Information processing apparatus

Abstract

To provide an information processing apparatus configured to determine settings to obtain extracted data that captures overall characteristics of original data.SOLUTION: A processing apparatus of an information processing apparatus executes search processing that 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 for some periods 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 in the extracted data; and a fifth step (S145) of calculating an error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data. The search processing repeatedly executes the second to fifth steps while modifying the settings of the time windows. The second step includes setting a plurality of time windows so that the ratio of periods before and after a specific maintenance may be equal to that of the entire original data.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 original data that includes multiple feature quantities. [Means for solving the problem]

[0006] An information processing device for solving the above problem reduces the amount of data used for analysis by extracting a portion of data from original data collected over a predetermined period of time using multiple sensors mounted on a vehicle. This information processing device includes a processing device for executing processing. In this information processing device, a search process executed by the processing device includes a first step of calculating a relative frequency distribution in the original data for each of multiple feature quantities 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 the relative frequency distribution for each feature quantity 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 by changing the settings of the multiple time windows, and extracts the extracted data for which the error is equal to or less than a threshold. In the second step, the processing device sets the multiple time windows so that the proportions of data before a specific maintenance and data after the specific maintenance in the entire original data and the extracted data are equal.

[0007] In one aspect of the information 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 find settings that can obtain extracted data that captures the overall characteristics of the original data. [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 change in oil flow rate, and (b) shows the change in gear shift frequency. [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 of the oil flow rate in the original data. [Figure 6] FIG. 6 is a graph showing an example of a relative frequency distribution of the gear change frequency in the original data. [Figure 7] FIG. 7 is a graph showing an example of the relationship between the index value and the running time. 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 80. The communication devices 80 are implemented as hardware such as a network adapter, various communication software, or a combination of these. The communication devices 80 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. The automatic transmission 30 includes a friction engagement device 31, a planetary gear train 32, and a hydraulic control circuit 33. The friction engagement device 31 changes the combination of the planetary gear train 32, which transmits power, by engaging or disengaging multiple friction engagement elements. In this way, the automatic transmission 30 forms multiple gear stages with different gear ratios. The friction engagement elements are, for example, clutches and brakes. The hydraulic control circuit 33 controls the hydraulic pressure supplied to each friction engagement element of the friction engagement device 31.

[0014] The automatic transmission 30 includes an oil pan 34, a pump 35, and an oil filter 36. The pump 35 draws oil stored in the oil pan 34 and supplies it to the planetary gear train 32 via the oil filter 36. The oil recovered from the planetary gear train 32 is stored again in the oil pan 34. The automatic transmission 30 may undergo an oil change as a specific maintenance routine.

[0015] The vehicle 10 includes an engine control device 40 and a transmission control device 50. The engine control device 40 controls the engine 20. The transmission control device 50 controls the automatic transmission 30 by controlling the hydraulic control circuit 33. The transmission control device 50 supplies oil to the planetary gear train 32 by controlling the pump 35.

[0016] The engine control unit 40 and the transmission control unit 50 are equipped with various sensors that collect information from various parts of the vehicle 10. Each vehicle 10 collects driving data from these various sensors. The driving data is transmitted from each vehicle 10 to the data center 500 via the communication device 80. For example, driving data including the driving distance, location 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 automatic transmission 30 acquired by the transmission 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. Data indicating the maintenance history of the automatic transmission 30 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, maintenance implementation history, and the travel data in the storage device 520. In this way, the travel data of a plurality of 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 driving 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 oil filter 36 of the specific vehicle 10 based on the driving 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 oil filter 36 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 oil filter 36 after 100,000 hours of driving by multiplying the index value calculated from the 20,000 hours of extracted data by five.

[0023] An example of the original data is shown in Figure 2. The original data shown in Figure 2 is driving data for 100,000 hours for one vehicle 10. The original data shown in Figure 2 includes, as feature quantities, the flow rate of oil passing through the oil filter 36 for which the damage index value is to be calculated, and the frequency of gear shifting of the automatic transmission 30.

[0024] 2(a) shows the change in oil flow rate over 100,000 hours. The oil flow rate can be detected by a flow rate sensor mounted on the vehicle 10. The oil flow rate may be calculated by the transmission control device 50. For example, the oil flow rate may be a discharge rate calculated from the specifications and rotation speed of the pump 35.

[0025] FIG. 2(b) shows the change in gear shift frequency of the automatic transmission 30 over 100,000 hours. The gear shift frequency indicates the number of times the gear ratio is changed per certain period of time. The gear shift frequency is calculated by the transmission control device 50. Data recording the time when the gear ratio was changed may be transmitted from the vehicle 10 to the data center 500, and the gear shift frequency may be calculated by the data center 500. The gear shift frequency may also be data indicating the interval between engagements using the same friction engagement element.

[0026] The oil flow rate and the frequency of gear changes are correlated with damage to the oil filter 36 of the vehicle 10. The processing device 510 of the data center 500 estimates damage to the oil filter 36 from the driving data that includes the oil flow rate and the frequency of gear changes as feature quantities.

[0027] The extracted data is created by extracting data from the original data using multiple time windows. In Figure 2, four time windows are shown by dashed lines as examples of multiple time windows: first time window W_1, second time window W_2, third time window W_3, and fourth time window W_4. 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 is 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.

[0028] When there is a history of specific maintenance, each time window is set so that the proportion of data before the specific maintenance and data after the specific maintenance is equal in the entire original data and the extracted data. For example, suppose that the proportions of the periods before and after the specific maintenance in the entire period of the original data are 50% each. In this case, each time window is set so that 50% of the total period of all time windows is the period before the specific maintenance and 50% is the period after the specific maintenance.

[0029] 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.

[0030] <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.

[0031] As shown in Fig. 3, the processing device 510 acquires original data in the processing 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 degree of damage accumulated in the oil filter 36 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 oil filter 36 after 100,000 hours of driving, the original data is driving data of the target vehicle 10 over a predetermined period.

[0032] 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.

[0033] 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.

[0034] 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 oil flow rate and the gear shift frequency shown in FIG. 2. 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.

[0035] 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.

[0036] 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 coordinate space.

[0037] 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.

[0038] Next, in step S120, the processing device 510 calculates the relative frequency distribution of the original data. As described above, the original data includes multiple feature amounts. The processing device 510 calculates the relative frequency distribution of the original data for each feature amount.

[0039] 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.

[0040] Figure 5 shows the relative frequency distribution of the oil flow rate in the original data shown in Figure 2. In this relative frequency distribution, the oil flow rate in the original data is divided into m classes from 1 to m, and the relative frequency distribution is shown.

[0041] Fig. 6 shows the relative frequency distribution of the gear change frequency in the original data shown in Fig. 2. In this relative frequency distribution, the gear change frequency classes in the original data are divided into m classes from 1 to m, and the relative frequency distribution is shown.

[0042] 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.

[0043] For example, as shown in the example of FIG. 2, if the original data includes two feature quantities, namely, oil flow rate and gear shift frequency, the processing device 510 calculates the relative frequency distribution of each of these two feature quantities.

[0044] 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 shows four time windows W_1 to W_4 as an example of multiple time windows: a first time window W_1, a second time window W_2, a third time window W_3, and a fourth time window W_4. In the example shown in FIG. 2, the time windows are all the same in duration. As shown in FIG. 2, the data extracted by each extraction window is data of each feature amount for the same period.

[0045] 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.

[0046] 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.

[0047] When there is a history of specific maintenance, the processing device 510 sets multiple time windows so that the proportions of data before the specific maintenance and data after the specific maintenance are equivalent in the entire original data and the extracted data. In the process of step S125, the processing device 510 sets multiple time windows so that the absolute value of the difference between the proportions of each period before and after the specific maintenance in the extracted data and the proportions of each period before and after the specific maintenance in the entire original data is equal to or less than a threshold.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] For example, as in the example shown in FIG. 2, if the original data includes two feature quantities, namely, oil flow rate and gear shift frequency, the processing device 510 also calculates the relative frequency distribution of each of these two feature quantities in step S140.

[0053] 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.

[0054]

number

[0055] In the above formula, "n" is the number of features. "m" is the number of classes in the relative frequency distribution. "Y" is the frequency of the corresponding feature in the corresponding class in the original data. "y" is the frequency of the corresponding feature in the corresponding class in the extracted data.

[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 processing of step S160, the processing device 510 calculates a target index value using the extracted data created in the processing of the most recent step S130. Here, the index value calculated indicates the degree of damage accumulated in the oil filter 36. For example, the processing device 510 calculates the damage level Sh as the index value indicating the degree of damage accumulated in the oil filter 36.

[0059] The damage level Sh is an index value that represents the proportion of accumulated damage, assuming that damage to the oil filter 36 gradually accumulates, with damage that results in damage being set to "1." Here, the damage inflicted on the oil filter 36 over a certain period of time is calculated from the oil flow rate and gear shift frequency. Then, the magnitude of damage that results in damage to the oil filter 36 is set to "1," and the calculated proportion of damage is calculated as an index value. By repeating this process, the damage level Sh is calculated, which is the proportion of accumulated damage to the calculated damage that results in damage. When the damage level Sh reaches "1," it means that damage has occurred, and the calculated damage level Sh 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 Sh 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 Sh 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 predetermined value for the damage level Sh. 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, when the calculated index value is equal to or greater than a predetermined value, the processing device 510 issues a notification informing the user that a failure has been predicted. The failure prediction may also be information about the lifespan until a failure occurs.

[0066] 7, for example, when the damage level Sh calculated using extracted data extracted from 100,000 hours of original data is an index value, the processing device 510 calculates the running time until the damage level Sh reaches "1" and outputs it as information on the lifespan. The processing device 510 may convert the information on the lifespan into a running distance based on the running distance for 100,000 hours and output it.

[0067] For example, the processing device 510 predicts the lifespan of the oil filter 36 from the gradient of the change in the index value. The processing device 510 calculates the remaining running time t until the damage level Sh reaches "1" by using an approximate straight line or an approximate curve calculated based on multiple data of the damage level Sh and the running time T.

[0068] The approximate curve L1 shown by a dashed line is calculated based on multiple data of the damage level Sh and the running time T in a period before specific maintenance. The approximate curve L2 shown by a solid line is calculated based on multiple data of the damage level Sh and the running time T in a period after specific maintenance. The approximate curve L2 is after the specific maintenance and includes a known period Pb up to the current time Tr. The approximate curve L2 is after the specific maintenance and includes a predicted period Pa after the current time Tr. For example, the processing device 510 calculates the damage level Sh for each predetermined period. The processing device 510 calculates the approximate curve for each predetermined period based on multiple data of the damage level Sh and the running time T.

[0069] When there is a history of specific maintenance, the processing device 510 calculates the remaining running time t until the time Tov at which the damage level Sh is predicted to reach "1" based on the damage level Sh at the current time Tr and the approximate curve L2 after the specific maintenance, and outputs the calculated time as lifespan information. For example, the processing device 510 calculates the remaining running time t from the slope of the tangent to the approximate curve L2 at the current time Tr. The processing device 510 may also calculate the remaining running time t from the mathematical formula of the approximate curve L2. The processing device 510 may also calculate the remaining running time t from the slope of the approximate straight line or the mathematical formula of the approximate straight line.

[0070] 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.

[0071] 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 over a predetermined period of time using multiple sensors installed on the vehicle 10 and calculates an index value indicating the extent of damage accumulated in the oil filter 36.

[0072] The data center 500 includes a processing device 510 that executes processing. The original data includes oil flow rate data and gear shift frequency data as feature quantities. 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 quantity included in the original data. The search process includes a second step (step S125) of setting multiple time windows to extract data for a portion of the original data so that the total duration of all the time windows is shorter than a predetermined duration. In the second step (step S125), the processing device 510 sets the multiple time windows so that the proportions of data before a specific maintenance and data after a specific maintenance are equal in the entire original data and the extracted data. The search process includes a third step (step S130) of extracting data from the original data using the multiple time windows. The search process includes a fourth step (step S140) of calculating, for each feature, the relative frequency distribution in extracted data obtained by combining all data extracted using multiple time windows. The search process also 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 the settings of multiple time windows. The processing device 510 then 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).

[0073] According to this data center 500, extracted data that captures the overall characteristics of the original data including multiple feature quantities can be obtained. Therefore, this data center 500 can calculate index values ​​with the same accuracy as when using the original data, using extracted data that has a smaller amount of data than the original data.

[0074] <Effects of this embodiment> (1) 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.

[0075] (2) According to the data center 500, which is an information processing apparatus of this embodiment, the index value can be calculated in a shorter time than when original data is used. (3) 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.

[0076] 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.

[0077] 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. (4) In the second step of the search process (step S125), the processing device 510 sets multiple time windows so that the proportions of data before specific maintenance and data after specific maintenance are equal in the entire original data and the extracted data. Therefore, it is possible to find settings that allow for obtaining extracted data that is closer to the characteristics of the entire original data by taking into account the implementation history of the specific maintenance.

[0078] (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.

[0079] (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.

[0080] (7) The processor 510 calculates the damage level Sh as an index value, so that the data center 500 can inform the user how much time there is before a failure occurs.

[0081] (8) The processing device 510 predicts the life of the oil filter 36 from the gradient of the change in the index value. Therefore, the data center 500 can predict the life of the oil filter 36 based on the change in the index value after specific maintenance.

[0082] <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.

[0083] The flow rate of oil passing through the oil filter 36, which is the target for calculating the damage index value, and the shift frequency of the automatic transmission 30 have been exemplified as feature quantities. However, the feature quantities are not limited to these, and may include the engagement frequency of a clutch that can connect so as to transmit the power of the engine 20 to the automatic transmission 30 or that can disconnect the engine 20 from the automatic transmission 30. The feature quantities may include the engagement frequency of a friction engagement element in the friction engagement device 31. The feature quantities may include the engagement time of a friction engagement element in the friction engagement device 31. The feature quantities may include the engagement time of a lock-up clutch. The feature quantities may include a learned value of the shift control by the transmission control device 50.

[0084] 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 a control device of the vehicle 10. In this case, the index value can also be calculated by the control device of the vehicle 10. For example, the index value can also be calculated by the transmission control device 50 of the vehicle 10.

[0085] 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.

[0086] 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.

[0087] Although the damage level Sh is given as an example of the index value to be calculated, the index value to be calculated is not limited to the damage level Sh. The specific maintenance may be a part replacement and an overhaul of the automatic transmission 30.

[0088] It is also possible to calculate index values ​​for damage to a plurality of oil filters 36. It is also possible to calculate the relative frequency distribution for each feature of each oil filter 36, and search for an extraction pattern that minimizes the error in the relative frequency distribution for all feature amounts.

[0089] A extraction pattern may be searched for so as to reduce the relative frequency distribution of the feature amount for each oil filter 36. Then, an index value may be calculated for each oil filter 36.

[0090] The present invention can also be applied to transmissions equipped with an oil filter 36 other than the automatic transmission 30 shown in the above embodiment, such as a stepped automatic transmission mounted on a hybrid vehicle, a forward / reverse switching mechanism of a continuously variable transmission, or a clutch of a manual transmission.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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]

[0095] 10...vehicle, 20...engine, 30...automatic transmission, 31...friction engagement device, 32...planetary gear train, 33...hydraulic control circuit, 34...oil pan, 35...pump, 36...oil filter, 40...engine control device, 50...transmission control device, 80...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 reduces the amount of data used for analysis by extracting a portion of data from original data collected over a predetermined period of time using multiple sensors mounted on a vehicle, a processing unit for performing processing; The processing device a first step of calculating a relative frequency distribution in the original data for each of a plurality of feature quantities 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 feature quantities; 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, wherein after performing the first step, a search process is performed in which trials from the second step to the fifth step are repeatedly performed with settings of the plurality of time windows changed to extract the extracted data for which the error is equal to or less than a threshold value, In the second step, the processing device sets the plurality of time windows so that the proportions of data before a specific maintenance and data after the specific maintenance in the entire original data and the extracted data are equal. Information processing device.

2. 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 .

3. the specific maintenance is an oil change; the original data includes oil flow rate data as a feature amount, The processing device calculates an index value indicating the degree of damage accumulated in the oil filter 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 .

4. If the calculated index value is equal to or greater than a predetermined value, the processing device issues a notification that it has predicted the occurrence of a failure. The information processing device according to claim 3 .

5. The processing device calculates the index value at predetermined intervals and predicts the life of the oil filter from the gradient of the change in the index value. The information processing device according to claim 3 .

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