JP7572908B2 - Analysis system, analysis method, and analysis program - Google Patents

Description

One aspect of the present disclosure relates to an analysis system, an analysis method, and an analysis program.

Cited Document 1 describes a device for identifying the mass of a vehicle. This identification device detects the engine speed and engine fuel injection amount during the period from immediately after the starting gear is set and the clutch is engaged when the vehicle starts to the time the clutch is disengaged. The identification device further determines the engine torque, and determines the engine rotation acceleration from the engine speed. The identification device then determines the mass of the vehicle based on a given equation.

Cited Document 2 describes a device that analyzes the vehicle's status. This analysis device calculates a coefficient that indicates the relationship between the operation status and the driving status based on the vehicle's operation status data and driving status data at a given time, and estimates the vehicle's load at that time by comparing this coefficient with information calculated in advance for each load.

Japanese Patent Application Publication No. 11-023353 JP 2007-264818 A

It is desirable to estimate the driving conditions of freight vehicles.

An analysis system according to one aspect of the present disclosure includes at least one processor. Based on driving data showing the actual driving of a freight vehicle, the at least one processor obtains pairs of the required time in a pre-gear-change section, which is a section from when the freight vehicle starts to when the freight vehicle reaches a shift reference speed corresponding to the first shift, and the fuel consumption in the pre-gear-change section, for a plurality of pre-gear-change sections, calculates a regression line showing the relationship between the required time and the fuel consumption based on the plurality of pairs corresponding to the plurality of pre-gear-change sections, and estimates the driving status of the freight vehicle based on the regression line.

An analysis method according to one aspect of the present disclosure is executed by an analysis system having at least one processor. The analysis method includes a step of calculating, based on driving data showing the actual driving of a freight vehicle, a required time in a pre-gear shift section, which is a section from when the freight vehicle starts to when the freight vehicle reaches a shift reference speed corresponding to the first gear shift, and a regression line showing the relationship between the required time and fuel consumption based on a plurality of pairs corresponding to a plurality of pre-gear shift sections, and a step of estimating the driving status of the freight vehicle based on the regression line.

An analysis program according to one aspect of the present disclosure causes a computer to execute the steps of: acquiring pairs of the required time in a pre-gear-change section, which is the section from when the freight vehicle starts to when the freight vehicle reaches the shift reference speed corresponding to the first shift, and the fuel consumption in the pre-gear-change section, for multiple pre-gear-change sections, based on driving data showing the actual driving of the freight vehicle; calculating a regression line showing the relationship between the required time and the fuel consumption, based on multiple pairs corresponding to the multiple pre-gear-change sections; and estimating the driving conditions of the freight vehicle based on the regression line.

In this respect, a regression line is calculated based on driving data from the pre-shift section, where the characteristics of freight vehicles are more likely to appear. Using this statistical method, the driving conditions of freight vehicles can be estimated.

According to one aspect of the present disclosure, the driving conditions of a freight vehicle can be estimated.

1 is a diagram illustrating an example of an overall configuration of an analysis system according to an embodiment. FIG. 2 is a diagram showing a general hardware configuration of a computer used in the analysis system according to the embodiment. FIG. 2 is a diagram illustrating an example of a functional configuration of an analysis system according to an embodiment. 1 is a flowchart illustrating an example of a regression analysis performed by the analysis system according to the embodiment. 1 is a graph showing an example of a regression line. 1 is a graph showing an example of mapping onto a regression line. 11 is a graph showing an example of a group of projected points on a regression line. 11 is a histogram showing an example of a frequency distribution of a projected point cloud. 10 is a flowchart illustrating an example of estimation of a load amount by the analysis system according to the embodiment. 4 is a flowchart showing an example of estimation of fuel efficiency by the analysis system according to the embodiment. 4 is a graph for explaining a method of calculating fuel consumption. FIG. 13 is a diagram illustrating an example of a display of an analysis result.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. In the description of the drawings, identical or equivalent elements are given the same reference numerals, and duplicate descriptions will be omitted.

[System Overview]
The analysis system 1 according to the embodiment is a computer system that analyzes driving data showing the actual driving of a freight vehicle and provides the analysis results to a user. A freight vehicle is a vehicle intended to transport freight. The driving data is electronic data showing the driving or stopping of the freight vehicle from when the engine of the freight vehicle starts to when it stops. As at least a part of the analysis, the analysis system 1 estimates the driving conditions of the freight vehicle based on the driving data. In one example, the analysis system 1 estimates how much freight the freight vehicle actually carried while driving based on the distribution of the load. The load refers to the weight of the freight loaded on the freight vehicle. In another example, the analysis system 1 estimates the fuel efficiency of the freight vehicle. The fuel efficiency refers to a value indicating how far the freight vehicle has traveled with one unit amount (e.g., one liter) of fuel. The distribution of the load and the fuel efficiency are both examples of the driving conditions of the freight vehicle. The user can refer to the analysis results provided by the analysis system 1 to know what the actual driving conditions of the freight vehicle were like.

[System Configuration]
1 is a diagram showing an example of the overall configuration of an analysis system 1. In one example, the analysis system 1 includes a server 10, which is a computer that executes analysis of trucks. The server 10 is connected to a database 20 and at least one user terminal 30 via a communication network N.

The database 20 is a device that stores various data used in the analysis system 1. The database 20 may be a component of the analysis system 1, or may be provided in a computer system separate from the analysis system 1.

The user terminal 30 is a computer operated by a user of the analysis system 1. The user may be, for example, a clerk at a service station (e.g., a gas station) that provides services to trucks, an employee of a business that owns and manages trucks, a truck driver, etc. The user terminal 30 may be a mobile terminal such as a smartphone, a tablet terminal, or a laptop personal computer, or may be a stationary computer such as a desktop personal computer.

The communication network N is typically constructed by the Internet, an intranet, or a combination of these. The communication network N may be constructed by a wired network, a wireless network, or a combination of these.

Figure 2 is a diagram showing a general hardware configuration of a computer 100 that can function as a server 10. For example, the computer 100 includes a processor 101, a main memory unit 102, an auxiliary memory unit 103, a communication control unit 104, an input device 105, and an output device 106. The processor 101 executes an operating system and application programs. The main memory unit 102 is composed of, for example, a ROM and a RAM. The auxiliary memory unit 103 is composed of, for example, a hard disk or a flash memory, and generally stores a larger amount of data than the main memory unit 102. The auxiliary memory unit 103 stores a program 110 for causing at least one computer to function as a server 10. The communication control unit 104 is composed of, for example, a network card or a wireless communication module. The input device 105 is composed of, for example, a keyboard, a mouse, a touch panel, etc. The output device 106 is composed of, for example, a monitor and a speaker.

Each functional module of each device is realized by loading program 110 onto processor 101 or main memory 102 and executing the program. Program 110 includes code for realizing each functional module of server 10. Processor 101 operates communication control unit 104, input device 105, or output device 106 in accordance with program 110, and reads and writes data in main memory 102 or auxiliary memory 103. Each functional module of server 10 is realized by this processing. Data or databases required for processing may be stored in main memory 102 or auxiliary memory 103.

The server 10 may be composed of one or more computers. When multiple computers are used, a single server 10 is logically configured by connecting these computers to each other via a communication network.

The program 110 may correspond to an analysis program. The program 110 may be provided by being permanently recorded on a tangible recording medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. Alternatively, the program 110 may be provided via a communication network as a data signal superimposed on a carrier wave.

Figure 3 is a diagram showing an example of the functional configuration of the analysis system 1 (server 10). In one example, the server 10 has a data registration unit 11, a data extraction unit 12, and an analysis unit 13 as functional components.

The data registration unit 11 is a functional module that registers the driving data of the freight vehicle in the database 20. In one example, the data registration unit 11 receives driving data recorded by the logger 40 of the freight vehicle and stores the driving data in the database 20. The data registration unit 11 may receive the driving data from the logger 40 via the communication network N. Alternatively, the user terminal 30 may access a portable recording medium (e.g., a memory card) on which the driving data has been written by the logger 40, transmit the driving data to the server 10, and the data registration unit 11 may receive the driving data. Examples of the logger 40 include a digital fuel consumption meter and a digital fuel gauge.

The driving data is time-series data consisting of multiple records recorded at a given time interval (e.g., every second). In one example, the driving data includes, as data items, a vehicle code, a file name, a logger ID, a recording date and time, a speed, an engine RPM, an accumulated mileage, and an accumulated fuel consumption. The vehicle code is an identifier that uniquely identifies each freight vehicle. The file name is the file name of the driving data on the file system. The logger ID is an identifier that uniquely identifies the logger of the freight vehicle. The recording date and time is the date and time when the record was recorded. The speed is the instantaneous value of the speed of the freight vehicle at the recording date and time. The engine RPM is the instantaneous value of the engine RPM of the freight vehicle at the recording date and time. The accumulated mileage is the total mileage of the freight vehicle in the period up to the recording date and time. The accumulated fuel consumption is the total fuel consumption of the freight vehicle in the period up to the recording date and time.

The data extraction unit 12 is a functional module that extracts partial data necessary for estimating the driving situation from the driving data. In one example, the data extraction unit 12 extracts short trip data, starting data, idling data, and constant speed driving data, and stores these extracted data in the database 20 for subsequent processing. The short trip data is data indicating the driving of the freight vehicle in a short trip, which is a section from when the freight vehicle starts to when it stops. The starting data is data indicating the driving of the freight vehicle in a pre-shift section, which is a section from when the freight vehicle starts to when the freight vehicle reaches a shift reference speed corresponding to the first shift. The first shift is typically an upshift. The shift reference speed is a reference value that is set with reference to the speed at the general first shift of a freight vehicle, and is set in advance as a common value independent of the vehicle type. In one example, the shift reference speed is set based on the typical maximum speed of the freight vehicle for starting in first gear and starting in second gear. For example, the reference speed for shifting when starting in first gear may be set to 8 km/h, taking into consideration that the first shift generally occurs when the speed is 9-10 km/h. Similarly, the reference speed for shifting when starting in second gear may be set to 13 km/h, taking into consideration that the first shift generally occurs when the speed is 15-18 km/h. Idling data is data obtained when the truck is idling. Constant speed travel data is data obtained when the truck is traveling at a given speed range that can be considered as a constant speed.

The analysis unit 13 is a functional module that analyzes the driving data stored in the database 20. In one example, the analysis unit 13 performs the analysis in response to an analysis request from the user terminal 30, and transmits the analysis result to the user terminal 30. In one example, the analysis unit 13 includes a regression analysis unit 14, a load estimation unit 15, and a fuel efficiency estimation unit 16.

The regression analysis unit 14 is a functional module that calculates a regression line showing the relationship between the time required in the pre-shift section and the amount of fuel consumed, based on the starting data. The "time required in the pre-shift section" is the time required for the freight vehicle to reach the shifting reference speed after starting. The "fuel consumption in the pre-shift section" is the amount of fuel consumed from the time the freight vehicle starts until it reaches the shifting reference speed.

The load estimation unit 15 is a functional module that estimates the distribution of the load of a freight vehicle based on the calculated regression line. In one example, the load estimation unit 15 estimates the distribution of the load using a number of categories. In this disclosure, the categories are also referred to as "load categories." For example, the load estimation unit 15 estimates the distribution of the load using five load categories: "very light, light, normal, heavy, very heavy."

The fuel efficiency estimation unit 16 is a functional module that estimates the rate of change in fuel consumption between two time periods based on the calculated regression line, and estimates fuel efficiency for a specific time period based on the rate of change. This rate of change is a value that indicates the degree of change in fuel consumption, and is also referred to as the "fuel consumption change rate" in this disclosure. The fuel consumption change rate can also be used to estimate values other than fuel efficiency.

[System Operation]
A specific description will be given of the processing executed by the analysis system 1. The processing for one particular truck will be described below, but it should be noted that the analysis system 1 can similarly execute processing for each of a plurality of trucks.

(Partial Data Extraction)
The extraction of partial data used to estimate the driving situation will now be described. As described above, this process is executed by the data extraction unit 12.

The data extraction unit 12 extracts short trip data from the travel data. In one example, the data extraction unit 12 identifies multiple short trips by referring to the travel data, and identifies a record group of the travel data for each short trip. The data extraction unit 12 then generates short trip data by associating a short trip ID with each record group, and stores the short trip data in the database 20. The short trip ID is an identifier for uniquely identifying each short trip.

The data extraction unit 12 extracts the starting data from the traveling data. In one example, the data extraction unit 12 refers to the traveling data to identify a plurality of pre-shifting sections, and identifies a record group of the traveling data for each pre-shifting section. The data extraction unit 12 identifies a section in which the freight vehicle reaches the shifting reference speed without decreasing the speed after starting as one pre-shifting section. As described above, the shifting reference speed may be set for each of the first-speed start and the second-speed start. For example, if the engine speed decreases before the speed reaches the shifting reference speed for the second-speed start, the data extraction unit 12 estimates that the freight vehicle has started in first gear, and identifies the section from the start to the shifting reference speed for the first-speed start as one pre-shifting section. If the engine speed does not decrease until the speed reaches the shifting reference speed for the second-speed start, the data extraction unit 12 estimates that the freight vehicle has started in second gear, and identifies the section from the start to the shifting reference speed for the second-speed start as one pre-shifting section. The data extraction unit 12 generates starting data by associating a pre-shift section ID with each of the identified record groups, and stores the starting data in the database 20. The pre-shift section ID is an identifier for uniquely identifying each pre-shift section.

The data extraction unit 12 extracts idling data from the driving data. In one example, the data extraction unit 12 refers to the driving data to identify multiple consecutive sections where the speed is 0 km/h over a given time span (e.g., 20 seconds), and identifies a record group of driving data for each section. The data extraction unit 12 then generates idling data by associating an idling section ID with each record group, and stores the idling data in the database 20. The idling section ID is an identifier for uniquely identifying each idling section.

The data extraction unit 12 extracts constant speed driving data from the driving data. In one example, the data extraction unit 12 refers to the driving data to identify multiple sections in which the speed fluctuation range is within a given numerical range (e.g., the average value for that time period ±10%) over a given time period (e.g., 20 seconds), and identifies a record group of driving data for each section. The data extraction unit 12 then associates a constant speed driving section ID with each record group to generate constant speed driving data, and stores the constant speed driving data in the database 20. The constant speed driving section ID is an identifier for uniquely identifying each constant speed driving section.

(Regression analysis)
With reference to FIG. 4, a regression analysis based on starting data, which is running data corresponding to the pre-shift section, will be described. FIG. 4 is a flowchart showing an example of the process as a processing flow S1. In one example, the processing flow S1 is executed in response to the analysis unit 13 receiving an analysis request from the user terminal 30. The analysis request is a data signal for requesting an analysis of the running of a freight vehicle. In one example, the analysis request includes specific information for uniquely identifying the freight vehicle to be analyzed (e.g., at least one of a vehicle code, a file name, and a logger ID) and a period to be analyzed (also referred to as a "target period" in this disclosure). The target period is typically a relatively long period, and may be, for example, several days, one week, several weeks, one month, or several months.

In step S11, the regression analysis unit 14 acquires departure data for the target period. The regression analysis unit 14 reads out the departure data corresponding to the specific information and target period indicated in the analysis request from the database 20.

In step S12, the regression analysis unit 14 interpolates the departure data. This interpolation is a process of calculating the values of individual data items of the departure data at time intervals (e.g., 0.1 seconds) that are smaller than the time intervals between records of the departure data (e.g., 1 second). This interpolation makes it possible to specify in more detail the point in time when the freight vehicle reaches the shift reference speed. An example of the interpolation is linear interpolation. If the time intervals between records are small enough to specify the point in time when the shift reference speed is reached, this interpolation may be omitted.

In step S13, the regression analysis unit 14 calculates, for each pre-shift section, the time required for the truck to reach the shift reference speed after starting, and the fuel consumption during that time, based on the interpolated start data. That is, the regression analysis unit 14 obtains a pair of the required time and the fuel consumption for each pre-shift section.

In step S14, the regression analysis unit 14 calculates a regression line showing the relationship between the required time and the amount of fuel consumed, based on multiple pairs of the required time and the amount of fuel consumed. In one example, the regression analysis unit 14 calculates the regression line by performing the least squares method on the multiple pairs.

In step S15, the regression analysis unit 14 maps each pair of required time and fuel consumption onto the regression line, and sets a group of projection points corresponding to the multiple pairs on the regression line.

In step S16, the regression analysis unit 14 calculates statistics for the group of projection points on the regression line. In one example, the regression analysis unit 14 generates a frequency distribution indicating the number of projection points for each class based on the required time, and determines a reference value and standard deviation for the group of projection points based on the frequency distribution. The reference value may be the average value or the median value.

As shown in process flow S1, in addition to calculating the regression line, the regression analysis unit 14 sets a projection point group and calculates statistical values based on the frequency distribution of the projection point group. An example of this series of processes will be described with reference to Figures 5 to 8. Figure 5 is a graph showing an example of a regression line. Figure 6 is a graph showing an example of mapping to the regression line. Figure 7 is a graph showing an example of a projection point group on the regression line. In the graphs shown in Figures 5 to 7, the horizontal axis indicates the required time and the vertical axis indicates the fuel consumption. Figure 8 is a histogram showing an example of the frequency distribution of the projection point group, where the horizontal axis indicates the required time and the vertical axis indicates the number of projection points, respectively.

As shown in FIG. 5, the regression analysis unit 14 generates a regression line 210 obtained from a set of pairs of required time and fuel consumption represented by a plurality of sample points 200. As shown in FIG. 6, the regression analysis unit 14 maps each of the plurality of pairs onto the regression line 210. As shown in the figure, this mapping is, for example, an operation of moving the sample points 200 representing the pairs onto the regression line 210 along a perpendicular line to the regression line 210. In the example of FIG. 6, the sample point 201 is mapped as a projection point 221, the sample points 202 and 203 are mapped as two projection points 222, and the sample point 204 is mapped as a projection point 223. FIG. 7 shows a projection point group 220 set by mapping all the sample points 200 onto the regression line 210. FIG. 8 shows the frequency distribution of the projection point group 220, and the reference value and standard deviation σ obtained from the frequency distribution. As described above, the reference value may be the average value or the median value. The position of the standard deviation σ in Figure 8 is the reference value ±σ.

(Estimated Load)
Estimation of load amounts based on the regression analysis will be described with reference to Fig. 9. Fig. 9 is a flowchart showing an example of the process as process flow S2. In one example, process flow S2 is executed following process flow S1, and in this case, the distribution of load amounts of freight vehicles in a specified target period is estimated.

In step S21, the payload estimation unit 15 sets a payload category based on a statistical value of the projected point cloud on the regression line. In one example, the payload estimation unit 15 sets a payload category as follows, using a reference value Mr and a standard deviation σ obtained from the frequency distribution of the point cloud on the regression line. As described above, the reference value Mr may be an average value or a median value.
・Mr±0.5σ: Medium load capacity.
· (Mr-σ) or more but less than (Mr-0.5σ): small load capacity.
Less than (Mr-σ): Very small payload.
Greater than (Mr + 0.5σ) and less than (Mr + σ): Large load capacity.
Greater than (Mr+σ): Very large payload.
This example shows that the load bins are set using at least −σ, −0.5σ, +0.5σ, and +σ, which means that the standard deviation is used to set the boundaries of the load bins.

In step S22, the load estimation unit 15 estimates the load distribution based on the load classification. In one example, the load estimation unit 15 counts the number of projection points that belong to each load classification, and estimates the load distribution based on the count. The load estimation unit 15 may express the load distribution by the number or percentage of projection points that belong to each load classification. For example, the load estimation unit 15 may estimate the load distribution as "very low = 0%, low = 33%, medium = 45%, high = 5%, very high = 17%".

(Theory of Load Estimation)
The theory of load estimation shown by the process flows S1 and S2 will be described below. When a freight vehicle with mass m is traveling at a shift reference speed v, the kinetic energy E of the freight vehicle is defined by the formula (1).
E=(1/2)mv ² ...(1)

If the shift reference speed v is considered to be constant, then equation (1) can be transformed into equation (2) using a constant α.
m = αE ... (2)

On the other hand, the formula (1) can be expanded to the formula (3) using the acceleration a, the time t, and Newton's equation of motion. The time t indicates the time required for the truck to reach the shift reference speed after starting.
^{E=(1/2)mv2=(1/2)ma2t2 = (} 1/2) ^Fat2 ...(3)

Since at=c is a constant, equation (3) can be transformed into equation (4).
E=(1/2)Fct...(4)

Since the engine torque (Nm) of a freight vehicle is almost constant in the practical range, (1/2)Fc=C can be regarded as a constant, and therefore, formula (4) can be transformed into formula (5). That is, the kinetic energy E is proportional to the time t required for the freight vehicle to reach the shift reference speed after starting.
E = Ct ... (5)

Therefore, there is a positive correlation between the kinetic energy E and the required time t. This means that the longer it takes to reach the shift reference speed, the more energy is required (i.e., the more fuel is consumed).

From equations (2) and (5), equation (6) is derived by setting the constant αC to C′.
m = C't ... (6)

Therefore, there is also a positive correlation between mass m and time required t. This means that the heavier the load, the longer it takes to reach the shift reference speed.

From equations (5) and (6), it can be said that the load weight, fuel consumption in the section before the shift, and the required time are parameters that are correlated with each other. Therefore, it is possible to estimate the load weight from the relationship between the required time and fuel consumption.

(Estimated fuel economy)
With reference to FIG. 10, the estimation of fuel efficiency based on the regression analysis will be described. FIG. 10 is a flowchart showing an example of the process as a process flow S3. The process flow S3 is a process of calculating the fuel consumption in the pre-shift section for each of two target periods, the first target period and the second target period, and estimating the fuel efficiency in the second target period based on the fuel consumption change rate between the two target periods. In one example, the second target period is a period after the first target period. For example, the start point of the second target period is after the end point of the first target period. In one example, the process flow S3 is executed in response to the analysis unit 13 receiving an analysis request from the user terminal 30. This analysis request includes specific information for uniquely identifying the freight vehicle, and the first target period and the second target period.

In step S31, the regression analysis unit 14 calculates a first regression line based on the departure data of freight vehicles in the first target period. The regression analysis unit 14 executes process flow S1 to calculate the first regression line, and a reference value and standard deviation σ based on the first regression line. As described above, the reference value may be the average value or the median value.

In step S32, the fuel efficiency estimation unit 16 calculates the first fuel consumption in the pre-shift section in the first target period based on the first regression line. The fuel efficiency estimation unit 16 integrates the first regression line in a section based on the reference value and the standard deviation σ, and calculates the first fuel consumption based on the integral value. This integral value can be said to be a value that indicates the overall trend or characteristic of the fuel consumption in the pre-shift section. Since the integral value is the area of the integral section, the fuel efficiency estimation unit 16 may obtain the square root of the integral value as the first fuel consumption in order to lower the order of the value. Figure 11 is a diagram for explaining a method of calculating the fuel consumption. In this example, the fuel efficiency estimation unit 16 calculates the square root of the integral value (area) 250 defined by the regression line 240, (reference value - σ), and (reference value + σ) as the fuel consumption.

Returning to FIG. 10, in step S33, the regression analysis unit 14 calculates a second regression line based on the departure data of freight vehicles in the second target period. The regression analysis unit 14 executes process flow S1 to obtain the second regression line, and a reference value and standard deviation σ based on the second regression line. As described above, the reference value may be the average value or the median value.

In step S34, the fuel efficiency estimation unit 16 calculates a second fuel consumption amount in the pre-shift section in the second target period based on the second regression line. The fuel efficiency estimation unit 16 integrates the second regression line in the section based on the reference value and the standard deviation σ, and calculates the second fuel consumption amount based on the integrated value. The idea behind the integration and the method of calculating the fuel consumption amount are the same as in step S32.

In step S35, the fuel efficiency estimation unit 16 calculates the fuel consumption change rate based on the first fuel consumption amount and the second fuel consumption amount. In one example, the fuel efficiency estimation unit 16 calculates the fuel consumption change rate by the following formula.
Fuel consumption change rate=(first fuel consumption amount−second fuel consumption amount)/first fuel consumption amount

In step S36, the fuel efficiency estimation unit 16 calculates a first fuel efficiency in the pre-shift section in the first target period. In one example, the fuel efficiency estimation unit 16 calculates the travel distance and fuel consumption in each pre-shift section based on the accumulated travel distance and accumulated fuel consumption indicated in each record of the departure data of the freight vehicle in the first target period. Then, the fuel efficiency estimation unit 16 calculates the first fuel efficiency based on the average travel distance and the average fuel consumption.

In step S37, the fuel efficiency estimation unit 16 calculates the second fuel efficiency in the pre-shift section in the second target period based on the first fuel efficiency and the fuel consumption change rate. In one example, the fuel efficiency estimation unit 16 calculates the second fuel efficiency by the following formula.
Second fuel efficiency = First fuel efficiency x (1 + fuel consumption change rate)

If the rate of change in fuel consumption is a positive value, the amount of fuel consumed in the pre-shift section in the second target period is less than that in the first target period, and therefore the second fuel efficiency is higher than the first fuel efficiency. If the rate of change in fuel consumption is a negative value, the amount of fuel consumed in the pre-shift section in the second target period is more than that in the first target period, and therefore the second fuel efficiency is lower than the first fuel efficiency.

(Display of analysis results)
The analysis unit 13 transmits the analysis result to the user terminal 30, and the user terminal 30 displays the analysis result. Fig. 12 is a diagram showing an example of the display of the analysis result. A screen 300 shown in this example includes fuel efficiency information 310 indicating how much fuel was reduced in the second target period (second period) compared to the first target period (first period), and load information 320 indicating the load distribution in the two target periods. The user can know the driving conditions of the freight vehicle in each target period and the change in the driving conditions over the two target periods by using the screen 300.

The fuel efficiency information 310 indicates the fuel reduction rate, the fuel reduction amount, the reduction cost, and the reduction amount of carbon dioxide (reduced CO ₂ ). The fuel reduction rate is the calculated fuel consumption change rate. That is, the fuel consumption change rate can be used as a value indicating how much the fuel consumption in the second target period has been reduced compared to the value in the first target period. As shown in FIG. 12 , the analysis unit 13 may calculate the fuel reduction amount, the reduction cost, and the reduced CO ₂ . For example, the analysis unit 13 calculates the total fuel consumption based on the driving data corresponding to the first target period, and calculates the fuel reduction amount by multiplying the total fuel consumption by the fuel consumption change rate. The analysis unit 13 multiplies the fuel reduction amount by the unit price of fuel to obtain the reduction cost. The analysis unit 13 calculates the reduced CO ₂ by a given method based on the fuel reduction amount.

The load information 320 indicates the load distribution using five load categories: "very light, light, normal, heavy, very heavy." The analysis unit 13 executes process flows S1 and S2 for the first and second target periods, respectively, to estimate the load distribution for each target period.

The analysis unit 13 may perform further analysis based on at least one of the driving data, the short trip data, the starting data, the idling data, and the constant speed driving data, and may include the results of the analysis on the screen 300. For example, the analysis unit 13 may calculate various parameters such as the total driving distance, the total driving time, the average speed, the maximum speed, the number of trips, the number of short trips, and the fuel consumption during idling during the target period. Here, a trip refers to a section from when the engine of the truck starts to when it stops. The analysis unit 13 may calculate the first fuel efficiency during the entire first target period based on the driving data, and calculate the second fuel efficiency during the entire second target period based on the first fuel efficiency and the fuel consumption change rate. The analysis unit 13 may calculate the first fuel efficiency in the idling section during the first target period based on the idling data, and calculate the second fuel efficiency in the idling section during the second target period based on the first fuel efficiency and the fuel consumption change rate. The analysis unit 13 may calculate a first fuel efficiency in a constant speed driving section in a first target period based on the constant speed driving data, and calculate a second fuel efficiency in a constant speed driving section in a second target period based on the first fuel efficiency and the fuel consumption change rate. That is, the analysis unit 13 may calculate fuel efficiency in various sections or situations.

Figure 12 shows the analysis results for one freight vehicle. By analyzing multiple freight vehicles, it is possible to understand the differences in driving conditions between the vehicles. The analysis system 1 may provide the user terminal 30 with a screen that shows the driving conditions of each of the multiple freight vehicles at once.

[effect]
As described above, the analysis system according to one aspect of the present disclosure includes at least one processor, which obtains pairs of a required time in a pre-gear-shift section, which is a section from when the freight vehicle starts to when the freight vehicle reaches a shift reference speed corresponding to a first shift, and a fuel consumption amount in the pre-gear-shift section, for a plurality of pre-gear-shift sections based on driving data indicating the actual driving of the freight vehicle, calculates a regression line indicating a relationship between the required time and the fuel consumption amount based on a plurality of pairs corresponding to the plurality of pre-gear-shift sections, and estimates the driving status of the freight vehicle based on the regression line.

An analysis method according to one aspect of the present disclosure is executed by an analysis system having at least one processor. The analysis method includes the steps of: acquiring pairs of the required time in a pre-gear-shift section, which is a section from when the freight vehicle starts until the freight vehicle reaches a shift reference speed corresponding to the first shift, and the fuel consumption in the pre-gear-shift section, for a plurality of pre-gear-shift sections, based on driving data showing the actual driving of the freight vehicle; calculating a regression line showing the relationship between the required time and the fuel consumption, based on a plurality of pairs corresponding to the plurality of pre-gear-shift sections; and estimating the driving conditions of the freight vehicle based on the regression line.

An analysis program according to one aspect of the present disclosure causes a computer to execute the steps of: acquiring pairs of the required time in a pre-gear-change section, which is the section from when the freight vehicle starts until the freight vehicle reaches the shift reference speed corresponding to the first shift, and the fuel consumption in the pre-gear-change section, for multiple pre-gear-change sections, based on driving data showing the actual driving of the freight vehicle; calculating a regression line showing the relationship between the required time and the fuel consumption, based on multiple pairs corresponding to the multiple pre-gear-change sections; and estimating the driving conditions of the freight vehicle based on the regression line.

In this respect, a regression line is calculated based on the driving data of the pre-shift section, where the characteristics of the freight vehicle are likely to appear. Using this statistical method, the driving conditions of the freight vehicle can be estimated. Since there is almost no difference in the driver's operation in the pre-shift section, the characteristics of the freight vehicle itself are likely to appear in the driving data of that section. In addition, there is a correlation between the required time and fuel consumption in the pre-shift section. Therefore, by focusing on the required time and fuel consumption in the pre-shift section obtained from the driving data, the driving conditions of the freight vehicle can be estimated more objectively. Furthermore, by using the statistical method of regression line, the driving conditions of the freight vehicle can be estimated for various situations or vehicle types without preparing unified standards and conditions for estimation. For example, it is possible to estimate the driving conditions of one freight vehicle over various periods, or for various vehicle types.

In the analysis system according to another aspect, at least one processor may estimate the distribution of the load of the freight vehicle as the driving condition. The inventors have found that the load correlates with the time required and fuel consumption in the pre-shift section. The load distribution can be estimated by using a regression line that shows the relationship between the time required and the fuel consumption.

In another aspect of the analysis system, at least one processor may estimate the load distribution using multiple categories. By introducing these categories, the load distribution can be presented to the user in an easy-to-understand format.

In the analysis system according to another aspect, at least one processor may map each of the plurality of pairs onto a regression line, set a cloud of projection points corresponding to the plurality of pairs on the regression line, and estimate the driving conditions based on the distribution of the cloud of projection points. Since the distribution of the cloud of projection points can be said to be information that represents the characteristics of the driving conditions in the pre-shift section, the distribution can be used to accurately estimate the driving conditions of the freight vehicle.

In the analysis system according to another aspect, at least one processor may map each of the pairs onto the regression line along a perpendicular line to the regression line. By such mapping, the information of each pair can be more accurately reflected in the projected point cloud.

In another aspect of the analysis system, at least one processor may generate a frequency distribution indicating the number of projection points for each class based on the required time as the distribution of the cloud of projection points. This frequency distribution reveals the characteristics of the driving conditions in the pre-shift section, allowing the driving conditions of the freight vehicle to be accurately estimated.

In another aspect of the analysis system, at least one processor may calculate a reference value, which is one of the average and median of the projection point cloud, and a standard deviation of the projection point cloud based on the frequency distribution, and estimate the driving conditions based on the reference value and the standard deviation. By using these statistical values related to the projection point cloud, the driving conditions of the freight vehicle can be accurately estimated.

In an analysis system according to another aspect, at least one processor may estimate the driving conditions based on the median and the standard deviation. The inventors have found that the frequency distribution of the projected point cloud tends not to be a normal distribution. By using the median taking such a tendency into account, the driving conditions of the freight vehicle can be estimated more accurately.

In another aspect of the analysis system, at least one processor may use a reference value and a standard deviation to set a number of categories for representing the distribution of the load of trucks, and estimate the distribution of the load based on the number of projection points in each of the categories. By considering the number of projection points in each category, the distribution of the load can be accurately estimated.

In an analysis system according to another aspect, at least one processor may set a plurality of categories using at least -σ, -0.5σ, +0.5σ, and +σ, where σ is the standard deviation. By setting each category based on the standard deviation, a plurality of categories representing the distribution of loads can be set to suit the actual conditions of freight vehicles.

In another aspect of the analysis system, at least one processor may calculate a regression line for each of two target periods, a first target period and a second target period, based on a plurality of pairs corresponding to the driving data in the target period, and calculate a rate of change in fuel consumption between the first target period and the second target period based on the two regression lines. This rate of change allows an appropriate estimation of the difference in fuel consumption between the two target periods.

In another aspect of the analysis system, at least one processor may calculate the fuel consumption in the pre-shift section for each of the two target periods based on the integral value of the regression line corresponding to the target period, and calculate the rate of change based on the two fuel consumption amounts. Since the integral value represents the tendency or characteristics of the fuel consumption amount, the difference in the fuel consumption amount between the two target periods can be appropriately estimated. In addition, by calculating the rate of change in the fuel consumption amount for the pre-shift section, where there is little difference in the driver's operation and where the characteristics of the freight vehicle itself are more likely to appear, the difference in the fuel consumption amount can be estimated more objectively.

In another aspect of the analysis system, at least one processor may map, for each of two target periods, each of a plurality of pairs corresponding to the target period onto a regression line corresponding to the target period, set a group of projection points corresponding to the plurality of pairs on the regression line, generate a frequency distribution indicating the number of projection points for each class based on the required time based on the group of projection points, calculate a reference value that is one of the average value and median of the group of projection points and a standard deviation of the group of projection points based on the frequency distribution, and integrate the regression line in an interval based on the reference value and the standard deviation to calculate an integral value. By setting the integral interval based on these statistical values for the group of projection points, it is possible to obtain an integral value that indicates a trend or characteristic of fuel consumption while reducing the amount of integral calculation.

In an analysis system according to another aspect, at least one processor may calculate an integral value by integrating the regression line in an interval based on the median and standard deviation. The inventors have found that the frequency distribution of the projected point cloud tends not to be a normal distribution. By taking such a tendency into account and using the median, it is possible to calculate an integral value that more accurately represents the trend or characteristics of fuel consumption.

In an analysis system according to another aspect, at least one processor may estimate a first fuel efficiency of the freight vehicle during a first target period based on driving data during the first target period, and estimate a second fuel efficiency of the freight vehicle during a second target period based on the first fuel efficiency and the rate of change. This process allows the second fuel efficiency to be easily estimated without referring to the driving data.

In another aspect of the analysis system, at least one processor may estimate the fuel efficiency of the freight vehicle in the pre-shift section in the first target period as the first fuel efficiency, and may estimate the fuel efficiency of the freight vehicle in the pre-shift section in the second target period as the second fuel efficiency based on the first fuel efficiency and the rate of change. This process makes it possible to easily estimate the second fuel efficiency in the pre-shift section without referring to the driving data. In addition, by determining the fuel efficiency for the pre-shift section, where there is little difference in the driver's operation and where the characteristics of the freight vehicle itself are more likely to appear, the change in fuel efficiency between the two periods can be estimated more objectively.

In another aspect of the analysis system, at least one processor may identify the pre-gear-shift section as the section in which the freight vehicle reaches the shift reference speed without slowing down after starting. This process allows the pre-gear-shift section to be accurately identified taking into account the actual driving conditions of the freight vehicle.

In another aspect of the analysis system, at least one processor may interpolate driving data corresponding to the pre-shift section, and determine the required time and fuel consumption in the pre-shift section based on the interpolated driving data. This interpolation allows the required time and fuel consumption to be accurately determined.

[Modification]
The present disclosure has been described in detail above based on the embodiments. However, the present disclosure is not limited to the above embodiments. The present disclosure can be modified in various ways without departing from the spirit and scope of the present disclosure.

The load distribution may be shown using load categories expressed as a ratio to the maximum load. If the maximum load capacity of a freight vehicle is obtained separately, the absolute load distribution can be determined based on the maximum load capacity and the ratio.

For the purposes of this disclosure, "transmitting" data or information from a first computer to a second computer means transmitting the data or information for ultimate delivery to the second computer. Note that this term also includes cases where another computer or communication device relays the data or information in the transmission.

In this disclosure, the expression "at least one processor executes a first process, executes a second process, ... executes an nth process" or a corresponding expression indicates a concept including cases where the entity executing the n processes from the first process to the nth process (i.e., the processor) changes midway. In other words, this expression indicates a concept including both cases where all n processes are executed by the same processor and cases where the processor changes among the n processes according to an arbitrary policy.

The processing procedure of the method executed by at least one processor is not limited to the example in the embodiment. For example, some of the steps (processing) described above may be omitted, or each step may be executed in a different order. In addition, any two or more of the steps described above may be combined, or some of the steps may be modified or deleted. Alternatively, other steps may be executed in addition to each of the steps described above.

1...analysis system, 10...server, 11...data registration section, 12...data extraction section, 13...analysis section, 14...regression analysis section, 15...load estimation section, 16...fuel consumption estimation section, 20...database, 30...user terminal, 40...logger, 110...program, 200-204...sample points (pairs of required time and fuel consumption), 210...regression line, 220...projection point cloud, 221-223...projection points, 300...screen, 310...fuel consumption information, 320...load information.

Claims (20)

At least one processor;
the at least one processor:
Based on driving data showing the actual driving of the freight vehicle, pairs of a required time in a pre-gear-change section, which is a section from when the freight vehicle starts to when the freight vehicle reaches a shift reference speed corresponding to a first gear change, and a fuel consumption amount in the pre-gear-change section are obtained for a plurality of the pre-gear-change sections;
calculating a regression line indicating a relationship between the required time and the fuel consumption amount based on a plurality of the pairs corresponding to the plurality of pre-shift sections;
Estimating a traveling state of the truck based on the regression line.
Analysis system. The at least one processor estimates a distribution of loads of the trucks as the driving conditions.
The analysis system according to claim 1 . the at least one processor estimating the payload distribution using a plurality of segments;
The analysis system according to claim 2 . the at least one processor:
Mapping each of the plurality of pairs onto the regression line, and setting a group of projected points corresponding to the plurality of pairs on the regression line;
estimating the traveling situation based on a distribution of the projected point cloud;
The analysis system according to any one of claims 1 to 3. the at least one processor maps each of the plurality of pairs onto the regression line along a perpendicular to the regression line.
The analysis system according to claim 4 . the at least one processor generates, as a distribution of the cloud of projection points, a frequency distribution indicating the number of the projection points for each class based on the required time;
The analysis system according to claim 4 or 5. the at least one processor:
calculating a reference value, which is one of an average value and a median value of the projection point cloud, and a standard deviation of the projection point cloud based on the frequency distribution;
estimating the driving situation based on the reference value and the standard deviation;
The analysis system according to claim 6. the at least one processor estimates the driving situation based on the median and the standard deviation.
The analysis system according to claim 7. the at least one processor:
Using the reference value and the standard deviation, a plurality of categories are set to represent a distribution of the load of the trucks;
estimating a distribution of the payload based on the number of the projection points in each of the plurality of sections;
The analysis system according to claim 8 . The at least one processor sets the plurality of categories using at least −σ, −0.5σ, +0.5σ, and +σ, where σ is the standard deviation.
The analysis system according to claim 9 . the at least one processor:
Calculating the regression line based on the plurality of pairs corresponding to the running data in each of two target periods, namely a first target period and a second target period;
Calculating a rate of change in fuel consumption between the first target period and the second target period based on the two regression lines;
The analysis system according to any one of claims 1 to 10. the at least one processor:
Calculating the fuel consumption in the pre-shift section for each of the two target periods based on an integral value of the regression line corresponding to the target period;
Calculating the rate of change based on the two fuel consumption amounts;
The analysis system according to claim 11. The at least one processor, for each of the two time periods,
Mapping each of the plurality of pairs corresponding to the target period onto the regression line corresponding to the target period, and setting a group of projected points corresponding to the plurality of pairs on the regression line;
generating a frequency distribution indicating the number of the projection points for each class based on the required time based on the group of projection points;
calculating a reference value, which is one of an average value and a median value of the projection point cloud, and a standard deviation of the projection point cloud based on the frequency distribution;
integrating the regression line in an interval based on the reference value and the standard deviation to calculate the integral value;
The analysis system according to claim 12. the at least one processor integrates the regression line in an interval based on the median and the standard deviation to calculate the integral value;
The analysis system according to claim 13. the at least one processor:
estimating a first fuel efficiency of the cargo vehicle during the first target period based on the driving data during the first target period;
estimating a second fuel efficiency of the cargo vehicle during the second target period based on the first fuel efficiency and the rate of change;
The analysis system according to any one of claims 11 to 14. the at least one processor:
A fuel efficiency of the freight vehicle in the pre-shift section during the first target period is estimated as the first fuel efficiency;
a fuel efficiency of the freight vehicle in the pre-shift section during the second target period is estimated as the second fuel efficiency based on the first fuel efficiency and the rate of change;
The analysis system according to claim 15. The at least one processor identifies a section in which the truck reaches the shift reference speed without decreasing its speed after starting, as the pre-shift section.
The analysis system according to any one of claims 1 to 16. the at least one processor:
Interpolating the travel data corresponding to the pre-shift section;
determining the required time and the fuel consumption in the pre-shift section based on the interpolated running data;
The analysis system according to any one of claims 1 to 17. 1. A method of analysis performed by an analysis system having at least one processor, comprising:
acquiring pairs of a required time for a pre-shift section, which is a section from when the freight vehicle starts to when the freight vehicle reaches a shift reference speed corresponding to a first shift, and a fuel consumption amount for the pre-shift section, for a plurality of the pre-shift sections, based on driving data indicating the actual driving of the freight vehicle;
calculating a regression line indicating a relationship between the required time and the fuel consumption amount based on a plurality of the pairs corresponding to the plurality of pre-shift sections;
estimating a traveling state of the truck based on the regression line;
Analysis methods including: acquiring pairs of a required time for a pre-shift section, which is a section from when the freight vehicle starts to when the freight vehicle reaches a shift reference speed corresponding to a first shift, and a fuel consumption amount for the pre-shift section, for a plurality of the pre-shift sections, based on driving data indicating the actual driving of the freight vehicle;
calculating a regression line indicating a relationship between the required time and the fuel consumption amount based on a plurality of the pairs corresponding to the plurality of pre-shift sections;
estimating a traveling state of the truck based on the regression line;
An analysis program that causes a computer to execute the above.

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