Information processing device

Abstract

To extract data suitable for analyzing the vibration-damping performance of a vibration-damping member that supports a unit mounted on a vehicle. [Solution] The information processing device executes the steps of: setting a plurality of time windows for extracting data from original data using the vertical acceleration of the vehicle body and the vertical acceleration of the unit 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 divided feature quantities based on information that affects the magnitude of at least one of the vertical acceleration of the vehicle body and the vertical acceleration of the unit (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] A unit mounted on a vehicle is supported by a vibration-damping member. The transmission of vibration from the unit to the vehicle body is suppressed by the vibration-damping member. When analyzing the vibration-damping performance of the vibration-damping member, if data suitable for analyzing the vibration-damping performance of the vibration-damping member can be extracted from the original data, the vibration-damping performance can be analyzed in a shorter time by using the extracted data than by using the original data. The above-mentioned information processing device analyzes the traveling speed pattern using the extracted data. Therefore, the above-mentioned information processing device cannot extract data suitable for analyzing the vibration-damping performance of the vibration-damping member from the original data. [Means for solving the problem]

[0006] An information processing device for solving the above problem acquires original data collected over a predetermined period of time using a sensor mounted on a vehicle, and extracts from the original data data used to analyze the vibration-damping performance of a vibration-damping member that supports a unit mounted on the vehicle and suppresses the transmission of vibration from the unit to the vehicle body. This information processing device includes a processing circuit. The processing circuit 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 multiple feature quantities included in the original data, including the vertical acceleration of the vehicle body and the vertical acceleration of the unit; a second step of setting multiple time windows to extract data for a portion of the original data so 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 multiple time windows; a fourth step of calculating a frequency distribution in extracted data for each of the feature quantities, which is obtained by combining all of the data extracted using the multiple time windows for the multiple feature quantities; a fifth step of calculating each error between the frequency distribution for each feature quantity in the original data and the frequency distribution for each feature quantity in the extracted data; and a sixth step of determining whether the original data and the extracted data are similar based on each error.The processing circuit repeatedly executes these steps by changing the settings of the multiple time windows, and when it is determined that the original data and the extracted data are similar, extracts the extracted data as data to be used for analyzing the vibration damping performance of the vibration damping member. [Effects of the Invention]

[0007] According to the information processing device, data suitable for analyzing the vibration damping performance of the vibration damping member can be extracted from the original data. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a schematic diagram showing the relationship between a data center, which is an embodiment of an information processing device, a vehicle, and an information processing terminal. [Figure 2]Figure 2 shows graphs of some of the original data. (a) shows the transition of the vehicle's vertical acceleration, (b) shows the transition of the unit's vertical acceleration, (c) shows the transition of tire air pressure, and (d) shows the transition of the driving area. [Figure 3] FIG. 3 is a flowchart showing the flow of processing executed by the processing circuitry of the data center. [Figure 4] FIG. 4 shows the frequency distribution of the vertical acceleration of the vehicle body and the vertical acceleration of the unit 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 vertical acceleration of the vehicle body and the vertical acceleration of the unit 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 a vehicle body 12, an internal combustion engine 21, a transmission 30, a mount 50, tires 60, and a vehicle control unit 90. The transmission 30 is fastened to the internal combustion engine 21 with bolts or the like. Therefore, the internal combustion engine 21 and the transmission 30 form a single unit 40. The unit 40 is supported on the body 12 of the vehicle 10 via a plurality of mounts 50. The mounts 50 are vibration-damping members that support the unit 40 mounted on the vehicle 10 and suppress the transmission of vibrations from the unit 40 to the body 12, and are made of, for example, rubber.

[0014] The tires 60 support the vehicle body 12 via a suspension. 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] An example of data collected by the vehicle control unit 90 is the travel distance and vehicle speed of the vehicle 10. Further, an example of data collected by the vehicle control unit 90 is the vertical acceleration of the vehicle body 12, the vertical acceleration of the unit 40, the tire pressure which is the air pressure of the tires 60, and position information of the vehicle 10.

[0016] <Data Extraction> The information processing terminal 600 is used to analyze the vibration damping performance of the mount 50. When analyzing the vibration damping performance of the mount 50, the information processing terminal 600 transmits instructions to the data center 500.

[0017] 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 vibration-damping performance of the mount 50 collected using multiple sensors installed on the vehicle 10. These pieces of data are called feature quantities. The processing circuit 510 uses the feature quantities to analyze the vibration-damping performance of the mount 50 equipped on a specific vehicle 10. In this case, the feature quantities are the vertical acceleration of the vehicle body 12, the vertical acceleration of the unit 40, the tire pressure, and the driving area obtained from the position information of the vehicle 10, for the vehicle 10 being analyzed.

[0018] Processing circuitry 510 utilizes large amounts of data collected over a long period of time to programmatically analyze the vibration damping performance of mount 50 for a particular vehicle 10. In this analysis, processing circuitry 510 performs a large amount of calculations, which can take a long time.

[0019] 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.

[0020] Fig. 2 shows a portion of the original data of feature quantities related to the vibration damping performance of the mount 50. The original data shown in Fig. 2 is a portion of 100,000 hours of data for one vehicle 10. The original data shown in Fig. 2 includes, as feature quantities, the vertical acceleration of the vehicle body 12, the vertical acceleration of the unit 40, tire air pressure, and driving area.

[0021] Figure 2(a) shows the vertical acceleration of the vehicle body 12. Figure 2(b) shows the vertical acceleration of the unit 40. Figure 2(c) shows the tire pressure. Figure 2(d) shows the driving area divided into urban and rural areas.

[0022] The vertical acceleration of the vehicle body 12, the vertical acceleration of the unit 40, the tire air pressure, and the driving area are data related to the vibration-damping performance of the mount 50. The processing circuit 510 extracts data for analyzing the vibration-damping performance of the mount 50 from data including the vertical acceleration of the vehicle body 12, the vertical acceleration of the unit 40, and the tire air pressure as feature quantities.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] The original data for analyzing the vibration damping performance of the mount 50 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Next, in step S130, 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 vertical acceleration of vehicle body 12 and the vertical acceleration of unit 40 are defined as first feature quantities. In addition, another feature quantity different from the first feature quantity, which is information that affects the magnitude of at least one of the vertical acceleration of vehicle body 12 and the vertical acceleration of unit 40, is defined as second feature quantity. In this embodiment, the second feature quantity is tire air pressure.

[0033] 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.

[0034] 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.

[0035] The frequency distribution classifies the data for each first feature amount into a plurality of classes, and shows the distribution of the frequency, which is the number of data in each class. In this embodiment, the data for the first feature amount is the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40. Therefore, the frequency distribution of the vertical acceleration of the vehicle body 12 and the frequency distribution of the vertical acceleration of the unit 40 are calculated, respectively.

[0036] 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.

[0037] FIG. 4 shows the frequency distribution of the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40 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 vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40 in the original data when the tire air pressure is below the predetermined pressure.

[0038] As shown in Figures 4 and 5, in these frequency distributions, the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40 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 vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40 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.

[0039] 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:

[0040]

number

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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 vibration damping performance of mount 50.

[0048] <Operation of this embodiment> The data center 500, which is an information processing device of this embodiment, acquires original data created by collecting data over a predetermined period of time using multiple sensors mounted on the vehicle 10. The data center 500 then extracts data used to analyze the vibration damping performance of the mount 50 from the original data.

[0049] The data center 500 includes a processing circuit 510. The original data includes, as a plurality of feature amounts, the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40. In this data center 500, the processing circuit 510 executes a search process. The search process includes a first step (step S130) of calculating a frequency distribution in the original data for each of the plurality of feature amounts.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] The search process includes a sixth step (step S150) of determining whether the original data and the extracted data are similar based on the above errors. After executing step 1, processing circuit 510 executes a search process in which steps 2 to 6 are repeatedly executed while changing the settings of multiple time windows. When processing circuit 510 determines that the original data and the extracted data are similar, it extracts the extracted data as data to be used for analyzing the vibration damping performance of mount 50 (step S160).

[0054] 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 vibration-damping performance of the mount 50 is similar to the original data. Therefore, the above-mentioned data center 500 can obtain analysis results of the vibration-damping performance of the mount 50 that are close to the analysis results obtained using the original data.

[0055] 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 vibration damping performance of the mount 50 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.

[0056] <Effects of this embodiment> (1) According to the data center 500, whether the original data and the extracted data are similar to each other is determined based on a plurality of feature quantities including the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40. Therefore, data suitable for analyzing the vibration damping performance of the mount 50 can be extracted from the original data.

[0057] (2) In the first step, the processing circuit 510 of the data center 500 divides the original data into a plurality of data based on information included in the original data that affects the magnitude of at least one of the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40. Then, the processing circuit 510 calculates a frequency distribution in the original data for each of the above-mentioned feature amounts, including the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40, for each data division.

[0058] 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. Then, the processing circuit 510 calculates a frequency distribution of the extracted data for each of the feature amounts including the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40 for each data segment.

[0059] 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.

[0060] Therefore, when analyzing the vibration damping performance of the mount 50, data can be extracted that allows for consideration of information that influences the magnitude of at least one of the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40.

[0061] (3) The above information is the tire pressure of the vehicle 10. Therefore, data can be extracted that can take into account the tire pressure, which affects the magnitude of at least one of the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40.

[0062] <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.

[0063] Information regarding the roughness of the road surface can be obtained based on the position information of the vehicle 10. The roughness of the road surface affects the magnitude of the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40. Therefore, the information that determines the magnitude of at least one of the vertical accelerations of the vehicle body 12 and the unit 40 may be the position information of the vehicle 10. In this case, for example, the vertical accelerations of the vehicle body 12 and the unit 40 included in the original data and the extracted data may be divided into two driving areas, rural and urban, based on the position information. The processing circuit 510 may then calculate the frequency distribution for each of the two driving areas. This modification makes it possible to extract data that takes into account the position of the vehicle 10, which determines the magnitude of at least one of the vertical accelerations of the vehicle body 12 and the unit 40.

[0064] The processing circuit 510 divides the original data into multiple pieces of data based on information contained in the original data that affects the magnitude of at least one of the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40. 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.

[0065] 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 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 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.

[0066] 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.

[0067] The unit supported by the vibration-damping member may be a unit other than the above-described unit 40. For example, it may be an electric motor, a gearbox, a battery that supplies power to the electric motor, or a power control unit that controls the power supplied to the electric motor. [Explanation of symbols]

[0068] 10...vehicle 12...vehicle body 21...internal combustion engine 30...transmission 40...unit 50...mount 60...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 that is collected and created over a predetermined period of time using a sensor mounted on a vehicle, and extracts from the original data data that is used to analyze vibration-damping performance of a vibration-damping member that supports a unit mounted on the vehicle and suppresses transmission of vibration from the unit to a vehicle body, processing circuitry; The processing circuitry a first step of calculating a frequency distribution in the original data for each of a plurality of feature amounts including the vertical acceleration of the vehicle body and the vertical acceleration of the unit included in the original data; 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 vibration damping performance of the vibration damping member. Information processing device.

2. The processing circuitry In the first step, the original data is divided into a plurality of data based on information included in the original data that affects the magnitude of at least one of the vertical acceleration of the vehicle body and the vertical acceleration of the unit, and the frequency distribution in the original data for each of the feature amounts is calculated for each data division; In the fourth step, the extracted data is divided into a plurality of data corresponding to the plurality of data divisions of the original data, and the frequency distribution in the extracted data for each of the feature amounts is calculated for each data division; In the fifth step, each error between the frequency distribution for each feature amount in the original data and the frequency distribution for each feature amount in the extracted data is calculated for each data section. The information processing device according to claim 1 .

3. The information is the tire pressure of the vehicle. The information processing device according to claim 2 .

4. The information is the location information of the vehicle. The information processing device according to claim 2 .

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