Information processing device

Abstract

To provide an information processing device capable of achieving both, with respect to extracted data, reducing an amount of data from original data and maintaining accuracy.SOLUTION: A processing device of an information processing device: includes a first step (S120) of calculating a relative frequency distribution of original data, a second step (S125) of setting a plurality of time windows for extracting data from a partial period of the original data, a third step (S130) of extracting data from the original data, a fourth step (S140) of calculating a relative frequency distribution of the extracted data; and a fifth step (S145) of calculating a difference between the relative frequency distribution of the original data and the relative frequency distribution of the extracted data; and executes a search process of repeatedly executing trials from the second step to the fifth step while changing settings of the plurality of time windows.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] The above-described information processing device extracts data by focusing only on vehicle speed. Therefore, the above-described information processing device cannot extract data based on data characteristics other than vehicle speed. There is a need for an information processing device that can obtain extracted data that captures the overall characteristics of the original data, including features other than vehicle speed. [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 from the original data to be used to calculate an index value indicating the magnitude of damage accumulated in a torque limiter. This information processing device includes a processing device that executes processing. The original data includes data on the amount of slippage of the torque limiter as a feature. In this information processing device, a search process executed by the processing device includes a first step of calculating, for each feature, a relative frequency distribution in the original data for the feature included in the original data. The search process includes a second step of setting multiple 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. The search process includes a third step of extracting data from the original data using the multiple time windows. The search process includes a fourth step of calculating, for each feature, the relative frequency distribution in extracted data obtained by combining the data extracted using the multiple time windows. The search process includes a fifth step of calculating an error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data. After executing the first step, the processing device executes the search process by repeatedly executing trials from the second step to the fifth step while changing the settings of the plurality of time windows, and extracts the extracted data for which the error is equal to or less than a threshold.

[0007] In one embodiment of the processing device, the processing device performs clustering, which is machine learning, to classify data in each interval obtained by dividing the original data into a predetermined number of clusters. In the second step, the processing device sets the multiple time windows so that the difference between the ratio of each cluster in the extracted data and the ratio of each cluster in the entire original data is equal to or less than a threshold. [Effects of the Invention]

[0008] This information processing device can extract extracted data that is smaller in volume than the original data and that provides analysis results with the same accuracy as the original data. Therefore, with regard to the extracted data, this information processing device can achieve both a reduction in the data volume from the original data and maintenance of accuracy. [Brief explanation of the drawings]

[0009] [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] FIG. 2 is a graph showing the original data, where (a) shows the transition of the slippage of the torque limiter, and (b) shows an enlarged view of a part of (a). [Figure 3] FIG. 3 is a flowchart showing the flow of processing executed by the processing device in the data center. [Figure 4] FIG. 4 is a graph showing an example of clustering original data using two feature quantities. [Figure 5] FIG. 5 is a graph showing an example of the relative frequency distribution in the original data, where (a) shows an example of the relative frequency distribution for the amount of slippage of the torque limiter, and (b) shows an example of the relative frequency distribution for the duration of slippage of the torque limiter. [Figure 6] FIG. 6 is a graph showing an example of the relative frequency distribution in the original data, where (a) shows an example of the relative frequency distribution for the interval of torque limiter slippage, and (b) shows an example of the relative frequency distribution for the product of the amount of torque limiter slippage and the duration of the slippage. DETAILED DESCRIPTION OF THE INVENTION

[0010] A data center 500, which is an embodiment of an information processing device, will be described below with reference to FIGS. <Configuration of information processing system> Fig. 1 shows the configuration of an information processing system including a data center 500. As shown in Fig. 1, the data center 500 communicates with a vehicle 10 via a communication network 400. The data center 500 also communicates with an information processing terminal 600 via the communication network 400. The data center 500 communicates with a plurality of vehicles 10 and a plurality of information processing terminals 600 via the communication network 400.

[0011] <Configuration of Data Center 500> As shown in FIG. 1, the data center 500 includes a processing device 510. The data center 500 also includes a storage device 520 and a communication device 530. The processing device 510 includes a CPU that executes processing according to a program and a ROM in which the program is stored. The storage device 520 stores a large amount of data. The communication device 530 is implemented as hardware such as a network adapter, various communication software, or a combination of these. The communication device 530 realizes wired or wireless communication via the communication network 400.

[0012] The data center 500 may be configured using a plurality of computers. For example, the data center 500 may be configured by a plurality of server devices. <Vehicle 10 Configuration> Each of the vehicles 10 is equipped with a communication device 60. The communication devices 60 are implemented as hardware such as a network adapter, various communication software, or a combination of these. The communication devices 60 are configured to realize wired or wireless communication via the communication network 400.

[0013] Each vehicle 10 is equipped with an engine 20 and an automatic transmission 30. For example, the automatic transmission 30 is a planetary gear type transmission. Each vehicle 10 is equipped with a torque limiter 40 provided on a power transmission path between the engine 20 and the automatic transmission 30. The torque limiter 40 includes an input rotating member 41 and an output rotating member 42. The input rotating member 41 is connected to an engine connecting shaft 21 that rotates integrally with the output shaft of the engine 20 on the power transmission path. The output rotating member 42 is connected to a transmission connecting shaft 31 that rotates integrally with the input shaft of the automatic transmission 30 on the power transmission path. An input surface 43 of the input rotating member 41 is pressed directly or indirectly against an output surface 44 of the output rotating member 42. Friction between the input surface 43 and the output surface 44 causes the input rotating member 41 and the output rotating member 42 to rotate integrally.

[0014] The input surface 43 is not fixed to the output surface 44. Therefore, when excessive torque exceeding a preset torque acts between the engine 20 and the automatic transmission 30, the output surface 44 rotates relative to the input surface 43. In this way, a phase difference occurs between the input rotating member 41 and the output rotating member 42. The phase difference between the input rotating member 41 and the output rotating member 42 is slippage of the torque limiter 40. The slippage of the torque limiter 40 suppresses the transmission of excessive torque between the input rotating member 41 and the output rotating member 42.

[0015] The vehicle 10 is equipped with a control device 50. The control device 50 controls the engine 20 and the automatic transmission 30. The control device 50 is equipped with various sensors that collect information from various parts of the vehicle 10.

[0016] Each vehicle 10 collects driving data from these various sensors. The driving data is transmitted from each vehicle 10 to the data center 500 by the communication device 60. For example, driving data including the driving distance, position information, and vehicle speed of each vehicle 10 is transmitted from each vehicle 10 to the data center 500. The driving data also includes various data indicating the state of the torque limiter 40 acquired by the control device 50 of the vehicle 10. Identification information for identifying each vehicle 10 is also transmitted from each vehicle 10 to the data center 500 together with the driving data.

[0017] The data center 500 stores the received identification information and the travel data in the storage device 520. In this way, the travel data of the multiple vehicles 10 is accumulated in the storage device 520 of the data center 500.

[0018] <Configuration of information processing terminal 600> The information processing terminal 600 includes a processing device 610, a storage device 620, and a communication device 630. The processing device 610 includes a CPU that executes processing according to a program and a ROM in which the program is stored. The storage device 620 stores data. The communication device 630 is implemented as hardware such as a network adapter, various communication software, or a combination of these. The communication device 630 realizes wired or wireless communication via the communication network 400. The information processing terminal 600 is, for example, a personal computer.

[0019] <Analysis of vehicle 10's driving data> The information processing terminal 600 is used to analyze the driving data. When analyzing the driving data, the information processing terminal 600 transmits an instruction to the data center 500 to perform the analysis. Upon receiving the instruction, the processing device 510 of the data center 500 performs the analysis using a portion of the vast amount of driving data stored in the storage device 520 of the data center 500. The driving data to be used is selected from the vast amount of driving data stored in the storage device 520 according to the purpose of the analysis.

[0020] For example, the processing device 510 calculates the load on a specific part of a specific vehicle 10 based on the running data of the specific vehicle 10. The processing device 510 estimates the damage accumulated in that part based on the calculated load. For example, the processing device 510 calculates an index value indicating the amount of damage accumulated in the torque limiter 40 of the specific vehicle 10 based on the running data of the specific vehicle 10. The processing device 510 of the data center 500 outputs the calculated result by transmitting it to the information processing terminal 600. The information processing terminal 600 that receives the result displays the received result.

[0021] To perform such an analysis, the processing unit 510 analyzes a large amount of driving data collected over a long period of time. Because the processing unit 510 needs to perform a huge amount of calculations, the analysis takes a long time.

[0022] Therefore, it is conceivable to extract extracted data that captures the overall characteristics of the original data from the large amount of driving data that constitutes the original data. If such extracted data can be extracted, the processing device 510 can use the extracted data to perform analysis in a shorter time. For example, when estimating damage to the torque limiter 40 after 100,000 hours of driving, the processing device 510 estimates the damage using 20,000 hours of extracted data extracted from 100,000 hours of original data. The processing device 510 then calculates an index value for damage to the torque limiter 40 after 100,000 hours of driving by multiplying the index value calculated from the 20,000 hours of extracted data by five.

[0023] Fig. 2 shows an example of original data. The original data shown in Fig. 2(a) is driving data for 100,000 hours for one vehicle 10. The original data shown in Fig. 2 includes, as a feature, data on the amount of slippage of the torque limiter 40 for which a damage index value is to be calculated.

[0024] 2(a) shows the transition of the amount of slippage over 100,000 hours. The amount of slippage is the difference in rotational speed between the input rotating member 41 and the output rotating member 42. The control device 50 obtains the amount of slippage from the rotational speed of the output shaft of the engine 20 and the rotational speed of the input shaft of the automatic transmission 30. Data on the rotational speed of the output shaft of the engine 20 and the rotational speed of the output shaft of the automatic transmission 30 may be transmitted from the vehicle 10 to the data center 500, and the amount of slippage may be calculated by the data center 500.

[0025] The amount of slippage correlates with damage to the torque limiter 40 of the vehicle 10. The processing device 510 of the data center 500 estimates damage to the torque limiter 40 from the driving data that includes the amount of slippage as a feature amount.

[0026] Figure 2(b) shows an enlarged view of a portion of the horizontal axis of Figure 2(a). Where the amount of slippage is zero, the input rotating member 41 and output rotating member 42 of the torque limiter 40 rotate together, and no slippage occurs. When slippage begins in the torque limiter 40, the amount of slippage becomes greater than zero, and when slippage ends in the torque limiter 40, the amount of slippage returns to zero. In this specification, the time from when slippage begins in the torque limiter 40 to when that slippage ends is referred to as the slippage duration. The period from when slippage ends in the torque limiter 40 to when the next slippage begins is referred to as the slippage interval.

[0027] The original data may include, as a feature, at least one of the duration of slippage of the torque limiter 40, the interval between slippage of the torque limiter 40, and the multiplication value of the amount of slippage and the duration of slippage. The duration of slippage and the interval between slippage can be calculated based on the change in the amount of slippage over time. The control device 50 calculates the duration of slippage and the interval between slippage based on the change in the amount of slippage over time. The data center 500 may also calculate the duration of slippage and the interval between slippage based on the change in the amount of slippage over time.

[0028] The multiplication value of the amount of slippage and the duration of the slippage is calculated for each slippage. The multiplication value of the amount of slippage and the duration of the slippage is calculated by the control device 50 based on the amount of slippage and the duration of the slippage. The multiplication value of the amount of slippage and the duration of the slippage may also be calculated in the data center 500. In calculating the multiplication value of the amount of slippage and the duration of the slippage, the amount of slippage multiplied by the duration of the slippage may be the maximum value of the amount of slippage during that duration of the slippage, or may be the average value of the amount of slippage during that duration of the slippage.

[0029] The duration of slippage, the interval between slippages, and the multiplication value of the amount of slippage and the duration of slippage are correlated with damage to the torque limiter 40 of the vehicle 10. The processing device 510 of the data center 500 may estimate damage to the torque limiter 40 from the driving data that includes, in addition to the amount of slippage, at least one of the duration of slippage, the interval between slippage, and the multiplication value of the amount of slippage and the duration of slippage as feature quantities.

[0030] The extracted data is created by extracting data from the original data using multiple time windows. In Figure 2(a), three time windows, the first time window W_1, the second time window W_2, and the third time window W_3, are shown by dashed lines as examples of some of the multiple time windows. The start and end periods of each time window are set so that they do not overlap. In this example, 20,000 hours of driving data are extracted as the extracted data. Therefore, the start and end periods of each time window are set so that the total length of all the time windows is 20,000 hours.

[0031] The data center 500 searches for the setting of the start and end of each time window that indicates a cutout pattern for extracting extracted data that captures the characteristics of the entire original data. The data center 500 extracts extracted data from the original data using the extraction pattern found by the search, and performs analysis using the extracted data.

[0032] <Searching for extraction patterns> 3 is a flowchart showing a series of processes related to the extraction pattern search process. This series of processes is executed by the processing device 510 of the data center 500.

[0033] As shown in Fig. 3, the processing device 510 acquires original data in the process of step S100. The original data is a portion of driving data selected according to the purpose of analysis from the vast amount of driving data stored in the storage device 520 of the data center 500. For example, the original data for calculating an index value indicating the amount of damage accumulated in the torque limiter 40 of one vehicle 10 is driving data of the target vehicle 10 over a predetermined period selected from the vast amount of driving data of multiple vehicles 10. For example, when estimating damage to the torque limiter 40 after 100,000 hours of driving, the original data is driving data of the target vehicle 10 over a predetermined period.

[0034] In the process of step S110, the processing device 510 assigns labels to the original data by clustering. Specifically, the processing device 510 divides the original data into fixed periods. The length of the periods into which the original data is divided is, for example, several minutes. Then, the processing device 510 executes clustering, which is machine learning, to classify the data in each period into a predetermined number of clusters. For example, the k-means method is used as the clustering algorithm. The k-means method is a clustering algorithm that classifies data into a pre-specified number of clusters. The clustering algorithm is not limited to the k-means method.

[0035] The original data includes driving data collected under different environments, such as driving data when driving in urban areas, driving data when driving in suburban areas, and driving data when driving on highways. By performing clustering, the driving data included in the original data can be classified into clusters of driving data with similar characteristics. The number of clusters to be classified can be set arbitrarily depending on the content of the analysis.

[0036] FIG. 4 is a graph showing an example of clustering original data into four clusters using the k-means method, using two feature quantities contained in the original data as explanatory variables. For example, the two feature quantities are the amount of slippage and the duration of the slippage. In FIG. 4, each data section into which the original data is divided is represented by a single point. When performing clustering, the processing device 510 uses a representative value of the explanatory variables in the data for each section. For example, the processing device 510 sets the average value of the feature quantities in the data for each section as the representative value. The processing device 510 may also use a moving average value of the feature quantities for multiple consecutive sections in a time series as the representative value.

[0037] In Figure 4, these points are shown in a two-dimensional space with the first feature FV_a and the second feature FV_b as coordinate axes. Figure 4 shows an example of original data clustered into four clusters: a first cluster M_1, a second cluster M_2, a third cluster M_3, and a fourth cluster M_4. In Figure 4, the boundaries of the four clusters are shown with solid lines. In Figure 4, the center of gravity of each cluster is shown with a hollow triangle. The center of gravity cgM_1 is the center of gravity of the first cluster M_1. The center of gravity cgM_2 is the center of gravity of the second cluster M_2. The center of gravity cgM_3 is the center of gravity of the third cluster M_3. The center of gravity cgM_4 is the center of gravity of the fourth cluster M_4.

[0038] 4 shows an example in which there are two explanatory variables, but the number of explanatory variables is not limited to two. For example, if the original data includes three feature amounts, the processing device 510 may perform clustering using these three feature amounts as explanatory variables. In this case, the processing device 510 clusters the original data in a three-dimensional space.

[0039] The processing device 510 assigns labels indicating the clustering results to the original data. Specifically, each piece of data represented by a point in the coordinate space is assigned a label identifying the cluster into which the data is classified. In this way, the processing device 510 creates labeled original data.

[0040] Next, in the process of step S120, the processing device 510 calculates the relative frequency distribution of the original data. If the original data includes multiple feature amounts, the processing device 510 calculates the relative frequency distribution in the original data for each feature amount.

[0041] A frequency distribution classifies data into multiple classes and shows the distribution of the frequency, which is the number of data in each class. Relative frequency indicates what percentage of the frequency of that class is in relation to the total frequency.

[0042] Figure 5(a) shows the relative frequency distribution of the amount of slippage in the original data shown in Figure 2. In this relative frequency distribution, the amount of slippage in the original data is divided into m classes from 1 to m, and the relative frequency distribution is shown.

[0043] Figure 5(b) shows the relative frequency distribution of the duration of slippage in the original data shown in Figure 2. In this relative frequency distribution, the duration of slippage in the original data is divided into m classes from 1 to m, and the relative frequency distribution is shown.

[0044] Figure 6(a) shows the relative frequency distribution of slip intervals in the original data shown in Figure 2. In this relative frequency distribution, the slip interval classes in the original data are divided into m classes from 1 to m, and the relative frequency distribution is shown.

[0045] Figure 6(b) shows the relative frequency distribution of the product of the amount of slippage and the duration of the slippage in the original data shown in Figure 2. In this relative frequency distribution, the product of the amount of slippage and the duration of the slippage in the original data is divided into m classes from 1 to m, and the relative frequency distribution is shown.

[0046] In the process of step S120, the processing device 510 calculates such a relative frequency distribution for each feature amount included in the original data. The number of classes in the relative frequency distribution for each feature amount is the same.

[0047] Next, in the process of step S125, the processing device 510 sets a plurality of time windows to extract extracted data from the original data. 2(a) shows three time windows W_1 to W_3 as an example of multiple time windows. In the example shown in FIG. 2(a), the durations of the time windows are all equal.

[0048] In the process of step S125, the processing device 510 randomly sets multiple time windows so that the total duration of all the time windows is shorter than a predetermined duration, which is the duration of the entire original data. As will be described later, the processing device 510 combines all the data extracted by the multiple time windows set here to create extracted data. The total duration of all the time windows is a value that determines the volume of the extracted data. Therefore, the total duration of all the time windows is set in advance.

[0049] For example, each time the processing device 510 executes the process of step S125, the processing device 510 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 device 510 sets each time window so that the time windows do not overlap. The processing device 510 thus randomly sets multiple time windows so that the total duration of all the time windows is a predetermined duration. In the process of step S125, the processing device 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 S125, the processing device 510 may set multiple time windows by fixing the number of time windows to a constant value.

[0050] In addition to the above requirements, when setting multiple time windows through step S125, the processing device 510 sets the multiple time windows so that the difference between the proportion of each cluster in the extracted data and the proportion of each cluster in the entire original data is below a threshold.

[0051] 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 S125. After determining the cut-out pattern in this way, processing device 510 advances the process to step S130.

[0052] In the process of step S130, the processing device 510 extracts data from the original data using the determined extraction pattern. That is, in the process of step S130, the processing device 510 extracts data from the original data using the multiple time windows that have been set. Then, the processing device 510 combines all of the data extracted using the multiple time windows to create extracted data.

[0053] In the next step S140, the processing device 510 calculates the relative frequency distribution of the extracted data. The processing device 510 calculates the relative frequency distribution of the extracted data using the same method as the method used to calculate the relative frequency distribution in step S120. That is, in the processing of step S140, the processing device 510 calculates the relative frequency distribution of the extracted data for each feature amount. At this time, the processing device 510 sets the number of classes in the relative frequency distribution of each feature amount to be the same as the relative frequency distribution in step S120.

[0054] Next, in the process of step S145, the processing device 510 calculates the error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data. For example, the processing device 510 calculates the mean absolute error (MAE). The mean absolute error (MAE) is expressed by the following formula.

[0055]

number

[0056] As shown in the above formula, the processing device 510 calculates the error as the sum of the errors in the frequencies in each class for each feature between the relative frequency distribution in the entire original data and the relative frequency distribution in the extracted data.

[0057] After calculating the error, the processing device 510 proceeds to step S150. In step S150, the processing device 510 determines whether the calculated error is equal to or less than a threshold value. The threshold value is a value for determining whether extracted data having a relative frequency distribution close to the relative frequency distribution in the original data has been extracted by 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 relative frequency distribution close to the relative frequency distribution in the original data has been extracted based on the error being equal to or less than the threshold value.

[0058] If it is determined in the process of step S150 that the error is equal to or smaller than the threshold value (step S150: YES), processing device 510 proceeds to step S160. In the process of step S160, the processing device 510 calculates a target index value using the extracted data created in the most recent process of step S130. Here, the processing device 510 calculates an index value indicating the magnitude of damage accumulated in the torque limiter 40. For example, the processing device 510 calculates the damage level as an index value indicating the magnitude of damage accumulated in the torque limiter 40.

[0059] The damage level is an index value that represents the proportion of accumulated damage, assuming that damage to the torque limiter 40 gradually accumulates, with damage that results in damage being set to "1." Here, damage inflicted on the torque limiter 40 over a certain period of time is calculated based on data on the amount of slippage. In addition to the amount of slippage, the damage inflicted on the torque limiter 40 may be calculated from at least one of the following feature quantities: the duration of the slippage, the interval between the slippage, and the multiplication of the amount of slippage and the duration of the slippage. Then, the magnitude of damage that results in damage to the torque limiter 40 is set to "1," and the proportion of the calculated damage is calculated as an index value. By repeating this process, the damage level, which is the proportion of accumulated damage to damage that results in damage, is calculated. When the damage level reaches "1," it means that damage has occurred, and the calculated damage level is a value between "0" and "1."

[0060] Here, the damage degree is calculated using the extracted data, which is a part of the original data, so the processing device 510 converts the calculated damage degree into a magnitude corresponding to the original data and calculates the damage degree as an index value. For example, if the original data is 100,000 hours of driving data and the extracted data is 20,000 hours of driving data, the calculated damage degree is multiplied by 5 to obtain the damage degree as an index value.

[0061] On the other hand, if it is determined in the process of step S150 that the error is greater than the threshold value (step S150: NO), processing device 510 returns the process to step S125. Then, processing device 510 executes the search process from step S125 to step S145 again.

[0062] In this way, the processing device 510 repeatedly executes the search process of steps S125 to S145 by changing the settings of multiple time windows, and extracts extracted data from the original data whose error is equal to or less than the threshold. Then, the processing device 510 calculates an index value using the extracted extracted data. After calculating the index value, the processing device 510 proceeds to step S170.

[0063] In the process of step S170, the processing device 510 determines whether the index value is equal to or greater than a predetermined value. The predetermined value is a value for predicting an increased likelihood of damage occurring based on the index value being equal to or greater than the predetermined value. For example, here, "0.9" can be set as the default value for the damage level. In this case, it is possible to predict an increased likelihood of damage occurring based on the fact that 90% of the damage that will result in damage has been reached.

[0064] In the process of step S170, if it is determined that the index value is equal to or greater than the predetermined value (step S170: YES), the processing device 510 proceeds to the process of step S180. In the process of step S180, the processing device 510 outputs the index value and the failure prediction. Specifically, the processing device 510 transmits the index value and the failure prediction to the information processing terminal 600 that transmitted the instruction requesting the analysis.

[0065] The failure prediction is, for example, a message indicating that a failure has been predicted. In this way, the processing device 510 issues a notification informing the user that a failure has been predicted if the calculated index value is equal to or greater than a predetermined value. The failure prediction may also be information on the lifespan until a failure occurs. For example, if the index value is a damage level calculated using extracted data extracted from 100,000 hours of original data, the processing device 510 calculates the driving time until the damage level reaches "1" and outputs this as lifespan information. The lifespan information may also be converted into a driving distance based on the driving distance for 100,000 hours and output.

[0066] In the process of step S170, if it is determined that the index value is less than the predetermined value (step S170: NO), processing device 510 proceeds to the process of step S190. In the process of step S190, processing device 510 outputs the index value. Specifically, processing device 510 transmits the index value to information processing terminal 600 that transmitted the instruction requesting analysis.

[0067] After executing the process of step S180 or step S190, the processing device 510 ends this series of processes. <Operation of this embodiment> The data center 500, which is an information processing device in this embodiment, acquires original data collected and created over a predetermined period using sensors installed in the vehicle 10, and calculates an index value indicating the amount of damage accumulated in the torque limiter 40.

[0068] The data center 500 includes a processing device 510 that executes processing. The original data includes data on the amount of slippage of the torque limiter 40 as a feature. In addition to the amount of slippage, the original data further includes at least one of the following data as feature amounts: the duration of slippage, the interval between slippages, and the multiplication of the amount of slippage and the duration of slippage. In this data center 500, the search process executed by the processing device 510 includes a first step (step S120) of calculating a relative frequency distribution in the original data for each feature amount included in the original data. The search process includes a second step (step S125) of setting multiple time windows that extract data for a portion of the original data so that the sum of the periods of all the time windows is shorter than a predetermined period. The search process includes a third step (step S130) of extracting data from the original data using multiple time windows. The search process includes a fourth step (step S140) of calculating, for each feature amount, the relative frequency distribution in extracted data that combines all the data extracted using the multiple time windows. The search process includes a fifth step (step S145) of calculating the error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data. After executing the first step, the processing device 510 executes the search process by repeatedly executing trials from the second step to the fifth step while changing multiple time window settings. Then, the processing device 510 extracts extracted data for which the error is equal to or less than a threshold (step S150: YES). The processing device 510 calculates an index value using the extracted data for which the error is equal to or less than the threshold (step S160).

[0069] According to this data center 500, extracted data that captures the overall characteristics of the original data, which includes multiple feature quantities, can be obtained. The extracted data has a smaller amount of data than the original data, and has the same accuracy as the original data. Therefore, the data center 500 can use the extracted data to calculate index values ​​with the same accuracy as when using the original data.

[0070] <Effects of this embodiment> (1) According to the data center 500, which is an information processing apparatus of this embodiment, it is possible to extract extracted data that has a smaller amount of data than the original data and that can obtain analysis results equivalent to those of the original data.

[0071] (2) According to the data center 500, which is an information processing apparatus of this embodiment, it is possible to achieve both a reduction in the amount of data and an improvement in the calculation accuracy of the index value. (3) According to the data center 500, which is an information processing device of this embodiment, the index value can be calculated in a shorter time than when original data is used.

[0072] (4) The processing device 510 performs clustering, which is machine learning, to classify data in each section obtained by dividing the original data into a fixed period into a predetermined number of clusters (step S110). Then, in the second step of the search process (step S125), the processing device 510 sets multiple time windows so that the difference between the ratio of each cluster in the extracted data and the ratio of each cluster in the entire original data is equal to or less than a threshold.

[0073] Multiple sections classified into the same cluster have similar characteristics. In the above search process, the settings output from the processing device 510 are settings that allow the difference in the ratio between the entire original data and each cluster to be equal to or less than a threshold, and that allow extraction of extracted data with similar relative frequency distributions of each feature.

[0074] Therefore, the search process executed by the data center 500 can find settings that can obtain extracted data that is closer to the characteristics of the entire original data. (5) The processing device 510 ends the search process when one piece of extracted data with an error equal to or less than the threshold is extracted, and calculates an index value using the extracted data with an error equal to or less than the threshold. Therefore, the data center 500 can calculate an index value when one piece of extracted data with an error equal to or less than the threshold is extracted, and quickly output the result.

[0075] (6) If the calculated index value is equal to or greater than the predetermined value (step S170: YES), the processing device 510 notifies the user that a failure has been predicted. This allows the data center 500 to notify the user that a failure has been predicted before the failure actually occurs.

[0076] (7) The processor 510 calculates the damage level as an index value, so that the data center 500 can inform the user how much time there is before a failure occurs.

[0077] <Example of change> This embodiment can be modified as follows: This embodiment and the following modifications can be combined and implemented within the scope of technical compatibility.

[0078] In the above information processing method, in the fifth step, the processing device 510 calculates the interval error, which is the difference in relative frequency for each class between the relative frequency distribution of the original data and the relative frequency distribution of the extracted data, and simply calculates the sum of these. Alternatively, each calculated interval error may be adjusted by multiplying it by a weight, and then the sum of the interval errors may be calculated as the error.

[0079] In this case, in the process of step S140, the processing device 510 calculates the relative frequency distribution of the extracted data, and then calculates the interval error, which is the difference in relative frequency for each class between the relative frequency distribution of the original data and the relative frequency distribution of the extracted data. Next, the processing device 510 adjusts the interval error for each class by multiplying the calculated interval error by a weight for each class. Then, in step S145, the processing device 510 calculates the sum of the adjusted interval errors as the error. The processing device 510 executes the processes from step S150 onwards using the error calculated in this way.

[0080] An example of a variable for determining the weight is the temperature when slippage occurs in the torque limiter 40. The processing device 510 adjusts the error based on the temperature when slippage occurs in the torque limiter 40. In step S140, the processing device 510 weights slippage that occurs when the temperature is high. Specifically, when calculating the relative frequency distribution of the extracted data in step S140, the processing device 510 adjusts the amount of slippage in the slippage to be larger if the temperature when the slippage occurs is high. Alternatively, when calculating the relative frequency distribution of the extracted data in step S140, the processing device 510 adjusts the amount of slippage in the slippage to be smaller if the temperature when the slippage occurs is low. Then, in the process of step S145, the processing device 510 calculates the error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data adjusted based on the temperature.

[0081] When the temperature is high, damage to the torque limiter 40 tends to be greater than when the temperature is low. By adjusting the error based on the temperature when slippage occurs in the torque limiter 40 as described above, the accuracy of calculating the index value can be further improved.

[0082] In the above embodiment, an example has been shown in which the information processing device is embodied as the data center 500. Then, an example has been shown in which the index value is calculated in the data center 500. In contrast, the above information processing device may be embodied as the information processing terminal 600. In this case, the index value is calculated by the processing device 610 of the information processing terminal 600. The above information processing device may be embodied as the control device 50 of the vehicle 10. In this case, the index value can also be calculated by the control device 50 of the vehicle 10.

[0083] An example has been shown in which the data center 500, which is an information processing device, extracts extracted data from the original data and calculates an index value. However, the information processing device may also be a device that performs the process up to extracting the extracted data from the original data. For example, the data center 500 may extract the extracted data from the original data and transmit the extracted data to the information processing terminal 600. In this case, the information processing terminal 600 that receives the extracted data calculates the index value. For example, the control device 50 of the vehicle 10 may extract the extracted data from the original data and transmit the extracted data to the data center 500. In this case, the data center 500 that receives the extracted data calculates the index value.

[0084] In the above embodiment, an example was shown in which one extracted data item was extracted and an index value was calculated. However, a final index value may be determined using multiple index values ​​calculated using multiple extracted data items. For example, the minimum value, maximum value, mode value, and average value may be used as the final index value. Also, multiple index values ​​may be output.

[0085] In the above embodiment, an example was shown in which a notification that a failure has been predicted is given when the index value is equal to or greater than a predetermined value. This may be omitted. After the index value is calculated, only the process of step S190 may be executed and only the index value may be output.

[0086] Although the damage level is used as an example of the index value to be calculated, the index value to be calculated is not limited to the damage level. In the above example, the processing device 510 sets multiple time windows so that the difference between the ratio of each cluster in the original data and the ratio of each cluster in the extracted data is equal to or less than a threshold. However, the processing device 510 may set multiple time windows without imposing such a constraint. In this case, the process of step S110 for performing clustering may be omitted.

[0087] The method for determining the setting of the time window in the cutout pattern does not have to be random. The setting of the time window in the cutout pattern may be changed according to a predetermined rule and repeated trials may be performed.

[0088] The error calculated in the process of step S145 is not limited to the mean absolute error (MAE). For example, the processing device 510 may calculate the mean square error as the error. The processing device 510 may calculate the root mean square error as the error.

[0089] An example has been shown in which extracted data is created by combining all of the extracted data. However, extracted data can also be created by combining some of the extracted data. [Explanation of symbols]

[0090] 10...vehicle, 20...engine, 30...automatic transmission, 40...torque limiter, 41...input rotating member, 42...output rotating member, 43...input surface, 44...output surface, 50...control device, 60...communication device, 400...communication network, 500...data center, 510...processing device, 520...storage device, 530...communication device, 600...information processing terminal, 610...processing device, 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 data from the original data that is used to calculate an index value that indicates the magnitude of damage accumulated in a torque limiter, a processing unit for performing processing; the original data includes data on the amount of slippage of the torque limiter as a feature amount, the processing device includes a first step of calculating a relative frequency distribution in the original data for each of the features included in the original data; a second step of setting a plurality of time windows to extract data for a partial period of the original data so that the total period of all of the time windows is shorter than the predetermined period; a third step of extracting data from the original data using the plurality of time windows; a fourth step of calculating the relative frequency distribution in extracted data obtained by combining the data extracted using the plurality of time windows for each of the features; and a fifth step of calculating an error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data, and after executing the first step, a search process is executed in which trials from the second step to the fifth step are repeatedly executed by changing the settings of the plurality of time windows to extract the extracted data for which the error is equal to or less than a threshold. Information processing device.

2. The original data further includes, as the characteristic amount, at least one of a duration of slippage of the torque limiter, an interval of slippage of the torque limiter, and a multiplication value of the amount of slippage and the duration of slippage. The information processing device according to claim 1 .

3. The processing unit adjusts the error based on the temperature when slippage occurs in the torque limiter.

3. The information processing device according to claim 1.

4. the processing device performs clustering, which is machine learning, to classify data in each section obtained by dividing the original data into a predetermined number of clusters; In the second step, the processing device sets the plurality of time windows so that a difference between a ratio of each cluster in the extracted data and a ratio of each cluster in the entire original data is equal to or less than a threshold. The information processing device according to claim 1 .

5. The processing device calculates, as the index value, a damage degree indicating a ratio of accumulated damage to damage that will result in damage, using the extracted data in which the error is equal to or less than a threshold value. The information processing device according to claim 1 .

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