Information processing apparatus

Abstract

The invention provides an information processing apparatus. Data suitable for analyzing a usage mode of a sub-drive unit among the drive units is extracted from the raw data. The information processing device executes: a step (S110) for setting a plurality of time windows for cutting out data from raw data, using the drive torque of the sub-drive unit and the total drive torque of the drive unit, which are included in the raw data, as feature quantities; a step (S120) for cutting out data from the original data; a step (S130) for calculating the frequency distribution of the raw data and the frequency distribution of the extracted data for each of the plurality of feature quantities on the basis of information relating to the traveling state of the vehicle; and a step (S140 and S150) for determining whether the original data and the extracted data are similar using an error in the frequency distribution. The information processing device repeatedly executes steps S110-S150 by changing settings of a plurality of time windows, and extracts extracted data similar to the original data from the original data.

Description

Technical Field

[0001] This invention relates to an information processing device. Background Technology

[0002] Patent Document 1 discloses an information processing apparatus that reduces the size of the data used for analysis by compressing the raw data. The raw data used for analysis is data collected continuously for a predetermined period using sensors mounted on a vehicle.

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

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 2008-108247 Summary of the Invention

[0007] In a vehicle where the drive unit comprises a main drive unit and a secondary drive unit, data acquired using multiple sensors mounted on the vehicle can be used to analyze the usage pattern of the secondary drive unit. If data suitable for analyzing the usage pattern of the secondary drive unit can be extracted from the raw data, then by using the extracted data, the usage pattern of the secondary drive unit can be analyzed in a shorter time compared to using the raw data. The aforementioned information processing device uses the extracted data to analyze the driving speed pattern. Therefore, the aforementioned information processing device cannot extract data suitable for analyzing the usage pattern of the secondary drive unit from the raw data.

[0008] An information processing device for solving the above-mentioned problem acquires raw data collected continuously for a predetermined period using sensors mounted on a vehicle, and extracts data from the raw data for analyzing the usage of the secondary drive unit in the drive unit that drives the vehicle, including the main drive unit and the secondary drive unit. The information processing device includes a processing circuit that performs the following steps: a first step of calculating the frequency distribution of a plurality of the features in the original data for each feature, including the drive torque of the sub-drive unit contained in the original data and the total drive torque of the drive unit; and a second step of repeatedly performing the following steps by changing the settings of a plurality of time windows: a third step of setting a plurality of time windows to cut out data for a portion of the original data in such a way that the period formed by combining the periods of all the time windows is shorter than the predetermined period; a fourth step of cutting out data from the original data using the plurality of time windows; a fifth step of calculating the frequency distribution of the extracted data for each feature in the extracted data, which is formed by combining all the data cut out using the plurality of time windows for the plurality of features; a sixth step of calculating errors in the frequency distribution of each feature in the original data and the frequency distribution of each feature in the extracted data; and a seventh step of determining whether the original data and the extracted data are similar based on the errors, and extracting the extracted data when the original data and the extracted data are determined to be similar, as data for analyzing the usage mode of the sub-drive unit in the drive unit.

[0009] According to the aforementioned information processing device, data suitable for the usage mode of the sub-drive unit in the analysis drive unit can be extracted from the raw data. Attached Figure Description

[0010] Figure 1 This is a schematic diagram illustrating the relationship between a data center, a vehicle, and an information processing terminal as one embodiment of an information processing device.

[0011] Figure 2 These are charts showing a portion of the raw data. (a) shows the shift in driving mode, (b) shows the shift in total drive torque, (c) shows the shift in secondary drive torque, (d) shows the shift in vehicle tilt angle, and (e) shows the shift in driving area.

[0012] Figure 3 This is a flowchart illustrating the processing flow performed by the processing circuitry of a data center.

[0013] Figure 4 It is the frequency distribution of the total driving torque and the secondary driving torque in the original data of the urban section.

[0014] Figure 5It is the frequency distribution of the total driving torque and the secondary driving torque in the original data of the field. Detailed Implementation

[0015] The following is for reference Figures 1-5 This describes one implementation of an information processing device.

[0016] <Structure of Information Processing Systems>

[0017] Figure 1 The structure of the information processing system is shown. The information processing system includes a data center 500 with information processing devices, an information processing terminal 600, multiple vehicles 10, and a communication network 400. The data center 500 can communicate with the multiple vehicles 10 and the information processing terminal 600 via the communication network 400.

[0018] <Data Center 500 Structure>

[0019] like Figure 1 As shown, 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, equipped with a CPU that executes processing according to a program and a ROM storing the program. The storage device 520 can store a large amount of data. The communication device 530 performs wired or wireless communication via a communication network 400. The communication device 530 includes hardware such as a network adapter, various communication software, or combinations thereof.

[0020] <Structure of Information Processing Terminal 600>

[0021] like Figure 1 As shown, 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 storing the program. The storage device 620 can store a large amount of data. The communication device 630 performs wired or wireless communication via a communication network 400. The communication device 630 includes hardware such as a network adapter, various communication software, or combinations thereof. The information processing terminal 600 is, for example, a personal computer.

[0022] <Structure of Vehicle 10>

[0023] Vehicle 10 is equipped with a communication device 99. The communication device 99 transmits data acquired by vehicle 10 and identification information of vehicle 10 to data center 500 via communication network 400.

[0024] The vehicle 10 includes an internal combustion engine 21, an electric generator 23, a power control unit (hereinafter referred to as "PCU (Power Control Unit)") 24, a battery 25, and a vehicle control unit 90.

[0025] The internal combustion engine 21 drives the front wheels 11F of the vehicle 10 via the front-wheel drive system. The internal combustion engine 21 and the front-wheel drive system are the main drive units for driving the vehicle 10.

[0026] The electric generator 23 is an electric motor operated by electricity. The electric generator 23 utilizes power from the battery 25, which is appropriately converted by the PCU 24, for its rotational operation. The electric generator 23 drives the rear wheels 11R of the vehicle 10 via the rear-wheel drive system. The electric generator 23 and the rear-wheel drive system are auxiliary drive units for driving the vehicle 10.

[0027] The vehicle control unit 90 includes a first control device 91 for controlling the internal combustion engine 21 and a second control device 92 for controlling the PCU 24.

[0028] The vehicle control unit 90 is equipped with multiple sensors for data collection. The first control unit 91 has a CPU that controls the operating state of the internal combustion engine 21. The first control unit 91 controls the internal combustion engine 21 based on the data collected by the sensors. The second control unit 92 has a CPU that controls the PCU 24. The second control unit 92 controls the PCU 24 based on the data collected by the sensors.

[0029] One example of the data collected by the vehicle control unit 90 is the travel distance and speed of the vehicle 10. Another example of the data collected by the vehicle control unit 90 is the vehicle 10's position information, the total drive torque of the drive units including the main drive unit and the auxiliary drive unit, the auxiliary drive torque as the drive torque of the auxiliary drive unit, the vehicle 10's tilt angle, and the driving mode.

[0030] The total driving torque is the sum of the torque driving the front wheels 11F and the torque driving the rear wheels 11R. The auxiliary driving torque is the torque driving the rear wheels 11R. For example, the torque driving the front wheels 11F is calculated based on the required torque of the internal combustion engine 21 required to drive the vehicle 10 and the gear ratio of the front-wheel drive system. For example, the torque driving the rear wheels 11R is calculated based on the required torque of the electric generator 23 required to drive the vehicle 10 and the gear ratio of the rear-wheel drive system.

[0031] Driving modes include, for example, an energy-saving mode, a normal mode, and a sport mode. The normal mode is the basic driving mode. The energy-saving mode is a driving mode that reduces fuel and electricity consumption compared to the normal mode. The sport mode is a driving mode that improves the vehicle's handling compared to the normal mode. In each driving mode, the ratio of the drive torque to the front wheels 11F and the drive torque to the rear wheels 11R is adjusted according to the driving conditions of the vehicle 10. Furthermore, each driving mode can be selected manually by the driver of the vehicle 10 or automatically by the vehicle control unit 90.

[0032] <Data Extraction>

[0033] Information processing terminal 600 is used to analyze the usage of the secondary drive unit in the drive unit that includes the main drive unit and the secondary drive unit driving vehicle 10. When analyzing the usage of the secondary drive unit in the drive unit, information processing terminal 600 sends an instruction to data center 500. Upon receiving the instruction, processing circuit 510 of data center 500 uses a portion of the expanded data stored in storage device 520 of data center 500 for analysis. In accordance with the purpose of analysis, data is selected from the expanded data stored in storage device 520. This data includes multiple data related to the usage of the secondary drive unit in the drive unit, collected using multiple sensors mounted on vehicle 10. This data is referred to as feature quantities. Processing circuit 510 uses feature quantities to analyze the usage of the secondary drive unit in the drive unit of a specific vehicle 10. In this case, the feature quantities are the secondary 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 tilt angle of the vehicle 10.

[0034] In order for the processing circuit 510 to analyze the usage mode of the auxiliary drive unit in the drive unit of a specific vehicle 10 according to the program, the processing circuit 510 utilizes a large amount of data collected over a long period of time. During this analysis, the processing circuit 510 requires a long time due to the large amount of calculations involved.

[0035] Therefore, we consider extracting data from the large amount of raw data to capture the overall features of the raw data. If such extracted data can be extracted, the processing circuit 510 can perform parsing in a shorter time by using the extracted data.

[0036] Figure 2 This shows a portion of the raw data of characteristic quantities related to how the sub-drive unit in the drive unit is used. Figure 2 The raw data shown is a portion of the data from 100,000 hours of data from one vehicle (10). Figure 2 The raw data shown includes, as characteristic quantities, driving mode, total driving torque, secondary driving torque, tilt angle, and driving area.

[0037] Figure 2 (a) shows the driving mode. Figure 2 (b) shows the total driving torque. Figure 2 (c) shows the total driving torque. Figure 2 (d) shows the incline angle. The incline angle is positive when going uphill. The incline angle is negative when going downhill. Figure 2 (e) shows the driving areas divided into urban and rural sections.

[0038] The driving mode of vehicle 10, the total driving torque of vehicle 10, the secondary driving torque of vehicle 10, the tilt angle of vehicle 10, and the driving area of ​​vehicle 10 are related to the usage of the secondary driving unit in the drive unit of vehicle 10. The processing circuit 510 extracts data for analyzing the usage of the secondary driving unit in the drive unit from the data including the total driving torque, secondary driving torque, and driving area as feature quantities.

[0039] Extracted data is created by cutting data from the raw data using multiple time windows. Figure 2 In this example, using multiple time windows as an example, three time windows—W_1 (first time window), W_2 (second time window), and W_3 (third time window)—are represented by double-dotted lines. The start and end points of each time window are set in a way that ensures they do not overlap. In this example, 20,000 hours of data are extracted. Therefore, the start and end points of each time window are set so that the total length of the period formed by summing the durations of all time windows is 20,000 hours.

[0040] Data center 500 searches for the start and end points of each time window used to extract data slices that capture the features of the original data as a whole. Data center 500 stores the information of the slice patterns used to extract the aforementioned data slices in storage device 520. The stored slice pattern information is the information on the settings of each time window found through the search.

[0041] The processing circuit 510 extracts data from the raw data based on the cutting pattern information stored in the storage device 520.

[0042] <Sliceout style search processing>

[0043] Figure 3 This is a flowchart illustrating a series of processes related to the search process for the cutout pattern. The processing circuit 510 of the data center 500 executes this series of processes according to the program.

[0044] like Figure 3 As shown, in step S100, the processing circuit 510 obtains the raw data. The raw data is a portion of the data selected from the expanded data stored in the storage device 520 of the data center 500 that is consistent with the purpose of parsing.

[0045] The raw data used to analyze the usage of the auxiliary drive unit in the drive unit of a vehicle 10 is the data of the object selected from the expanded data of multiple vehicles 10.

[0046] Next, in the processing of step S110, the processing circuit 510 sets multiple time windows in order to extract data from the raw data.

[0047] exist Figure 2 In the example shown, the duration of each time window is all equal. Figure 2 As shown, the data cut out using each cutout window are data of each feature quantity in the same period.

[0048] Each time the processing circuit 510 executes step S110, it randomly sets the number of time windows, the start point of each time window, and the end point of each time window. At this time, the processing circuit 510 sets each time window in a manner that prevents them from overlapping. The processing circuit 510 randomly sets multiple time windows in such a way that the sum of the periods of all time windows becomes a preset period. The processing circuit 510 may also, during the processing of step S110, such as... Figure 2 As shown, the duration of each time window is fixed to a constant, and multiple time windows are set. Alternatively, in step S110, the processing circuit 510 can also set multiple time windows by fixing the number of time windows to a constant value.

[0049] Thus, by setting multiple time windows through step S110, the cutting pattern for extracting data from the original data is determined. After determining the cutting pattern, the processing circuit 510 proceeds to step S120.

[0050] In step S120, the processing circuit 510 extracts data from the original data using a determined cutting pattern. Specifically, in step S120, the processing circuit 510 extracts data from the original data using multiple predefined time windows. Furthermore, the processing circuit 510 combines all the data extracted using the multiple time windows to create the extracted data.

[0051] Next, in step S130, the processing circuit 510 calculates the frequency distribution of the raw data and the extracted data. The raw data includes multiple characteristic quantities. The secondary drive torque and the total drive torque are defined as the first characteristic quantity, and other characteristic quantities different from the first characteristic quantity and related to the driving state of the vehicle 10 are defined as the second characteristic quantity. In this embodiment, the second characteristic quantity is the driving area.

[0052] In step S130, the processing circuit 510 divides the data of the first feature quantity contained in the original data into multiple data based on the data of the second feature quantity collected when the first feature quantity is obtained. In this embodiment, the second feature quantity is the aforementioned driving area. Therefore, the data of the first feature quantity is divided into urban area data and rural area data. Furthermore, for each division of the data, the frequency distribution in the original data of each first feature quantity is calculated.

[0053] Similarly, the processing circuit 510 divides the data containing the first feature quantity of the extracted data into multiple data regions corresponding to the distinction between the multiple data regions of the original data. Furthermore, for each region of the data, the frequency distribution in the extracted data of each first feature quantity is calculated.

[0054] Regarding the frequency distribution, the data for each first characteristic quantity is classified into multiple levels, representing the frequency distribution of the number of data points at each level. In this embodiment, the data for the first characteristic quantity are the total driving torque and the secondary driving torque. Therefore, the frequency distribution of the total driving torque and the frequency distribution of the secondary driving torque are calculated separately.

[0055] Furthermore, the total frequency of the first characteristic of the data differs between the original and extracted data, making it impossible to simply compare the frequency distributions of the original and extracted data. When 20,000 hours of extracted data are drawn from 100,000 hours of original data, the total frequency of the extracted data becomes approximately one-fifth of the total frequency of the original data. In this case, by multiplying the frequency of each level of the extracted data by five, a frequency distribution of the extracted data with the same total frequency as the original data can be obtained. Even without relying on the above method, it is possible to compare the distribution of data in the original data with the distribution of data in the extracted data by calculating the relative frequency distributions of the original and extracted data. A relative frequency distribution represents the percentage of a particular level's frequency relative to the total frequency.

[0056] Figure 4 The frequency distribution of total driving torque and secondary driving torque is shown in the raw data when the driving area of ​​vehicle 10 is in an urban area.

[0057] Figure 5 The original data for the total driving torque and the frequency distribution of the secondary driving torque are shown when the vehicle 10 is traveling in the rural area.

[0058] like Figure 4 as well as Figure 5 As shown, in these frequency distributions, the total driving torque and the secondary driving torque are divided into m levels from "1" to "m". The frequency distribution of the extracted data is also divided into levels corresponding to the original data for calculation. The processing circuit 510 thus divides the total driving torque and the secondary driving torque contained in the original data and the extracted data into two categories: an urban area category and a rural area category. For each of the two driving areas, the processing circuit 510 calculates the frequency distribution as described above.

[0059] In execution Figure 3Following the processing in step S130, the processing circuit 510 then executes step S140. In step S140, for each of the multiple distinctions based on the second feature, the processing circuit 510 calculates the error between the frequency distribution of the first feature in the original data and the frequency distribution of the first feature 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.

[0060] Formula 1

[0061]

[0062] In the above formula, "n" is the number of the first characteristic quantity. In this embodiment, the first characteristic quantity is the total driving torque and the secondary driving torque, so "n" is "2". "m" is the total number of levels in the frequency distribution. "Y" is the frequency of the corresponding characteristic quantity in the corresponding level in the original data. "y" is the frequency of the corresponding characteristic quantity in the corresponding level in the extracted data.

[0063] As shown in the formula above, the processing circuit 510 calculates the error as the sum of the errors of the frequencies in each level of the first feature of the frequency distribution in the original data and the frequency distribution in the extracted data for each distinction.

[0064] After calculating the error for all distinctions, the processing circuit 510 proceeds to step S150. In step S150, the processing circuit 510 determines whether the error for each distinction is below a threshold. The threshold is a value used to determine whether extracted data with a frequency distribution close to the frequency distribution in the original data has been extracted using a set cutting pattern. The threshold is preset so that it can be determined that extracted data with a frequency distribution close to the frequency distribution in the original data has been extracted based on the error being below the threshold. This threshold can be determined to be a different value for each distinction.

[0065] In step S150, if it is determined that the error for each distinction is below the threshold (step S150: "Yes"), the processing circuit 510 records the cutout pattern. Specifically, the processing circuit 510 stores the start and end point data of each time window in the cutout pattern as information for determining the cutout pattern in the storage device 520. After recording the cutout pattern in this way, the processing circuit 510 proceeds to step S160.

[0066] On the other hand, in the processing of step S150, if it is determined that any error of each error is greater than the threshold (step S150: "No"), the processing circuit 510 returns the processing to step S110. That is, in order to reset the time window and extract the extracted data from the original data, the processing circuit 510 starts the processing of setting multiple new time windows.

[0067] Thus, the processing circuit 510 repeats steps S110 to S150 until it can extract data similar to the original data using the frequency distribution of the first feature value distinguished by the second feature value. As a result, in the storage device 520, the error of each distinction is stored as a cutout pattern below the threshold.

[0068] In step S160, the processing circuit 510 extracts data by cutting out data from the original data according to the data extraction pattern stored in the storage device 520, which is similar to the original data.

[0069] After the processing in step S160 is performed, the processing circuit 510 terminates the above-mentioned series of program-based processes.

[0070] In this way, the processing circuit 510 extracts data used to analyze the usage of the sub-drive unit in the drive unit.

[0071] <The function of this implementation method>

[0072] The data center 500, which is the information processing device in this embodiment, acquires raw data collected continuously for a predetermined period using multiple sensors mounted on the vehicle 10. Furthermore, the data center 500 extracts data from the raw data for analyzing the usage of the auxiliary drive unit in the drive unit that drives the vehicle 10, including the main drive unit and the auxiliary drive unit.

[0073] The data center 500 includes a processing circuit 510. In the raw data, multiple characteristic quantities include total drive torque and secondary drive torque. In this data center 500, the processing circuit 510 performs a search process. This search process includes a first step (step S130) of calculating the frequency distribution in the raw data for each characteristic quantity.

[0074] The search process includes a second step (step S110) which involves setting multiple time windows from a portion of the original data so that the sum of the periods of all time windows is shorter than the overall period of the original data.

[0075] The search process includes a third step (step S120) which involves extracting data from the original data using multiple time windows. The extracted data is the data obtained by combining all the data extracted using the multiple time windows.

[0076] The search process includes a fourth step (step S130) that calculates the frequency distribution in the extracted data for each feature.

[0077] The search process includes step 5 (step S140), which calculates the errors of the frequency distribution of each feature in the original data and the frequency distribution of each feature in the extracted data.

[0078] The search process includes step 6 (step S150) which determines whether the original data and the extracted data are similar based on the errors mentioned above.

[0079] After executing step 1, processing circuit 510 performs a search process that repeatedly executes steps 2 through 6 by changing the settings of multiple time windows. Furthermore, processing circuit 510 extracts the extracted data when it is determined that the original data and the extracted data are similar, and uses this extracted data as data for analyzing the usage mode of the sub-drive unit in the drive unit (step S160).

[0080] According to the data center 500, extracted data, whose distribution of feature quantities related to the usage mode of the sub-drive unit in the drive unit is similar to that of the original data, can be used to analyze such usage modes. Therefore, the data center 500 can obtain analysis results that are close to the analysis results of the usage mode of the sub-drive unit in the drive unit using the original data.

[0081] The extracted data from Data Center 500 is data obtained by cutting a portion of the original data. Therefore, the extracted data is smaller in volume compared to the original data. The more data used in parsing, the longer the processing time required for parsing in the sub-drive unit within the drive unit becomes. By using extracted data, Data Center 500 can reduce parsing time compared to using the original data.

[0082] <Effects of this implementation method>

[0083] (1) Based on data center 500, and based on multiple characteristic quantities including the driving torque of the sub-drive unit and the total driving torque of the drive unit, it is determined whether the original data and the extracted data are similar. Therefore, it is possible to extract data from the original data that is suitable for analyzing the usage mode of the sub-drive unit in the drive unit.

[0084] (2) In the first step described above, the processing circuit 510 of the data center 500 divides the original data into multiple data based on the information related to the driving state of the vehicle 10 contained in the original data. Furthermore, for each division of the data, the processing circuit 510 calculates the frequency distribution of the original data for each of the aforementioned characteristic quantities, including the secondary drive torque and the total drive torque.

[0085] Furthermore, in step 4 above, the processing circuit 510 divides the extracted data into multiple data points, corresponding to the division of the original data into multiple data points. And, for each division of the data, the processing circuit 510 calculates the frequency distribution of the extracted data for each of the aforementioned characteristic quantities, including the secondary drive torque and the total drive torque.

[0086] Then, in step 5 above, the processing circuit 510 calculates the frequency distribution of each feature in the original data and the error of the frequency distribution of each feature in the extracted data for each distinction of the data.

[0087] Therefore, it is possible to extract data on the usage of the secondary drive unit suitable for analyzing each driving state of vehicle 10.

[0088] (3) The information related to the driving state of the vehicle 10 is the vehicle's position. Therefore, it is possible to extract data suitable for analyzing the usage of the auxiliary drive unit at each position of the vehicle 10 as the driving state of the vehicle 10.

[0089] <Example of Change>

[0090] Furthermore, the following elements are common to be modified in the above embodiments. The following modification examples can be combined and implemented to the extent that they are not technically contradictory.

[0091] The information related to the driving state of the vehicle 10 can also be the tilt angle of the vehicle 10. In this case, for example, the secondary drive torque and total drive torque included in the raw data and extracted data are divided into three categories: a positive tilt angle, a negative tilt angle, and a zero tilt angle. Furthermore, the processing circuit 510 can also calculate the frequency distribution for each of the three tilt angle categories. According to this modified example, data suitable for analyzing the usage mode of the secondary drive unit of the vehicle 10 at each tilt angle as the driving state of the vehicle 10 can be extracted.

[0092] The information related to the driving state of the vehicle 10 can also be the driving mode of the vehicle 10. In this case, for example, the secondary drive torque and total drive torque included in the raw data and extracted data are divided into three categories: driving mode as energy-saving mode, driving mode as normal mode, and driving mode as sport mode. Furthermore, the processing circuit 510 can also calculate the frequency distribution for each of the three driving modes. According to this modified example, data suitable for analyzing the usage mode of the secondary drive unit of each driving mode of the vehicle 10 as the driving state of the vehicle 10 can be extracted.

[0093] The processing circuit 510 divides the original data into multiple data based on information related to the driving state of the vehicle 10 contained in the original data. On the other hand, the processing circuit 510 may also calculate the frequency distribution in the original data and the frequency distribution in the extracted data without performing such data division. Even in this case, effects other than those described in (2) can be obtained.

[0094] In the above embodiment, an example of embodying the information processing device as a data center 500 is shown. Alternatively, the information processing device can also be embodying as an information processing terminal 600. In this case, the processing circuit 610 of the information processing terminal 600... Figure 3 In step S100, raw data is obtained from the data center 500. Furthermore, the processing circuit 610 executes... Figure 3 The processing after step S110 shown. Alternatively, the aforementioned information processing device can also be implemented as a control device for the vehicle 10. In this case, for example, the processing circuit of the second control device 92 of the vehicle 10... Figure 3 In step S100, raw data is obtained from the data center 500. Furthermore, the processing circuit of the second control device 92 executes... Figure 3 The processing after step S110 shown.

[0095] The aforementioned data center 500 determines the similarity between the original and extracted data by calculating the error in the frequency distribution. Alternatively, data center 500 can determine the similarity between the original and extracted data without calculating the error. For example, statistical methods such as fitness assessment can be used to determine similarity when the difference between the original and extracted data is not significant.

[0096] In the case of a vehicle in which the torque generated by a drive unit such as an internal combustion engine or an electric generator is distributed to the front wheel 11F and the rear wheel 11R via a central differential, either the drive torque of the front wheel 11F or the drive torque of the rear wheel 11R can be set as the aforementioned secondary drive torque.

[0097] (Symbol Explanation)

[0098] 10: Vehicle; 11F: Front wheel; 11R: Rear wheel; 21: Internal combustion engine; 23: Electric 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 apparatus that acquires raw data collected continuously for a predetermined period using sensors mounted on a vehicle, and extracts data from the raw data for analyzing the usage mode of the secondary drive unit, which includes a main drive unit and a secondary drive unit driving the vehicle, wherein... The information processing device includes a processing circuit. The processing circuit: The first step involves calculating the frequency distribution of the original data for each of the multiple characteristic quantities, including the drive torque of the sub-drive unit and the total drive torque of the drive unit contained in the original data. Step 2: Repeatedly execute the setting of multiple time windows, so that the period of the sum of the periods of all time windows is shorter than the predetermined period, to cut out the data of a portion of the original data. Step 3: Extract data from the original data using the multiple time windows; Step 4: Calculate the frequency distribution of the extracted data, which is formed by combining all the data extracted using the multiple time windows for each feature; Step 5: Calculate the errors of the frequency distribution of each feature in the original data and the frequency distribution of each feature in the extracted data. And in step 6, based on the aforementioned errors, determine whether the original data and the extracted data are similar. The extracted data, which is determined to be similar to the original data, is used as data for parsing the usage mode of the sub-drive unit in the drive unit.

2. The information processing apparatus according to claim 1, wherein, The processing circuit: In the first step, the raw data is divided into multiple data sets based on information related to the vehicle's driving state contained within the raw data. For each data set, the frequency distribution of each feature quantity in the raw data is calculated. In step 4, the extracted data and the original data are divided into multiple data regions corresponding to the distinctions between the extracted data and the original data. For each region of the data, the frequency distribution of each feature quantity in the extracted data is calculated. In step 5, for each distinction of the data, the frequency distribution of each of the features in the original data and the frequency distribution of each of the features in the extracted data are calculated with respect to each distinction.

3. The information processing apparatus according to claim 2, wherein, Information related to the vehicle's driving status is the vehicle's driving mode.

4. The information processing apparatus according to claim 2, wherein, Information related to the vehicle's driving status is the vehicle's location.

5. The information processing apparatus according to claim 2, wherein, Information related to the vehicle's driving status is the vehicle's tilt angle.

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