Information processing apparatus

Abstract

To extract data suitable for analyzing the performance of a suspension from original data.SOLUTION: A step (S110) of setting a plurality of time windows for cutting out a datum from the original datum using the sprung acceleration and the unsprung acceleration included in the original datum as feature quantities, a step (S120) of cutting out the datum from the original datum, a step (S130) of calculating a frequency distribution of the original datum and a frequency distribution of the extracted datum for each of a plurality of divided feature quantities based on a physical quantity that affects a magnitude of at least one of the sprung acceleration and the unsprung acceleration, and steps (S140 and S150) of determining whether or not the original datum and the extracted datum are similar to each other using errors of the frequency distributions. The information processor repeatedly executes steps S110 to S150 while changing the setting of a plurality of time windows, and extracts an extraction datum similar to the original datum from the original datum.SELECTED DRAWING: Figure 3

Description

[Technical Field]

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

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

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

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

[0005] Suspension performance can be analyzed using data acquired by multiple sensors mounted on a vehicle. If data suitable for analyzing suspension performance can be extracted from the original data, suspension 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 suspension performance from the original data. [Means for solving the problem]

[0006] An information processing device for solving the above problem acquires original data collected and created over a predetermined period using a sensor mounted on a vehicle, and extracts data used to analyze the performance of a suspension mounted on the vehicle from the original data. The information processing device includes a processing circuit. The processing circuit executes a first step of calculating a frequency distribution in the original data for each of a plurality of feature quantities included in the original data, the feature quantities including sprung acceleration, which is the vertical acceleration of a sprung member of the vehicle, and unsprung acceleration, which is the vertical acceleration of an unsprung member of the vehicle, and a second step of setting a plurality of time windows for extracting data for a portion of the original data such that the total period of all the time windows is shorter than the predetermined period. A third step of extracting data from the original data using the plurality of time windows and a third step of extracting data from the original data using the plurality of time windows and a third step of extracting data from the plurality of time windows for the plurality of feature quantities. A fourth step of calculating, for each feature, a frequency distribution in extracted data obtained by combining all of the data extracted by the windows; a fifth step of calculating each error between the frequency distribution for each feature in the original data and the frequency distribution for each feature in the extracted data; and a sixth step of determining whether the original data and the extracted data are similar based on each error are repeatedly executed by changing the settings of the multiple time windows, and when it is determined that the original data and the extracted data are similar, the extracted data is extracted as data to be used for analyzing the performance of the suspension. [Effects of the Invention]

[0007] According to the information processing device, data suitable for analyzing the performance of the suspension 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 sprung acceleration, (b) shows the change in unsprung acceleration, (c) shows the change in tire air pressure, (d) shows the change in vehicle weight, and (e) shows the change in shock absorber oil temperature. [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 sprung and unsprung accelerations of the original data when the air pressure is equal to or higher than the predetermined pressure. [Figure 5] FIG. 5 shows the frequency distribution of the sprung acceleration and unsprung acceleration of the original data when the air pressure is lower than 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 a body 12, a suspension 30, tires 40 mounted on wheels, and a vehicle control unit 90. The suspension 30 includes an arm 32, a spring 34, and a shock absorber 36. A wheel with a tire 40 is attached to the arm 32 via a hub. The spring 34 and the shock absorber 36 are attached between the arm 32 and the vehicle body 12.

[0014] The vehicle control unit 90 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 multiple sensors that collect data related to the vehicle 10.

[0015] 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 are sprung acceleration, unsprung acceleration, and tire pressure, which is the air pressure of the tires 40. Other examples of data collected by the vehicle control unit 90 are the vehicle weight, which is the weight of the vehicle 10 that varies depending on the number of passengers and load of the vehicle 10, and the absorber oil temperature, which is the temperature of the hydraulic oil in the shock absorber 36 that affects the damping force of the shock absorber 36.

[0016] The sprung acceleration is the vertical acceleration of a sprung member of the vehicle 10. The sprung member is, for example, the vehicle body 12. The unsprung acceleration is the vertical acceleration of an unsprung member of the vehicle 10. The unsprung member is, for example, the arm 32.

[0017] <Data Extraction> The information processing terminal 600 is used to analyze the performance of the suspension 30 mounted on the vehicle 10. When analyzing the performance of the suspension 30, the information processing terminal 600 transmits an instruction to the data center 500.

[0018] 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 performance of the suspension 30 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 performance of the suspension 30 of a specific vehicle 10. In this case, the feature quantities are the sprung acceleration, unsprung acceleration, tire air pressure, vehicle weight, and absorber oil temperature of the vehicle 10 to be analyzed.

[0019] The processing circuitry 510, according to the program, utilizes large amounts of data collected over a long period of time to analyze the performance of the suspension 30 of a particular vehicle 10. In this analysis, the processing circuitry 510 performs a large amount of calculations, which can take a long time to complete.

[0020] 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.

[0021] Fig. 2 shows a portion of original data of feature quantities related to the performance of the suspension 30. 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, sprung acceleration, unsprung acceleration, tire air pressure, vehicle weight, and absorber oil temperature.

[0022] Figure 2(a) shows the sprung acceleration. Figure 2(b) shows the unsprung acceleration. Figure 2(c) shows the tire pressure. Figure 2(d) shows the vehicle weight. Figure 2(e) shows the absorber oil temperature.

[0023] The sprung acceleration, unsprung acceleration, tire pressure, vehicle weight, and absorber oil temperature are data related to the performance of the suspension 30 of the vehicle 10. The processing circuit 510 extracts data for analyzing the performance of the suspension 30 from the data including the sprung acceleration, unsprung acceleration, and tire pressure as feature quantities.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] The original data for analyzing the performance of the suspension 30 of one vehicle 10 is data of the target vehicle 10 selected from a huge amount of data of multiple vehicles 10 . Next, in step S110, the processing circuit 510 sets a plurality of time windows to extract extracted data from the original data.

[0029] 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.

[0030] 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.

[0031] In this way, a cut-out pattern for cutting out data from the original data is determined by setting a plurality of time windows through the process of step S110. After determining the cut-out pattern in this way, processing circuit 510 advances the process to S120.

[0032] 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.

[0033] 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 sprung acceleration and the unsprung acceleration are defined as the first feature quantities. In addition, a feature quantity other than the first feature quantity, which is a physical quantity that affects the magnitude of at least one of the sprung acceleration and the unsprung acceleration, is defined as the second feature quantity. In this embodiment, the second feature quantity is the tire pressure.

[0034] 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.

[0035] 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.

[0036] 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 sprung acceleration and unsprung acceleration. Therefore, the frequency distribution of sprung acceleration and the frequency distribution of unsprung acceleration are calculated separately.

[0037] Furthermore, because the total frequency of the first feature contained in the original data and the extracted data differs, the frequency distribution of the original data and the frequency distribution of the extracted data cannot be simply compared. If 20,000 hours of extracted data are extracted from 100,000 hours of original data, the total frequency of the extracted data will be approximately one-fifth of the total frequency of the original data. In this case, by multiplying the frequency of each class of the extracted data by five, a frequency distribution of the extracted data with a total frequency equivalent to that of the original data can be obtained. Even if the above method is not used, the data distribution in the original data and the data distribution in the extracted data can be compared by calculating a relative frequency distribution as the frequency distribution of the original data and the extracted data. A relative frequency distribution is a frequency distribution that indicates what percentage of the frequency of a class is made up of the total frequency.

[0038] FIG. 4 shows the frequency distributions of the sprung acceleration and unsprung acceleration of the original data when the tire air pressure is equal to or higher than the predetermined pressure. FIG. 5 shows the frequency distributions of the sprung acceleration and unsprung acceleration of the original data when the tire pressure is lower than the predetermined pressure.

[0039] As shown in Figures 4 and 5, in these frequency distributions, the sprung acceleration and unsprung acceleration 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 sprung acceleration and unsprung acceleration 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.

[0040] 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:

[0041]

number

[0042] In the above formula, "n" is the number of first feature quantities. In this embodiment, the first feature quantities are sprung acceleration and unsprung acceleration, so "n" is "2". "m" is the total number of classes in the frequency distribution. "Y" is the frequency of the corresponding class of the corresponding feature quantity in the original data. "y" is the frequency of the corresponding class of the corresponding feature quantity in the extracted data.

[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 amount 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 manner, processing circuitry 510 extracts data used to analyze the performance of suspension 30.

[0050] <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. Then, the data center 500 extracts data used to analyze the performance of the suspension 30 mounted on the vehicle 10 from the original data.

[0051] The data center 500 includes a processing circuit 510. The original data includes sprung acceleration and unsprung acceleration as a plurality of feature quantities. In the data center 500, the processing circuit 510 executes a search process. The search process includes a first step (step S130) of calculating a frequency distribution in the original data for each of the plurality of feature quantities.

[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 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.

[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 processing from the second step to the sixth step is 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 performance of the suspension 30 (step S160).

[0056] According to this data center 500, it is possible to analyze the performance of the suspension 30 using extracted data in which the distribution of feature quantities related to the performance of the suspension 30 is similar to that of the original data. Therefore, the data center 500 can obtain analysis results of the performance of the suspension 30 that are close 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 performance of the suspension 30 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 multiple feature quantities including the sprung acceleration and the unsprung acceleration. Therefore, data suitable for analyzing the performance of the suspension 30 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 a physical quantity that affects the magnitude of at least one of the sprung acceleration and the unsprung acceleration included in the original data. Then, the processing circuit 510 calculates a frequency distribution in the original data for each of the feature quantities including the sprung acceleration and the unsprung acceleration for each data classification.

[0060] In the fourth step, the processing circuit 510 divides the extracted data into a plurality of data segments corresponding to the plurality of data segments of the original data, and calculates a frequency distribution of each of the extracted data segments for each of the feature quantities including the sprung acceleration and the unsprung acceleration for each of the data segments.

[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 performance of the suspension 30, it is possible to extract data that allows consideration of the physical quantity that influences the magnitude of at least one of the sprung acceleration and the unsprung acceleration.

[0063] (3) The physical quantity is the tire pressure of the vehicle 10. Therefore, it is possible to extract data that can take into account the tire pressure, which is a physical quantity that affects the magnitude of at least one of the sprung acceleration and the unsprung acceleration.

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

[0065] The physical quantity that influences the magnitude of at least one of the sprung acceleration and the unsprung acceleration may be vehicle weight. In this case, for example, the sprung acceleration and the unsprung acceleration included in the original data and the extracted data may be divided into two categories: a category for vehicle weights equal to or greater than a predetermined value, and a category for vehicle weights less than the predetermined value. The processing circuit 510 may then calculate the frequency distribution for each of the two vehicle weight categories. This modification makes it possible to extract data that takes into account vehicle weight, which is a physical quantity that influences the magnitude of at least one of the sprung acceleration and the unsprung acceleration.

[0066] The physical quantity that influences the magnitude of at least one of the sprung acceleration and the unsprung acceleration may be the absorber oil temperature. In this case, for example, the sprung acceleration and the unsprung acceleration included in the original data and the extracted data are divided into two categories: one where the absorber oil temperature is equal to or higher than a predetermined temperature, and one where the absorber oil temperature is lower than the predetermined temperature. The processing circuit 510 may then calculate the frequency distribution for each of the two absorber oil temperature categories. This modification makes it possible to extract data that takes into account the absorber oil temperature, which is a physical quantity that influences the magnitude of at least one of the sprung acceleration and the unsprung acceleration.

[0067] Based on the position information of the vehicle 10, information regarding the roughness of the road surface can be obtained. The roughness of the road surface affects the magnitude of at least one of the sprung acceleration and the unsprung acceleration. Therefore, the processing circuit 510 acquires the position information of the vehicle 10 as information that affects the magnitude of at least one of the sprung acceleration and the unsprung acceleration included in the original data. Based on the position information, the processing circuit 510 divides the driving area into, for example, urban and rural areas. As with the physical quantities described above, the processing circuit 510 divides the sprung acceleration and the unsprung acceleration included in the original data and the extracted data into two sections, an urban section and a rural section. The processing circuit 510 may then calculate the frequency distribution described above for each of the two driving area sections. According to this modification, data can be extracted that takes into account the position information of the vehicle 10, which is information that affects the magnitude of at least one of the sprung acceleration and the unsprung acceleration.

[0068] The processing circuit 510 divides the original data into multiple data based on a physical quantity that affects the magnitude of at least one of the sprung acceleration and unsprung acceleration contained in the original data. Alternatively, 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 manner. 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 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 of the vehicle control unit 90 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.

[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. [Explanation of symbols]

[0071] 10...Vehicle 12...Vehicle body 30...Suspension 32...Arm 34...Spring 36...Shock absorber 40...Tire 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 data from the original data to be used for analyzing the performance of a suspension mounted on the vehicle, processing circuitry; The processing circuitry A first step is executed in which a frequency distribution in the original data is calculated for each of a plurality of feature quantities included in the original data, the feature quantities including a sprung acceleration that is a vertical acceleration of a sprung member of the vehicle and an unsprung acceleration that is a vertical acceleration of an unsprung member of the vehicle, and 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 performance of the suspension. Information processing device.

2. The processing circuitry In the first step, the original data is divided into a plurality of data based on a physical quantity that affects the magnitude of at least one of the sprung acceleration and the unsprung acceleration included in the original data, and the frequency distribution in the original data for each feature quantity 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 physical quantity is the air pressure of the tires of the vehicle. The information processing device according to claim 2 .

4. The physical quantity is the weight of the vehicle. The information processing device according to claim 2 .

5. The physical quantity is the temperature of hydraulic oil in a shock absorber provided in the suspension. The information processing device according to claim 2 .

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