JP7717320B2 - Data processing system, data processing method, data processing device and program - Google Patents

Description

The present invention relates to a data processing system, data processing method, data processing device, and program for analyzing information about moving objects.

There is a demand for acquiring information about moving objects used by users (including, but not limited to, automobiles, bicycles, airplanes, helicopters, drones, ships, and even walking) such as acceleration, speed, position information, and angular velocity, and using this information for some kind of analysis. For example, there is a societal demand for evaluating the driving of moving objects based on this information, or the roads on which moving objects travel. Patent Document 1 discloses a driving diagnosis system that generates statistical information based on time-series information about acceleration as vehicle behavior data collected from various sensors installed in the vehicle via a car navigation system.

Japanese Patent Application Laid-Open No. 2006-243856

However, no sufficiently simple tools have been proposed for evaluating driving behavior, etc., and the vehicle user or manager must perform some or all of the analysis themselves. In particular, when there are multiple vehicles to be analyzed, analyzing those multiple vehicles as a whole or in combination is difficult unless you are an expert.

The objective of this invention is to make it possible to easily obtain analytical information about moving objects.

One aspect of the present invention is a data processing system comprising: a storage unit that stores mobile body information acquired in chronological order in association with time information; a synthesis method identification unit that identifies a synthesis method; a mobile body information identification unit that identifies multiple pieces of mobile body information related to synthesis processing from the stored mobile body information; an allocation unit that allocates feature information included in each of the multiple pieces of mobile body information related to the synthesis processing based on a predetermined classification; an acquisition unit that acquires the frequency or occurrence probability of the feature information for each of the multiple pieces of mobile body information related to the synthesis processing for each of the predetermined classifications; a synthesis result acquisition unit that acquires a synthesis result by applying the identified synthesis method to the frequency or occurrence probability of the acquired feature information for each of the multiple pieces of mobile body information related to the synthesis processing; and an output unit that outputs the acquired synthesis result.

According to the present invention, analytical information about moving objects can be easily obtained.

1 is a diagram showing a vehicle equipped with a data processing device according to the present embodiment, and a collection result of acceleration occurring as the vehicle travels; FIG. 2 is an explanatory diagram of communication between a data processing device and a server according to the present embodiment. 4 is a flowchart of data processing in the present embodiment. FIG. 4 is an explanatory diagram of the compression process of FIG. 3; FIG. 4 is an explanatory diagram of adding time information to FIG. 3 . 1 is an example of an interface used to display the results of an analysis of data stored on a server. 10 is another example of an interface used to display the results of an analysis of data stored on a server. FIG. 8 is an enlarged view of the area 406 in FIG. 7, showing an example of a display of the results of analyzing acceleration information about one moving object. The situation in which the acceleration corresponding to the area 501 in FIG. 7 was acquired is displayed in a selectable list. The figure shows a detailed display of one of the situations in which the acceleration corresponding to the area 501 was acquired. 10 is a flowchart of a synthesis process of acceleration distributions in the present embodiment. 10 is an example of an interface displayed when specifying a portion of the compositing targets in a case where the remaining compositing targets are specified by specifying a portion of the compositing targets and a compositing method. 10 is an example of an interface that is displayed when specifying a compositing method after specifying a portion of the compositing objects, in a case where the remaining compositing objects are specified by specifying a compositing method and a portion of the compositing objects. 10 is an example of an interface displayed after specifying a portion of the compositing objects and a compositing method in a case where the remaining compositing objects are specified by specifying a portion of the compositing objects and a compositing method. 10 is a display example of the difference between two acceleration distributions. 10 is a display example of the results of analyzing acceleration information about a plurality of moving objects. 10 is a display example of the results of analyzing acceleration information of a plurality of moving bodies limited under one condition. 10 is an example showing a selection area pattern for a displayed acceleration distribution. In this example, the situations in which accelerations corresponding to a region selected in FIG. 18 have been acquired are displayed in a selectable list, and their trends and commonalities are displayed to the right of the list.

Hereinafter, an example of an embodiment of the present invention will be described with reference to the drawings.
In the description of the drawings, the same elements are denoted by the same reference numerals, and duplicated descriptions may be omitted.
Furthermore, the components described in these embodiments are merely examples and are not intended to limit the scope of the present invention.

This embodiment will now be outlined. Figure 1 shows a vehicle from which mobile object information is collected, and the acceleration information generated as the vehicle travels, collected and visualized by a data processing device in this embodiment.

FIG. 1 shows a plan view of a vehicle (an example of a "moving body") as seen from above, and cumulatively shows the collected results of acceleration information that occurs as the vehicle travels.
The definitions of the symbols at the top of FIG. 1 are as follows:
- "FR" indicates the front in the direction of travel of the vehicle (FR-RR direction).
"RR" indicates the rear in the direction of travel of the vehicle (FR-RR direction).
・"LS" indicates the left side based on the direction of travel of the vehicle (FR-RR direction).
"RS" indicates the right side based on the vehicle's direction of travel (FR-RR direction).
"RP" indicates a plane (hereinafter referred to as the "reference plane") defined by the vehicle's direction of travel (FR-RR direction) and the lateral direction (LS-RS direction) perpendicular to the vehicle's direction of travel (FR-RR direction).

The lower part of FIG. 1 shows the magnitude of acceleration in the reference plane RP using the distance from the origin.
The directions in the reference plane RP are expressed using angles from a predetermined direction (for example, the direction of travel (FR-RR direction)).
The number or density of plots in the reference plane RP is represented by shading.

In this embodiment, the sensing data collected in this manner, such as acceleration information, is processed according to the data content before being transmitted to the server 230 connected to the data processing device 210.

This section explains communication between the data processing device 210 and the server 230 in this embodiment. Figure 2 is an explanatory diagram of communication between the data processing device 210 and the server 230 in this embodiment.

The data processing device 210 can be fixed inside the vehicle by being inserted into a socket (e.g., a cigarette lighter socket, a power supply socket, or a connection socket) of the vehicle. The power supply socket or connection socket is, for example, a socket that supports USB (Universal Serial Bus). Of course, the data processing device 210 is not limited to such a socket and may be, for example, a drive recorder or any other IoT device. These devices may be installed in the vehicle in any manner, either individually or in combination, as long as they include the components of the data processing device 210 described below. The data processing device 210 is connected to the server 230 directly or via the mobile terminal 220. Note that the data processing device 210 may be a device such as a smartphone. In this case, for example, the functions of the data processing device 210 may be included in the mobile terminal 220 described below. In this case, for example, the acceleration information may be calculated from position information acquired by the position information acquisition unit 212 described below.
The data processing device 210 is configured to collect mobile information, which is data relating to the vehicle of FIG.

The mobile terminal 220 is a device that is located inside or near the vehicle, for example, held by a user (e.g., the driver) in the vehicle. The mobile terminal 220 is configured to control communication between the data processing device 210 and the server 230. The mobile terminal 220 is, for example, a smartphone or tablet. As described above, the data processing device 210 may communicate with the server 230 without going through the mobile terminal 220.

The server 230 stores and processes data collected by at least one data processing device 210. It is further configured to provide the results of data processing based on the stored data to an external device, such as a device used by a vehicle manager.

The configuration of the data processing device 210 of this embodiment will be described.

As shown in FIG. 2, the data processing device 210 includes, for example, an acceleration information acquisition unit 211, a position information acquisition unit 212, a time information acquisition unit 213, a processing unit 214, and a communication unit 215, and is configured to acquire mobile object information, which is information related to the vehicle in which the data processing device 210 is installed.
The acceleration information acquisition unit 211 acquires the acceleration of the vehicle, and is configured to acquire the acceleration using, for example, a piezoelectric acceleration sensor. Note that the acceleration of the vehicle may be calculated based on position information acquired by a position information acquisition unit 212 (described later) or speed information acquired by a speed information acquisition unit (not shown). In such a case, the acceleration information acquisition unit 211 may not be provided.
The location information acquisition unit 212 acquires location information (e.g., latitude and longitude information) of the data processing device 210 at predetermined intervals based on radio waves received from GNSS satellites (e.g., GPS satellites), for example. That is, it can acquire location information of the vehicle in which the data processing device 210 is installed.
The time information acquisition unit 213 is configured to acquire time information (e.g., a timestamp) generated based on information regarding time obtained from a built-in clock or an external time information providing server, and based on information indicating the collection period Pc corresponding to the information obtained from the acceleration sensor 212 and the position information acquisition unit 212.
The processing unit 214 is configured to generate mobile object information, which is information about the vehicle, by performing predetermined data processing on the values acquired by the acceleration information acquisition unit 211, the position information acquisition unit 212, and the time information acquisition unit. The processing unit 214 is, for example, a microcomputer. The processing unit 214 realizes the functions of the data processing device 210 by executing a predetermined program (for example, a program stored in a memory in the microcomputer).
The communication unit 215 is configured to transmit to the outside the mobile object information that is the processing result of the processing unit 214. The communication unit 215 is, for example, a communication interface.

The configuration of the server 230 in this embodiment is described below.

As shown in FIG. 2, the server 230 includes a communication unit 231 , a storage unit 232 , and a processing unit 233 .
The communication unit 231 is configured to control communication between the server 230 and a device (e.g., the mobile terminal 220 or the data processing device 210) connected directly or indirectly (e.g., via a network such as the Internet) to the server 230. The communication unit 231 is, for example, a communication interface.
The memory unit 232 is configured to store various data, for example, data received by the communication unit 231 and a program for realizing the functions of the server 230 (for example, the function of executing each process of the server 230 described below).
The processing unit 233 is configured to process the data stored in the storage unit 232. The processing unit 233 is, for example, a processor (for example, a CPU (Central Processing Unit)). The processing unit 233 executes the program stored in the storage unit 232 to realize the functions of the server 230.

For example, the processing unit 233 extracts the frequency of occurrence of each category based on a predetermined category for the acceleration information contained in each of multiple units of mobile body information identified as mobile body information related to the synthesis process from the stored mobile body information, and obtains the result of synthesizing the frequency extraction contents.
As another example, the processing unit 233 calculates a driving score using acceleration information acquired for the moving object based on the driving score calculation model stored in the storage unit 232. The driving score may be, for example and without limitation, a score obtained by converting the driving quality of the vehicle. In this embodiment, the description is based on logic in which the driving score tends to decrease when acceleration with a large scalar value is acquired, but the content of the logic is not particularly limited.

This section explains the process from data acquisition to data storage in this embodiment. Figure 3 is a flowchart of the process from data acquisition to data storage in this embodiment. Figure 5 is an explanatory diagram of the addition of time information in Figure 3.

As shown in FIG. 3, the data processing device 210 acquires acceleration information and position information (S100). In this embodiment, the description is given assuming that position information is acquired in addition to acceleration information, but the acquired information need only include at least position information. When only position information is acquired, as described above, acceleration information can be calculated based on the acquired position information, and this calculation may be performed by the data processing device 210 or the server 230, for example. Furthermore, for example, in addition to this information, at least one of vehicle identification information, speed information, steering information, accelerator information, brake information, remaining fuel or remaining battery capacity, and load information may be acquired and transmitted to the server 230 at the same time as the acceleration information is transmitted or at a separate timing as appropriate.

Specifically, the acceleration information acquisition unit 211 acquires the acceleration that occurs as the vehicle travels at a predetermined frequency Tm (e.g., every 0.2 seconds).

The processing unit 212 performs coordinate transformation on the values acquired by the acceleration information acquisition unit 211 to align the axial direction of the acceleration information acquisition unit 211 with the axial direction of the vehicle (e.g., the direction of travel (FR-RR direction)). The processing unit 212 stores the values after coordinate transformation in a storage device (e.g., memory within the microcomputer or a storage device connected to the microcomputer).

Similarly, the location information acquisition unit 214 acquires location information that changes as the vehicle travels at a predetermined frequency Tn (e.g., every 0.5 seconds). Here, the periods Tm and Tn may be the same, and there are no particular restrictions on the period, whether synchronous or asynchronous.

The processing unit 212 stores the location information acquired from the location information acquisition unit 214 in the storage device. As a result, the storage device stores these multiple pieces of acquired information.

After step S100, the data processing device 210 performs compression processing (S101) on the acceleration information.

Specifically, in the compression process in step S101, the processing unit 212 allocates each of the acquired values obtained in step S100 to each of the discrete divisions of the reference plane RP, as shown in FIG.
In the example shown in FIG. 4 , the magnitude of acceleration is expressed in eight levels (arbitrary units) from 0 to 7, and the direction is divided into 32 levels (arbitrary units) from 0 to 31 clockwise using polar coordinates representing 360 degrees with the vehicle's traveling direction x as the base.
The processing unit 212 converts the acquired value into a certain data format by approximating the acquired point located in each section with one of the vertices of the section. For example, the acquired value v1 in FIG. 4 is converted into the closest vertex (r: 5, d: 29) among the vertices of the section that includes the acquired point corresponding to the acquired value.

For example, if the scalar value of the acquired acceleration information is less than a predetermined threshold, the corresponding acceleration information may be compressed as all 0s. This compression method is one example of reducing the capacity to improve data handling efficiency, and the compression method is not particularly limited. Alternatively, the acquired data may be retained without compressing the data, and the process may proceed to step 102.

After step S101, the data processing device 210 adds time information (S102).
When adding time information, for example, as shown in FIG. 5, the processing unit 212 adds time information specific to each collection period Pc to a combination of multiple pieces of acceleration information and position information collected within one collection period Pc (e.g., 1000 milliseconds).
The time information is, for example, information indicating each collection period Pc (for example, information indicating the start time and/or end time of the collection period Pc), or a code generated based on the information indicating each collection period Pc.
For example, at least one piece of acceleration information or one piece of position information may be added per timestamp, or one piece of acceleration information or one piece of position information may be added every predetermined time (for example, 5 seconds). In other words, the method for adding the time information is not particularly limited as long as the acceleration information or position information can be obtained with sufficient frequency when using this information for later analysis.

After step S102, the data processing device 210 executes a transmission process (S103).
Specifically, the communication unit 213 transmits the generated data (i.e., compressed acceleration information and position information with time information added) to the server 230 at a predetermined transmission interval (e.g., 1.0 second).

Here, the timing for linking the time information is not limited to the timing when the mobile object information is stored in the memory device inside the data processing device 210, but may be linked to the time information at that time when the mobile object information is transmitted from the data processing device 210 to the outside, or may be linked to the time information at that time when the transmitted mobile object information is received by the server 230 and stored in the memory unit 232 inside the server 230.

It is preferable that the data processing device 210 obtains at least one of an identifier identifying the user of the vehicle in which the data processing device 210 is installed and an identifier for the data processing device 210, and transmits this to the server 230. This data transmission may be performed in association with the data each time the transmission process of S104 is performed, or may be performed at a separate time. In this case, the server 230 stores the data transmitted from the data processing device 210 in S104 in association with at least one of the user identifier and the identifier for the data processing device 210.

When a user specifies, for example, via a mobile terminal 220, at least one of the identifier of the data processing device 210 and the user's identifier, and information about the data to be extracted (for example, a time or period), the server 230 has the function of extracting the data corresponding to the user's specifications. This function can be provided externally, for example, as an application programming interface (API).

Server 230 stores acceleration values and position information compressed into snapshots at each time determined by the time information. Therefore, server 230 can extract data for each moving object at any time, and can also extract cumulative data over a specified period of time.

The mobile object information stored in the memory unit 232 of the server 230 is not limited to acceleration information, time information, and location information, and may also be stored in association with any other information related to the mobile object. Examples include driver information, passenger information, related person (e.g., owner, manager, etc.) information, affiliation (group, company, etc.) information, remaining fuel information, payload information, remaining power information, weather information (weather, temperature, humidity, etc.), and driving score (a score indicating driving quality, calculated based on information such as acceleration). This information may be obtained from a separate device equipped on the mobile object, obtained by user input via the mobile terminal 220, obtained through an administrator terminal (not shown) operated by the person managing the mobile object, or, in the case of general information (e.g., weather information), obtained via the Internet from a website that provides such information as appropriate.

Below is an example of analyzing data acquired and stored from the data processing device 210 and displaying the analysis results.

Figure 6 shows an example of an interface used to display the analysis results of data stored in a server. Similarly, Figure 7 shows another example of an interface used to display the analysis results of stored data. Figure 8 is an enlarged view of the acceleration distribution diagram included in Figure 7. Figure 9 is a diagram displayed when 501 is selected in the distribution diagram 406 shown in Figure 8, and lists the situations in which the acceleration corresponding to area 501 was acquired.

The interface shown in FIG. 6 is made up of the following areas:
Area 401: This area shows the content items to be displayed in areas 402 to 404, such as the type of analysis result, and allows the user to select an item. The content corresponding to the selected item ("Daily Report" in FIG. 6) is displayed in areas 402 to 404.
Area 402: This area displays information about the vehicle or individual (driver) and date to be analyzed. In addition to the target date, the driver's name, driver's identifier, vehicle number, telephone number, etc. are displayed, and the user can specify the vehicle or individual and the date by inputting the information.
Area 403: Displays the total driving time, total driving distance, destinations, distances between destinations, driving time, etc. for the driving content corresponding to the vehicle or individual and date information specified in area 402.
Area 404: This area shows, on a map, information from a geographical perspective about the driving content corresponding to the vehicle or individual and date information specified in area 402. The route corresponding to the driving content is shown, and locations defined as destinations are indicated by numbered circles, which correspond to the numbers of each destination in area 403.

The interface shown in FIG. 7 is made up of the following areas:
Area 401: This is the same as area 401 in Fig. 6, and is in a state where "driving history" has been selected by user input. Contents corresponding to the selected "driving history" are displayed in areas 402, 404 to 407.
Area 402: This is similar to area 402 in Fig. 6 and displays information about the vehicle or individual (driver) to be analyzed and the date. Note that the analysis may be performed on multiple objects, and for example, all managed vehicles may be specified and a display related to that group of vehicles may be displayed.
Area 405: Shows the driving scores corresponding to the vehicle or person, date and time period identified in area 402, including an overall score, a handling score, an acceleration score and a deceleration score.
Area 406: A map of the acceleration obtained for the vehicle or individual, date, and time period specified in area 402 is displayed. Details will be described later.
Area 407: This area displays, on a map, geographical information about driving behavior corresponding to the vehicle or individual and date information specified in area 402. It is substantially similar to area 404 in FIG. 6 , except that instead of numbered circles indicating locations defined as destinations, marks corresponding to specific driving behavior (handling, acceleration, or deceleration) are displayed at locations where specific driving behavior is detected. That is, a handling mark is displayed at locations where specific handling-related driving behavior (e.g., acceleration with a scalar value greater than or equal to a predetermined value in the left-right direction) is acquired, and similarly, acceleration and deceleration marks are displayed at locations where specific acceleration and deceleration-related driving behavior (acceleration greater than or equal to a predetermined value in the forward or backward direction) is acquired. Furthermore, check boxes are displayed adjacent to the displayed marks, allowing the user to select the check boxes.

In the interface shown in Figure 7, when a user checks a checkbox located at a position where a specific driving behavior was detected, the driving score is recalculated in a way that does not reflect the driving information observed at the checked position (for example, the driving information related to that specific driving behavior may be deleted from the information input to the driving score calculation logic and then recalculated, but this is not limited to this and any appropriate method may be used). In other words, it is possible to determine the driving score that would have been obtained if that specific driving behavior had not occurred.

Figure 8 is an enlarged view of area 406 in the interface shown in Figure 7. Area 406 is a map of acceleration information obtained from a data processing device (hereinafter referred to as acceleration distribution). In this embodiment, the acceleration information obtained from the data processing device 210 installed in a vehicle is counted for each predetermined category, and the area of each category is displayed based on the count value. Here, the predetermined category is preferably the same as the category used in the compression process of step S101 by the data processing device 210. However, due to the fact that mobile unit information is obtained from multiple mobile units, different types of data processing devices 210 or equivalent devices are used, and therefore the acceleration information is not necessarily generated based on a common category. Therefore, when generating the acceleration distribution, it is preferable for the server 230 to assign each piece of acceleration information to a unified category.

In the example of Figure 8, the greater the distance from the center, the greater the acceleration, and the direction of the area from the center indicates the direction of the acquired acceleration. An area without a display indicates that the corresponding acceleration was not acquired, i.e., the corresponding acceleration was not observed. The density of the corresponding area is expressed according to the frequency of acquisition. For example, area 501 corresponds to a case where a large acceleration was acquired in the deceleration direction, and is expressed darker than area 503, which corresponds to a case where a slightly large acceleration was acquired in the acceleration direction. This means that the frequency of occurrence of acceleration corresponding to area 501 is higher than that of area 503. Similarly, area 502 indicates that a slightly large acceleration was acquired for steering to the left.

Here, when the first region 501 is selected by user input, the screen shown in Figure 9 is displayed. Specifically, this screen displays a map of the area around the location in a situation where acceleration corresponding to the first region 501 was acquired on the map, and information about the driving route on that map. Furthermore, in this embodiment, assuming that there are multiple situations in which acceleration corresponding to the first region 501 was acquired, information about the multiple corresponding situations is displayed in a selectable list. The display order is based on the time the information was acquired closest to the current time, but this is not particularly limited. When one of the multiple displayed situations is selected by user input, a screen showing details of the selected situation, as shown in Figure 10, is displayed.

In FIG. 10 , an area (e.g., a 1-kilometer square area) near the location where the acceleration was acquired is enlarged and displayed, allowing the user to understand the shape of the road. The display also shows the acceleration acquired along the vehicle's route. Specifically, the circle in FIG. 10 indicates the magnitude of the acceleration, and the arrow within the circle indicates the direction of the acceleration. Here, when the magnitude of the acceleration is small, the arrow is not displayed. Furthermore, the circle display itself may be omitted. Additionally, as shown in FIG. 10 , in this embodiment, driver information, information related to the location, information related to the display range, date and time information, remaining fuel information, driving time information, speed information, weather information (weather, temperature, humidity, etc.), and a driving score are displayed. Of course, the information is not limited to these, and any information that can be acquired from the data processing device 210, inferred from the acquired information, or acquired by the server 230 via the Internet or the like may be displayed. For example, the following information may be displayed:
- Information about the area around the location (traffic congestion, nearby construction, etc.)
- Status of the vehicle (load weight, charge level of an electric vehicle, tire wear, etc.)
- Information about other moving objects that passed near the location at a similar time - Driving details (such as acceleration details) of the vehicle (or its driver) at nearby locations over a certain period of time in the past

Furthermore, the interface shown in area 406 can display not only a map of acceleration distribution for a specific driver or mobile unit, but also a composite of acceleration distributions for multiple units. In this case, the content of the mobile unit information to be combined is not particularly limited, but it is preferable to combine mobile unit information for approximately the same period. For example, if one of the combined objects is mobile unit information for one month, it is preferable that the other combined objects are also mobile unit information for one month. Note that the data may or may not be accumulated continuously for one month, and may be extracted and added together for one month's worth of data from different, non-contiguous time periods.
The synthesis method may be any one of addition, subtraction, multiplication and division, or an appropriate combination of these, and is not particularly limited to these.

For example, the following synthesis content can be given:
Example 1: For one moving object, the difference between the acceleration distributions in different periods (for example, the previous month and the current month) is calculated.
Example 2: Calculate the difference in acceleration distribution for two moving bodies over the same period (for example, the previous month).
<Example 3> The acceleration distributions of multiple moving bodies over the same period (for example, the first week of the current month) are added together to obtain the result.
Example 4: The acceleration distributions of a plurality of moving bodies are summed over a period of time under certain conditions (for example, two hours after the lunch break on a working day this month) to obtain the sum.
<Example 5> Calculate the difference between the sum of acceleration distributions for multiple moving objects managed by one department during a period of the first condition and the sum of acceleration distributions for multiple moving objects managed by another department during a period of the second condition.
Example 6: For one moving object, the rate of change (proportion) of acceleration distribution in different periods (for example, last month and this month) is calculated.
<Example 7> For a mobile object operating in a certain area, an OR operation is performed on the acceleration distribution over a certain period to determine the tendency of acceleration that does not occur.
<Example 8> For one moving object, an XOR operation is performed on the acceleration distributions for different periods (for example, the month of the previous year and the current month) to find the trend of the accelerations that are different.

The following describes the analysis results that are generated and displayed when multiple acceleration distributions are combined and an arbitrary acceleration region is selected from the combined results in this embodiment. Figure 11 is a flowchart of the processing from combining multiple acceleration distributions to displaying the analysis results in this embodiment.

The process performed according to the flowchart below is triggered by the following events, for example and not by limitation, assuming that a certain amount of mobile data has been accumulated: - In the safe driving diagnosis service, when the time for analysis has come (for example, once a month).
A user's analysis request has been accepted. Predetermined mobile object information has been detected (for example, an acceleration value above a predetermined threshold has been obtained for three consecutive days in the target mobile object, a collision accident has occurred, etc.).

11 , the server 230 specifies the type of compositing to be performed (S1100). Specifically, for example, a user may directly input a compositing method from a terminal connected to the server 230 via a network, or the compositing method may be specified by the user inputting, for example, one of the following predetermined compositing processes:
- Calculating the difference between two acceleration distributions obtained over different periods of time for one moving body → Subtraction of two acceleration distributions - Calculating the sum of multiple acceleration distributions obtained over different periods of time for one moving body → Addition of multiple acceleration distributions - Calculating the difference between two acceleration distributions obtained over the same period of time for two moving bodies → Subtraction of two acceleration distributions - Calculating the sum of acceleration distributions obtained over the same period of time for multiple moving bodies → Addition of multiple acceleration distributions - Calculating the sum of acceleration distributions obtained over different periods of time for multiple moving bodies → Addition of multiple acceleration distributions - Calculating the ratio of acceleration distributions obtained over two different periods of time for one moving body → Division of two acceleration distributions Note that the synthesis methods are not limited to these, and any appropriate calculation can be applied.

Next, the server 230 identifies information that can identify the mobile object information related to the synthesis process (S1101). Specifically, for example, a user inputs information that can identify the mobile object information from a terminal connected to the server 230, thereby identifying the corresponding mobile object information and identifying the acceleration distribution included in the identified mobile object information.
Here, the information specified to identify the mobile object information related to the synthesis process only needs to include at least the mobile object and the period, and may also include other information, such as one or more of the following information: location (area), driver, weather, and load capacity (e.g., limited to cases where the load capacity is 80% or more of the maximum load capacity).

The identification of the moving object and period in S1101 is preferably performed based on the synthesis method identified in S1100. For example, if the synthesis method identified in S1100 is "the difference between two acceleration distributions acquired over different periods for one moving object," the server 230 outputs an interface that allows the user to input one target moving object and multiple periods. Examples of the interface will be described later.

The order of S1100 and S1101 is not particularly limited, and may be reversed. That is, the moving object and period in the moving object information to be combined may be specified first, and then the combining method may be specified.
Alternatively, first, the moving object and the period related to the moving object information that is part of the compositing object may be identified, then the compositing method may be identified, and then the remaining moving object information that is the compositing object may be identified. In other words, first, a part of the compositing object that is the axis of the compositing process may be identified, then the compositing method may be identified, and then the remaining compositing object may be identified.
In this case, after identifying at least one piece of moving object information that will be the axis of the synthesis process and identifying the synthesis method, it is preferable that the server 230 identifies the remaining synthesis targets by presenting information about the remaining candidates for synthesis and allowing the user to select one.

For example, if acceleration information in last month's mobile body information for a certain mobile body is first identified as one of the combination targets, and subtraction is identified as the combination method, it is preferable that mobile body information relating to approximately the same period be used as the combination target by subtraction, and considering that time, mobile body, region, etc. can be considered as comparison criteria, for example, the following mobile body information may be extracted as a candidate for the remaining combination target and presented preferentially to the user to recommend selection, or may be identified as the remaining combination target. Here, preferential presentation may include, for example, a method of displaying some of the mobile body information of the identified combination target and one or more remaining combination target candidates extracted based on the identified combination method in a manner different from the other candidates.
- Mobile object information of the same mobile object for the past month (for example, the month before last, or the same month one year ago, etc.) - Mobile object information of another mobile object for the past month. This other mobile object may be, for example, a mobile object selected by the user, a mobile object that travels in the same area as the target mobile object, another mobile object under the control of the same person, a mobile object driven by another driver with a similar driving history or driving quality to the driver, a mobile object of the same model, a randomly selected mobile object, etc.
- Average mobile unit information for a specified range (e.g., the same region, the same prefecture, the entire country, etc.) for the last month. In other words, the mobile units may be the same or different, may be different units in the same region, may have the same or different managers, and may have the same or different driving history or driving quality, and various selection methods can be applied without any particular restrictions.

As a further example, if the acceleration distribution for the past month for a certain mobile body is first identified as one of the objects to be synthesized, and if additive is identified as the synthesis method, it is preferable to use mobile body information relating to approximately the same period as the objects to be synthesized using additive synthesis. Also, considering that time, organization, region, etc. can be considered as comparison criteria, for example, the following mobile body information is extracted as a candidate for the remaining objects to be synthesized, and is presented to the user preferentially to recommend selection, or is identified as the remaining object to be synthesized.
- Mobile object information for the same mobile object for several months in the past (for example, the month before last and several months before that) - Mobile object information for the same month for some or all mobile objects in the group to which the target mobile object belongs

As a further example, if the acceleration distribution for the past month of a certain moving body is identified as one of the synthesis targets, for example, the following combinations of moving body information and synthesis methods are extracted as candidates and presented preferentially to the user to recommend selection, or are identified as the remaining synthesis targets and synthesis methods.
- Mobile object information of the same mobile object for several months in the past (for example, the month before last and several months before that) and subtraction - Mobile object information of the same mobile object for several months in the past (for example, the month before last and several months before that) and addition - Mobile object information of another mobile object for the past month and subtraction - Average mobile object information of a specified range in the past month (for example, the same region, the same prefecture, the whole country, etc.) and subtraction - Mobile object information of some or all mobile objects in the group to which the target mobile object belongs for the same month and addition

Here, when identifying the remaining mobile body information to be combined and the combination method based on a certain mobile body information identified as one of the combination targets, for example, candidates for the remaining mobile body information to be combined that have been identified based on the identified mobile body information may be presented to the user, and the user may be allowed to select one to determine the remaining mobile body information to be combined, and then the combination method may be identified or presented as a candidate based on all of the identified mobile body information to be combined, and the user may be allowed to decide.
When extracting candidates for the remaining mobile body information to be combined based on the mobile body information corresponding to the identified mobile body information, for example, location information contained in the mobile body information, as well as driver information, passenger information, related person (e.g., owner, manager, etc.) information, affiliation (group, company, etc.) information, remaining fuel information, load information, remaining power information, weather information, driving score, etc. contained in the mobile body information may be used.

Similarly, for example, the synthesis method may be determined by presenting candidates for synthesis methods identified based on the identified mobile body information to the user and having the user select one, and then, based on the identified mobile body information and the identified synthesis method, the remaining mobile body information to be synthesized may be identified or presented as candidates and the user may be allowed to decide.
Similarly, for example, one or more candidate synthesis methods may be identified based on the moving body information identified as one of the synthesis targets, and the remaining candidate moving body information to be synthesized may be identified based on the moving body information identified as one of the synthesis targets and each of those candidate synthesis methods, and combinations of the candidate synthesis methods and the corresponding remaining candidate synthesis targets may be presented to the user for decision.

In other words, by identifying all or part of the compositing targets and the compositing method, the remaining compositing targets and compositing methods may be preferentially presented, recommended, or determined based on the content of the mobile object information corresponding to the identified compositing targets and/or the compositing method.

12, 13, and 14 show examples of interfaces for specifying a portion of the compositing objects and the compositing method, and then specifying the remaining compositing objects.
The interface is provided, for example, in a display unit or an input unit of an administrator terminal, etc. As shown in each figure, the interface is composed of areas A1, A2, and A3, where area A1 is an area for specifying a part of the compositing target, area A2 is an area for specifying a compositing method, and area A3 is an area for specifying the rest of the compositing target.

12 shows an example of an interface displayed when specifying a portion of a compositing target. The corresponding region, region A1, includes object A11 showing the acceleration distribution of the portion of the compositing target, and object A12, which is an region for setting information for specifying the portion of the compositing target. In object A12, for example, the driver, driving period, driving area, and weather are set as information for specifying the portion of the compositing target. However, the information set in object A12 is not particularly limited as long as it is information for specifying the portion of the compositing target. Furthermore, in FIG. 12, the interface is configured such that the driver, start date and end date of the driving period, and weather are determined using a pull-down menu, and the driving area is input directly. However, the input method is not limited to these, and any input method can be applied, such as a check box, a radio button, or, for the driving area, an operation for selecting a desired area on a map.
The interface shown in Figure 12 is for a situation where the driver is AAA, the driving period is from January 1, 2022 to January 31, 2022, the driving area is the 23 wards of Tokyo, Mitaka City, Musashino City, Komae City, Chofu City, and Nishitokyo City, and the weather is about to be selected from no preference, sunny, or rainy using a pull-down menu.
In area A2, no information is displayed because the synthesis method has not yet been specified, and in area A3, some of the synthesis objects and the synthesis method have not yet been specified, so the remaining synthesis objects cannot yet be specified, and similarly no information is displayed.

Figure 13 shows an example of an interface that is displayed when specifying a compositing method after a portion of the compositing target has been identified. The corresponding area, area A2, is composed of an object A21 that displays candidate compositing methods, "addition (+),""subtraction(-),""division(÷)," and "multiplication (x)," in a pull-down menu.
In the area A1 for specifying a part of the compositing object, the driver, driving period, driving area, and weather, which are information for specifying a part of the compositing object, are already set, and are colored gray to indicate a fixed state so that further changes cannot be made. Here, once the setting in the area A1 is completed, the setting in the area A2 may be made possible with the contents of the area A1 fixed, or both the area A1 and the area A2 may be made possible to set in any order.
In addition, in area A3, although some of the objects to be combined have been identified, the combining method has not yet been identified, so the remaining objects to be combined cannot be identified and no information is displayed.

14 shows an example of an interface that displays information about remaining compositing target candidates based on the specified compositing target parts and compositing method after a portion of the compositing target and a compositing method have been specified. The corresponding area A3 includes an object A311 showing the acceleration distribution of candidate 1, which is the first of the remaining compositing target candidates, an object A312 showing information for identifying candidate 1, and an object A313 showing a checkbox for selecting candidate 1 as the remaining compositing target, and an object A321 showing the acceleration distribution of candidate 2, which is the second of the remaining compositing target candidates, an object A322 showing information for identifying candidate 2, and an object A313 showing a checkbox for selecting candidate 2 as the remaining compositing target.
In addition, in area A1 for identifying part of the composite object, the information for identifying part of the composite object, such as the driver, driving period, driving area, and weather, has already been set and is grayed out, and in area A2, the composite method has already been identified as "subtraction" and is grayed out.

As shown in FIG. 14, a portion of the synthesis targets is specified as follows: the driver is "AAA," the driving period is one month (31 days) from January 1, 2022 to January 31, 2022, the driving area is the 23 wards of Tokyo, Mitaka City, Musashino City, Komae City, Chofu City, and Nishitokyo City, and the weather is "not specified" (i.e., weather does not matter), and the synthesis method is specified as "subtraction (-)." Therefore, as the remaining synthesis targets, comparison targets having a similar amount to the portion of the synthesis targets are extracted. is preferable, and therefore, candidate 1 is one with the same driver, driving area (note that since the amount of information exceeds the amount that can be displayed, the portion that cannot be displayed is omitted as "..."), weather, and driving period length (1 month; 31 days), but a different driving period timing (December 1, 2021 to December 31, 2021), or candidate 2 is one with a different driver (driver is "BBB") but similar driving period length and timing, driving area, and weather.
Since the user has checked the checkbox for object A323 of candidate 2, candidate 2 is identified as the remaining object to be composited.

A specific example of specifying a synthesis method based on all of the identified synthesis targets will also be described below.
For example, if last month's worth of mobile body information for one mobile body and last month's worth of mobile body information for another mobile body are identified as targets for compositing, since the mobile body information is of approximately the same amount for different mobile bodies, the purpose of comparison is assumed, and subtraction is determined to be a preferable compositing method, for example, and subtraction is presented to the user as the compositing method or recommended (for example, presented preferentially by changing the display mode from other candidates), or subtraction is determined as the compositing method. Also, if the purpose of comparison is assumed, division rather than subtraction may be presented, recommended, or determined as the compositing method (for example, by presenting it preferentially). Conversely, if different amounts of mobile body information are identified as targets for compositing, subtraction and division are considered undesirable, and therefore addition may be recommended, for example.
Furthermore, for example, if mobile body information for the previous month for a number of mobile bodies and mobile body information for the month before last for a number of other mobile bodies owned by the same owner are identified as targets for compositing, since the mobile body information is from the same owner, it is determined that additive is a preferable compositing method for the purpose of referring to the cumulative results, and additive is either presented to the user as a preferred compositing method (for example, by presenting it as a preferred compositing method), or additive is determined to be the compositing method.

In the above explanation, two pieces of moving body information are identified as objects to be combined, and one combination method is identified as a combination method for the two objects to be combined. However, the number of objects to be combined and the number of combination methods are not limited to this, and any number can be applied.
For example, assume that four pieces of mobile object information, A, B, C, and D, are identified as targets for compositing, and these identified targets for compositing are combined by adding A and B (A+B), adding C and D (C+D), and subtracting A+B and C+D ((A+B)-(C+D)). In this case, for example, A may first be identified, and then the remaining targets for compositing, B, C, and D, may be identified based on the identified A, and a combination of addition and subtraction may be identified as a compositing method. Alternatively, these candidates may be identified and presented to the user for selection. As an example, if A is mobile object information for a certain mobile object in a certain region (region 1) from the previous month, B may be identified as mobile object information for the same mobile object in another region (region 2) in the same month, C may be identified as mobile object information for the same mobile object in region 1 in a different month, and D may be identified as mobile object information for the same mobile object in region 2 in the same month as C. That is, for example, it corresponds to a case where it is desired to compare the driving conditions of a certain mobile object not only in area 1 but also in area 2 between the previous month and another month.
Similarly, for example, if B and C are identified as synthesis targets and a similar synthesis method such as ((○ + B) - (C + ○)) is identified (○ indicates that no synthesis targets have been identified), the remaining synthesis targets may be identified as A and D or presented as candidates based on B and C and the above synthesis method.

Note that the object of synthesis here can be interpreted as a set of mobile object information, which is a set of mobile object information divided into certain sections as described above, or as a unit of each piece of acquired mobile object information. In other words, in the former case, it can be understood as performing synthesis by addition, subtraction, etc., using a set based on a certain criterion as a unit, and in the latter case, it means synthesizing by addition until a certain set is reached, and then further synthesizing the set resulting from the additive synthesis by addition, subtraction, etc.

Next, the server 230 identifies the mobile object information to be combined based on the information on the mobile object and period identified in S1101 (S1102). Specifically, the server 230 identifies the mobile object information corresponding to the identified information on the mobile object and period from the information stored in the memory unit 232 of the server 230.

Next, the server 230 synthesizes the acceleration distributions corresponding to the identified mobile body information using the identified synthesis method (S1103). Specifically, the server 230 applies the identified synthesis method to the frequency of each of the identified mobile body information for each specified category. For example, if the identified synthesis method is "the difference between two acceleration distributions obtained over different periods for one mobile body," the server 230 calculates the difference between the two frequency values for each specified category.

The server 230 then outputs the synthesis results (S1104), and the synthesis results are displayed, for example, on the display unit of the user terminal. At this time, noteworthy categories, such as those with high frequency values, may be displayed in a manner that makes them distinguishable from other categories. This allows the user to easily identify categories with high values and encourages them to select them as categories for analysis processing.
Note that a category is not necessarily worthy of attention if its value is greater than a predetermined threshold. Other examples include a category whose value is in the top 10% of all categories, a category whose frequency values vary greatly (for example, its variance is greater than a predetermined threshold), or a category whose frequency values are significantly greater than those of the surrounding categories (for example, its variance is greater than a predetermined multiple of the average or maximum value of the four adjacent categories).

Fig. 15 shows an example of the difference in acceleration distribution between the two moving bodies mentioned above in Example 1. Fig. 15 shows an example of comparing the acceleration distribution between a certain month and the previous month for a vehicle driven by a certain driver.
As shown in Figure 15, each region is represented by a density corresponding to the magnitude of the difference value, and it is possible to recognize which of the two regions has a higher occurrence frequency. For example, region 601 is represented by a vertical line with a high density, which indicates that the acceleration corresponding to this region occurred much more frequently in that month than in the previous month. On the other hand, region 602 is represented by a diagonal line with a high density, which indicates that the acceleration corresponding to this region occurred much less frequently in that month than in the previous month.
By comparing the acceleration distributions in this way, it is possible to perform a relative evaluation of driving from the viewpoints of time, area, person, etc.

16 shows an example of adding up the acceleration distributions of multiple moving objects, referring to the above-mentioned Example 3. FIG. 13 shows the addition of the acceleration distributions of multiple moving objects managed by a certain department, regarding their operation in a certain week.
16, similar to FIG. 8, each region is represented by a density corresponding to the magnitude of the summed value. That is, the higher the density, the more frequently the acceleration corresponding to that region occurred.

Furthermore, the user may specify a limiting condition for the content displayed in Fig. 15 or 16, and the result when the corresponding limiting condition is applied may be displayed (S1105). Fig. 17 is a distribution diagram of acceleration when the limiting condition is to limit the moving objects to those in the top 20% of driving scores, and further the target area is limited to the vicinity of YY in XX city.
In the distribution diagram shown in Figure 17, it can be seen that the frequency of acquisition of accelerations corresponding to sudden steering, sudden acceleration, and sudden deceleration is lower than in Figure 16, due to the fact that this is a collection of mobile bodies with high driving scores. Furthermore, because the density of area 801 and its surrounding divisions remains high, even though this is a collection of mobile bodies with high driving scores, there are many accelerations corresponding to area 801 in this target area, and it can be inferred that there is some background (for example, there are roads with poor visibility, making sudden braking more likely, etc.). Therefore, this is a division that deserves attention and is displayed in a manner that allows it to be distinguished from other divisions.

The user can then specify any category to be analyzed in order to identify mobile entity information relevant to the analysis process. The analysis process, which will be described in detail later, is a process of determining whether there are any correlations, such as trends or commonalities, between the identified group of mobile entity information, and if so, what those trends or commonalities are.
When specifying a section, it is possible to select any range including one or more sections, and for example, it is possible to specify an area as shown in FIG.
FIG. 18A specifies a section in which the scalar value of acceleration in all directions is greater than a predetermined value.
18B shows a case where the section with the largest value is designated among all sections. As described above, the section to be noted may be displayed in a manner different from the other sections.
FIG. 18C designates a section in which the scalar value of acceleration in the forward direction is greater than a predetermined value.
FIG. 18D specifies a section in the forward direction regardless of the magnitude of the scalar value of acceleration.
18E shows a section that specifies a section whose value is within the top 10% of sections whose acceleration scalar value is greater than a predetermined value. The section to be noted may be displayed in a different manner from the other sections.
18F shows an area arbitrarily designated by a user operation. For example, the area may be designated by filling it with a finger on a tablet or smartphone. The designated section may be a section that shares at least one pixel with the area, or a section that shares a predetermined number of pixels (e.g., 50% or more of the size of the section) with the area; the relationship between the area and the section is not particularly limited.

As a result, the server 230 identifies the segmented area specified by the user (S1106). For example, if area 803 is specified in the example of Figure 17, the server 230 not only displays map information and route information for the vicinity of the position where the acceleration corresponding to area 803 was acquired, but also analyzes the circumstances under which the acceleration was acquired, and derives and outputs trends, commonalities, etc. (S1107).

19 is an example of a screen displayed on a user terminal when a certain acceleration region is selected. As shown in FIG. 19, information about trends is output for the following items, for example:
Time period: For example, early morning (4am to 8am), morning (8pm to 12pm), afternoon (12pm to 4pm), evening (4pm to 8pm), night (8pm to midnight), and late night (midnight to 4am). If the frequency of such acceleration acquired during a certain time period is higher than a predetermined value (for example, 30% or more of the total; here, the proportion of the frequency at which such acceleration is acquired compared to the total will be referred to as the tendency), the tendency for that time period is deemed to be high and the time period will be displayed.
Weather: When the frequency of a certain weather is higher than a predetermined value, the weather is considered to have a high tendency and is displayed. Note that, since driving is likely to be difficult in some weather conditions, such as rain and snow, information about the tendency may be displayed only when the tendency is high for those weather conditions.
・Load capacity (ratio to maximum load capacity)
The load information is acquired from a load acquisition unit (not shown) provided in the mobile object or the data processing device 210, or by a user inputting, for example, each unit of driving. While the load information in FIG. 19 indicates the ratio of the load to the maximum load, it may also be the load (e.g., in kilograms). Based on the acquired load information and corresponding acceleration information, information about loads with a high tendency is displayed. Here, as with the weather, since a high maximum load ratio or load is likely to make driving difficult, information about the tendency may be displayed only when the load ratio or load is equal to or greater than a predetermined value.
Continuous driving time If the difference in time between the time when the situation occurred and the time of the last break is a time period that shows a large tendency with the selected acceleration, the tendency is deemed to be large and the time period is displayed.
Vehicle type/class When a particular vehicle type or class has a high tendency for the selected acceleration to occur, it is determined that the tendency is high and the vehicle is to be displayed.

Of this information, information about the time period and continuous driving time can be calculated from the corresponding time information, and weather information is obtained by server 230 via the Internet or other means based on the corresponding time information and location information. Information about load capacity and vehicle type and class is obtained by user input into the user terminal.

The content of the information about trends is, for example, information about the most frequent element and the percentage of that frequency, as shown in Figure 19. However, the content of the information about trends is not limited to this, and is not particularly limited as long as it is information that allows the trend to be confirmed in some form.

Similarly, by selecting the area 701 in the example of FIG. 16 or the area 601 in the example of FIG. 15 before the application of the limiting conditions, the tendency may be calculated and output in the same manner as in the example of FIG. 19 .
When a region is selected as a result of comparing two acceleration distributions, the display may show the conditions for both of the two comparison targets, or only the condition with a higher frequency or the condition with a lower frequency. Alternatively, the display may be arranged in a manner that distinguishes between the conditions with a higher frequency and the conditions with a lower frequency (for example, by placing the condition with a higher frequency on the left and the condition with a lower frequency on the right).

[Modification]
A modification of the above embodiment will now be described.
The lower part of Figure 1 shows the results of collecting acceleration within the reference plane RP, but if acceleration is also obtained in a direction perpendicular to the reference plane RP (i.e., a vertical direction (e.g., up and down direction of the vehicle) that is orthogonal to both the forward direction (FR-RR direction) and the lateral direction (LS-RS direction)), the angle from this vertical direction may also be used to plot the position in polar coordinates or spherical coordinates.

The magnitude of acceleration may be calculated in three dimensions, including the vertical direction, and plotted within a two-dimensional reference plane RP.

The data processing device 210 may store acquired mobile object information for a predetermined period of time or a predetermined distance traveled in the memory unit 232, and when predetermined communication becomes possible while traveling (for example, when passing near a roadside device installed on the side of the road that is capable of data communication with the mobile object using a predetermined communication protocol), it may transmit and store all or part of the stored mobile object information to the server 230 or a temporary data storage server (not shown) that temporarily stores data. In this way, when mobile object information is stored in the temporary data storage server, the server 230 accesses the temporary data storage server as appropriate (for example, every hour), extracts the mobile object information, and stores it in the memory unit 232.

In the above description, an example was described in which acceleration information and position information are acquired in the data processing device 210, and time information is linked to these to transmit and store as mobile body information, but the configuration of the mobile body information is not limited to this.
For example, speed information may be used instead of acceleration information. In this case, the speed information linked to position information and time information is transmitted to the server 230 as mobile body information and stored therein, and the same processing as in the case of acceleration information is performed. In this case, the data processing device 210 includes a speed information acquisition unit that acquires the speed information of the vehicle. Alternatively, angular velocity information may be used instead of acceleration information. In this case, the angular velocity information linked to position information and time information is transmitted to the server 230 as mobile body information and stored therein, and the same processing as in the case of acceleration information is performed therein. In this case, the data processing device 210 includes an angular velocity information acquisition unit that acquires the angular velocity information of the vehicle.
For example, the mobile information may further include at least one of the following:
Information about the load capacity Information about the charge amount of the electric vehicle (e.g., remaining capacity, SOC, SOH, etc.)
- Information regarding the temperature of the luggage compartment in a temperature-controlled vehicle - Information regarding the engine RPM - Information regarding the gear during driving In this case, the data processing device 210 is equipped with corresponding means (a load information acquisition unit, a charge amount information acquisition unit, a temperature information acquisition unit, an engine RPM information acquisition unit, and a gear information acquisition unit, respectively), and the acquired information is sent to the server 230 and stored.

In the above explanation, an example was described in which the data processing device 210 is inserted into a socket in an automobile, but this embodiment is not limited to this. This embodiment can also be applied when the data processing device 210 is connected to the vehicle via a connection terminal other than a cigarette lighter socket, when it is configured as an integral part of the vehicle, or when it is simply placed on the dashboard or the like inside the vehicle without being permanently connected to the vehicle.

In the above description, the data processing device 210 is described as including an acceleration information acquisition unit 211 and a position information acquisition unit 214, but this is not the only possible configuration. For example, the acceleration information acquisition unit 211 may be provided in a device different from the data processing device 210, and the acquired acceleration information may be transmitted to the data processing device 210, where the acceleration information and position information may be linked. Conversely, the position information acquisition unit 214 may be provided in a device different from the data processing device 210, and the acquired position information may be transmitted to the data processing device 210, where the acceleration information and position information may be linked. In this way, the elements of the data processing device 210 do not have to be configured as an integrated unit; separate devices may have the elements, and the acquired information may be transmitted to the data processing device 210, where it may be aggregated and linked. Alternatively, the information may be aggregated and linked in the server 230.

In the above explanation, a vehicle is used as an example of a moving object, but the scope of applicability of the present invention is not limited to vehicles. For example, the invention can be applied to any moving object, such as motorcycles, bicycles, airplanes, helicopters, drones, and ships.

In the above explanation, we have shown a mode in which information on the situation related to acceleration identified in the acceleration distribution for a single moving object is displayed, but this mode is not limited to this. For example, any area may be specified on map information, and acceleration information for vehicles in that area may be displayed. In this case, for example, when the "Display acceleration distribution in that area" button is pressed in Figure 10, acceleration information for vehicles that have passed through that area is collected, and an acceleration distribution like that shown in Figure 8 is displayed.

In the above description, addition, subtraction, division, and multiplication have been shown as examples of the combination method, but the combination method is not limited to these, and any combination method can be applied. For example, a combination method that combines these methods may be used. When combining mobile object information sets A and B, the frequency value in each division for A may be tripled, and the frequency value in each division for B may be quadrupled, and the difference therebetween may be calculated (i.e., 3×A−4×B). This is effective, for example, when mobile object information set A has a four-month amount of information and mobile object information set B has a three-month amount of information, and the difference is obtained based on the assumed annual information volume of both sets.
Furthermore, an acceleration distribution calculated by using a multiple of an arbitrary number or an arbitrary number as a divisor may be output.

Furthermore, as an example of another synthesis method, comparison operations or logical operations may be performed. For example, an operation may be performed in which values are compared for each category, and if the first value is less than the second value, a true result of 1 is output; conversely, if the first value is greater than or equal to the second value, a false result of 0 is output. Similarly, an operation may be performed to output 1 or 0 (or any arbitrary numerical value) based on criteria such as whether the first value is less than or equal to the second value, whether the first value is greater than the second value, whether the first value is greater than or equal to the second value, whether the first value is equal to or greater than the second value, whether the first value is equal to or equal to the second value (in this case, a determination can be made as to whether the first value is within a predetermined range with a certain margin), whether the first value is not equal to the second value, whether both the first and second values are greater than or equal to 1 (or a predetermined value), whether either the first and second values are greater than or equal to 1 (or a predetermined value), whether the first and second values are different (or whether the difference is greater than or equal to a predetermined value), or whether either the first or second value is greater than or equal to 1 (or a predetermined value) and both are not greater than or equal to 1.

In the above description, the mobile object information set to be combined is identified based on information such as the driver, driving period, driving area, and weather, but the identification method is not limited to this. For example, the average of acceleration distributions corresponding to a mobile object set targeting multiple drivers may be used as the combination target, or the average of all mobile object information in a certain area collected over a certain period of time may be used as the combination target as a region average.
In this case, these average acceleration distributions may be obtained by the processes from step S1100 to step S1104. In this case, a method of calculating a weighted average may also be applied.

In the above description, the synthesis process is performed on the acceleration information or velocity information included in the moving body information, but the target of the synthesis process is not limited to these. The target of the synthesis process may be any feature amount information related to the movement of the moving body, such as jerk, angular velocity, angular acceleration, etc.
Furthermore, distributions of multiple types of feature information may be prepared for each of multiple pieces of moving body information to be combined, and these may be combined using a specified combination method. For example, for each of multiple pieces of moving body information to be combined, an acceleration distribution based on the first division and a speed distribution based on the second division may be prepared, and combination processing may be performed on the multiple acceleration distributions using the specified combination method, and combination processing may be performed on the multiple speed distributions using the same combination method, and each may be output.

Furthermore, in the above description, multiple pieces of moving body information to be combined are assigned to two-dimensional divisions, and the combination process is performed based on the two-dimensional divisions. However, the divisions to be assigned are not limited to this. The divisions to be assigned may be one-dimensional or three or more-dimensional, and the number of dimensions is not particularly limited. The manner of division is also not particularly limited. For example, for the purpose of performing a detailed analysis of acceleration occurring in a specific direction (e.g., the right direction), the divisions in a specific direction may be finer than the divisions in other directions.
The user may be allowed to select a category to which multiple pieces of moving body information to be combined are to be assigned. In this case, the category displayed by default may be changed to the selected category, or the default may remain as is and only the output may be performed in that category, allowing the user to select the category before or after selecting the combining method, for example.

In the above description, the mobile object information is assigned to a predetermined category, and the frequency in each category is acquired and used as the subject of synthesis, but the information acquired as the subject of synthesis in a predetermined category is not limited to frequency. For example, the occurrence probability calculated based on the frequency in each category may be acquired and used as the subject of synthesis.
In this case, there is an advantage that comparison is easy even if there is a difference in the amount of moving object information to be combined. In addition, from this background, it is preferable to recommend a combination method such as subtraction or division.

Although the embodiments of the present invention have been described in detail above, the scope of the present invention is not limited to the above embodiments. Furthermore, various improvements and modifications to the above embodiments are possible without departing from the spirit of the present invention. Furthermore, the above embodiments and modifications can be combined.

210 Data processing device 211 Acceleration information acquisition unit 212 Processing unit 213 Communication unit 214 Position information acquisition unit 220 Portable terminal 230 Server 231 Communication unit 232 Storage unit 233 Processing unit

Claims (11)

1. A data processing system comprising:
a storage unit that stores the moving object information acquired in time series in association with time information;
a synthesis method specification unit that specifies a synthesis method;
a mobile object information specifying unit that specifies a plurality of pieces of mobile object information related to a synthesis process from the stored mobile object information;
an allocation unit that allocates feature amount information included in each of the plurality of pieces of moving object information related to the synthesis process based on a predetermined classification;
an acquisition unit that acquires a frequency or occurrence probability of the feature amount information for each of the predetermined categories for each of the plurality of pieces of moving object information related to the synthesis process;
a synthesis result acquisition unit that acquires a synthesis result by applying the specified synthesis method to the frequency or occurrence probability of the feature amount information for each of the acquired predetermined categories in each of the plurality of pieces of moving object information related to the synthesis process;
an output unit that outputs the obtained synthesis result;
Equipped with
the mobile object information identification unit identifies a part of the plurality of pieces of mobile object information related to the synthesis process;
the combining method specifying unit specifies at least one candidate combining method for the combining process based on a part of the specified plurality of pieces of moving body information;
the mobile body information specifying unit specifies remaining mobile body information candidates of the plurality of mobile body information related to the combining process based on each of the specified at least one candidate combining method and part of the specified plurality of mobile body information;
The mobile body information identification unit and the synthesis method identification unit identify a combination of candidates selected by user input from among combinations of the identified at least one synthesis method and the corresponding identified remaining mobile body information candidates as the synthesis method for the synthesis process and the remaining mobile body information of the multiple mobile body information. 1. A data processing method in a data processing system, comprising:
storing the moving body information acquired in time series in association with time information;
Identifying a synthesis method;
Identifying a plurality of pieces of moving body information related to a synthesis process from the stored moving body information;
Allocating feature information included in each of the plurality of pieces of moving object information related to the synthesis process based on a predetermined classification; and acquiring a frequency or occurrence probability of the feature information for each of the plurality of pieces of moving object information related to the synthesis process for each of the predetermined classifications.
acquiring a synthesis result by applying the specified synthesis method to the frequency or occurrence probability of the feature amount information for each of the acquired predetermined categories in each of the plurality of pieces of moving object information related to the synthesis process;
outputting the obtained synthesis result;
Including,
Identifying the mobile object information includes identifying a part of a plurality of mobile object information related to the synthesis process;
identifying the combining method includes identifying at least one candidate combining method for the combining process based on a portion of the identified plurality of pieces of moving object information;
The data processing method includes:
Identifying remaining candidates for mobile body information of the plurality of mobile body information related to the compositing process based on each of the identified at least one candidate compositing method and part of the identified plurality of mobile body information;
The method further includes identifying a combination of candidates selected by user input from among combinations of the identified at least one synthesis method and the corresponding identified remaining mobile body information candidates as the synthesis method for the synthesis process and the remaining mobile body information of the plurality of mobile body information. In the data processing system,
storing the moving body information acquired in time series in association with time information;
Identifying a synthesis method;
Identifying a plurality of pieces of moving body information related to a synthesis process from the stored moving body information;
Allocating feature information included in each of the plurality of pieces of moving object information related to the synthesis process based on a predetermined category; and acquiring a frequency or occurrence probability of feature information for each of the plurality of pieces of moving object information related to the synthesis process for each of the predetermined categories.
acquiring a synthesis result by applying the specified synthesis method to the frequency or occurrence probability of the feature amount information for each of the acquired predetermined categories in each of the plurality of pieces of moving object information related to the synthesis process;
outputting the obtained synthesis result;
A program that causes a program to execute
Identifying the mobile object information includes identifying a part of a plurality of mobile object information related to the synthesis process;
identifying the combining method includes identifying at least one candidate combining method for the combining process based on a portion of the identified plurality of pieces of moving object information;
The program
Identifying remaining candidates for mobile body information of the plurality of mobile body information related to the compositing process based on each of the identified at least one candidate compositing method and part of the identified plurality of mobile body information;
The method further executes the step of identifying a combination of candidates selected by user input from among the combinations of the identified at least one synthesis method and the corresponding identified remaining candidate mobile body information as the synthesis method for the synthesis process and the remaining mobile body information of the plurality of mobile body information. 1. A data processing system comprising:
a storage unit that stores the moving object information acquired in time series in association with time information;
a synthesis method specification unit that specifies a synthesis method;
a mobile object information specifying unit that specifies a plurality of pieces of mobile object information related to a synthesis process from the stored mobile object information;
an allocation unit that allocates feature amount information included in each of the plurality of pieces of moving object information related to the synthesis process based on a predetermined classification;
an acquisition unit that acquires an occurrence probability of the feature amount information for each of the predetermined categories for each of the plurality of pieces of moving object information related to the synthesis process;
a synthesis result acquisition unit that acquires a synthesis result by applying the specified synthesis method to the occurrence probability of the feature amount information for each of the acquired predetermined categories in each of the plurality of pieces of moving object information related to the synthesis process;
an output unit that outputs the obtained synthesis result;
Equipped with
The synthesis method specification unit
Identifying at least one candidate compositing method based on the plurality of pieces of moving object information related to the identified compositing process;
A candidate compositing method selected by a user input from the identified at least one candidate compositing method is identified as the compositing method for the compositing process. 1. A data processing method in a data processing system, comprising:
storing the moving body information acquired in time series in association with time information;
Identifying a synthesis method;
Identifying a plurality of pieces of moving body information related to a synthesis process from the stored moving body information;
Allocating feature information included in each of the plurality of pieces of moving object information related to the synthesis process based on a predetermined classification; and acquiring an occurrence probability of the feature information for each of the plurality of pieces of moving object information related to the synthesis process for each of the predetermined classifications.
acquiring a synthesis result by applying the specified synthesis method to the occurrence probability of the acquired feature amount information for each predetermined category in each of the plurality of pieces of moving object information related to the synthesis process;
outputting the obtained synthesis result;
Including,
Identifying the synthesis method includes identifying at least one candidate synthesis method based on multiple pieces of mobile body information related to the identified synthesis process, and identifying a candidate synthesis method selected by user input from among the identified at least one candidate synthesis method as the synthesis method related to the synthesis process. 1. A data processing system comprising:
a storage unit that stores the moving object information acquired in time series in association with time information;
a synthesis method specification unit that specifies a synthesis method;
a mobile object information specifying unit that specifies a plurality of pieces of mobile object information related to a synthesis process from the stored mobile object information;
an allocation unit that allocates feature amount information included in each of the plurality of pieces of moving object information related to the synthesis process based on a predetermined classification;
an acquisition unit that acquires an occurrence probability of the feature amount information for each of the predetermined categories for each of the plurality of pieces of moving object information related to the synthesis process;
a synthesis result acquisition unit that acquires a synthesis result by applying the specified synthesis method to the occurrence probability of the feature amount information for each of the acquired predetermined categories in each of the plurality of pieces of moving object information related to the synthesis process;
an output unit that outputs the obtained synthesis result;
Equipped with
the mobile object information identification unit identifies a part of the plurality of pieces of mobile object information related to the synthesis process;
the combining method specifying unit specifies a combining method based on a part of the specified plurality of pieces of moving body information;
The mobile body information specifying unit specifies the remaining mobile body information of the plurality of mobile body information related to the combining process based on the specified part of the plurality of mobile body information and the specified combining method. 1. A data processing method in a data processing system, comprising:
storing the moving body information acquired in time series in association with time information;
Identifying a synthesis method;
Identifying a plurality of pieces of moving body information related to a synthesis process from the stored moving body information;
Allocating feature information included in each of the plurality of pieces of moving body information related to the synthesis process based on a predetermined category; and acquiring an occurrence probability of the feature information for each of the plurality of pieces of moving body information related to the synthesis process for each of the predetermined categories.
acquiring a synthesis result by applying the specified synthesis method to the occurrence probability of the feature amount information for each of the acquired predetermined categories in each of the plurality of pieces of moving object information related to the synthesis process;
outputting the obtained synthesis result;
Including,
Identifying the mobile object information includes identifying a part of a plurality of mobile object information related to the synthesis process;
the step of identifying the combining method is performed based on a part of the identified plurality of pieces of moving body information;
Identifying the mobile body information includes identifying the remaining mobile body information of the multiple mobile body information related to the synthesis process based on a portion of the identified multiple mobile body information and the identified synthesis method. 1. A data processing system comprising:
a storage unit that stores the moving object information acquired in time series in association with time information;
a synthesis method specification unit that specifies a synthesis method;
a mobile object information specifying unit that specifies a plurality of pieces of mobile object information related to a synthesis process from the stored mobile object information;
an allocation unit that allocates feature amount information included in each of the plurality of pieces of moving object information related to the synthesis process based on a predetermined classification;
an acquisition unit that acquires an occurrence probability of the feature amount information for each of the predetermined categories for each of the plurality of pieces of moving object information related to the synthesis process;
a synthesis result acquisition unit that acquires a synthesis result by applying the specified synthesis method to the occurrence probability of the feature amount information for each of the acquired predetermined categories in each of the plurality of pieces of moving object information related to the synthesis process;
an output unit that outputs the obtained synthesis result;
Equipped with
the combining method specification unit specifies at least one candidate combining method for the combining process, and specifies a candidate combining method selected by a user input from among the specified at least one candidate combining method as the combining method for the combining process;
The mobile body information identification unit identifies at least one remaining mobile body information candidate among the remaining mobile body information in the multiple mobile body information related to the synthesis process based on a portion of the identified multiple mobile body information and the identified synthesis method, and identifies the remaining mobile body information candidate selected by user input from among the identified at least one remaining mobile body information candidate as the remaining mobile body information of the multiple mobile body information related to the synthesis process. 1. A data processing method in a data processing system, comprising:
storing the moving body information acquired in time series in association with time information;
Identifying a synthesis method;
Identifying a plurality of pieces of moving body information related to a synthesis process from the stored moving body information;
Allocating feature information included in each of the plurality of pieces of moving body information related to the synthesis process based on a predetermined category; and acquiring an occurrence probability of the feature information for each of the plurality of pieces of moving body information related to the synthesis process for each of the predetermined categories.
acquiring a synthesis result by applying the specified synthesis method to the occurrence probability of the feature amount information for each of the acquired predetermined categories in each of the plurality of pieces of moving object information related to the synthesis process;
outputting the obtained synthesis result;
Including,
specifying the combining method includes specifying at least one candidate combining method for the combining process, and specifying a candidate combining method selected by a user input from the specified at least one candidate combining method as the combining method for the combining process;
Identifying the mobile body information includes identifying at least one remaining mobile body information candidate among the remaining mobile body information in the multiple mobile body information related to the synthesis process based on a portion of the identified multiple mobile body information and the identified synthesis method, and identifying a remaining mobile body information candidate selected by user input from the identified at least one remaining mobile body information candidate as the remaining mobile body information in the multiple mobile body information related to the synthesis process. The data processing system according to any one of claims 1, 4, 6 and 8,
a limiting condition specifying unit that specifies a limiting condition for the synthesis result;
The output unit outputs a result of applying the specified limiting condition to the synthesis result. 9. A data processing system according to claim 1, 4, 6, or 8,
The output unit outputs the synthesis result in a manner in which a segment that satisfies a predetermined criterion for the feature amount information for each of the predetermined segments can be distinguished from a segment that does not satisfy the predetermined criterion.