Information processing device

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 apparatus. [Background Art]

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

[0003] The information processing apparatus disclosed in Patent Document 1 compresses data by extracting, from the original data, data acquired when a constant vehicle speed is reached and data acquired at an inflection point of vehicle speed. [Prior Art Documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Unexamined Patent Publication No. 2008-108247 [Summary of the Invention] [Problem to be Solved by the Invention]

[0005] The above information processing apparatus extracts data by focusing only on vehicle speed. Therefore, the above information processing apparatus cannot extract data according to characteristics of data other than vehicle speed. There is a need for an information processing apparatus capable of obtaining extracted data that captures the characteristics of the entire original data including a plurality of 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 some data from original data collected over a predetermined period using multiple sensors mounted on a vehicle. This information processing device includes a processing unit that performs processing. In this information processing device, the search processing performed by the processing unit includes a first step of calculating the relative frequency distribution in the original data for each of the multiple features contained in the original data. The search processing includes a second step of setting multiple time windows that extract data for a portion of the original data such that the sum of the periods of all time windows is shorter than the predetermined period. The search processing includes a third step of extracting data from the original data using the multiple time windows. The search processing includes a fourth step of calculating the relative frequency distribution in the extracted data, which is obtained by combining the data extracted using the multiple time windows, for each of the features. The search processing includes a fifth step of calculating the error between the relative frequency distribution in the original data and the relative frequency distribution in the extracted data. After performing the first step, the processing device performs the search process, which involves repeatedly performing the trials from the second to the fifth step by changing the settings of the plurality of time windows, to extract the extracted data in which the error is less than or equal to a threshold. In the second step, the processing device sets the plurality of time windows such that the ratio of data before a specific maintenance to data after a specific maintenance is equal in the entire original data and the extracted data.

[0007] In one embodiment of the information processing device, the device performs clustering, which is a machine learning method, to classify the data in each interval, obtained by dividing the original data into fixed-period intervals, into a predetermined number of clusters. In the second step, the device sets the multiple time windows such 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 less than or equal to a threshold. [Effects of the Invention]

[0008] This information processing device can find settings that allow it to obtain extracted data that captures the characteristics of the entire original data. [Brief explanation of the drawing]

[0009] [Figure 1] Figure 1 is a schematic diagram showing the relationship between a data center, which is one embodiment of an information processing device, a vehicle, and an information processing terminal. [Figure 2] Figure 2 is a graph showing the original data, where (a) shows the trend in oil flow rate and (b) shows the trend in gear shift frequency. [Figure 3] Figure 3 is a flowchart showing the processing flow performed by the processing unit in the data center. [Figure 4] Figure 4 is a graph showing an example of clustering the original data using two features. [Figure 5] Figure 5 is a graph showing an example of the relative frequency distribution of oil flow rate in the original data. [Figure 6] Figure 6 is a graph showing an example of the relative frequency distribution of gear shift frequency in the original data. [Figure 7] Figure 7 is a graph showing an example of the relationship between indicator values ​​and running time. [Modes for carrying out the invention]

[0010] Below, a data center 500, which is one embodiment of an information processing device, will be described with reference to Figures 1 to 7. <Configuration of the Information Processing System> Figure 1 shows the configuration of an information processing system including a data center 500. As shown in Figure 1, the data center 500 communicates with the vehicles 10 via a communication network 400. The data center 500 also communicates with information processing terminals 600 via the communication network 400. The data center 500 communicates with multiple vehicles 10 and multiple information processing terminals 600 via the communication network 400.

[0011] <Data Center 500 Configuration> As shown in Figure 1, the data center 500 includes a processing unit 510. The data center 500 also includes a storage device 520 and a communication device 530. The processing unit 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 thereof. The communication device 530 enables wired or wireless communication via the communication network 400.

[0012] The data center 500 may be configured using multiple computers. For example, the data center 500 may be configured using multiple server devices. <Vehicle 10 configuration> Each of the multiple vehicles 10 is equipped with a communication device 80. These communication devices 80 are implemented as hardware such as network adapters, various communication software, or a combination thereof. These communication devices 80 are configured to enable wired or wireless communication via a 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 comprises 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 that transmits power by engaging or disengaging a plurality of friction engagement elements. This allows the automatic transmission 30 to form multiple gears 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 sucks in the oil stored in the oil pan 34 and supplies the oil 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 can be subjected to oil change as a specific type of maintenance.

[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 device 40 and the transmission control device 50 are equipped with various sensors that collect information on each part of the vehicle 10. In each vehicle 10, travel data is collected from these various sensors. The travel data is transmitted from each vehicle 10 to a data center 500 by a communication device 80. For example, travel data including the travel distance, position information, and vehicle speed of each vehicle 10 is transmitted from each vehicle 10 to the data center 500. The travel 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 that identifies each vehicle 10 is also transmitted from each vehicle 10 to the data center 500 together with the travel 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 travel data.

[0017] The data center 500 stores the travel data in a storage device 520 together with the received identification information and maintenance history. In this way, 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 comprises a processing device 610, a storage device 620, and a communication device 630. The processing device 610 comprises a CPU that executes processing in accordance with a program, and a ROM storing the program. The storage device 620 stores data. The communication device 630 is implemented as hardware such as a network adapter, various types of communication software, or a combination thereof. The communication device 630 implements wired or wireless communication via the communication network 400. The information processing terminal 600 is, for example, a personal computer.

[0019] <Regarding Analysis of Travel Data of Vehicle 10> The information processing terminal 600 is used for work of analyzing travel data. When analyzing travel data, the information processing terminal 600 transmits an instruction to execute analysis to the data center 500. Upon receiving the instruction, the processing device 510 of the data center 500 performs analysis using a part of the massive travel data stored in the storage device 520 of the data center 500. The travel data to be used is selected from the massive travel data stored in the storage device 520 in accordance with the purpose of the analysis.

[0020] For example, the processing device 510 calculates the load applied to a specific component of a specific vehicle 10 based on the travel data of the specific vehicle 10. The processing device 510 estimates the damage accumulated in the component based on the calculated load. For example, the processing device 510 calculates an index value indicating the magnitude of damage accumulated in the oil filter 36 of the specific vehicle 10 based on the travel data of the specific vehicle 10. The processing device 510 of the data center 500 outputs the calculation result by transmitting it to the information processing terminal 600. The information processing terminal 600 that has received the result displays the received result.

[0021] In order to perform such analysis, the processing device 510 analyzes a large amount of travel data collected over a long period of time. Since the processing device 510 needs to perform a huge amount of calculation, the analysis takes a long time.

[0022] Therefore, it is conceivable to extract data that captures the characteristics of the entire original data from the large amount of original driving data. If such extracted data can be obtained, the processing unit 510 can perform analysis in a shorter time by using the extracted data. For example, when estimating the damage to the oil filter 36 after 100,000 hours of driving, the processing unit 510 estimates the damage using 20,000 hours of extracted data extracted from 100,000 hours of original data. Then, the processing unit 510 calculates an index value for the 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 5.

[0023] Figure 2 shows an example of the original data. The original data shown in Figure 2 is 100,000 hours of driving data for one vehicle 10. The original data shown in Figure 2 includes, as features, the flow rate of oil passing through the oil filter 36 for which the damage index value is calculated, and the shift frequency of the automatic transmission 30.

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

[0025] Figure 2(b) shows the trend of the gear shift frequency of the automatic transmission 30 over 100,000 hours. The gear shift frequency indicates the number of times the gear ratio was changed per unit of time. The gear shift frequency is calculated by the transmission control device 50. Alternatively, data recording the timing of gear ratio changes may be transmitted from the vehicle 10 to the data center 500, where the gear shift frequency is calculated. 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 gear shift frequency are correlated with the damage to the oil filter 36 of the vehicle 10. The processing unit 510 of the data center 500 estimates the damage to the oil filter 36 from driving data that includes the oil flow rate and gear shift frequency as features.

[0027] Extracted data is created by cutting out data from the original data using multiple time windows. Figure 2 shows four time windows as examples of multiple time windows: the first time window W_1, the second time window W_2, the third time window W_3, and the fourth time window W_4, each indicated by a dashed line. The start and end dates of each time window are set so that they do not overlap. In this example, 20,000 hours of driving data are extracted as data. Therefore, the start and end dates of each time window are set so that the total length of the periods of all time windows is 20,000 hours.

[0028] If there is a history of specific maintenance being performed, each time window will be set so that the proportion of data before the specific maintenance and data after the specific maintenance is equal in both the entire original data and the extracted data. For example, suppose the proportion of each period, before and after the specific maintenance, to the entire period of the original data is 50%. In this case, each time window will be 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] Data center 500 searches for start and end date settings for each time window that represent extraction patterns for extracting data that captures the characteristics of the entire original data. Data Center 500 extracts data from the original data using the extraction patterns found through exploration. Data Center 500 then performs analysis using the extracted data.

[0030] <Search process for extraction patterns> Figure 3 is a flowchart showing the sequence of processes related to the extraction pattern search process. This sequence of processes is performed by the processing unit 510 of the data center 500.

[0031] As shown in Figure 3, the processing unit 510 acquires original data in the processing of step S100. The original data is a portion of the driving data selected from the vast amount of driving data stored in the storage device 520 of the data center 500 according to the purpose of the analysis. For example, the original data for calculating an index value indicating the magnitude of damage accumulated in the oil filter 36 of one vehicle 10 is the 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 the damage to the oil filter 36 after 100,000 hours of driving, the original data is the driving data of the target vehicle 10 over a predetermined period.

[0032] In step S110, the processing unit 510 assigns labels to the original data by clustering. Specifically, the processing unit 510 divides the original data into fixed-period intervals. The length of the interval for dividing the original data is, for example, several minutes. Then, the processing unit 510 performs clustering, a machine learning method that classifies the data in each interval into a predetermined number of clusters. The clustering algorithm used is, for example, the k-means method. The k-means method is a clustering algorithm that classifies data into a predetermined number of clusters. The clustering algorithm is not limited to the k-means method.

[0033] The original data includes driving data collected under different conditions, such as driving in urban areas, driving in suburban areas, and 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 arbitrarily set depending on the content of the analysis.

[0034] Figure 4 is a graph showing an example of clustering the original data into four clusters using the k-means method, with two features included in the original data as explanatory variables. For example, the two features are the oil flow rate and the gear shift frequency, as shown in Figure 2. In Figure 4, each data point in the intervals into which the original data is divided is shown as a single point. When performing clustering, the processing unit 510 uses representative values ​​of the explanatory variables in the data for each interval. For example, the processing unit 510 uses the mean value of the features in the data for each interval as the representative value. The processing unit 510 may also use the moving average of the features over multiple consecutive intervals in a time series as the representative value.

[0035] In Figure 4, these points are shown on a two-dimensional space with the first feature FV_a and the second feature FV_b as the coordinate axes. Figure 4 is an example of the original data being clustered into four clusters: the first cluster M_1, the second cluster M_2, the third cluster M_3, and the fourth cluster M_4. In Figure 4, the boundaries of the four clusters are shown by solid lines. In Figure 4, the centroids of each cluster are indicated by white triangles. Centroid cgM_1 is the centroid of the first cluster M_1. Centroid cgM_2 is the centroid of the second cluster M_2. Centroid cgM_3 is the centroid of the third cluster M_3. Centroid cgM_4 is the centroid of the fourth cluster M_4.

[0036] Figure 4 shows an example with two explanatory variables, but the number of explanatory variables is not limited to two. For example, if the original data contains three features, the processing unit 510 may use these three features as explanatory variables to perform clustering. In that case, the processing unit 510 will cluster the original data in a three-dimensional coordinate space.

[0037] The processing unit 510 then assigns labels to the original data indicating the clustering results. Specifically, it assigns a label to each data point, which was represented as a point in coordinate space, to identify the classified cluster. In this way, the processing unit 510 creates the original data with labels.

[0038] Next, in step S120, the processing unit 510 calculates the relative frequency distribution of the original data. As mentioned above, the original data contains multiple features. The processing unit 510 calculates the relative frequency distribution of each feature in the original data.

[0039] A frequency distribution classifies data into multiple classes and represents the distribution of the number of data points in each class. Relative frequency indicates what percentage of the total sum of frequencies a particular class represents.

[0040] Figure 5 shows the relative frequency distribution for 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 for each class.

[0041] Figure 6 shows the relative frequency distribution of gear shift frequency in the original data shown in Figure 2. In this relative frequency distribution, the gear shift frequency in the original data is divided into m classes from 1 to m, and the relative frequency distribution is shown for each class.

[0042] In step S120, the processing unit 510 calculates this relative frequency distribution for each feature contained in the original data. The number of classes in the relative frequency distribution of each feature is the same.

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

[0044] Next, in the processing of step S125, the processing unit 510 sets multiple time windows in order to extract extracted data from the original data. Figure 2 shows four time windows W_1 to W_4 as an example of multiple time windows: the first time window W_1, the second time window W_2, the third time window W_3, and the fourth time window W_4. In the example shown in Figure 2, the duration of each time window is equal. As shown in Figure 2, the data extracted by each extraction window is the data for each feature over the same period.

[0045] In step S125, the processing unit 510 randomly sets multiple time windows such that the sum of the periods of all time windows is shorter than the default period, which is the period of the entire original data. As will be described later, the processing unit 510 combines all the data extracted using the multiple time windows set here to create extracted data. The sum of the periods of all time windows is a value that determines the capacity of the extracted data. Therefore, the sum of the periods of all time windows is set in advance.

[0046] For example, each time the processing unit 510 executes the process in step S125, it randomly sets the number of time windows, the start date of each time window, and the end date of each time window. At this time, the processing unit 510 sets each time window so that they do not overlap. In this way, the processing unit 510 randomly sets multiple time windows so that the sum of the periods of all time windows equals a predetermined period. In the process in step S125, the processing unit 510 may set multiple time windows by fixing the period of each time window to a constant value, as shown in Figure 2. In the process in step S125, the processing unit 510 may set multiple time windows by fixing the number of time windows to a constant value.

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

[0048] In addition to the requirements described above, when the processing unit 510 sets multiple time windows through step S125, it sets multiple time windows such that the difference between the ratio of each cluster in the extracted data and the ratio of each cluster in the original data as a whole is less than or equal to a threshold.

[0049] In this way, by setting multiple time windows through the process in step S125, an extraction pattern for extracting data from the original data is determined. Once the extraction pattern is determined, the processing unit 510 proceeds to step S130.

[0050] In step S130, the processing unit 510 extracts data from the original data according to the determined extraction pattern. In other words, in step S130, the processing unit 510 extracts data from the original data according to multiple set time windows. Then, the processing unit 510 combines all the data extracted according to the multiple time windows to create extracted data.

[0051] In the next step, S140, the processing unit 510 calculates the relative frequency distribution of the extracted data. The processing unit 510 calculates the relative frequency distribution of the extracted data in the same way as the method used to calculate the relative frequency distribution in step S120. That is, in the processing of step S140, the processing unit 510 calculates the relative frequency distribution of the extracted data for each feature. At this time, the processing unit 510 makes the number of classes in the relative frequency distribution of each feature the same as the relative frequency distribution in step S120.

[0052] For example, as shown in Figure 2, if the original data includes two features, oil flow rate and gear shift frequency, the processing unit 510 calculates the relative frequency distribution of each of these two features in step S140.

[0053] Next, in step S145, the processing unit 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 unit 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 formula above, the processing unit 510 calculates the error as the sum of the frequency errors 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] Once the error is calculated, the processing unit 510 proceeds to step S150. In step S150, the processing unit 510 determines whether the calculated error is less than or equal to a threshold. The threshold is a value used to determine whether extracted data having a relative frequency distribution similar to the relative frequency distribution in the original data has been extracted using the set extraction pattern. The magnitude of this threshold is pre-set so that, based on the error being less than or equal to the threshold, it can be determined that extracted data having a relative frequency distribution similar to the relative frequency distribution in the original data has been extracted.

[0058] If the processing in step S150 determines that the error is below a threshold (step S150: YES), the processing unit 510 proceeds to step S160. In step S160, the processing unit 510 calculates a target index value using the extracted data created in the most recent step S130. Here, an index value indicating the magnitude of damage accumulated in the oil filter 36 is calculated. For example, the processing unit 510 calculates the damage degree Sh as an index value indicating the magnitude of damage accumulated in the oil filter 36.

[0059] Damage level Sh is an index value that represents the percentage of accumulated damage, assuming that damage to the oil filter 36 gradually accumulates, with the damage leading to failure set as "1". Here, the damage inflicted on the oil filter 36 over a certain period is calculated from the oil flow rate and the frequency of gear changes. Then, the magnitude of the damage that leads to failure of the oil filter 36 is set as "1", and the percentage of the calculated damage is used as an index value. By repeating this process, the damage level Sh, which is the percentage of accumulated damage to the calculated damage leading to failure, is calculated. When the damage level Sh reaches "1", it means that failure has occurred, and the calculated damage level Sh is a value between "0" and "1".

[0060] Here, since the degree of damage is calculated using extracted data, which is part of the original data, the processing unit 510 converts the calculated degree of damage to a size corresponding to the original data and calculates the index value of damage Sh. 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 degree of damage is multiplied by 5 to obtain the index value of damage Sh.

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

[0062] In this way, the processing unit 510 repeatedly executes the search process in steps S125 to S145 by changing the settings of multiple time windows, and extracts data from the original data in which the error is below a threshold. Then, the processing unit 510 calculates an index value using the extracted data. Once the index value is calculated, the processing unit 510 proceeds to step S170.

[0063] In step S170, the processing unit 510 determines whether the index value is equal to or greater than a default value. The default value is a value used to predict that the likelihood of damage occurring is high based on the index value being equal to or greater than a default value. For example, here, for example, "0.9" can be set as the default value for damage level Sh. In this case, it is possible to predict that the likelihood of damage occurring is high, based on the fact that 90% of the damage leading to actual damage has been reached.

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

[0065] Failure prediction is, for example, a message indicating that a failure has been predicted. In this way, the processing unit 510 issues a notification indicating that a failure has been predicted if the calculated index value is greater than or equal to a predetermined value. Failure prediction may also be information about the lifespan until a failure occurs.

[0066] As shown in Figure 7, for example, if the damage level Sh calculated using extracted data from 100,000 hours of original data is the index value, the processing unit 510 calculates the driving time until the damage level Sh reaches "1" and outputs it as lifespan information. The processing unit 510 may also convert the lifespan information into driving distance based on the driving distance for 100,000 hours and output it.

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

[0068] The dashed approximation curve L1 is calculated based on multiple data points of damage degree Sh and travel time T during a period before a specific maintenance. The solid approximation curve L2 is calculated based on multiple data points of damage degree Sh and travel time T during a period after a specific maintenance. The approximation curve L2 includes a known period Pb after the specific maintenance and up to the current time Tr. The approximation curve L2 includes a predicted period Pa after the specific maintenance and up to the current time Tr. For example, the processing unit 510 calculates the damage degree Sh at predetermined intervals. The processing unit 510 calculates an approximation curve at predetermined intervals based on multiple data points of damage degree Sh and travel time T.

[0069] If there is a history of specific maintenance being performed, the processing unit 510 calculates the remaining travel time t until the predicted time Tov when the damage level Sh reaches "1", based on the current damage level Sh of Tr and the approximation curve L2 after the specific maintenance, and outputs this as life information. For example, the processing unit 510 calculates the remaining travel time t from the slope of the tangent line to the approximation curve L2 at the current Tr. The processing unit 510 may also calculate the remaining travel time t from the formula of the approximation curve L2. The processing unit 510 may also calculate the remaining travel time t from the slope of the approximation line or from the formula of the approximation line.

[0070] In step S170, if it is determined that the index value is less than the default value (step S170: NO), the processing unit 510 proceeds to step S190. In step S190, the processing unit 510 outputs the index value. Specifically, the processing unit 510 sends the index value to the information processing terminal 600 that sent the instruction requesting analysis.

[0071] When the processing in step S180 or step S190 is executed, the processing unit 510 terminates this series of processes. <Operation of this embodiment> The data center 500, which is an information processing device in this embodiment, acquires original data created by collecting data over a predetermined period of time using multiple sensors mounted on the vehicle 10, and calculates an index value indicating the magnitude of damage accumulated in the oil filter 36.

[0072] The data center 500 is equipped with a processing unit 510 that performs processing. The original data includes oil flow rate data and gear shift frequency data as features. In this data center 500, the search process performed by the processing unit 510 includes a first step (step S120) of calculating the relative frequency distribution in the original data for each of the multiple features included in the original data. The search process includes a second step (step S125) of setting up multiple time windows that extract data for a portion of the original data such that the sum of the periods of all time windows is shorter than a predetermined period. In the second step (step S125), the processing unit 510 sets up multiple time windows such that the ratio 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) in which the relative frequency distribution of the extracted data, obtained by combining all the data extracted using multiple time windows, is calculated for each feature. The search process includes a fifth step (step S145) in which the error between the relative frequency distribution of the original data and the relative frequency distribution of the extracted data is calculated. After executing the first step, the processing unit 510 executes the search process, repeatedly performing the trials from the second to the fifth step by changing the settings of the multiple time windows. The processing unit 510 then extracts the data for which the error is below a threshold (step S150: YES). The processing unit 510 calculates an index value using the extracted data for which the error is below a threshold (step S160).

[0073] According to this Data Center 500, it is possible to obtain extracted data that captures the characteristics of the entire original data, including multiple features. Therefore, this Data Center 500 can calculate index values ​​with the same accuracy as when using the original data, even though the extracted data has a smaller volume than the original data.

[0074] <Effects of this embodiment> (1) According to the data center 500, which is an information processing device of this embodiment, it is possible to achieve both a reduction in the amount of data and an improvement in the accuracy of calculating index values.

[0075] (2) 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 compared to when the original data is used. (3) The processing unit 510 performs clustering, a machine learning technique that classifies the data from each interval of the original data into a predetermined number of clusters (step S110). Then, in the second step of the search process (step S125), the processing unit 510 sets multiple time windows such 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 less than or equal to a threshold.

[0076] Multiple intervals classified into the same cluster are intervals with similar characteristics. In the search process described above, the settings output from the processing unit 510 are such that the difference in the ratio of the entire original data to each cluster is below a threshold, and the relative frequency distribution of each feature is similar, allowing for the extraction of relevant data.

[0077] Therefore, the search process performed by the data center 500 described above can find settings that allow for the acquisition of extracted data that more closely resembles the characteristics of the entire original data. (4) In the second step of the search process (step S125), the processing unit 510 sets multiple time windows so that the ratio of data before a specific maintenance and data after a specific maintenance in the entire original data and the extracted data are equal. Therefore, by taking into account the history of the specific maintenance performed, it is possible to find a setting that can obtain extracted data that is closer to the characteristics of the entire original data.

[0078] (5) The processing unit 510 terminates the search process when it has extracted one data point whose error is below the threshold, and calculates an index value using the extracted data point whose error is below the threshold. Therefore, the data center 500 can calculate the index value as soon as it has extracted one data point whose error is below the threshold, and output the results quickly.

[0079] (6) If the calculated indicator value is greater than or equal to a predetermined value (step S170: YES), the processing unit 510 issues a notification indicating that it has predicted the occurrence of a failure. Therefore, the data center 500 can inform the user that a failure has been predicted before the failure actually occurs.

[0080] (7) The processing unit 510 calculates the degree of damage Sh as an indicator value. As a result, the data center 500 can inform the user how much time is left before a failure occurs.

[0081] (8) The processing unit 510 predicts the lifespan of the oil filter 36 from the slope of the trend of the indicator value. Therefore, the data center 500 can predict the lifespan of the oil filter 36 according to the trend of the indicator value after a specific maintenance.

[0082] <Example of changes> This embodiment can be implemented with the following modifications. This embodiment and the following modifications can be combined with each other to the extent that they do not contradict each other technically.

[0083] As examples of features, the flow rate of oil passing through the oil filter 36, which is used to calculate the damage index value, and the gear shifting frequency of the automatic transmission 30 were given. However, features may also include the engagement frequency of the clutch that connects the engine 20 to the automatic transmission 30 so as to transmit power to it, or disconnects the engine 20 from the automatic transmission 30. Features may also include the engagement frequency of the friction engagement elements in the friction engagement device 31. Features may also include the engagement time of the friction engagement elements in the friction engagement device 31. Features may also include the engagement time of the lock-up clutch. Features may also include the learned value of the gear shifting control by the transmission control device 50.

[0084] In the above embodiment, an example was shown in which the information processing device is implemented as a data center 500. An example was shown in which the calculation of the index value is performed in the data center 500. Alternatively, the above information processing device may be implemented as an information processing terminal 600. In this case, the calculation of the index value is performed by the processing device 610 of the information processing terminal 600. The above information processing device may also be implemented as a control device of the vehicle 10. In this case, the calculation of the index value can also be performed by the control device of the vehicle 10. For example, the calculation of the index value can also be performed by the transmission control device 50 of the vehicle 10.

[0085] The above embodiment shows an example of extracting one data point and calculating an index value. In contrast, multiple data points may be extracted, and the final index value may be determined using multiple index values ​​calculated from each data point. For example, the minimum value, maximum value, mode, and mean may be used as the final index value. Alternatively, multiple index values ​​may be output.

[0086] • In the above embodiment, an example was shown in which a notification is given that a failure has been predicted when the index value is greater than or equal to a predetermined value. This may be omitted. After calculating the index value, only the processing in step S190 may be executed to output only the index value.

[0087] • While damage level Sh is used as an example of the index value to be calculated, the index value to be calculated is not limited to damage level Sh. Specific maintenance may include the replacement and overhaul of parts of the automatic transmission 30.

[0088] • Damage index values ​​for multiple oil filters 36 may be calculated. Alternatively, the relative frequency distribution for each feature of each oil filter 36 may be calculated, and an extraction pattern may be searched to minimize the error in the relative frequency distribution for all features.

[0089] Alternatively, a feature extraction pattern may be searched for for each of the 36 oil filters such that the relative frequency distribution is small. Then, an index value may be calculated for each of the 36 oil filters.

[0090] The embodiment can also be applied to transmissions equipped with an oil filter 36 other than the automatic transmission 30 shown above, such as stepped automatic transmissions, forward / reverse switching mechanisms of continuously variable transmissions, and clutches of manual transmissions installed in hybrid vehicles.

[0091] The example shown illustrates how the processing unit 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 less than or equal to a threshold. However, the processing unit 510 may set multiple time windows without such constraints. In this case, the clustering step S110 may be omitted.

[0092] The method for determining the time window setting in the extraction pattern does not have to be random. The time window setting in the extraction pattern can be changed according to a pre-defined rule, and the trial can be repeated.

[0093] The error calculated in step S145 is not limited to the mean absolute error (MAE). For example, the processing unit 510 may calculate the mean squared error as the error. The processing unit 510 may also calculate the root mean squared error as the error.

[0094] This example demonstrates the use of extracted data created by combining all the extracted data. Alternatively, extracted data can be created by combining only a portion 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. This information processing device reduces the amount of data used for analysis by extracting some data from the original data collected over a predetermined period using multiple sensors mounted on the vehicle. It includes a processing unit that performs processing, The aforementioned processing apparatus The process includes: a first step of calculating the relative frequency distribution in the original data for each of the multiple features contained in the original data; 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 time windows is shorter than the predetermined period; a third step of extracting data from the original data using the multiple time windows; a fourth step of calculating the relative frequency distribution in the extracted data obtained by combining the data extracted using the multiple time windows for each of the features; and a fifth step 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 process executes a search process that repeatedly performs the trials from the second to fifth steps by changing the settings of the multiple time windows, thereby extracting the extracted data for which the error is less than or equal to a threshold. In the second step, the processing device sets the multiple time windows such that the ratio of data before a specific maintenance to data after a specific maintenance is equal in the entire original data and the extracted data. Information processing device.

2. The processing unit performs clustering, a machine learning technique that classifies the data from each interval obtained by dividing the original data into fixed-period intervals, into a predetermined number of clusters. In the second step, the processing device sets the multiple time windows such that the difference between the ratio of each cluster in the extracted data and the ratio of each cluster in the original data as a whole is less than or equal to a threshold. The information processing apparatus according to claim 1.

3. The aforementioned specific maintenance is an oil change. The aforementioned original data includes oil flow rate data as a feature. The processing device uses the extracted data, where the error is below a threshold, to calculate an index value indicating the magnitude of damage accumulated in the oil filter. The information processing apparatus according to claim 1.

4. If the calculated index value is greater than or equal to a predetermined value, the processing device will issue a notification indicating that a malfunction has been predicted. The information processing apparatus according to claim 3.

5. The processing device calculates the index value at predetermined intervals and predicts the lifespan of the oil filter from the slope of the trend of the index value. The information processing apparatus according to claim 3.

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