Information processing apparatus

Abstract

To extract data used for analyzing a factor becoming a specific differential state for a differential device from original data.SOLUTION: The information processor executes a step (S110) of setting a plurality of time windows for extracting data from original data, a step (S120) of extracting data from the original data, a step (S130) of calculating a frequency distribution of the original data and a frequency distribution of the extracted data for each feature amount, and steps (S140 and S150) of determining whether or not the original data and the extracted data are similar to each other by using an error of the frequency distribution. The information processor repeatedly executes steps S110 to S150 while changing the setting of the plurality of time windows, and extracts the extracted data similar to the original data from the original data.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] A differential gear mounted on a vehicle enables differential rotation between a first rotating shaft and a second rotating shaft. When analyzing factors that cause the differential gear to be in a specific differential state, if data suitable for analyzing the factors that cause the specific differential state can be extracted from the original data, the factors can be analyzed in a shorter time by using the extracted data than by using the original data. The information processing device analyzes the traveling speed pattern using the extracted data. Therefore, the information processing device cannot extract data suitable for analyzing the factors that cause the specific differential state from the original data. [Means for solving the problem]

[0006] An information processing device for solving the above problem acquires original data collected over a predetermined period of time using a sensor mounted on a vehicle, and extracts from the original data data used to analyze factors that cause a specific differential state in a differential gear that enables differential rotation between a first rotating shaft and a second rotating shaft mounted on the vehicle. This information processing device includes a processing circuit. the processing circuit repeatedly executes the following steps while changing the settings of the plurality of time windows: a first step of calculating, for each feature quantity in the original data, a frequency distribution of a plurality of feature quantities, the feature quantities including information on the difference in rotational speeds between the first rotating shaft and the second rotating shaft and on factors that cause the specific differential state, and a second step of setting a plurality of time windows to extract data of a portion of the original data such that the total period of all of 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 feature quantity, a frequency distribution of 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 errors between the frequency distribution of each feature quantity in the original data and the frequency distribution of each feature quantity in the extracted data; and a sixth step of determining whether the original data and the extracted data are similar based on the errors; and when it is determined that the original data and the extracted data are similar, the processing circuit extracts the extracted data as data to be used for analyzing factors that cause the specific differential state. [Effects of the Invention]

[0007] According to the information processing device described above, it is possible to extract data suitable for analyzing factors that cause a specific differential state in a differential gear 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 is a graph showing part of the original data. (a) shows the change in the first rotational speed difference, (b) shows the change in the accelerator operation amount, (c) shows the change in the steering angle, and (d) shows the change in the yaw rate. [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 first rotational speed difference in the original data. [Figure 5] FIG. 5 shows the frequency distribution of accelerator operation amount in the original data. 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 is a four-wheel drive vehicle with front wheels 11F, ie, a right front wheel 11FR and a left front wheel 11FL, and rear wheels 11R, ie, a right rear wheel 11RR and a left rear wheel 11RL, as drive wheels. An output shaft of an internal combustion engine 21 , which is an example of a prime mover mounted on the vehicle 10 , is connected to a transmission 30 .

[0014] The output shaft of the transmission 30 is connected to a transfer 40 that distributes the torque generated by the internal combustion engine 21 to the front wheels 11F and the rear wheels 11R. A front propeller shaft 22F connected to a front wheel differential gear (hereinafter referred to as a front wheel differential) 23 and a rear propeller shaft 22R connected to a rear wheel differential gear (hereinafter referred to as a rear wheel differential) 26 are connected to the transfer 40. The transfer 40 is a differential device that enables differential rotation between the front propeller shaft 22F, which corresponds to the first rotation shaft, and the rear propeller shaft 22R, which corresponds to the second rotation shaft.

[0015] A right front drive shaft 24R connected to the right front wheel 11FR and a left front drive shaft 24L connected to the left front wheel 11FL are connected to the front wheel differential 23. The front wheel differential 23 is a differential device that enables differential rotation between the right front drive shaft 24R, which corresponds to the first rotation shaft, and the left front drive shaft 24L, which corresponds to the second rotation shaft.

[0016] A right rear drive shaft 27R connected to the right rear wheel 11RR and a left rear drive shaft 27L connected to the left rear wheel 11RL are connected to the rear wheel differential 26. The rear wheel differential 26 is a differential device that enables differential rotation between the right rear drive shaft 27R, which corresponds to the first rotation shaft, and the left rear drive shaft 27L, which corresponds to the second rotation shaft.

[0017] The steering mechanism 81 provided in the vehicle 10 changes the steering angle of the front wheels 11F, which are steered wheels, in accordance with the steering angle of the steering wheel 85. The vehicle control unit 90 provided in the vehicle 10 includes a processing circuit 95. The processing circuit 95 includes a CPU that executes processing according to a program and a ROM in which the program is stored. The vehicle control unit 90 also includes a plurality of sensors that collect data related to the vehicle 10 and the internal combustion engine 21. The vehicle control unit 90 performs various controls related to the vehicle 10 and the internal combustion engine 21.

[0018] Examples of data collected by the vehicle control unit 90 are the travel distance and vehicle speed of the vehicle 10. Other examples of data collected by the vehicle control unit 90 include a rotational speed difference, an accelerator operation amount which is an operation amount of an accelerator pedal which adjusts the output of the internal combustion engine 21, a steering angle which is a steering angle of the steering wheel 85, a yaw rate of the vehicle 10, and position information of the vehicle 10.

[0019] The rotational speed differences include a first rotational speed difference, a second rotational speed difference, and a third rotational speed difference. The first rotational speed difference is the difference in rotational speed between the right front drive shaft 24R and the left front drive shaft 24L, which are capable of differential rotation by the front wheel differential 23.

[0020] The second rotational speed difference is the difference in rotational speed between the right rear drive shaft 27R and the left rear drive shaft 27L, which are capable of differential rotation by the rear wheel differential 26. The third rotational speed difference is the rotational speed difference between the front propeller shaft 22F and the rear propeller shaft 22R, which are capable of differential rotation by the transfer 40.

[0021] The first rotational speed difference, the second rotational speed difference, and the third rotational speed difference are calculated from the detection values ​​of wheel speed sensors provided on the right front wheel 11FR, the left front wheel 11FL, the right rear wheel 11RR, and the left rear wheel 11RL, respectively.

[0022] In this embodiment, the steering angle is expressed as a positive value when steering to the right as viewed from the front of the vehicle 10, and a negative value when steering to the left. Similarly, the yaw rate is expressed as a positive value when steering to the right as viewed from the front of the vehicle 10, and a negative value when steering to the left.

[0023] <Data Extraction> The information processing terminal 600 is used to analyze factors that cause a specific differential state of a differential gear. In the following, a case will be described in which the differential gear to be analyzed is the front wheel differential 23, but the differential gear to be analyzed may also be the rear wheel differential 26 or the transfer case 40.

[0024] When analyzing factors that cause the front wheel differential 23 to be in a specific differential state, the information processing terminal 600 transmits an instruction to the data center 500. The processing circuit 510 of the data center 500, upon receiving the instruction, 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. The data includes multiple pieces of data related to factors that cause the front wheel differential 23 to be in a specific differential state, 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 factors that cause the specific differential state in the specific vehicle 10. In this case, the feature quantities are information related to the first rotational speed difference and factors that cause the specific differential state in the front wheel differential 23 in the vehicle 10 being analyzed. The information related to factors that cause the specific differential state includes the accelerator operation amount, the steering angle, the yaw rate, and position information of the vehicle 10.

[0025] The processing circuit 510 uses a large amount of data collected over a long period of time to analyze, in accordance with the program, the factors that cause a specific differential state for the front wheel differential 23 of a specific vehicle 10. In this analysis, the processing circuit 510 performs a huge amount of calculations, which takes a long time to complete.

[0026] 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.

[0027] Fig. 2 shows a portion of original data of feature quantities related to factors that result in a specific differential state for the front wheel differential 23. The original data shown in Fig. 2 is a portion of data for 100,000 hours for one vehicle 10. The original data shown in Fig. 2 includes, as feature quantities, for example, a first rotational speed difference, an accelerator operation amount, a steering angle, and a yaw rate.

[0028] FIG. 2(a) shows the first rotational speed difference. FIG. 2(b) shows the accelerator operation amount. FIG. 2(c) shows the steering angle. FIG. 2(d) shows the yaw rate. In this embodiment, the steering angle is expressed as a positive value when the steering angle is turned to the right as viewed from the front of the vehicle 10, and a negative value when the steering angle is turned to the left. Similarly, the yaw rate is expressed as a positive value when the steering angle is turned to the right as viewed from the front of the vehicle 10, and a negative value when the steering angle is turned to the left.

[0029] The first rotational speed difference, the accelerator operation amount, the steering angle, the yaw rate, and the position information are data related to factors that cause a specific differential state for the front wheel differential 23. The processing circuit 510 extracts data for analyzing factors that cause the specific differential state from the data that includes the first rotational speed difference and the accelerator operation amount as feature amounts.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] The original data for analyzing the factors that cause a specific differential state for the front wheel differential 23 of one vehicle 10 is data of the target vehicle 10 selected from a huge amount of data of multiple vehicles 10.

[0035] 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.

[0036] 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.

[0037] 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 step S120.

[0038] 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.

[0039] Next, in the process of step S130, the processing circuit 510 calculates the frequency distribution of the original data and the extracted data for each feature amount. The frequency distribution classifies the data for each feature into multiple classes, and shows the distribution of frequencies, which are the number of data in each class. In this embodiment, the feature data are the first rotation speed difference and the accelerator operation amount. Therefore, the frequency distribution of the first rotation speed difference and the frequency distribution of the accelerator operation amount are calculated.

[0040] Furthermore, because the total frequencies of the features contained in the original data and extracted data differ, 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 in 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 without using the above method, 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.

[0041] Figure 4 shows the frequency distribution of the first rotational speed difference in the original data. In the frequency distribution of the first rotational speed difference, the first rotational speed difference is divided into m classes from "1" to "m." The frequency distribution of the extracted data of the first rotational speed difference is also calculated by dividing it into classes corresponding to the original data.

[0042] Figure 5 shows the frequency distribution of accelerator operation amount in the original data. In the frequency distribution of accelerator operation amount, the accelerator operation amount is divided into m classes from "1" to "m". The frequency distribution of the extracted data of accelerator operation amount is also calculated by dividing it into classes corresponding to the original data.

[0043] After executing the process of step S130 shown in Fig. 3, the processing circuit 510 then executes the process of step S140. In step S140, 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 example, the processing circuit 510 calculates the mean absolute error (MAE). The mean absolute error (MAE) is expressed by the following formula:

[0044]

number

[0045] As shown in the above formula, the processing circuit 510 calculates the error as the sum of the errors in the frequencies in each class for each feature amount between the frequency distribution in the original data and the frequency distribution in the extracted data.

[0046] After calculating the error, processing circuit 510 proceeds to step S150. In step S150, processing circuit 510 determines whether the calculated error is 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 in 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 in the original data has been extracted based on the error being equal to or less than the threshold value.

[0047] If it is determined in the process of step S150 that the error is equal to or less than the threshold value (step S150: YES), the processing circuit 510 records the cut-out pattern. Specifically, the processing circuit 510 stores data on the start and end of each time window in the cut-out pattern in the storage device 520 as information that identifies the cut-out pattern. After recording the cut-out pattern in this way, the processing circuit 510 proceeds to the process of step S160.

[0048] On the other hand, if it is determined in the process of step S150 that the error 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.

[0049] In this way, processing circuit 510 repeats the processes of steps S110 to S150 using the frequency distribution for each feature amount until extracted data similar to the original data is extracted. As a result, storage device 520 stores an extraction pattern whose error is equal to or smaller than the threshold value.

[0050] 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.

[0051] After executing the process of step S160, the processing circuit 510 ends the above series of processes based on the program. In this manner, the processing circuit 510 extracts data used to analyze the factors that cause a particular differential state for the front wheel differential 23.

[0052] <Operation of this embodiment> The data center 500, which is an information processing device of this embodiment, acquires original data that is collected over a predetermined period of time using a plurality of sensors mounted on the vehicle 10. The data center 500 then extracts data from the original data that is used to analyze factors that cause a specific differential state for the front wheel differential 23.

[0053] The data center 500 includes a processing circuit 510. The original data includes, as a plurality of feature quantities, a first rotational speed difference and information on factors that cause a specific differential state for the front wheel differential 23. In this 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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, processing circuit 510 executes a search process in which steps 2 to 6 are repeatedly executed while changing the settings of multiple time windows. When processing circuit 510 determines that the original data and the extracted data are similar, processing circuit 510 extracts the extracted data as data to be used for analyzing factors that cause a specific differential state for front wheel differential 23 (step S160).

[0058] The data center 500 can analyze factors that result in a specific differential state for the front wheel differential 23 by using extracted data in which the distribution of feature quantities related to factors that result in the specific differential state for the front wheel differential 23 is similar to that of the original data. Therefore, the data center 500 can obtain analysis results that are close to the results of analysis of factors that result in a specific differential state for the front wheel differential 23 performed using the original data.

[0059] 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 the factors that cause a specific differential state for the front wheel differential 23 increases as the amount of data used in the analysis increases. By using the extracted data, the data center 500 can shorten the analysis time compared to when using the original data.

[0060] <Effects of this embodiment> (1) According to the data center 500, whether the original data and the extracted data are similar to each other is determined based on a plurality of feature quantities including information about the first rotational speed difference and the factors that cause a specific differential state for the front wheel differential 23. Therefore, data suitable for analyzing the factors that cause a specific differential state for the front wheel differential 23 can be extracted from the original data.

[0061] (2) The information regarding the cause of the specific differential state is the accelerator operation amount, so it is possible to extract data suitable for analyzing whether the cause of the specific differential state is the driver's acceleration operation.

[0062] <Example of change> This embodiment can be modified as follows: This embodiment and the following modifications can be combined and implemented within the scope of technical compatibility.

[0063] The information regarding the cause of the specific differential state when extracting data may be the steering angle. In this case, in the process of step S130 described above, processing circuit 510 calculates the frequency distribution of the steering angle instead of the frequency distribution of the accelerator operation amount in the original data and the extracted data. Then, when calculating the error in step S140, processing circuit 510 calculates the error between the frequency distribution of the steering angle in the original data and the frequency distribution of the steering angle in the extracted data instead of the error in the frequency distribution of the accelerator operation amount. This modification makes it possible to extract data suitable for analyzing whether the cause of the specific differential state of front wheel differential 23 is the driver's turning operation.

[0064] The information regarding the cause of the specific differential state when extracting data may be the yaw rate of the vehicle 10. In this case, in the process of step S130 described above, the processing circuit 510 calculates the frequency distribution of the yaw rate instead of the frequency distribution of the accelerator operation amount in the original data and the extracted data. Then, when calculating the error in step S140, the processing circuit 510 calculates the error between the frequency distribution of the yaw rate in the original data and the frequency distribution of the yaw rate in the extracted data instead of the error in the frequency distribution of the accelerator operation amount. According to this modification, it is possible to extract data suitable for analyzing whether the cause of the specific differential state of the front wheel differential 23 is due to the turning of the vehicle 10.

[0065] When extracting data, the information regarding the cause of the specific differential state may be the location information of the vehicle 10. According to this modification, it is possible to extract data suitable for analyzing whether the cause of the specific differential state of the front wheel differential 23 is due to the location of the vehicle 10.

[0066] The processing circuit 510 described above calculates the frequency distribution using the first rotation speed difference as a feature. Alternatively, the processing circuit 510 may calculate the frequency distribution using the second rotation speed difference as a feature instead of the first rotation speed difference. Furthermore, the processing circuit 510 may calculate the frequency distribution using the third rotation speed difference as a feature instead of the first rotation speed difference.

[0067] 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 95 included in the vehicle control unit 90 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 95 executes the process from step S110 onwards shown in FIG. 3 .

[0068] 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.

[0069] The prime mover mounted on the vehicle 10 may be an electric motor. The vehicle 10 may be a front-wheel drive vehicle. Alternatively, the vehicle 10 may be a rear-wheel drive vehicle. [Explanation of symbols]

[0070] 10...Vehicle 11FL...Left front wheel 11FR...Right front wheel 11RL...Left rear wheel 11RR...Right rear wheel 21...Internal combustion engine 22F...Front propeller shaft 22R...Rear propeller shaft 23...Front wheel differential gear 24L...Left front drive shaft 24R...Right front drive shaft 26...Rear wheel differential gear 27L...Left rear drive shaft 27R...Right rear drive shaft 30...Transmission 40...Transfer 81...Steering mechanism 85...Steering 90...Vehicle control unit 95...Processing circuit 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 collected over a predetermined period of time using a sensor mounted on a vehicle, and extracts from the original data data used to analyze factors that result in a specific differential state of a differential gear that enables differential rotation between a first rotating shaft and a second rotating shaft mounted on 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 amounts including information on the rotational speed difference between the first rotation axis and the second rotation axis and a factor causing the specific differential state, the frequency distribution being calculated for each of the feature amounts; 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 factors that cause the specific differential state. Information processing device.

2. The information about the cause of the specific differential state is the accelerator operation amount of the vehicle. The information processing device according to claim 1 .

3. The information regarding the cause of the specific differential state is the steering angle of the vehicle. The information processing device according to claim 1 .

4. The information about the cause of the specific differential state is the yaw rate of the vehicle. The information processing device according to claim 1 .

5. The information about the cause of the specific differential state is the position of the vehicle. The information processing device according to claim 1 .

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