Information processing apparatus

Abstract

To extract data suitable for analyzing how to use a sub-drive unit in a drive unit from original data.SOLUTION: A step (S110) of setting a plurality of time windows for cutting out a datum from the original datum, a step (S120) of cutting out the datum from the original datum, a step (S130) of calculating a frequency distribution of the original datum and a frequency distribution of the extracted datum for each of a plurality of divided feature quantities, and a step (S140 and S150) of determining whether or not the original datum and the extracted datum are similar using errors in the frequency distributions. The information processor repeatedly executes steps S110 to S150 while changing the setting of a plurality of time windows, and extracts an extraction datum similar to the original datum from the original datum.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an information processing device. [Background technology]

[0002] Patent Document 1 discloses an information processing device that reduces the size of original data for analysis by compressing the original data for analysis. The original data for analysis is data collected over a predetermined period of time using a sensor mounted on a vehicle.

[0003] The information processing device disclosed in Patent Document 1 compresses data by extracting from the original data data acquired when a certain vehicle speed is reached and data acquired at an inflection point in the vehicle speed. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2008-108247 Summary of the Invention [Problem to be solved by the invention]

[0005] In a vehicle equipped with a main drive unit and an auxiliary drive unit as drive units for driving the vehicle, the usage of the auxiliary drive unit in the drive unit can be analyzed using data acquired by multiple sensors mounted on the vehicle. If data suitable for analyzing the usage of the auxiliary drive unit can be extracted from the original data, the usage of the auxiliary drive unit can be analyzed in a shorter time by using the extracted data than when using the original data. The above information processing device analyzes the traveling speed pattern using the extracted data. Therefore, the above information processing device cannot extract data suitable for analyzing the usage of the auxiliary drive unit in the drive unit from the original data. [Means for solving the problem]

[0006] An information processing device for solving the above problem acquires original data created by collecting data over a predetermined period using sensors mounted on a vehicle, and extracts data from the original data to be used for analyzing how the auxiliary drive unit is used in a drive unit including a main drive unit and an auxiliary drive unit that drive the vehicle. This information processing device includes a processing circuit, and the processing circuit executes a first step of calculating a frequency distribution in the original data for each of a plurality of feature quantities included in the original data, including the drive torque of the auxiliary drive unit and the total drive torque of the drive unit, and a second step of setting a plurality of time windows to extract data for a portion of the original data so that the sum of the periods of all the time windows is shorter than the predetermined period. A third step of extracting data from the original data using the plurality of time windows and a third step of extracting data using the plurality of time windows for the plurality of feature quantities. a fourth step of calculating, for each of the feature quantities, a frequency distribution in extracted data obtained by combining all of the data obtained by the above-mentioned calculations; a fifth step of calculating each error between the frequency distribution for each of the feature quantities in the original data and the frequency distribution for each of the feature quantities in the extracted data; and a sixth step of determining whether the original data and the extracted data are similar based on each error. These steps are repeatedly executed by changing the settings of the plurality of time windows, and when it is determined that the original data and the extracted data are similar, the extracted data is extracted as data to be used for analyzing how the sub-drive unit is used in the drive unit. [Effects of the Invention]

[0007] According to the information processing device described above, data suitable for analyzing how the sub-drive unit is used in the drive unit can be extracted from the original data. [Brief explanation of the drawings]

[0008] [Figure 1]FIG. 1 is a schematic diagram showing the relationship between a data center, which is an embodiment of an information processing device, a vehicle, and an information processing terminal. [Figure 2] Figure 2 shows graphs of part of the original data. (a) shows the transition of the driving mode, (b) shows the transition of the total driving torque, (c) shows the transition of the auxiliary driving torque, (d) shows the transition of the vehicle tilt angle, and (e) shows the transition of the driving area. [Figure 3] FIG. 3 is a flowchart showing the flow of processing executed by the processing circuitry of the data center. [Figure 4] Figure 4 shows the frequency distribution of the total drive torque and auxiliary drive torque of the original data in urban areas. [Figure 5] Figure 5 shows the frequency distribution of the total driving torque and auxiliary driving torque of the original data in a rural area. DETAILED DESCRIPTION OF THE INVENTION

[0009] An embodiment of an information processing device will be described below with reference to FIGS. <Configuration of information processing system> 1 shows the configuration of an information processing system. The information processing system includes a data center 500 equipped with an information processing device, an information processing terminal 600, a plurality of vehicles 10, and a communication network 400. The data center 500 is capable of communicating with the plurality of vehicles 10 and the information processing terminal 600 via the communication network 400.

[0010] <Configuration of Data Center 500> As shown in Fig. 1, the data center 500 includes a processing circuit 510, a storage device 520, and a communication device 530. The processing circuit 510 is an information processing device, and includes a CPU that executes processing according to a program, and a ROM in which the program is stored. The storage device 520 is capable of storing large amounts of data. The communication device 530 performs wired or wireless communication via the communication network 400. The communication device 530 includes hardware such as a network adapter, various types of communication software, or a combination of these.

[0011] <Configuration of information processing terminal 600> As shown in FIG. 1, the information processing terminal 600 includes a processing circuit 610, a storage device 620, and a communication device 630. The processing circuit 610 includes a CPU that executes processing according to a program and a ROM in which the program is stored. The storage device 620 is capable of storing large amounts of data. The communication device 630 performs wired or wireless communication via the communication network 400. The communication device 630 includes hardware such as a network adapter, various types of communication software, or a combination of these. The information processing terminal 600 is, for example, a personal computer.

[0012] <Vehicle 10 Configuration> The vehicle 10 includes a communication device 99. The communication device 99 transmits data acquired by the vehicle 10 and identification information for identifying the vehicle 10 to the data center 500 via the communication network 400.

[0013] The vehicle 10 includes an internal combustion engine 21, a motor generator 23, a power control unit (hereinafter referred to as a “PCU (Power Control Unit)”) 24, a battery 25, and a vehicle control unit 90.

[0014] The internal combustion engine 21 drives the front wheels 11F of the vehicle 10 via a front wheel drive system. The internal combustion engine 21 and the front wheel drive system are a main drive unit that drives the vehicle 10. The motor generator 23 is an electric motor that runs on electricity. The motor generator 23 rotates using the power of the battery 25 that has been appropriately converted by the PCU 24. The motor generator 23 drives the rear wheels 11R of the vehicle 10 via a rear-wheel drive system. The motor generator 23 and the rear-wheel drive system are an auxiliary drive unit that drives the vehicle 10.

[0015] The vehicle control unit 90 includes a first control device 91 that controls the internal combustion engine 21 and a second control device 92 that controls the PCU 24 . The vehicle control unit 90 includes a plurality of sensors that collect data. The first control device 91 includes a CPU that controls the operating state of the internal combustion engine 21. The first control device 91 controls the internal combustion engine 21 based on the data collected by the sensors. The second control device 92 includes a CPU that controls the PCU 24. The second control device 92 controls the PCU 24 based on the data collected by the sensors.

[0016] An example of data collected by the vehicle control unit 90 is the travel distance and vehicle speed of the vehicle 10. Further, an example of data collected by the vehicle control unit 90 is position information of the vehicle 10, the total drive torque of the drive units including the main drive unit and the auxiliary drive unit, the auxiliary drive torque which is the drive torque of the auxiliary drive unit, the tilt angle of the vehicle 10, and the driving mode.

[0017] The total drive torque is the sum of the torque that drives the front wheels 11F and the torque that drives the rear wheels 11R. The auxiliary drive torque is the torque that drives the rear wheels 11R. The torque that drives the front wheels 11F is calculated based on, for example, the torque required by the internal combustion engine 21 to run the vehicle 10 and the gear ratio of the front wheel drive system. The torque that drives the rear wheels 11R is calculated based on, for example, the torque required by the motor generator 23 to run the vehicle 10 and the gear ratio of the rear wheel drive system.

[0018] The driving modes include, for example, eco mode, normal mode, and sport mode. Normal mode is the basic driving mode. Eco mode is a driving mode that improves fuel economy and power consumption compared to normal mode. Sport mode is a driving mode that enhances the sporty driving characteristics of the vehicle 10 compared to normal mode. In each driving mode, the ratio between the drive torque of the front wheels 11F and the drive torque of the rear wheels 11R is changed in various ways depending on the driving conditions of the vehicle 10. Furthermore, each driving mode is selected manually by the driver of the vehicle 10 or automatically by the vehicle control unit 90.

[0019] <Data Extraction> The information processing terminal 600 is used to analyze how the auxiliary drive unit in a drive unit including a main drive unit and an auxiliary drive unit that drive the vehicle 10 is used. When analyzing how the auxiliary drive unit in the drive unit is used, the information processing terminal 600 sends an instruction to the data center 500. Upon receiving the instruction, the processing circuit 510 of the data center 500 performs analysis using a portion of the vast amount of data stored in the storage device 520 of the data center 500. The data to be used is selected from the vast amount of data stored in the storage device 520 according to the purpose of the analysis. This data includes multiple pieces of data related to how the auxiliary drive unit in the drive unit is used, collected using multiple sensors mounted on the vehicle 10. These pieces of data are referred to as feature quantities. The processing circuit 510 uses the feature quantities to analyze how the auxiliary drive unit is used in the drive unit of a specific vehicle 10. In this case, the features are the auxiliary drive torque of the vehicle 10 being analyzed, the total drive torque of the vehicle 10, the driving mode of the vehicle 10, the driving area obtained from the position information of the vehicle 10, and the inclination angle of the vehicle 10.

[0020] The processing circuit 510 uses a large amount of data collected over a long period of time to analyze, according to the program, how the auxiliary drive unit is used in the drive unit of the particular vehicle 10. In this analysis, the processing circuit 510 performs a huge amount of calculations, which can take a long time to complete.

[0021] Therefore, it is possible to extract extracted data that captures the overall characteristics of the original data from the large amount of data that is the original data. If such extracted data can be extracted, the processing circuit 510 can use the extracted data to perform analysis in a shorter time.

[0022] Figure 2 shows part of the original data of feature quantities related to how the auxiliary drive unit is used in the drive unit. The original data shown in Figure 2 is part of 100,000 hours of data for one vehicle 10. The original data shown in Figure 2 includes the driving mode, total drive torque, auxiliary drive torque, tilt angle, and driving area as feature quantities.

[0023] Figure 2(a) shows the driving modes. Figure 2(b) shows the total drive torque. Figure 2(c) shows the total drive torque. Figure 2(d) shows the slope angle. The slope angle is positive for uphill roads. The slope angle is negative for downhill roads. Figure 2(e) shows the driving area divided into urban and rural areas.

[0024] The driving mode of the vehicle 10, the total driving torque of the vehicle 10, the auxiliary driving torque of the vehicle 10, the tilt angle of the vehicle 10, and the driving area of ​​the vehicle 10 are correlated with how the auxiliary driving unit is used in the drive unit of the vehicle 10. The processing circuit 510 extracts data for analyzing how the auxiliary driving unit is used in the drive unit from data including the total driving torque, the auxiliary driving torque, and the driving area as feature quantities.

[0025] The extracted data is created by extracting data from the original data using multiple time windows. In Figure 2, three time windows are shown as examples of multiple time windows: a first time window W_1, a second time window W_2, and a third time window W_3, each indicated by a two-dot chain line. The start and end of each time window are set so that they do not overlap. In this example, 20,000 hours of data is extracted as the extracted data. Therefore, the start and end of each time window are set so that the total length of all the time windows is 20,000 hours.

[0026] The data center 500 searches for settings of the start and end of each time window that indicate a cutout pattern for extracting extracted data that captures the characteristics of the entire original data. The data center 500 stores information on the cutout pattern for extracting the extracted data described above in the storage device 520. The stored cutout pattern information is information on the settings of each time window found by the search.

[0027] The processing circuit 510 extracts data from the original data based on the information of the extraction pattern stored in the storage device 520 . <Searching for extraction patterns> 3 is a flowchart showing the flow of a series of processes related to the extraction pattern search process, which are executed by the processing circuit 510 of the data center 500 in accordance with a program.

[0028] 3, the processing circuit 510 acquires original data in the process of step S100. The original data is a portion of data selected from the vast amount of data stored in the storage device 520 of the data center 500 according to the purpose of analysis.

[0029] The original data for analyzing how the auxiliary drive unit in the drive unit of one vehicle 10 is used is data of the target vehicle 10 selected from a huge amount of data of multiple vehicles 10.

[0030] Next, in step S110, the processing circuit 510 sets a plurality of time windows to extract extracted data from the original data. In the example shown in Fig. 2, the duration of each time window is the same. As shown in Fig. 2, the data extracted by each extraction window is data of each feature amount for the same period.

[0031] Each time the processing circuit 510 executes the process of step S110, it randomly sets the number of time windows, the start time of each time window, and the end time of each time window. At this time, the processing circuit 510 sets each time window so that the time windows do not overlap. In this way, the processing circuit 510 randomly sets multiple time windows so that the total duration of all the time windows is a predetermined duration. In the process of step S110, the processing circuit 510 may set multiple time windows by fixing the duration of each time window to a constant value, as shown in FIG. 2. In the process of step S110, the processing circuit 510 may set multiple time windows by fixing the number of time windows to a constant value.

[0032] In this way, a cut-out pattern for cutting out data from the original data is determined by setting a plurality of time windows through the process of step S110. After determining the cut-out pattern in this way, processing circuit 510 advances the process to S120.

[0033] In the process of step S120, the processing circuit 510 extracts data from the original data using the determined extraction pattern. That is, in the process of step S120, the processing circuit 510 extracts data from the original data using the multiple time windows that have been set. Then, the processing circuit 510 combines all of the data extracted using the multiple time windows to create extracted data.

[0034] Next, in step S130, the processing circuit 510 calculates the frequency distribution of the original data and the extracted data. The original data includes a plurality of feature quantities. Among them, the auxiliary driving torque and the total driving torque are defined as first feature quantities, and other feature quantities different from the first feature quantities, which are information relating to the traveling state of the vehicle 10, are defined as second feature quantities. In this embodiment, the second feature quantity is the traveling area.

[0035] In step S130, the processing circuit 510 classifies the data of the first feature included in the original data into multiple data based on the data of the second feature when the first feature was collected. In this embodiment, the second feature is the driving area described above. Therefore, the data of the first feature is classified into urban area data and rural area data. Then, the frequency distribution of the original data for each first feature is calculated for each data classification.

[0036] Similarly, the processing circuit 510 divides the data of the first feature amount included in the extracted data into a plurality of data corresponding to the plurality of data divisions of the original data, and calculates a frequency distribution of each first feature amount in the extracted data for each data division.

[0037] The frequency distribution is calculated by classifying the data for each first characteristic amount into a plurality of classes, and showing the distribution of frequencies, which are the number of data in each class. In this embodiment, the data for the first characteristic amount is the total drive torque and the auxiliary drive torque. Therefore, the frequency distribution of the total drive torque and the frequency distribution of the auxiliary drive torque are calculated separately.

[0038] Furthermore, because the total frequency of the first feature contained in the original data and the extracted data differs, the frequency distribution of the original data and the frequency distribution of the extracted data cannot be simply compared. If 20,000 hours of extracted data are extracted from 100,000 hours of original data, the total frequency of the extracted data will be approximately one-fifth of the total frequency of the original data. In this case, by multiplying the frequency of each class of the extracted data by five, a frequency distribution of the extracted data with a total frequency equivalent to that of the original data can be obtained. Even if the above method is not used, the data distribution in the original data and the data distribution in the extracted data can be compared by calculating a relative frequency distribution as the frequency distribution of the original data and the extracted data. A relative frequency distribution is a frequency distribution that indicates what percentage of the frequency of a class is made up of the total frequency.

[0039] FIG. 4 shows the frequency distribution of the total driving torque and the auxiliary driving torque of the original data when the vehicle 10 is traveling in an urban area. FIG. 5 shows the frequency distribution of the total driving torque and the auxiliary driving torque of the original data when the driving area of ​​the vehicle 10 is a rural area.

[0040] As shown in Figures 4 and 5, in these frequency distributions, the total driving torque and auxiliary driving torque are divided into m classes from "1" to "m." The frequency distribution of the extracted data is also calculated by dividing it into classes corresponding to the original data. In this way, the processing circuit 510 divides the total driving torque and auxiliary driving torque contained in the original data and the extracted data into two classes: an urban area class and a rural area class. The processing circuit 510 calculates the frequency distribution as described above for each of the two driving area classes.

[0041] 3, the processing circuit 510 then executes the processing of step S140. In step S140, the processing circuit 510 calculates the error between the frequency distribution of the first feature amount in the original data and the frequency distribution of the first feature amount in the extracted data for each of a plurality of sections based on the second feature amount. For example, the processing circuit 510 calculates a mean absolute error (MAE). The mean absolute error (MAE) is expressed by the following formula:

[0042]

number

[0043] In the above formula, "n" is the number of first feature quantities. In this embodiment, the first feature quantities are the total driving torque and the auxiliary driving torque, so "n" is "2". "m" is the total number of classes in the frequency distribution. "Y" is the frequency of the corresponding class of the corresponding feature quantity in the original data. "y" is the frequency of the corresponding class of the corresponding feature quantity in the extracted data.

[0044] As shown in the above formula, the processing circuit 510 calculates, for each section, the error as the sum of the errors in the frequencies in each class of the first feature between the frequency distribution in the original data and the frequency distribution in the extracted data.

[0045] After calculating the errors for all the segments, the processing circuit 510 proceeds to step S150. In step S150, the processing circuit 510 determines whether all of the calculated errors for each segment are equal to or less than a threshold value. The threshold value is a value for determining whether extracted data having a frequency distribution similar to that of the original data has been extracted using the set extraction pattern. The magnitude of this threshold value is set in advance so that it can be determined that extracted data having a frequency distribution similar to that of the original data has been extracted based on whether the errors are equal to or less than the threshold value. This threshold value can be set to a different value for each segment.

[0046] In the process of step S150, if it is determined that all of the errors for each section are equal to or less than the threshold value (step S150: YES), the processing circuit 510 records the extraction pattern. Specifically, the processing circuit 510 stores data on the start and end of each time window in the extraction pattern as information that identifies the extraction pattern in the storage device 520. After recording the extraction pattern in this way, the processing circuit 510 proceeds to the process of step S160.

[0047] On the other hand, if it is determined in the process of step S150 that any of the errors is greater than the threshold value (step S150: NO), the processing circuit 510 returns the process to step S110. That is, the processing circuit 510 starts the process of setting new multiple time windows in order to reset the time windows and extract extracted data from the original data.

[0048] In this way, processing circuit 510 repeats the processes of steps S110 to S150 using the frequency distribution of the first feature amount divided by the second feature amount until extracted data similar to the original data is extracted. As a result, storage device 520 stores extraction patterns in which all errors for each division are equal to or less than the threshold.

[0049] In the process of step S160, the processing circuit 510 extracts extracted data by extracting data from the original data based on an extraction pattern of extracted data that is similar to the original data and that is stored in the storage device 520.

[0050] After executing the process of step S160, the processing circuit 510 ends the above series of processes based on the program. In this way, the processing circuit 510 extracts data that can be used to analyze how the auxiliary drive units are used in the drive unit.

[0051] <Operation of this embodiment> The data center 500, which is an information processing device of this embodiment, acquires original data created by collecting data over a predetermined period of time using multiple sensors mounted on the vehicle 10. The data center 500 then extracts from the original data data used to analyze how the auxiliary drive unit is used in a drive unit that includes a main drive unit and an auxiliary drive unit that drive the vehicle 10.

[0052] The data center 500 includes a processing circuit 510. The original data includes a total driving torque and a secondary driving torque as a plurality of feature quantities. In the data center 500, the processing circuit 510 executes a search process. The search process includes a first step (step S130) of calculating a frequency distribution in the original data for each of the plurality of feature quantities.

[0053] The search process includes a second step (step S110) of setting a plurality of time windows that extract data for a partial period of the original data so that the total period of all the time windows is shorter than the period of the entire original data.

[0054] The search process includes a third step (step S120) of extracting data from the original data using a plurality of time windows. The extracted data is the data obtained by combining all of the data extracted using the plurality of time windows.

[0055] The search process includes a fourth step (step S130) of calculating a frequency distribution in the extracted data for each feature amount. The search process includes a fifth step (step S140) of calculating each error between the frequency distribution of each feature amount in the original data and the frequency distribution of each feature amount in the extracted data.

[0056] The search process includes a sixth step (step S150) of determining whether the original data and the extracted data are similar based on the above errors. After executing step 1, the processing circuit 510 executes a search process in which steps 2 to 6 are repeatedly executed by changing the settings of multiple time windows. When the processing circuit 510 determines that the original data and the extracted data are similar, it extracts the extracted data as data to be used for analyzing how the auxiliary drive unit is used in the drive unit (step S160).

[0057] According to this data center 500, it is possible to analyze how the auxiliary drive units in the drive units are used by using extracted data in which the distribution of feature quantities related to such usage is similar to that of the original data. Therefore, the data center 500 can obtain analysis results that are close to the results of an analysis of how the auxiliary drive units in the drive units are used by using the original data.

[0058] The extracted data extracted by the data center 500 is a portion of the original data. Therefore, the amount of extracted data is smaller than that of the original data. The processing time required to analyze how the auxiliary drive unit is used in the drive unit increases as the amount of data used for the analysis increases. By using the extracted data, the data center 500 can shorten the analysis time compared to when using the original data.

[0059] <Effects of this embodiment> (1) The data center 500 determines whether the original data and the extracted data are similar based on multiple feature quantities, including the drive torque of the auxiliary drive unit and the total drive torque of the drive unit. Therefore, data suitable for analyzing how the auxiliary drive unit is used in the drive unit can be extracted from the original data.

[0060] (2) In the first step, the processing circuit 510 of the data center 500 classifies the original data into a plurality of data based on information included in the original data and related to the running state of the vehicle 10. Then, the processing circuit 510 calculates the frequency distribution of the original data for each of the above feature quantities, including the auxiliary driving torque and the total driving torque, for each data classification.

[0061] In the fourth step, the processing circuit 510 divides the extracted data into a plurality of data segments corresponding to the plurality of data segments of the original data, and calculates a frequency distribution of each of the extracted data for each of the feature quantities including the auxiliary driving torque and the total driving torque for each data segment.

[0062] Then, in the fifth step, the processing circuit 510 calculates the error between the frequency distribution of each feature amount in the original data and the frequency distribution of each feature amount in the extracted data for each data section.

[0063] Therefore, data suitable for analyzing how the auxiliary drive unit is used for each running state of the vehicle 10 can be extracted. (3) The information relating to the running state of the vehicle 10 is the vehicle position. Therefore, it is possible to extract data suitable for analyzing how the auxiliary drive unit is used for each position of the vehicle 10 as the running state of the vehicle 10.

[0064] <Example of change> Other elements that can be modified in common to the above embodiments include the following: The following modifications can be implemented in combination with each other to the extent that they are not technically inconsistent.

[0065] The information relating to the running state of the vehicle 10 may be the inclination angle of the vehicle 10. In this case, for example, the auxiliary drive torque and total drive torque included in the original data and the extracted data are divided into three categories: a category with a positive inclination angle, a category with a negative inclination angle, and a category with a zero inclination angle. The processing circuit 510 may then calculate the frequency distribution for each of the three inclination angle categories. This modification makes it possible to extract data suitable for analyzing how the auxiliary drive unit is used for each inclination angle of the vehicle 10 as a running state of the vehicle 10.

[0066] The information relating to the driving state of the vehicle 10 may be the driving mode of the vehicle 10. In this case, for example, the auxiliary drive torque and total drive torque included in the original data and the extracted data are divided into three categories: an eco mode category, a normal mode category, and a sport mode category. The processing circuit 510 may then calculate the frequency distribution for each of the three driving mode categories. This modification makes it possible to extract data suitable for analyzing how the auxiliary drive unit is used for each driving mode of the vehicle 10 as the driving state of the vehicle 10.

[0067] The processing circuit 510 divides the original data into multiple data based on information related to the running state of the vehicle 10 contained in the original data. However, the processing circuit 510 may calculate the frequency distribution of the original data and the frequency distribution of the extracted data without dividing the data in this way. Even in this case, effects other than those described in (2) above can be obtained.

[0068] In the above embodiment, an example has been shown in which the information processing device is embodied as the data center 500. However, the above information processing device may also be embodied as the information processing terminal 600. In this case, the processing circuit 610 of the information processing terminal 600 acquires the original data from the data center 500 in the process of step S100 shown in FIG. 3 . Then, the processing circuit 610 executes the process from step S110 onwards shown in FIG. 3 . Alternatively, the above information processing device may also be embodied as a control device of the vehicle 10. In this case, for example, the processing circuit included in the second control device 92 of the vehicle 10 acquires the original data from the data center 500 in the process of step S100 shown in FIG. 3 . Then, the processing circuit of the second control device 92 executes the process from step S110 onwards shown in FIG. 3 .

[0069] The data center 500 determines the similarity between the original data and the extracted data by calculating the error in the frequency distribution. Alternatively, the data center 500 may determine whether the original data and the extracted data are similar without calculating the error. For example, by using a statistical method such as a goodness-of-fit test, it can be determined that the original data and the extracted data are similar when the difference between them is not significant.

[0070] In the case of a vehicle in which torque generated by a drive unit such as an internal combustion engine or a motor generator is distributed to the front wheels 11F and rear wheels 11R via a center differential, either the drive torque of the front wheels 11F or the drive torque of the rear wheels 11R may be used as the auxiliary drive torque. [Explanation of symbols]

[0071] 10...Vehicle 11F...Front wheel 11R...Rear wheel 21...Internal combustion engine 23...Motor generator 24...PCU 25...Battery 90...Vehicle control unit 91...First control device 92...Second control device 99...Communication device 400...Communication network 500...Data center 510...Processing circuit 520...Storage device 530...Communication device 600...Information processing terminal 610...Processing circuit 620...Storage device 630...Communication device

Claims

1. An information processing device that acquires original data that is collected and created over a predetermined period of time using sensors mounted on a vehicle, and extracts from the original data data that is used to analyze how an auxiliary drive unit is used in a drive unit that includes a main drive unit and an auxiliary drive unit that drive the vehicle, a processing circuit; The processing circuitry a first step of calculating a frequency distribution in the original data for each of a plurality of feature quantities including the drive torque of the auxiliary drive unit and the total drive torque of the drive unit, the first step being to calculate a frequency distribution in the original data for each of the feature quantities; a second step of setting a plurality of time windows to extract data for a portion of the original data such that the sum of the periods of all the time windows is shorter than the predetermined period; a third step of extracting data from the original data using the plurality of time windows; a fourth step of calculating, for each of the plurality of feature quantities, a frequency distribution in extracted data obtained by combining all of the data extracted using the plurality of time windows for the plurality of feature quantities; a fifth step of calculating each error between the frequency distribution for each of the feature quantities in the original data and the frequency distribution for each of the feature quantities in the extracted data; and a sixth step of determining whether the original data and the extracted data are similar based on each error, while changing the settings of the plurality of time windows; When it is determined that the original data and the extracted data are similar, the extracted data is extracted as data to be used for analyzing how the auxiliary drive unit is used in the drive unit. Information processing device.

2. The processing circuitry In the first step, the original data is divided into a plurality of data based on information related to a running state of the vehicle included in the original data, and the frequency distribution in the original data for each of the feature amounts is calculated for each data division; In the fourth step, the extracted data is divided into a plurality of data corresponding to the plurality of data divisions of the original data, and the frequency distribution in the extracted data for each of the feature amounts is calculated for each data division; In the fifth step, each error between the frequency distribution for each feature amount in the original data and the frequency distribution for each feature amount in the extracted data is calculated for each data section. The information processing device according to claim 1 .

3. The information relating to the vehicle running state is the running mode of the vehicle. The information processing device according to claim 2 .

4. The information relating to the running state of the vehicle is the position of the vehicle. The information processing device according to claim 2 .

5. The information relating to the running state of the vehicle is the inclination angle of the vehicle. The information processing device according to claim 2 .

Images