Information processing apparatus

Abstract

The invention relates to an information processing apparatus. Data suitable for analyzing the vibration damping performance of a vibration damping member supporting a unit mounted on a vehicle is extracted. The information processing device executes: a step (S110) for setting a plurality of time windows for cutting out data from raw data, using the vertical acceleration of the vehicle body and the vertical acceleration of the unit as feature quantities; a step (S120) for cutting out data from the original data; a step (S130) for calculating the frequency distribution of the original data and the frequency distribution of the extracted data for each of the plurality of feature quantities on the basis of information on the magnitude of at least one of the vertical acceleration of the left and right vehicle bodies and the vertical acceleration of the unit; and a step (S140 and S150) for determining whether the original data and the extracted data are similar using an error in the frequency distribution. The information processing device repeatedly executes steps S110-S150 by changing settings of a plurality of time windows, and extracts extracted data similar to the original data from the original data.

Description

Technical Field

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

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

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

[0004] Existing technical documents

[0005] Patent documents

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

[0007] A unit mounted on a vehicle is supported by vibration damping components. These components suppress the transmission of vibrations from the unit to the vehicle body. When analyzing the vibration damping performance of these components, if suitable data for analyzing the vibration damping performance can be extracted from the raw data, then by using the extracted data, the vibration damping performance can be analyzed in a shorter time compared to using the raw data. The aforementioned information processing device uses extracted data to analyze the driving speed pattern. Therefore, the aforementioned information processing device cannot extract suitable data for analyzing the vibration damping performance of the components from the raw data.

[0008] An information processing apparatus for solving the above-mentioned problem acquires raw data collected continuously for a predetermined period using sensors mounted on a vehicle, and extracts data from the raw data for analyzing the vibration damping performance of vibration damping components that support the vehicle-mounted unit and suppress the transmission of vibrations from the unit to the vehicle body. The information processing apparatus includes a processing circuit. The processing circuit performs a first step, in which the frequency distribution of the raw data for each of a plurality of characteristic quantities is calculated, including the vertical acceleration of the vehicle body and the vertical acceleration of the unit included in the raw data, and the process is repeated by changing the settings of a plurality of time windows. A second step involves setting a plurality of time windows from which data of a portion of the raw data is cut, such that the sum of the periods of all time windows is shorter than the predetermined period. A third step involves using the plurality of time windows to cut data from the raw data. The data; Step 4, for each of the said features, calculate the frequency distribution of the extracted data obtained by combining all the data cut out using the multiple time windows for the multiple features; Step 5, calculate the errors between the frequency distribution of each of the said features in the original data and the frequency distribution of each of the said features in the extracted data; and Step 6, determine whether the original data and the extracted data are similar based on the errors, and extract the extracted data when the original data and the extracted data are determined to be similar, as data for analyzing the vibration reduction performance of the vibration reduction component.

[0009] According to the aforementioned information processing device, data suitable for analyzing the vibration reduction performance of vibration reduction components can be extracted from the raw data. Attached Figure Description

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

[0011] Figure 2 These are graphs showing a portion of the raw data. (a) shows the vertical acceleration shift of the vehicle body, (b) shows the vertical acceleration shift of the unit, (c) shows the tire air pressure shift, and (d) shows the shift of the driving area.

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

[0013] Figure 4 It is the frequency distribution of the vertical acceleration of the vehicle body and the vertical acceleration of the unit in the raw data when the tire air pressure is above the predetermined pressure.

[0014] Figure 5It is the frequency distribution of the vertical acceleration of the vehicle body and the vertical acceleration of the unit in the raw data when the tire air pressure is less than the predetermined pressure. Detailed Implementation

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

[0016] <Structure of Information Processing Systems>

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

[0018] <Data Center 500 Structure>

[0019] like Figure 1 As shown, the data center 500 includes a processing circuit 510, a storage device 520, and a communication device 530. The processing circuit 510 is an information processing device, equipped with a CPU that executes processing according to a program and a ROM storing the program. The storage device 520 can store a large amount of data. The communication device 530 performs wired or wireless communication via a communication network 400. The communication device 530 includes hardware such as a network adapter, various communication software, or a combination thereof.

[0020] <Structure of Information Processing Terminal 600>

[0021] like Figure 1 As shown, the information processing terminal 600 includes a processing circuit 610, a storage device 620, and a communication device 630. The processing circuit 610 includes a CPU that executes processing according to a program and a ROM storing the program. The storage device 620 can store a large amount of data. The communication device 630 performs wired or wireless communication via a communication network 400. The communication device 630 includes hardware such as a network adapter, various communication software, or a combination thereof. The information processing terminal 600 is, for example, a personal computer.

[0022] <Structure of Vehicle 10>

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

[0024] The vehicle 10 includes a body 12, an internal combustion engine 21, a transmission 30, a bracket 50, tires 60, and a vehicle control unit 90.

[0025] The internal combustion engine 21 is connected to the transmission 30 using 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 vehicle body 12 of the vehicle 10 via multiple brackets 50. The brackets 50 are vibration damping components that support the unit 40 mounted on the vehicle 10 and suppress the transmission of vibrations from the unit 40 to the vehicle body 12, and are made of materials such as rubber.

[0026] The tires 60 support the vehicle body 12 via the suspension.

[0027] 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 that stores the program. In addition, the vehicle control unit 90 includes multiple sensors that collect data related to the vehicle 10.

[0028] One example of the data collected by the vehicle control unit 90 is the travel distance and speed of the vehicle 10. Another example of the data collected by the vehicle control unit 90 is the vertical acceleration of the vehicle body 12, the vertical acceleration of the unit 40, the tire air pressure as the air pressure of the tire 60, and the position information of the vehicle 10.

[0029] <Data Extraction>

[0030] The information processing terminal 600 is used to analyze the vibration reduction performance of the support frame 50. When analyzing the vibration reduction performance of the support frame 50, the information processing terminal 600 sends instructions to the data center 500.

[0031] Upon receiving the instruction, the processing circuit 510 of the data center 500 analyzes a portion of the expanded data stored in the storage device 520 of the data center 500. In accordance with the purpose of analysis, it selects data to be used from the expanded data stored in the storage device 520. This data includes multiple data related to the vibration damping performance of the support 50, collected using multiple sensors mounted on the vehicle 10. These data are referred to as feature quantities. The processing circuit 510 uses the feature quantities to analyze the vibration damping performance of the support 50 of 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 air pressure, and the driving area obtained from the position information of the vehicle 10 within the vehicle 10 being analyzed.

[0032] In order for the processing circuit 510 to analyze the vibration damping performance of the bracket 50 of a specific vehicle 10 according to a program, the processing circuit 510 utilizes a large amount of data collected over a continuous period of time. During this analysis, the processing circuit 510 requires a long time to perform calculations of the expansion quantities.

[0033] Therefore, it is considered to extract extractable data that captures the overall characteristics of the original data from the large amount of data that serves as the raw data. If such extractable data can be extracted, the processing circuit 510 can perform parsing in a shorter time by using the extracted data.

[0034] Figure 2 This shows a portion of the raw data for characteristic quantities related to the vibration damping performance of bracket 50. Figure 2 The raw data shown is a portion of the data from 100,000 hours of data from one vehicle (10). Figure 2 The raw data shown includes, as feature quantities, the vertical acceleration of the vehicle body 12, the vertical acceleration of the unit 40, the tire air pressure, and the driving area.

[0035] Figure 2 (a) shows the vertical acceleration of the vehicle body 12. Figure 2 (b) shows the vertical acceleration of unit 40. Figure 2 (c) shows the tire air pressure. Figure 2 (d) shows the driving area divided into urban and rural sections.

[0036] The vertical acceleration of the vehicle body 12, the vertical acceleration of the unit 40, the tire air pressure, and the travel area are data related to the vibration damping performance of the bracket 50. The processing circuit 510 extracts data for analyzing the vibration damping performance of the bracket 50 from the data, which include the vertical acceleration of the vehicle body 12, the vertical acceleration of the unit 40, and the tire air pressure as characteristic quantities.

[0037] Data extraction is done by cutting data from the raw data using multiple time windows. Figure 2 In this example, using multiple time windows as an example, three time windows—W_1 (first time window), W_2 (second time window), and W_3 (third time window)—are represented by double-dotted lines. The start and end points of each time window are set in a way that prevents them from overlapping. In this example, 20,000 hours of data are extracted. Therefore, the start and end points of each time window are set so that the total length of the sum of all time window periods is 20,000 hours.

[0038] Data center 500 searches for the start and end points of each time window in a data extraction pattern that captures the overall characteristics of the original data. Data center 500 stores the information about the data extraction pattern used to extract the aforementioned data in storage device 520. The stored data extraction pattern information is the setting of each time window found through the search.

[0039] The processing circuit 510 extracts data from the raw data based on the cutting mode information stored in the storage device 520.

[0040] <Search Processing in Cutout Mode>

[0041] Figure 3 This is a flowchart illustrating a series of processes related to the search process for the cut-out mode. The processing circuit 510 of the data center 500 executes this series of processes according to the program.

[0042] like Figure 3 As shown, in step S100, the processing circuit 510 obtains the raw data. The raw data is a portion of the data selected from the expanded data stored in the storage device 520 of the data center 500 that is consistent with the purpose of parsing.

[0043] The raw data used to analyze the vibration damping performance of the bracket 50 of a vehicle 10 is the data of the object selected from the expanded data of multiple vehicles 10.

[0044] Next, in the processing of step S110, the processing circuit 510 sets multiple time windows in order to extract data from the raw data.

[0045] exist Figure 2 In the example shown, the duration of each time window is all equal. Figure 2 As shown, the data cut out using each cutout window are data of each feature quantity in the same period.

[0046] Each time step S110 is executed, the processing circuit 510 randomly sets the number of time windows, the start point of each time window, and the end point of each time window. At this time, the processing circuit 510 sets each time window in a manner that prevents them from overlapping. The processing circuit 510 randomly sets multiple time windows in such a way that the sum of the durations of all time windows becomes a preset period. The processing circuit 510 may also, in step S110, perform the following... Figure 2 As shown, multiple time windows are set by fixing the duration of each time window to a constant. In the processing step S110, the processing circuit 510 can also set multiple time windows by fixing the number of multiple time windows to a constant.

[0047] In this way, multiple time windows are set through the processing in step S110, thereby determining the data cutting mode from the original data. After determining the cutting mode, the processing circuit 510 causes the processing to proceed to step S120.

[0048] In step S120, the processing circuit 510 extracts data from the original data using a determined extraction mode. Specifically, in step S120, the processing circuit 510 extracts data from the original data using multiple predefined time windows. Furthermore, the processing circuit 510 combines all the data extracted using the multiple time windows to create the extracted data.

[0049] Next, in step S130, the processing circuit 510 calculates the frequency distribution of the raw data and the extracted data. The raw data includes multiple feature quantities. The vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40 are defined as the first feature quantity. Furthermore, information that modifies the magnitude of at least one of the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40 (which is different from the first feature quantity) is defined as the second feature quantity. In this embodiment, the second feature quantity is tire air pressure.

[0050] In step S130, the processing circuit 510 divides the data of the first feature quantity contained in the original data into multiple data based on the data of the second feature quantity when the first feature quantity was collected. In this embodiment, the second feature quantity is tire air pressure. Therefore, the data of the first feature quantity is divided using tire air pressure. For each division of tire air pressure, the processing circuit 510 calculates the frequency distribution in the original data of each first feature quantity.

[0051] Similarly, the processing circuit 510 divides the data containing the first feature quantity of the extracted data into multiple data corresponding to the multiple data divisions of the original data. Furthermore, for each division of the data, the processing circuit 510 calculates the frequency distribution in the extracted data of each first feature quantity.

[0052] The data for each first feature quantity are classified into multiple levels, and the frequency distribution represents the number of data in each level, i.e., the frequency distribution. In this embodiment, the data for the first feature quantity are the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40. Therefore, the frequency distributions of the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40 are calculated respectively.

[0053] Furthermore, the total frequency of the first characteristic of the data differs between the original and extracted data, making it impossible to simply compare the frequency distributions of the original and extracted data. When 20,000 hours of extracted data are drawn from 100,000 hours of original data, the total frequency of the extracted data becomes approximately one-fifth of the total frequency of the original data. In this case, by multiplying the frequency of each level of the extracted data by five, a frequency distribution of the extracted data with the same total frequency as the original data can be obtained. Even without relying on the above method, it is possible to compare the distribution of data in the original data with the distribution of data in the extracted data by calculating the relative frequency distributions of the original and extracted data. A relative frequency distribution represents the percentage of the frequency at a given level relative to the total frequency.

[0054] Figure 4The frequency distributions 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 above the predetermined pressure are shown respectively.

[0055] Figure 5 The frequency distributions of the vertical acceleration of vehicle body 12 and unit 40 in the original data when the tire air pressure is less than the predetermined pressure are shown respectively.

[0056] like Figure 4 as well as Figure 5 As shown, in these frequency distributions, the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40 are divided into m levels from "1" to "m". The frequency distribution of the extracted data is also calculated based on the levels corresponding to the original data. As described above, 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 categories: those with tire air pressure above a predetermined value and those with tire air pressure below a predetermined value. For each of the two tire air pressure categories, the processing circuit 510 calculates the frequency distribution as described above.

[0057] In execution Figure 3 Following the processing in step S130, the processing circuit 510 then executes step S140. In step S140, for each of the multiple partitions based on the second feature, the processing circuit 510 calculates the error between the frequency distribution of the first feature in the original data and the frequency distribution of the first feature in the extracted data. For example, the processing circuit 510 calculates the Mean Absolute Error (MAE). The Mean Absolute Error (MAE) is expressed by the following formula.

[0058] Formula 1

[0059]

[0060] In the above formula, "n" is the number of the first feature quantity. In this embodiment, the first feature quantity is the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40, so "n" is "2". "m" is the total number of levels in the frequency distribution. "Y" is the frequency of the corresponding feature quantity in the corresponding level in the original data. "y" is the frequency of the corresponding feature quantity in the corresponding level in the extracted data.

[0061] As shown in the formula above, for each partition, the processing circuit 510 calculates the error as the sum of the errors between the frequency distribution in the original data and the frequency distribution in the extracted data at each level of the first feature.

[0062] After calculating the errors for all partitions, the processing circuit 510 proceeds to step S150. In step S150, the processing circuit 510 determines whether any error of the calculated error for each partition is below a threshold. The threshold is a value used to determine whether extracted data with a frequency distribution close to the frequency distribution in the original data was extracted using a set cutting pattern. The threshold is preset so that it can determine whether extracted data with a frequency distribution close to the frequency distribution in the original data has been extracted based on the error being below the threshold. This threshold can be determined as a different value for each partition.

[0063] If, during the processing in step S150, it is determined that any error in each division is below a threshold (step S150: "Yes"), the processing circuit 510 records the cut-out pattern. Specifically, the processing circuit 510 stores the start and end data of each time window in the cut-out pattern as information for determining the cut-out pattern in the storage device 520. After recording the cut-out pattern in this way, the processing circuit 510 proceeds to step S160.

[0064] On the other hand, in the processing of step S150, if it is determined that any one of the errors is greater than the threshold (step S150: "No"), the processing circuit 510 returns the processing to step S110. That is, in order to reset the time window and extract the extracted data from the original data, the processing circuit 510 starts the process of setting multiple new time windows.

[0065] Thus, the processing circuit 510 repeatedly executes steps S110 to S150 until it can extract data similar to the original data using the frequency distribution of the first feature divided by the second feature. As a result, in the storage device 520, any error storing the error of each division becomes a cutting pattern below the threshold.

[0066] In step S160, the processing circuit 510 extracts data from the original data according to the data extraction pattern stored in the storage device 520, which is similar to the original data.

[0067] After the processing in step S160 is performed, the processing circuit 510 terminates the above-mentioned series of program-based processes.

[0068] In this way, the processing circuit 510 extracts data for analyzing the vibration damping performance of the support 50.

[0069] <The function of this implementation method>

[0070] The data center 500, which is the information processing device in this embodiment, acquires raw data collected continuously for a predetermined period using multiple sensors mounted on the vehicle 10. Furthermore, the data center 500 extracts data from the raw data for analyzing the vibration damping performance of the support 50.

[0071] Data center 500 includes processing circuitry 510. The raw data includes the vertical acceleration of vehicle body 12 and the vertical acceleration of unit 40 as multiple feature quantities. In this data center 500, processing circuitry 510 performs a search process. This search process includes a first step (step S130) of calculating the frequency distribution of the raw data for each feature quantity.

[0072] The search process includes a second step (step S110) of setting multiple time windows for data that cut out a portion of the original data, in a manner that makes the sum of the periods of all time windows shorter than the overall period of the original data.

[0073] The search process includes a third step (step S120) which involves extracting data from the original data using multiple time windows. The extracted data is obtained by combining all the data extracted using the multiple time windows.

[0074] The search process includes a fourth step (step S130) that calculates the frequency distribution in the extracted data for each feature.

[0075] The search process includes step 5 (step S140), which calculates the errors between the frequency distribution of each feature in the original data and the frequency distribution of each feature in the extracted data.

[0076] The search process includes step 6 (step S150) which determines whether the original data and the extracted data are similar based on the errors mentioned above.

[0077] After executing step 1, the processing circuit 510 performs a search process that repeatedly executes steps 2 through 6 by changing the settings of multiple time windows. Furthermore, the processing circuit 510 extracts the extracted data when it is determined that the original data and the extracted data are similar, and uses this extracted data as data for analyzing the vibration damping performance of the support 50 (step S160).

[0078] According to the data center 500, such analysis can be performed using extracted data with a distribution similar to that of the original data, which is related to the vibration reduction performance of the support 50. Therefore, the data center 500 can obtain analytical results that are close to those obtained using the original data for analyzing the vibration reduction performance of the support 50.

[0079] The extracted data from data center 500 is obtained by cutting off a portion of the original data. Therefore, the amount of extracted data is smaller compared to the original data. The more data used in the analysis, the longer the processing time required to analyze the vibration damping performance of support 50 becomes. By using extracted data, data center 500 is able to shorten the analysis time compared to using the original data.

[0080] <Effects of this implementation method>

[0081] (1) Based on the data center 500, and based on multiple characteristic quantities including the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40, it is determined whether the original data and the extracted data are similar. Therefore, data suitable for analyzing the vibration reduction performance of the support 50 can be extracted from the original data.

[0082] (2) In the first step described above, the processing circuit 510 of the data center 500 divides the original data into multiple data based on information about the magnitude of at least one of the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40 contained in the original data. Furthermore, for each division of the data, the processing circuit 510 calculates the frequency distribution of the original data including each of the aforementioned characteristic quantities of the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40.

[0083] Furthermore, in step 4 above, the processing circuit 510 divides the extracted data into multiple data points corresponding to the division of the original data into multiple data points. And, for each division of the data, the processing circuit 510 calculates the frequency distribution of each of the aforementioned characteristic quantities in the extracted data, including the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40.

[0084] Furthermore, in step 5 above, the processing circuit 510 calculates the errors between the frequency distribution of each feature in the original data and the frequency distribution of each feature in the extracted data for each data partition.

[0085] Therefore, whenever the vibration damping performance of the bracket 50 is analyzed, data can be extracted that takes into account the magnitude of at least one of the vertical acceleration of the left and right vehicle bodies 12 and the vertical acceleration of the unit 40.

[0086] (3) The above information is the tire air pressure of vehicle 10. Therefore, it is possible to extract tire air pressure data that takes into account at least one of the vertical acceleration of the left and right vehicle bodies 12 and the vertical acceleration of unit 40.

[0087] <Example of Change>

[0088] Furthermore, the following elements are common to be modified in all the above embodiments. The following modification examples can be combined and implemented to the extent that they are not technically contradictory.

[0089] Based on the position information of vehicle 10, information related to the roughness of the road surface can be obtained. This roughness affects the magnitude of the vertical acceleration of vehicle body 12 and unit 40. Therefore, the aforementioned information regarding the magnitude of at least one of the vertical acceleration of the left and right vehicle bodies 12 and the vertical acceleration of unit 40 can also be considered as the position information of vehicle 10. In this case, for example, the vertical acceleration of vehicle body 12 and unit 40 included in the original data and extracted data are divided into two sections: a rural area and an urban area, representing the driving region as defined by the position information. Furthermore, the processing circuit 510 can calculate the aforementioned frequency distribution for each of the two driving regions. According to this modified example, data regarding the position of vehicle 10, taking into account the magnitude of at least one of the vertical acceleration of the left and right vehicle bodies 12 and the vertical acceleration of unit 40, can be extracted.

[0090] The processing circuit 510 divides the original data into multiple data based on information about the magnitude of at least one of the vertical acceleration of the vehicle body 12 and the vertical acceleration of the unit 40 contained in the original data. Alternatively, the processing circuit 510 may not perform such data division, but instead calculate the frequency distribution in the original data and the frequency distribution in the extracted data. In this case, effects other than those described in (2) can also be obtained.

[0091] In the above embodiment, an example of embodying the information processing device as a data center 500 is shown. Alternatively, the information processing device can also be embodying as an information processing terminal 600. In this case, the processing circuit 610 of the information processing terminal 600... Figure 3 In step S100, raw data is obtained from the data center 500. Furthermore, the processing circuit 610 executes... Figure 3 The processing after step S110 shown. Alternatively, the aforementioned information processing device can also be implemented as a control device for the vehicle 10. In this case, for example, the processing circuit 95 of the vehicle control unit 90... Figure 3 In step S100, raw data is obtained from the data center 500. Furthermore, the processing circuit 95 executes... Figure 3 The processing after step S110 shown.

[0092] The aforementioned data center 500 determines the similarity between the original and extracted data by calculating the error in the frequency distribution. Alternatively, data center 500 can determine the similarity between the original and extracted data without calculating the error. For example, using statistical methods such as fitness assessment, similarity can be determined when the difference between the original and extracted data is not intentional.

[0093] • The unit supporting the vibration damping component can also be a unit other than the aforementioned unit 40. For example, it can 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.

[0094] (Symbol Explanation)

[0095] 10: Vehicle; 12: Vehicle body; 21: Internal combustion engine; 30: Transmission; 40: Unit; 50: Bracket; 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 apparatus that acquires raw data collected continuously for a predetermined period using sensors mounted on a vehicle, extracts data from the raw data for analyzing the vibration damping performance of a damping component, the damping component supporting a unit mounted on the vehicle and suppressing the transmission of vibration from the unit to the vehicle body, wherein, The information processing device includes a processing circuit. The processing circuit performs a first step, in which the frequency distribution of the original data regarding multiple feature quantities is calculated for each feature quantity, the multiple feature quantities including the vertical acceleration of the vehicle body and the vertical acceleration of the unit contained in the original data, and... The processing circuit repeatedly executes the following step by changing the settings of multiple time windows: Step 2, in order to set the multiple time windows from which a portion of the original data is cut out, such that the sum of the periods of all time windows is shorter than the predetermined period; Step 3: Extract data from the original data using the multiple time windows; Step 4: For each feature, calculate the frequency distribution of the extracted data obtained by combining all the data extracted using the multiple time windows for the multiple features; Step 5: Calculate the errors between the frequency distribution of each feature in the original data and the frequency distribution of each feature in the extracted data. And in step 6, based on the aforementioned errors, determine whether the original data and the extracted data are similar. The processing circuit extracts the extracted data when the original data and the extracted data are similar, and uses this extracted data as data for analyzing the vibration reduction performance of the vibration reduction component.

2. The information processing apparatus according to claim 1, wherein, The processing circuit: In the first step, the original data is divided into multiple data segments based on information about the magnitude of at least one of the vertical acceleration of the vehicle body and the vertical acceleration of the unit contained in the original data. For each segment of the data, the frequency distribution of each feature quantity in the original data is calculated. In step 4, the extracted data is divided into multiple data points corresponding to the multiple data points of the original data. For each data point, the frequency distribution of each feature quantity in the extracted data is calculated. In step 5, for each partition of the data, the errors between the frequency distribution of each feature in the original data and the frequency distribution of each feature in the extracted data are calculated.

3. The information processing apparatus according to claim 2, wherein, The information is the tire air pressure of the vehicle.

4. The information processing apparatus according to claim 2, wherein, The information is the location information of the vehicle.

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