used, driving conditions when the vehicle 10 is used, and conditions related to the frequency with which the vehicle 10 is used. Changes in the components of the vehicle 10 may include, for example, changes in hardware such as parts of the vehicle 10, changes in the software of the vehicle 10, changes in the type of coolant or lubricant used in the vehicle 10, etc. In addition, the virtual vehicle model generation unit 201 may generate a digital twin as a dedicated virtual model (a specified base model) for the vehicle 10 by updating the virtual model in real time based on vehicle data successively uploaded from the vehicle 10.

In addition, the virtual model dedicated to each vehicle 10 as a predetermined base model may take into account differences in characteristics for each vehicle 10 due to manufacturing variations of the vehicle 10 and the parts mounted on the vehicle 10. Specifically, the virtual vehicle model generation unit 201 reflects differences in characteristics due to manufacturing variations of the vehicle 10 and the parts mounted on the vehicle 10 by updating the above-mentioned general-purpose virtual model based on manufacturing data acquired from the data server 30. In this way, the virtual vehicle model generation unit 201 can reflect differences in characteristics due to manufacturing variations of the vehicle 10 and the parts mounted on the vehicle 10 in the first virtual vehicle model and the second virtual vehicle model by using a virtual model dedicated to the vehicle 10 as a predetermined base model.

When the above-mentioned general-purpose virtual model is used as a predetermined base model for generating the first virtual vehicle model, the first vehicle data does not need to be uploaded from the vehicle 10 to the analysis server 20. In this case, the vehicle data acquisition unit 191, the storage unit 192, and the vehicle data transmission unit 193 of the vehicle 10 may be omitted.

The virtual vehicle model storage unit 202 stores the virtual vehicle model generated by the virtual vehicle model generation unit 201. If the vehicle analysis system 1 includes multiple vehicles 10, a virtual vehicle model for each of the multiple vehicles 10 is stored.

The simulator unit 203 performs a virtual simulation of a predetermined performance of the vehicle 10 using the virtual vehicle model of the vehicle 10 registered (stored) in the virtual vehicle model storage unit 202. The virtual simulation of the predetermined performance of the vehicle 10 (for example, a target performance item among multiple performance items specified by a performance improvement request signal) includes a driving simulation of the predetermined performance of the vehicle 10 under predetermined driving conditions.

When a performance improvement request signal is received from the vehicle 10 or the user terminal 40, the request reception unit 204 (an example of a reception unit) receives the user's request content specified in the performance improvement request signal and outputs one or more countermeasures according to the user's request content. As described above, the user's request content may include the target performance item among the multiple performance items for which performance improvement is requested and the performance level required for that performance item. For example, the request reception unit 204 extracts one or more countermeasures from list information in which countermeasures for each of the multiple performance items are listed. In addition, the request reception unit 204 (an example of a second acquisition unit) may acquire data (for example, manufacturing data, replacement/additional parts data, etc.) necessary for the effect prediction unit 205 described later to predict the effect of the countermeasure on the target performance item of the vehicle 10.

The effect prediction unit 205 (an example of a prediction unit) predicts the effect on the target performance items of the vehicle 10 that will be obtained by the countermeasures output from the request reception unit 204, specifically, by measures such as replacing or adding parts. Specifically, the effect prediction unit 205 predicts the effect on the target performance items of the vehicle 10 that will be obtained by the countermeasures (measures) by having the simulator unit 203 perform a virtual simulation using a virtual vehicle model of the vehicle 10.

For example, the simulator unit 203 uses the second virtual vehicle model in response to an instruction from the effect prediction unit 205 to perform a virtual simulation of the vehicle 10 running under specified running conditions. The specified running conditions are, of course, variable depending on the target performance item. In addition, the specified running conditions are set, for example, according to the running state of the vehicle 10 at the time the second vehicle data is acquired. The effect prediction unit 205 then compares the second vehicle data acquired from the actual vehicle 10 with the data of the virtual simulation result corresponding to the vehicle data of the vehicle 10 after the implementation of the countermeasure (treatment), thereby predicting the effect of the implementation of the countermeasure on the target performance item of the vehicle 10. As a result, the effect prediction unit 205 can predict the effect of the implementation of the countermeasure on the target performance item of the vehicle 10 in a relatively simple manner, assuming that the second vehicle data is the state of the vehicle 10 before the implementation of the countermeasure.

For example, the simulator unit 203 performs a virtual simulation of the running of the vehicle 10 under the same running conditions using each of the first virtual vehicle model and the second virtual vehicle model in response to an instruction from the effect prediction unit 205. The effect prediction unit 205 may then predict the effect of the implementation of the countermeasure plan on the target performance item of the vehicle 10 by comparing the results of the virtual simulation corresponding to each vehicle 10 before and after the implementation of the countermeasure plan. This allows the effect prediction unit 205 to roughly match the running conditions, etc. that are the premise of the data to be compared. Therefore, the effect prediction unit 205 can predict the effect with higher accuracy.

The effect prediction unit 205 may predict the presence or absence of an effect on the target performance item of the vehicle 10, for example, based on whether or not the index value for the target performance item exceeds a predetermined standard. The effect prediction unit 205 may also predict the degree of effect on the target performance item of the vehicle 10 based on the index value for the target performance item.

The effect prediction unit 205 may select a method of predicting the effect depending on, for example, the contents of the countermeasure (treatment) and the target performance item. Specifically, the effect prediction unit 205 may select whether to compare the second vehicle data with the results of a virtual simulation corresponding to after the countermeasure is implemented, or to compare the results of virtual simulations corresponding to before and after the countermeasure is implemented, depending on the contents of the countermeasure and the target performance item. This is because, depending on the contents of the countermeasure and the target performance item, there may be cases where the desired prediction accuracy is obtained even if the second vehicle data is used as a comparison target as is, and cases where the desired prediction accuracy is not obtained if the second vehicle data is used as a comparison target as is. Whether or not to use the second vehicle data as a comparison target as is may be specified in advance, for example, for the contents of the countermeasure (treatment) and the target performance item, and may be automatically determined according to the specified contents. Whether or not to use the second vehicle data as a comparison target as is may be determined, for example, using table information or the like based on the contents of the countermeasure (treatment) and the target performance item.

<Functional configuration of data server>
4, the data server 30 includes, as functional units, a data transmission unit 301 and a storage unit 302. The function of the data transmission unit 301 is realized, for example, by loading a program installed in the auxiliary storage device into a memory device and executing the program on a CPU. The function of the storage unit 302 is realized, for example, by a storage area defined in the auxiliary storage device.

In response to a signal representing a request to transmit specific data received from the analysis server 20, the data transmission unit 301 extracts the specific data specified by the signal from the memory unit 302 and transmits it to the analysis server 20.

The memory unit 302 stores various data related to the vehicle 10.

<Functional configuration of user terminal>
4, the user terminal 40 includes, as functional units, a performance improvement request unit 401 and a notification unit 402. The functions of the performance improvement request unit 401 and the notification unit 402 are realized, for example, by loading a program installed in the auxiliary storage device of the user terminal 40 into a memory device and executing the program on a CPU.

The performance improvement request unit 401 transmits a signal (performance improvement request signal) representing a request for improving the performance of the vehicle 10 to the analysis server 20 in response to a request from the user of the vehicle 10 received through the input device. This allows the user of the vehicle 10 to make a request for improving the performance of the vehicle 10 to the analysis server 20 when the vehicle 10 is not in use.

For example, the user may input a request for improving the performance of the vehicle 10 to the vehicle 10 using a performance improvement request screen displayed on the display device, similar to the case of the performance improvement request unit 194 of the vehicle 10.

Similar to the case of the notification unit 195 of the vehicle 10, when a performance improvement request signal is sent to the analysis server 20 by the performance improvement request unit 401, the notification unit 402 notifies the user of the contents of the reply to the performance improvement request signal from the analysis server 20 via a display device or the like.

[Specific examples of predicted effects of replacing or adding vehicle parts]
Next, a specific example of a prediction result by the effect prediction unit 205 of an effect on a specific performance of the vehicle 10 resulting from replacement or addition of a vehicle part of the vehicle 10 will be described with reference to FIG.

Figure 5 is a diagram showing an example of the predicted effect of replacing or adding a vehicle part on a specific vehicle performance. Specifically, Figure 5 shows a specific example of the predicted effect of replacing the transmission of the vehicle 10 or adding a part (mass damper) to the transmission on the performance of the gear noise of the vehicle 10, and shows the results of a virtual simulation of the noise levels inside the vehicle 10 before and after the treatment.

As shown in FIG. 5, the results of a virtual simulation of a vehicle 10 before measures (such as replacing the transmission or adding a mass damper) were taken showed that the noise level in frequency band 501, which corresponds to the gear noise of the transmission, was extremely high.

In contrast, the virtual simulation results for vehicle 10 after measures (such as replacing the transmission or adding a mass damper) have been implemented show that the noise level in frequency band 501 has dropped significantly.

In this way, the effect prediction unit 205 can predict the effect on noise performance caused by gear noise due to replacement of the transmission of the vehicle 10 by comparing the results of the virtual simulation of the interior noise levels corresponding to each vehicle 10 before and after the treatment.

[Specific example of reflecting vehicle-specific circumstances in a virtual vehicle model]
Next, a specific example of the result of reflecting circumstances specific to the vehicle 10 in the virtual vehicle model by the virtual vehicle model generating unit 201 will be described with reference to FIGS.

Figure 6 is a diagram showing an example of the variation during manufacturing of a transmission of the same type as the transmission mounted on the vehicle 10. Specifically, Figure 6 is a diagram showing a specific example of the number distribution of gear noise levels during manufacturing of a transmission of the same type as the transmission mounted on the vehicle 10. Figure 7 is a diagram showing an example of the result of reflecting circumstances unique to the vehicle 10 in a virtual vehicle model. Specifically, Figure 7 is a diagram showing a specific example of the result of reflecting variations during manufacturing of the transmission of the vehicle 10 in a virtual vehicle model. Figure 8 is a diagram showing another example of the result of reflecting circumstances unique to the vehicle 10 in a virtual vehicle model. Specifically, Figure 9 is a diagram showing a specific example of the result of reflecting variations during manufacturing of the transmission of the vehicle 10 and the usage conditions of the vehicle 10 in a virtual vehicle model.

<First Specific Example>
As shown in FIG. 6, there is of course a tolerable manufacturing variation in the gear noise level (specifically, the actual measured value in the inspection process) of a transmission of the same type as the transmission mounted on the vehicle 10.

For example, if the transmission currently installed in the vehicle 10 has a relatively high predetermined value GN1 within the range of previously tolerated gear noise levels, the noise level inside the vehicle 10 will be higher than if the transmission has a gear noise level of a predetermined value GN2 near the median value of the variation.

For example, consider a case where a virtual vehicle model of vehicle 10 is generated and updated using a general-purpose virtual model based on a transmission with a median gear noise variation as a specified base model. In this case, as shown in FIG. 7, the noise sensitivity inside the vehicle 10 increases approximately linearly with the trend of line 701 according to the cumulative travel distance (travel time) of vehicle 10. Then, as a result of a virtual simulation of vehicle 10 corresponding to the state before treatment of vehicle 10 (transmission replacement) based on the virtual vehicle model is performed, the noise sensitivity corresponding to point 702 is output.

In contrast, consider a case where the virtual vehicle model is generated and updated after the fact that the gear noise level of the transmission mounted on the vehicle 10 is a predetermined value GN1 is reflected in the general-purpose virtual model. In this case, as shown in FIG. 7, the noise sensitivity in the cabin of the vehicle 10 increases approximately linearly with respect to the cumulative travel distance (travel time) of the vehicle 10, with the tendency of line 703 being offset with the same inclination as line 701 in the direction in which the noise sensitivity increases. Then, as a result of a virtual simulation of the vehicle 10 corresponding to the state before the treatment (replacement of the transmission) of the vehicle 10 based on the virtual vehicle model, a noise sensitivity corresponding to point 704, which is higher than point 702, is output. This allows the effect prediction unit 205 to more appropriately evaluate the current noise sensitivity of the vehicle 10 regarding the gear noise of the transmission. As a result, the effect prediction unit 205 can improve the prediction accuracy of the effect of the treatment (replacement of the transmission, etc.) on the noise sensitivity of the vehicle 10 regarding the gear noise of the transmission.

Note that noise sensitivity represents the ratio of the level of noise heard by the user as gear noise inside the vehicle 10 to the level of gear noise near the transmission.

<Second Specific Example>
For example, consider a case where the gear noise is based on a transmission with a median variation and the usage conditions specific to the vehicle 10 are not taken into consideration (usually assumed usage conditions are automatically assumed). In this case, as shown in Fig. 8, the noise sensitivity inside the vehicle 10 increases approximately linearly with a relatively gentle slope of line 801 according to the cumulative travel distance (travel time) of the vehicle 10. Then, as a result of a virtual simulation for the vehicle 10 corresponding to the state before the treatment of the vehicle 10 (replacement of the transmission) based on the virtual vehicle model is performed, the noise sensitivity corresponding to point 802 is output.

In contrast to this, for example, a case is considered in which the gear noise level of the transmission mounted on the vehicle 10 is a predetermined value GN1, and then the virtual vehicle model is generated and updated in such a way that the usage conditions of the vehicle 10 are reflected in the general-purpose virtual model. Specifically, a case is considered in which the usage conditions of the vehicle 10 are reflected in which the frequency of traveling on roads with very good pavement conditions and relatively low road noise is relatively high, and background noise is relatively low. In this case, as shown in FIG. 8, the noise sensitivity inside the vehicle 10 increases approximately linearly with respect to the cumulative traveling distance (traveling time) of the vehicle 10, with the tendency of line 703 in which line 801 is offset in the direction of increasing the noise sensitivity and the inclination is relatively large. Then, as a result of a virtual simulation of the vehicle 10 corresponding to the vehicle 10 before the treatment (replacement of the transmission) is performed based on the virtual vehicle model, a noise sensitivity corresponding to point 804, which is greater than point 802, is output. This allows the effect prediction unit 205 to more appropriately evaluate the current noise sensitivity of the vehicle 10 regarding the gear noise of the transmission. As a result, the effect prediction unit 205 can further improve the prediction accuracy of the effect of a measure (such as replacing the transmission) on the noise sensitivity of the vehicle 10 regarding transmission gear noise.

[Notification process of the effect of replacing vehicle parts or taking additional measures]
Next, a process for notifying a user of the effect on a specified performance of the vehicle 10 resulting from replacement of a part of the vehicle 10 or additional treatment will be described with reference to FIG.

Figures 9 and 10 are flowcharts showing an example of a process for notifying a user of the effect on a specified performance of the vehicle 10 due to replacement of a part of the vehicle 10 or additional treatment. Specifically, Figure 9 is a flowchart showing an example of a prediction process by the analysis server 20 of the effect on a specified performance of the vehicle 10 due to replacement of a part of the vehicle 10 or additional treatment. Figure 10 is a flowchart showing an example of a second vehicle data acquisition process by the central ECU 19.

The flowchart in FIG. 9 is executed repeatedly, for example, at a predetermined interval. The flowchart in FIG. 10 is started when a data acquisition request signal is received from the analysis server 20.

As shown in FIG. 9, in step S102, the request reception unit 204 determines whether or not a performance improvement request signal has been received from the vehicle 10. If a performance improvement request signal has been received from the vehicle 10, the request reception unit 204 proceeds to step S104, and if a performance improvement request signal has not been received, the request reception unit 204 ends the processing of this flowchart.

In step S104, the request receiving unit 204 transmits a data acquisition request signal to the vehicle 10.

When the analysis server 20 completes the processing of step S104, it proceeds to step S106.

As shown in FIG. 10, in step S202, the vehicle data acquisition unit 196 determines whether or not the second vehicle data acquisition, which is started in step S206 described below, is in progress. If the second vehicle data acquisition is in progress, the vehicle data acquisition unit 196 proceeds to step S206, and otherwise proceeds to step S204.

In step S204, the vehicle data acquisition unit 196 determines whether or not a data acquisition request signal has been received from the analysis server 20. If a data acquisition request signal has been received from the analysis server 20, the vehicle data acquisition unit 196 proceeds to step S206, and if a data acquisition request signal has not been received, the vehicle data acquisition unit 196 ends the processing of this flowchart.

In step S206, the vehicle data acquisition unit 196 starts acquiring the second vehicle data.

When the central ECU 19 completes processing of step S206, it proceeds to step S208.

In step S208, the vehicle data acquisition unit 196 determines whether acquisition of the second vehicle data has been completed. For example, if there are multiple acquisition conditions as described above, the vehicle data acquisition unit 196 determines whether acquisition of the second vehicle data for each of the multiple acquisition conditions has been completed. If acquisition of the second vehicle data has not been completed, the vehicle data acquisition unit 196 proceeds to step S210, and if acquisition has been completed, the vehicle data acquisition unit 196 proceeds to step S218.

In step S210, the vehicle data acquisition unit 196 determines whether the vehicle 10 has been stopped (e.g., whether the ignition switch has been turned off). If the vehicle 10 has not been stopped, the vehicle data acquisition unit 196 proceeds to step S212. On the other hand, if the vehicle 10 has been stopped, the vehicle data acquisition unit 196 turns ON a flag indicating that the second vehicle data is in the middle of being acquired, and ends the processing of this flowchart. This allows the vehicle data acquisition unit 196 to determine in step S202 that the second vehicle data is in the middle of being acquired the next time the vehicle 10 is started.

In step S212, the vehicle data acquisition unit 196 determines whether a predetermined time has elapsed since the start of acquisition of the second vehicle data. The predetermined time may be fixed regardless of the content of the treatment or the characteristic items of the target, or may be variable depending on the content of the treatment or the characteristic items of the target. If the predetermined time has elapsed since the start of acquisition of the second vehicle data, the vehicle data acquisition unit 196 proceeds to step S214, and if the predetermined time has not elapsed, the vehicle data acquisition unit 196 returns to step S208.

In step S214, the route guidance unit 197 determines whether route guidance suitable for obtaining second vehicle data in step S216 described below has been performed. If route guidance has not been performed, the route guidance unit 197 proceeds to step S216, and if route guidance has been performed, the route guidance unit 197 returns to step S208.

In step S216, the route guidance unit 197 controls the navigation device 16 to search for a suitable route for acquiring the second vehicle data, and guides (directs) the user (driver) of the vehicle 10 along that route.

When the central ECU 19 completes processing of step S216, it returns to step S208.

In addition, the route guidance unit 197 may search for a route suitable for acquiring the second vehicle data in accordance with the start of acquisition of the second vehicle data by the vehicle data acquisition unit 196, and may start guiding the user (driver) of the vehicle 10 to pass through the route. In this case, the process of step S216 is performed immediately before step S208. In addition, the processes of steps S212 and S214 are also omitted, and if it is determined in step S210 that the vehicle 10 is not stopped, the process returns to step S208. In addition, the process of step S216 may be omitted. In this case, the processes of steps S212 and S214 are also omitted, and if it is determined in step S210 that the vehicle 10 is not stopped, the process returns to step S208. In addition, the vehicle data acquisition unit 196 may acquire the second vehicle data of the same acquisition condition two or more predetermined times. Thereby, the vehicle data acquisition unit 196 can acquire two or more predetermined numbers of second vehicle data for the same acquisition condition. Therefore, the analysis server 20 can perform statistical processing on the predetermined number of second vehicle data (two or more), or can remove vehicle data that corresponds to an abnormal value or does not meet the acquisition conditions from the predetermined number of second vehicle data (two or more). Therefore, the analysis server 20 can further improve the prediction accuracy when predicting the effect of implementing a treatment on the predetermined performance of the vehicle 10 based on the second vehicle data that corresponds to the current state of the vehicle 10.

In step S218, the vehicle data transmission unit 199 transmits the second vehicle data stored in the memory unit 198 to the analysis server 20.

When the central ECU 19 completes processing of step S218, it ends the processing of this flowchart.

As shown in FIG. 9, in step S108, the effect prediction unit 205 acquires manufacturing data for parts installed in the vehicle 10 related to the target performance item and replacement/additional part data related to the target performance item from the data server 30.

When the processing of step S108 is completed, the analysis server 20 proceeds to step S110.

In step S210, the effect prediction unit 205 selects a base response plan from one or more response plans output from the request reception unit 204.

For example, when there is a request for performance improvement regarding gear noise (reduction of interior noise of the vehicle 10 caused by gear noise), the request receiving unit 204 outputs the following countermeasures (1) to (3).
(1) Replacing the transmission of the vehicle 10. (2) Adding a mass damper to the sound generating portion of the transmission of the vehicle 10. (3) Adding an insulator to the interior of the vehicle 10.

Then, the effect prediction unit 205 selects a countermeasure plan from among (1) to (3). The selection method may be arbitrary.

For example, the effect prediction unit 205 selects the response plan that is expected to be the most effective among (1) to (3) as the base response plan.

Also, for example, the effect prediction unit 205 may select the least expensive response plan among (1) to (3) as the base response plan.

In addition, for example, the effect prediction unit 205 may not only select a countermeasure, but also select a part to be added or replaced to the vehicle 10 from a group of parts of the same type, taking into consideration the manufacturing variation of the part to be added or replaced to the vehicle 10 in the selected countermeasure. Specifically, when there are multiple candidate parts within the range of manufacturing variation expected in advance for the part to be added or replaced to the vehicle 10 in the selected countermeasure, the effect prediction unit 205 may select from among them a part with a higher target performance item.

For example, as shown in FIG. 6 above, consider a case where the current transmission of the vehicle 10 has a gear noise level of a predetermined value GN1, and there are candidate transmissions with gear noise levels of predetermined values GN2 and GN3. In this case, the effect prediction unit 205 may select the transmission with the lower gear noise level of the predetermined value GN3 from the two candidate transmissions.

When the analysis server 20 completes processing of step S110, it proceeds to step S112.

In step S112, the effect prediction unit 205 causes the simulator unit 203 to perform a virtual simulation of the target performance items of the vehicle 10 using the virtual vehicle model. For example, the simulator unit 203 performs a virtual simulation of the target performance items of the vehicle 10 after the treatment is performed using the second virtual vehicle model as described above. The simulator unit 203 may also perform virtual simulations of the target performance items of the vehicle 10 before and after the treatment is performed using each of the first virtual vehicle model and the second virtual vehicle model as described above.

When the analysis server 20 completes processing of step S112, it proceeds to step S114.

In step S114, the effect prediction unit 205 predicts the effect of the treatment corresponding to the countermeasure on the target performance item of the vehicle 10 based on the result of the virtual simulation in step S112. For example, as described above, the effect prediction unit 205 predicts the effect of the treatment on the target performance item of the vehicle 10 by comparing the second vehicle data acquired from the vehicle 10 with the result of the virtual simulation on the target performance item of the vehicle 10 corresponding to after the treatment is implemented. In addition, the effect prediction unit 205 may predict the effect of the treatment on the target performance item of the vehicle 10 by comparing the results of the virtual simulation on the target performance item of the vehicle 10 corresponding to before and after the treatment is implemented, respectively, as described above.

When the analysis server 20 completes processing of step S114, it proceeds to step S116.

In step S116, the effect prediction unit 205 determines whether the countermeasure (treatment) is effective, specifically, whether the effect of the countermeasure (treatment) on the target performance item of the vehicle 10 reaches the level required by the user, based on the prediction result of step S114. If the countermeasure (treatment) is effective, the effect prediction unit 205 proceeds to step S120, and if the countermeasure (treatment) is not effective, the effect prediction unit 205 proceeds to step S118.

In step S118, the effect prediction unit 205 determines whether or not there are any additional countermeasures. If there are any additional countermeasures, the effect prediction unit 205 returns to step S112 and causes the simulator unit 203 to perform a virtual simulation in which a measure corresponding to the additional countermeasure is added. On the other hand, if there are no additional countermeasures, the effect prediction unit 205 proceeds to step S120.

The effect prediction unit 205 may predict the effect of a treatment for all combinations of countermeasures that can be added cumulatively based on one countermeasure, regardless of whether the treatment has an effect. In this case, step S116 is omitted. The effect prediction unit 205 may also predict the effect of a treatment for all treatments that can be implemented by selecting one or more from all the countermeasures.

In step S120, the effect prediction unit 205 transmits a notification signal including the contents of the measures corresponding to the countermeasures and the effects of the measures to at least one of the vehicle 10 and the user terminal 40. At this time, the effect prediction unit 205 may transmit the notification signal only to the sender of the performance improvement request among the vehicle 10 and the user terminal 40, or may transmit the notification signal to both the vehicle 10 and the user terminal 40. As a result, the notification unit 195 of the central ECU 19 can notify the user of the vehicle 10 of the countermeasures (contents of the measures) for improving the target performance items of the vehicle 10 and the effects of the countermeasures through the display device 18, etc. Similarly, the user terminal 40 can notify the user of the vehicle 10 of the countermeasures (contents of the measures) for improving the target performance items of the vehicle 10 and the effects of the countermeasures through its display device, etc.

When the analysis server 20 completes the processing of step S120, it ends the processing of this flowchart.

In this way, in this example, the analysis server 20 can predict and notify the effect of a countermeasure (treatment) on the target performance item of the vehicle 10 in response to a performance improvement request from the user. Therefore, the user of the vehicle 10 can comprehensively consider the countermeasure (content of the treatment) for improving the target performance item of the vehicle 10, the effect of the countermeasure, the cost of the countermeasure (treatment), etc., and determine whether to implement the treatment and the content of the treatment to be implemented.

In this example, the analysis server 20 may predict the effect of a predetermined treatment on the specified performance of the vehicle 10 in response to a request from a user, or in addition to predicting the effect of a predetermined treatment on the specified performance of the vehicle 10 in response to a request from a dealer where the vehicle 10 is in stock, for example. The analysis server 20 may also predict the effect of a predetermined treatment on the specified performance of the vehicle 10 regardless of a request from a user, dealer, etc. For example, when a newly developed part that can be replaced with a part of the vehicle 10 or added to the vehicle 10 is released, the analysis server 20 may predict the effect on the specified performance of the vehicle 10 of the treatment of replacing the part with the newly developed part or adding the part. This allows the dealer's staff, etc. to use the data of the prediction results to suggest replacement with or addition of the newly developed part to the user.

[Action]
Next, the operation of the vehicle analysis system 1 according to this embodiment will be described.

In this embodiment, the vehicle analysis system 1 includes a vehicle data acquisition unit 196, a virtual vehicle model generation unit 201, and an effect prediction unit 205. Specifically, the vehicle data acquisition unit 196 acquires data (second vehicle data) related to the state of the vehicle 10, including data related to a predetermined performance of the vehicle 10. The virtual vehicle model generation unit 201 generates a virtual model corresponding to the vehicle 10 based on the second vehicle data acquired by the vehicle data acquisition unit 196. The effect prediction unit 205 performs a virtual simulation of the vehicle 10 related to a predetermined performance when at least one of the following measures is implemented: replacing a part mounted on the vehicle 10 and adding a part to the vehicle 10, based on the virtual model generated by the virtual vehicle model generation unit 201 through the simulator unit 203, and predicts the effect of the measures on the predetermined performance of the vehicle 10 based on the results.

This allows the vehicle analysis system 1 to carry out a virtual simulation of the specified performance of the vehicle 10 using a virtual model that reflects the second vehicle data corresponding to the current state (specific circumstances) of the vehicle 10. Therefore, the vehicle analysis system 1 can predict the effect of replacing parts or performing additional measures on the specified performance based on the results of the virtual simulation that is in line with the specific circumstances of the vehicle 10, and as a result, can predict the effect of the measures with higher accuracy.

In addition, in this embodiment, the vehicle analysis system 1 may include a request receiving unit 204. Specifically, the request receiving unit 204 may acquire information regarding characteristics that take into account manufacturing variations for parts that will be newly installed in the vehicle 10 as a result of the treatment (e.g., manufacturing data, replacement/additional part data, etc.). Then, the effect prediction unit 205 may perform a virtual simulation of what would happen if the treatment were carried out, via the simulator unit 203, based on the information acquired by the request receiving unit 204.

This allows the vehicle analysis system 1 to predict the effect of part replacement or addition on the specified performance of the vehicle 10, taking into account the manufacturing variability of the parts installed in the vehicle 10 due to the part replacement or addition. Therefore, the vehicle analysis system 1 can predict the effect of the treatment with higher accuracy.

In addition, in this embodiment, the effect prediction unit 205 may compare the second vehicle data acquired by the vehicle data acquisition unit 196 with the results of a virtual simulation of the vehicle 10 when the treatment is implemented, and predict the effect of the treatment on a specified performance of the vehicle 10.

This allows the vehicle analysis system 1 to more easily predict the effect on a specified performance of the vehicle 10 by using the second vehicle data as is.

In addition, in this embodiment, the effect prediction unit 205 may compare the results of a virtual simulation of the vehicle 10 regarding the specified performance before and after the treatment is performed, and predict the effect of the treatment on the specified performance of the vehicle 10.

This allows the effect prediction unit 205 to make the conditions for performing the virtual simulation exactly the same before and after the treatment is performed, and as a result, the effect of the treatment on the specified performance of the vehicle 10 can be predicted with greater accuracy.

In addition, in this embodiment, the effect prediction unit 205 may select whether to predict the effect by comparing the data acquired by the vehicle data acquisition unit 196 with the results of a virtual simulation of the vehicle 10 when the treatment is implemented based on at least one of the contents of the treatment and the contents of the specified performance, or to predict the effect by comparing the results of a virtual simulation of the vehicle 10 regarding the specified performance before and after the treatment is implemented.

As a result, the vehicle analysis system 1 can improve the efficiency of effect prediction without performing a virtual simulation based on the first virtual vehicle model, depending on the content (type) of the treatment or the content (type) of the specified performance, or can predict the effect with higher accuracy by performing a virtual simulation based on the first virtual vehicle model. Therefore, the vehicle analysis system 1 can balance the efficiency of effect prediction with the high accuracy of effect prediction, depending on the content (type) of the treatment or the content (type) of the specified performance.

In addition, in this embodiment, the virtual vehicle model generation unit 201 may generate a virtual model (virtual vehicle model) corresponding to the vehicle 10 by updating a general-purpose virtual model for the vehicle 10 as a predetermined base model based on the data acquired by the vehicle data acquisition unit 196.

This allows the vehicle analysis system 1 to generate a virtual vehicle model based on, for example, a virtual model (general-purpose model) that is common to multiple vehicles 10 of the same vehicle type. As a result, the vehicle analysis system 1 can reduce the effort and time required to prepare a virtual vehicle model for predicting the effect of a treatment on a specific performance of the vehicle 10, for example, and more efficiently predict the effect of a treatment on the specific performance of the vehicle 10.

In addition, in this embodiment, the virtual vehicle model generation unit 201 may generate a virtual model corresponding to the vehicle 10 by updating a dedicated virtual model for the vehicle 10 based on the data acquired by the vehicle data acquisition unit 196.

As a result, the vehicle analysis system 1 can predict the effect of a treatment on the specified performance of the vehicle 10 using a virtual vehicle model generated based on a dedicated virtual model for each vehicle 10. Therefore, the vehicle analysis system 1 can reflect the unique circumstances of each vehicle 10 in the virtual vehicle model, and as a result, can predict with higher accuracy the effect of the treatment on the specified performance of the vehicle 10.

In addition, in this embodiment, the virtual vehicle model generation unit 201 may update the dedicated virtual model in accordance with at least one of the changes over time of the vehicle 10, the usage conditions of the vehicle 10, the characteristics of the components of the vehicle 10, and the changes in the components of the vehicle 10.

This allows the vehicle analysis system 1 to specifically reflect the unique circumstances of the vehicle 10, such as changes in the vehicle 10 over time, the conditions of use of the vehicle 10, the characteristics of the vehicle 10 (e.g., manufacturing variations, etc.), and changes in the components of the vehicle 10, in the virtual vehicle model.

In addition, in this embodiment, the virtual vehicle model generation unit 201 may generate and update a digital twin of the vehicle 10 as a dedicated virtual model based on a general-purpose virtual model of the vehicle 10 and data representing the actual state of the vehicle 10 that is sequentially acquired.

This allows the vehicle analysis system 1 to generate a virtual vehicle model based on a virtual model (digital twin) dedicated to the vehicle 10, which always reflects the latest state of the vehicle 10. As a result, the vehicle analysis system 1 can predict with greater accuracy the effect of a treatment on a specific performance of the vehicle 10.

In addition, in this embodiment, the vehicle analysis system 1 may include a request receiving unit 204. Specifically, the request receiving unit 204 may receive a request from a user for improving a specified performance. Then, the effect prediction unit 205 may predict the effect on the specified performance of the vehicle 10 due to a measure in response to the request.

This allows the vehicle analysis system 1 to notify the user of the effect on the specified performance of the vehicle 10 as a result of measures taken in accordance with the user's request. Therefore, the vehicle analysis system 1 can improve the convenience of users who wish to improve the performance of the vehicle 10, for example.

Although the embodiments have been described in detail above, the present disclosure is not limited to such specific embodiments, and various modifications and improvements are possible within the scope of the gist of the invention as described in the claims.

1. Vehicle analysis system (information processing system)
10 vehicle 11 communication device 12 gateway ECU
13 ECU
15 Sensor 16 Navigation device 17 Input device 18 Display device 19 Central ECU
19A Auxiliary storage device 19B Memory device 19C CPU
19D Interface device 19E Recording medium 20 Analysis server (information processing device)
21 External interface 21A Recording medium 22 Auxiliary storage device 23 Memory device 24 CPU
25 High-speed arithmetic unit 26 Communication interface 27 Input device 28 Display device 30 Data server 40 User terminal 121 Communication unit 191 Vehicle data acquisition unit 192 Storage unit 193 Vehicle data transmission unit 194 Performance improvement request unit 195 Notification unit 196 Vehicle data acquisition unit (first acquisition unit)
197 Route guidance unit 198 Storage unit 199 Vehicle data transmission unit 201 Virtual vehicle model generation unit (generation unit)
202 Virtual vehicle model storage unit 203 Simulator unit 204 Request reception unit (second acquisition unit, reception unit)
205 Effect Prediction Department (Prediction Department)
301 Data transmission section 302 Storage section 401 Performance improvement request section 402 Notification section

Claims (23)

A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed based on the virtual model generated by the generation unit, and predicts an effect of the treatment on the predetermined performance of the vehicle based on the result of the virtual simulation;
the prediction unit selects, based on at least one of the content of the action and the content of the predetermined performance, whether to predict the effect by comparing the data acquired by the first acquisition unit with a result of a virtual simulation of the vehicle when the action is performed, or to predict the effect by comparing results of a virtual simulation of the vehicle regarding the predetermined performance before and after the action is performed .
Information processing system. A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed based on the virtual model generated by the generation unit, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation;
The generation unit generates a virtual model corresponding to the vehicle by updating a general-purpose virtual model related to the vehicle based on the data acquired by the first acquisition unit .
Information processing system. A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed based on the virtual model generated by the generation unit, and predicts an effect of the treatment on the predetermined performance of the vehicle based on the result of the virtual simulation;
The generation unit generates a virtual model corresponding to the vehicle by updating a dedicated virtual model related to the vehicle based on the data acquired by the first acquisition unit .
Information processing system. A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed based on the virtual model generated by the generation unit, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation;
a reception unit that receives a request for improving the predetermined performance from a user,
The prediction unit predicts the effect of the treatment in response to the request .
Information processing system. The prediction unit compares the data acquired by the first acquisition unit with a result of a virtual simulation of the vehicle in a case where the treatment is performed, and predicts the effect.
5. An information processing system according to claim 2 . The prediction unit compares results of a virtual simulation of the vehicle regarding the predetermined performance before and after the implementation of the measures, and predicts the effect.
5. An information processing system according to claim 2 . the prediction unit selects, based on at least one of the content of the action and the content of the predetermined performance, whether to predict the effect by comparing the data acquired by the first acquisition unit with a result of a virtual simulation of the vehicle when the action is performed, or to predict the effect by comparing results of a virtual simulation of the vehicle regarding the predetermined performance before and after the action is performed.
5. An information processing system according to claim 2. The generation unit updates the dedicated virtual model in accordance with at least one of a change over time of the vehicle, a use condition of the vehicle, a characteristic of a component of the vehicle, and a change in the component of the vehicle.
The information processing system according to claim 3 . The generation unit generates and updates a digital twin of the vehicle as the dedicated virtual model based on a general-purpose virtual model of the vehicle and data representing the actual state of the vehicle that is sequentially acquired.
The information processing system according to claim 8. a second acquisition unit that acquires information about characteristics of the part that is to be newly installed in the vehicle by the process, the information taking into account manufacturing variations;
The prediction unit performs a virtual simulation of a case in which the treatment is performed based on the information acquired by the second acquisition unit.
10. An information processing system according to any one of claims 1 to 9 . A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed using the virtual model generated by the generation unit, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation ;
the prediction unit selects, based on at least one of the content of the action and the content of the predetermined performance, whether to predict the effect by comparing the data acquired by the first acquisition unit with a result of a virtual simulation of the vehicle when the action is performed, or to predict the effect by comparing results of a virtual simulation of the vehicle regarding the predetermined performance before and after the action is performed .
Information processing device. A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed using the virtual model generated by the generation unit, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation;
The generation unit generates a virtual model corresponding to the vehicle by updating a general-purpose virtual model related to the vehicle based on the data acquired by the first acquisition unit.
Information processing device. A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed using the virtual model generated by the generation unit, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation;
The generation unit generates a virtual model corresponding to the vehicle by updating a dedicated virtual model related to the vehicle based on the data acquired by the first acquisition unit.
Information processing device. A first acquisition unit that acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation unit that generates a virtual model corresponding to the vehicle based on the data acquired by the first acquisition unit;
a prediction unit that performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed using the virtual model generated by the generation unit, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation;
a reception unit that receives a request for improving the predetermined performance from a user,
The prediction unit predicts the effect of the treatment in response to the request.
Information processing device. A first acquisition step in which an information processing device acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation step in which the information processing device generates a virtual model corresponding to the vehicle based on the data acquired in the first acquisition step;
a prediction step in which the information processing device performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a treatment of replacing a part mounted on the vehicle and a treatment of adding a part to the vehicle is performed, using the virtual model generated in the generation step, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the simulation,
In the prediction step, based on at least one of the content of the treatment and the content of the predetermined performance, a selection is made as to whether to predict the effect by comparing the data acquired in the first acquisition step with a result of a virtual simulation of the vehicle when the treatment is performed, or to compare results of a virtual simulation of the vehicle regarding the predetermined performance before and after the treatment is performed, and to predict the effect.
Information processing methods. A first acquisition step in which an information processing device acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation step in which the information processing device generates a virtual model corresponding to the vehicle based on the data acquired in the first acquisition step;
a prediction step in which the information processing device performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a treatment of replacing a part mounted on the vehicle and a treatment of adding a part to the vehicle is performed, using the virtual model generated in the generation step, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the simulation,
In the generating step, a general-purpose virtual model of the vehicle is updated based on the data acquired in the first acquiring step, thereby generating a virtual model corresponding to the vehicle.
Information processing methods. A first acquisition step in which an information processing device acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation step in which the information processing device generates a virtual model corresponding to the vehicle based on the data acquired in the first acquisition step;
a prediction step in which the information processing device performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a treatment of replacing a part mounted on the vehicle and a treatment of adding a part to the vehicle is performed, using the virtual model generated in the generation step, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the simulation,
In the generating step, a dedicated virtual model for the vehicle is updated based on the data acquired in the first acquiring step, thereby generating a virtual model corresponding to the vehicle.
Information processing methods. A first acquisition step in which an information processing device acquires data related to a state of the vehicle, the data including data related to a predetermined performance of the vehicle;
a generation step in which the information processing device generates a virtual model corresponding to the vehicle based on the data acquired in the first acquisition step;
a prediction step in which the information processing device performs a virtual simulation of the vehicle regarding the predetermined performance when at least one of a treatment of replacing a part mounted on the vehicle and a treatment of adding a part to the vehicle is performed, using the virtual model generated in the generation step, and predicts an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the simulation;
a receiving step of receiving a request for improving the predetermined performance from a user by the information processing device;
In the prediction step, the effect of the treatment in response to the request is predicted.
Information processing methods. In the information processing device,
a first acquisition step of acquiring data relating to a state of the vehicle, the data including data relating to a predetermined performance of the vehicle;
a generating step of generating a virtual model corresponding to the vehicle based on the data acquired in the first acquiring step;
a prediction step of performing a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed using the virtual model generated in the generation step, and predicting an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation ;
In the prediction step, based on at least one of the content of the treatment and the content of the predetermined performance, a selection is made as to whether to predict the effect by comparing the data acquired in the first acquisition step with a result of a virtual simulation of the vehicle when the treatment is performed, or to compare results of a virtual simulation of the vehicle regarding the predetermined performance before and after the treatment is performed, and to predict the effect .
program. In the information processing device,
a first acquisition step of acquiring data relating to a state of the vehicle, the data including data relating to a predetermined performance of the vehicle;
a generating step of generating a virtual model corresponding to the vehicle based on the data acquired in the first acquiring step;
a prediction step of performing a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed using the virtual model generated in the generation step, and predicting an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation;
In the generating step, a general-purpose virtual model of the vehicle is updated based on the data acquired in the first acquiring step, thereby generating a virtual model corresponding to the vehicle.
program. In the information processing device,
a first acquisition step of acquiring data relating to a state of the vehicle, the data including data relating to a predetermined performance of the vehicle;
a generating step of generating a virtual model corresponding to the vehicle based on the data acquired in the first acquiring step;
a prediction step of performing a virtual simulation of the vehicle regarding the predetermined performance when at least one of a replacement of a part mounted on the vehicle and an addition of a part to the vehicle is performed using the virtual model generated in the generation step, and predicting an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the virtual simulation;
In the generating step, a dedicated virtual model for the vehicle is updated based on the data acquired in the first acquiring step, thereby generating a virtual model corresponding to the vehicle.
program. In the information processing device,
a first acquisition step of acquiring data relating to a state of the vehicle, the data including data relating to a predetermined performance of the vehicle;
a generating step of generating a virtual model corresponding to the vehicle based on the data acquired in the first acquiring step;
a prediction step of performing a virtual simulation of the vehicle regarding the predetermined performance when at least one of a treatment of replacing a part mounted on the vehicle and a treatment of adding a part to the vehicle is performed using the virtual model generated in the generation step, and predicting an effect of the treatment regarding the predetermined performance of the vehicle based on the result of the simulation;
a receiving step of receiving a request for improving the predetermined performance from a user;
In the prediction step, the effect of the treatment in response to the request is predicted.
program. A computer-readable recording medium having the program according to any one of claims 19 to 22 recorded thereon.

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