Information processing device

Abstract

Extract data suitable for analyzing tire grip performance. [Solution] An information processing device executes the following steps: setting a plurality of time windows for extracting data from original data using the accelerator operation amount and the vehicle slip amount when the vehicle starts as feature quantities (S110); extracting data from the original data (S120); calculating a frequency distribution of the original data and a frequency distribution of the extracted data for each of a plurality of feature quantities divided based on information on factors that affect tire grip (S130); and determining whether the original data and the extracted data are similar using an error in the frequency distribution (S140 and S150). The information processing device repeatedly executes steps S110 to S150 by changing the settings of the plurality of time windows, and extracts extracted data that is similar to the original data from the original data.

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] When analyzing the grip performance of a vehicle's tires, if data suitable for analyzing grip performance can be extracted from the original data, the grip performance 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 tire grip performance 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 data used for analyzing the grip performance of tires on the vehicle from the original data. This information processing device includes a processing circuit. the processing circuit repeatedly executes the following steps by changing the settings of the multiple time windows: 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, the frequency distribution being calculated for the plurality of feature quantities, the frequency distribution being calculated for the plurality of feature quantities included in the original data, the frequency distribution being calculated for the plurality of feature quantities, and the slip amount or slip ratio of the vehicle; a second step of setting a plurality of time windows to extract data for 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 a frequency distribution for each of the feature quantities 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; 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 the grip performance of the tire. [Effects of the Invention]

[0007] According to the information processing device, data suitable for analyzing the grip performance of a tire 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 is a graph showing part of the original data. (a) shows the change in accelerator operation amount, (b) shows the change in slip amount, and (c) shows the change in tire air pressure. [Figure 3] FIG. 3 is a flowchart showing the flow of processing executed by the processing circuitry of the data center. [Figure 4] FIG. 4 shows the frequency distribution of the accelerator operation amount and the slip amount in the original data when the tire air pressure is equal to or higher than the predetermined pressure. [Figure 5] FIG. 5 shows the frequency distribution of the accelerator operation amount and the slip amount in the original data when the tire air pressure is below the predetermined pressure. 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, two motor generators 23, a planetary gear mechanism 50 constituting a power split device, a power control unit (hereinafter referred to as a "PCU (Power Control Unit)") 24, a battery 25, and a vehicle control unit 90. In the following description, the two motor generators 23 will be referred to as a first motor generator 23A and a second motor generator 23B, respectively.

[0014] The crankshaft of the internal combustion engine 21 is mechanically connected to a carrier C of the planetary gear mechanism 50. A rotating shaft of a first motor generator 23A, which is an electric motor, is mechanically connected to a sun gear S of the planetary gear mechanism 50. Furthermore, a rotating shaft of a second motor generator 23B, which is an electric motor, and a driving wheel 60 of the vehicle 10 are mechanically connected to a ring gear R of the planetary gear mechanism 50. The driving wheel 60 includes a wheel and a tire 61 mounted on the wheel.

[0015] The battery 25 exchanges electric power with the motor generators 23. That is, when the first motor generator 23A or the second motor generator 23B functions as a motor that outputs torque, the electric power of the battery 25 is converted by the PCU 24 and then supplied to each motor generator. On the other hand, when the first motor generator 23A or the second motor generator 23B functions as a generator, the electric power regenerated by each motor generator is converted by the PCU 24 and then supplied to the battery 25.

[0016] 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.

[0017] Examples of data collected by the vehicle control unit 90 are the travel distance, vehicle speed V, and location information of the vehicle 10. Other examples of data collected by the vehicle control unit 90 include an accelerator operation amount, which is an operation amount of an accelerator pedal that adjusts the acceleration of the vehicle 10, an amount of slip of the vehicle 10, tire air pressure, which is the air pressure of the tires 61, and tire temperature, which is the temperature of the tires 61. The amount of slip is a value obtained by subtracting the vehicle speed V from the wheel speed ω of the drive wheels 60. Another example of data collected by the vehicle control unit 90 is a steering angle, which is the steering angle of the steering wheel that operates the steered wheels of the vehicle 10. Another example of data collected by the vehicle control unit 90 is weather information acquired via the communication network 400 based on the location information. The weather information includes, for example, information on sunny, cloudy, rainy, and snowy weather.

[0018] <Data Extraction> The information processing terminal 600 is used to analyze the grip performance of the tires 61 of the vehicle 10. When analyzing the grip performance of the tires 61, the information processing terminal 600 transmits an instruction to the data center 500.

[0019] The processing circuit 510 of the data center 500 that received 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. This data includes multiple pieces of data related to the grip performance of the tires 61 collected using multiple sensors mounted on the vehicle 10. These pieces of data are called feature quantities. The processing circuit 510 uses the feature quantities to analyze the grip performance of the tires 61 equipped on the specific vehicle 10. In this case, the feature quantities are the accelerator operation amount, slip amount, and tire air pressure when the vehicle 10 to be analyzed starts moving.

[0020] The processing circuit 510 uses a large amount of data collected over a long period of time in order to analyze the grip performance of the tires 61 on a particular vehicle 10 according to a program. In this analysis, the processing circuit 510 performs a huge amount of calculations, which takes 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] The original data shown in Fig. 2 is a chronological arrangement of part of the data when one vehicle 10 starts 100,000 times. The original data shown in Fig. 2 includes, as feature quantities, the accelerator operation amount, slip amount, and tire pressure when the vehicle 10 starts.

[0023] Fig. 2(a) shows the accelerator operation amount at each start of the vehicle 10. Fig. 2(b) shows the slip amount when the accelerator operation amount was collected. Fig. 2(c) shows the tire air pressure when the accelerator operation amount was collected.

[0024] The accelerator operation amount, slip amount, and tire pressure when the vehicle 10 starts are data related to the grip performance of the tires 61. The processing circuit 510 extracts data for analyzing the grip performance of the tires 61 from the data including the accelerator operation amount, slip amount, and tire pressure when the vehicle 10 starts, as feature amounts.

[0025] The extracted data is created by extracting data from the original data using multiple time windows. In FIG. 2, three time windows, a first time window W_1, a second time window W_2, and a third time window W_3, are shown by two-dot chain lines as examples of multiple time windows. The start and end periods of each time window are set so that they do not overlap. In this example, data for 20,000 vehicle starts is extracted as the extracted data. Therefore, the start and end periods of each time window are set so that the total number of starts of vehicle 10 over the duration of all the time windows is 20,000.

[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 the grip performance of the tires 61 of one vehicle 10 is data of the target vehicle 10 selected from a huge amount of data of a plurality of vehicles 10 . Next, in step S110, the processing circuit 510 sets a plurality of time windows to extract extracted data from the original data.

[0030] 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 step 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 these, the accelerator operation amount and the slip amount are defined as first feature quantities. In addition, other feature quantities different from the first feature quantities, information relating to factors that affect the grip force of the tire 61, are defined as second feature quantities. In this embodiment, the second feature quantity is tire air pressure.

[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 tire pressure. Therefore, the data of the first feature is classified by tire pressure. The processing circuit 510 calculates a frequency distribution in the original data for each first feature for each tire pressure 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 then 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 feature 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 feature amount is the accelerator operation amount and the slip amount. Therefore, the frequency distribution of the accelerator operation amount and the frequency distribution of the slip amount 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 extracted data of 20,000 vehicle starts is extracted from original data of 100,000 vehicle starts, the total frequency of the extracted data will be 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. The 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 accelerator operation amount and the slip amount when the vehicle starts moving in the original data when the tire air pressure is equal to or higher than a predetermined pressure. FIG. 5 shows the frequency distribution of the accelerator operation amount and the slip amount when the vehicle starts in the original data when the tire air pressure is below the predetermined pressure.

[0040] As shown in FIGS. 4 and 5, in these frequency distributions, the accelerator operation amount and slip amount 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 accelerator operation amount and slip amount contained in the original data and the extracted data into two classes: a class where the tire pressure is equal to or greater than a predetermined value, and a class where the tire pressure is less than the predetermined value. The processing circuit 510 calculates the frequency distribution as described above for each of the two tire pressure 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] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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 is used to analyze the grip performance of the tire 61.

[0050] <Operation of this embodiment> The data center 500, which is an information processing device of this embodiment, acquires original data that is collected and created over a predetermined period of time using a plurality of sensors mounted on the vehicle 10. Then, the data center 500 extracts data to be used for analyzing the grip performance of the tire 61 from the original data.

[0051] The data center 500 includes a processing circuit 510. The original data includes, as a plurality of feature quantities, the accelerator operation amount and the slip amount when the vehicle starts. 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.

[0052] 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.

[0053] 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.

[0054] 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 the errors between the frequency distribution of each feature amount in the original data and the frequency distribution of each feature amount in the extracted data.

[0055] 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 the first step, the processing circuit 510 executes a search process in which the processes from the second step to the sixth step are repeatedly executed by changing the settings of a plurality of time windows. Then, when the processing circuit 510 determines that the original data and the extracted data are similar, the processing circuit 510 extracts the extracted data as data to be used for analyzing the grip performance of the tire 61 (step S160).

[0056] According to this data center 500, such an analysis can be performed using extracted data in which the distribution of feature quantities related to the grip performance of the tire 61 is similar to that of the original data. Therefore, the data center 500 can obtain analysis results of the grip performance of the tire 61 that are similar to the analysis results obtained using the original data.

[0057] 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 grip performance of the tire 61 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.

[0058] <Effects of this embodiment> (1) The data center 500 determines whether the original data and the extracted data are similar based on a plurality of feature quantities, including the accelerator operation amount and slip amount, when the vehicle 10 starts moving. Therefore, data suitable for analyzing the grip performance of the tire 61 can be extracted from the original data.

[0059] (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 contained in the original data about factors that affect the grip of the tires 61. Then, the processing circuit 510 calculates, for each data class, a frequency distribution in the original data for each of the above feature amounts, including the accelerator operation amount and slip amount when the vehicle 10 starts moving.

[0060] In the fourth step, the processing circuit 510 divides 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 of the extracted data for each of the feature amounts including the accelerator operation amount and the slip amount for each data division.

[0061] 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.

[0062] Therefore, when analyzing the grip performance of the tire 61, data can be extracted that allows factors that affect the grip force of the tire 61 to be taken into consideration. (3) The above information regarding the factor affecting the grip of the tire 61 is the air pressure of the tire 61. Therefore, data that can take into account the air pressure of the tire 61 as a factor affecting the grip of the tire 61 can be extracted.

[0063] <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.

[0064] The tire temperature is data related to the grip performance of the tire 61. Therefore, the information regarding the factors affecting the grip of the tire 61 may be the temperature of the tire 61 when the vehicle 10 starts moving. In this case, for example, the accelerator operation amount and slip amount contained in the original data and the extracted data are divided into two categories: a category where the tire temperature is equal to or higher than a predetermined temperature, and a category where the tire temperature is lower than the predetermined temperature. The processing circuit 510 may then calculate the frequency distribution described above for each of the two tire temperature categories. According to this modification, it is possible to extract data that can take into account the temperature of the tire 61 as a factor affecting the grip of the tire 61.

[0065] The weather information is data related to the grip performance of the tires 61. Therefore, the information on factors affecting the grip of the tires 61 may be weather information at the time of starting the vehicle 10. In this case, for example, the accelerator operation amount and slip amount included in the original data and the extracted data may be divided into three categories: a category for when the weather was sunny or cloudy, a category for when the weather was rainy, and a category for when the weather was snowy. The processing circuit 510 may then calculate the frequency distribution for each of the three weather categories. This modification makes it possible to extract data that takes into account weather as a factor affecting the grip of the tires 61. Note that in the above embodiment, the vehicle control unit 90 acquired weather information via the communication network 400 based on location information. Alternatively, for example, the data center 500 may acquire weather information via the communication network 400 based on the collected location information.

[0066] The steering angle is data related to the grip performance of the tires 61. Therefore, the information regarding the factors affecting the grip of the tires 61 may be the steering angle at the time of starting the vehicle 10. In this case, for example, the accelerator operation amount and slip amount included in the original data and the extracted data are divided into two categories: a category where the steering angle is within a predetermined range, and a category where the steering angle is outside the predetermined range. The processing circuit 510 may then calculate the frequency distribution for each of the two steering angle categories. According to this modification, data that can take into account the steering angle as a factor affecting the grip of the tires 61 can be extracted.

[0067] Based on the position information of the vehicle 10, information regarding the slipperiness of the road surface can be obtained. Such slipperiness is data related to the grip performance of the tires 61. Therefore, as the above information regarding factors affecting the grip of the tires 61, the processing circuit 510 acquires position information at the time of starting the vehicle 10. Based on the position information, the processing circuit 510 divides the driving area into, for example, urban and rural areas. As with the above information, the processing circuit 510 divides the accelerator operation amount and slip amount contained in the original data and the extracted data into two sections, an urban section and a rural section. Then, the processing circuit 510 may calculate the frequency distribution for each of the two driving area sections. According to this modification, data can be extracted that can take into account the position information of the vehicle 10 as a factor affecting the grip of the tires 61.

[0068] The processing circuit 510 divides the original data into multiple data based on information contained in the original data about factors that affect the grip of the tire 61. 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.

[0069] 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 processes from step S110 onwards shown in FIG. 3 . 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 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 of the vehicle control unit 90 executes the processes from step S110 onwards shown in FIG. 3 .

[0070] 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.

[0071] The slip amount included in the feature amount may be changed to the slip ratio of the vehicle 10. The slip ratio is a value calculated from the formula "(wheel speed ω - vehicle speed V) / wheel speed ω". The number of motor generators 23 provided in the vehicle 10 can be changed as appropriate.

[0072] The vehicle 10 is not limited to a hybrid vehicle equipped with the series-parallel system shown in FIG. 1, but may be a hybrid vehicle equipped with another system. The vehicle 10 may be a vehicle equipped with only an electric motor as a prime mover.

[0073] The vehicle 10 may be a vehicle equipped with only an internal combustion engine as a prime mover. [Explanation of symbols]

[0074] 10...vehicle 21...internal combustion engine 23...motor generator 23A...first motor generator 23B...second motor generator 24...PCU 25...battery 50...planetary gear mechanism 60...drive wheel 61...tire 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 collected over a predetermined period of time using a sensor mounted on a vehicle, and extracts data from the original data to be used for analyzing tire grip performance of 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 included in the original data, the feature quantities including an accelerator operation amount at the time of starting the vehicle and a slip amount or a slip ratio of the vehicle; 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 the grip performance of the tire. Information processing device.

2. The processing circuitry In the first step, the original data is divided into a plurality of data based on information on factors that affect the tire grip force, which is included in the original data, and the frequency distribution in the original data for each of the feature quantities 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 is the tire pressure. The information processing device according to claim 2 .

4. The information is the temperature of the tire. The information processing device according to claim 2 .

5. The information is weather information. The information processing device according to claim 2 .

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