Downsampling method

Abstract

To provide a downsampling method which accurately performs downsampling of traveling data.SOLUTION: A traveling data downsampling method using machine learning includes steps of: acquiring traveling data; calculating a predetermined feature quantity of the traveling data; generating cutout data by using a window from the traveling data; calculating an error between appearance frequencies of the feature quantity of the traveling data and the feature quantity of the cutout data; and setting time length and a position of the window by a randomized method in a manner to make the error a predetermined value or less.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a downsampling method. [Background technology]

[0002] If travel data is collected while a vehicle is traveling and stored, it can be used for various information processing. Travel data stored over a long period of time will be a huge amount of data, so there is a demand for a technology to compress the travel data. For example, Patent Document 1 discloses an invention that compresses travel data by outputting vehicle speed data at the time when the vehicle speed becomes equal to a set vehicle speed and at the time when the change in vehicle speed becomes an inflection point. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2008-108247 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the conventional technology, the accuracy of the vehicle speed data after compression is not sufficient. In order to efficiently compress the driving data, it is desirable to shorten the sampling time as much as possible, but if the sampling time is too short, there is a possibility that the accuracy will decrease.

[0005] In view of the above-mentioned technical problems, one aspect of the present disclosure aims to perform downsampling of driving data with high accuracy. [Means for solving the problem]

[0006] A downsampling method according to one aspect of the present disclosure includes the steps of acquiring driving data, calculating predetermined features for the driving data, generating cut-out data from the driving data using a window, calculating an error between the occurrence frequency of the feature of the driving data and the occurrence frequency of the feature of the cut-out data, and setting the time length and position of the window by a random selection method so that the error is equal to or less than a predetermined value. Effect of the Invention

[0007] According to one aspect of the present disclosure, downsampling of driving data can be performed with high accuracy. [Brief description of the drawings]

[0008] [Figure 1] 1 is a flowchart illustrating an example of a downsampling method. [Diagram 2] FIG. 11 is a diagram illustrating an example of a feature amount. [Diagram 3] 1 is a flowchart illustrating an example of a failure symptom detection method. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. In this specification and the drawings, components having substantially the same functional configurations are denoted by the same reference numerals, and redundant description will be omitted.

[0010] [Embodiment] An embodiment of the present disclosure is a downsampling method for driving data using artificial intelligence (AI). In this embodiment, the downsampling method calculates a predetermined feature amount for the entire driving data, compares the appearance frequency of the feature amount of the entire driving data with the appearance frequency of the feature amount of cut-out data cut out from the driving data, and selects cut-out data such that an error in the appearance frequency is equal to or less than a predetermined value.

[0011] In the downsampling method, when extracting cut-out data from the driving data, a window with an arbitrary time length and position is used by a random selection method. In the random selection method, a window with an arbitrary time length may be set. In addition, in the random selection method, a window may be set at an arbitrary position.

[0012] Also, for example, the setting of the window by the random selection method may be performed with the total window time fixed. The setting of the window by the random selection method may be performed after extending the total window time. In this case, when the error in the occurrence frequency is large, the proportion of the time to be extended may be increased. Furthermore, for example, the setting of the window by the random selection method may be performed so that the error in the sum of the feature amounts is equal to or less than a predetermined value.

[0013] Various indices can be used for the frequency of occurrence error, such as the root mean squared error (RMSE), the mean squared error (MSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE), etc.

[0014] <Driving data> In this embodiment, the processing target is driving data acquired from a drive unit used in a hybrid vehicle. This type of drive unit is disclosed in, for example, Reference 1. [Reference 1] JP 2008-232287 A

[0015] The drive device disclosed in Reference 1 is configured as follows. The drive device has a plurality of multi-plate clutches. Each multi-plate clutch has a cancel chamber. A plurality of multi-plate clutches may share one cancel chamber.

[0016] Among the multiple multi-plate clutches, one multi-plate clutch is configured to have a clutch drum, a clutch piston, a clutch hub, a return spring, and a cancel plate. The clutch drum is a bottomed cylindrical member connected to the first intermediate shaft so as to rotate integrally, and a plurality of ring-shaped friction plates are splined to its inner circumferential surface so as to rotate integrally. The clutch piston is disposed in the clutch drum, and forms a hydraulic chamber between the clutch drum and itself. The clutch hub has a cylindrical portion facing the inner circumferential surface of the cylindrical portion of the clutch drum, and is a cylindrical member connected to the second intermediate shaft so as to rotate integrally, and a plurality of ring-shaped friction mating plates are splined to its outer circumferential surface so as to rotate integrally.

[0017] The driving data is output from a control device (ECU; Electronic Control Unit) that controls the operation of the drive device. Data such as vehicle speed, access opening, shift position, oil temperature of the power transmission mechanism, and mode select switch are input to the control device. The control device controls the operation of the drive device based on the input data. For example, the torque of the motor generator, the electric oil pump, line pressure, and engagement pressure are output from the control device. Multiple engagement pressures are output corresponding to each friction material in the drive device.

[0018] In this embodiment, the travel data is uploaded to an external storage device via a wireless communication network such as a mobile phone network. The external storage device may be, for example, a cloud storage service provided by cloud computing. Travel data from various vehicles is uploaded to the external storage device, and big data is constructed.

[0019] <Downsampling method> The downsampling method in this embodiment will be described with reference to Fig. 1. Fig. 1(A) is a flowchart showing an example of the downsampling method.

[0020] The downsampling method in this embodiment is executed by an information processing device realized by a computer, etc. The information processing device includes a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), a HDD (Hard Disk Drive), an input device, an output device, an external I / F (Interface), and a communication I / F, each of which is mutually connected by a system bus.

[0021] In step S101, the information processing device acquires driving data acquired in each driving mode. The driving mode may be, for example, an urban mode, a highway mode, a mountain mode, etc.

[0022] In step S102, the information processing device combines the driving data of each driving mode acquired in step S101 to generate one driving data. Hereinafter, the combined driving data will be referred to as "total data." When evaluating parts, it is necessary to create an evaluation mode taking into account all of the ways in which each part is used, so it is necessary to combine the driving data of each driving mode. The purpose of the evaluation may be, for example, calculation of a damage rate.

[0023] In step S103, the information processing device performs machine learning and clustering on the entire data generated in step S102.

[0024] In step S104, the information processing device calculates a predetermined feature amount (explanatory variable) for the entire data clustered in step S103. The explanatory variable may be, for example, the number of engagements of the friction material for shifting, the engagement torque, the oil temperature, the relative rotation speed during idling, etc.

[0025] In step S105, the information processing device extracts N pieces of data with a time series length of TL from the entire data by using a window with a variable time length. Hereinafter, the extracted running data will be referred to as "extracted data".

[0026] In step S106, the information processing device calculates the frequency of appearance of the feature amount calculated in step S104 for each piece of extracted data extracted in step S105.

[0027] In step S107, the information processing device calculates the error between the occurrence frequency of the feature of the entire data and the occurrence frequency of the feature of the cut-out data. Next, the information processing device determines whether the error is less than a predetermined value. If the error is less than the predetermined value (YES), the information processing device proceeds to step S108. On the other hand, if the error is equal to or greater than the predetermined value (NO), the information processing device returns to step S105.

[0028] After returning to step S105, the information processing device changes the window length TL and the window position by a random selection method, and extracts N pieces of data again. In this manner, the information processing device repeats extracting the extracted data while adjusting the window length TL and the window position until the error in the occurrence frequency of the feature becomes less than a predetermined value.

[0029] In step S108, the information processing device selects the N pieces of extracted data extracted in step S105 as compressed driving data.

[0030] FIG. 1B is a flowchart showing an example of the data extraction process (step S105 in FIG. 1A).

[0031] In step S111, the information processing device determines whether the counter Sk is greater than a predetermined value S1. If the counter Sk is greater than the predetermined value S1 (YES), the information processing device proceeds to step S115. On the other hand, if the counter Sk is equal to or less than the predetermined value S1 (NO), the information processing device proceeds to step S112.

[0032] In step S112, the information processing device fixes (maintains) the total time of the window.

[0033] In step S113, the information processing device changes the time length and the position of each window by a random selection method.

[0034] In step S114, 1 is added to the counter Sk.

[0035] In step S115, the information processing device determines whether the counter Sh is greater than the predetermined value S2. If the counter Sh is greater than the predetermined value S2 (YES), the information processing device proceeds to step S119. On the other hand, if the counter Sh is equal to or less than the predetermined value S2 (NO), the information processing device proceeds to step S116.

[0036] In step S116, the information processing device extends the total time of the window by X%, where X is a predetermined positive number. In this embodiment, the sampling time cannot be shortened, but this is a constraint that should be tolerated in order to reduce the error. The percentage X% by which the total time is changed may be set according to the magnitude of the error. For example, when the error is large, X can be set to a large value.

[0037] In step S117, the information processing device changes the time length and the position of each window by a random selection method.

[0038] In step S118, the information processing device adds 1 to the counter Sh.

[0039] In step S119, the information processing device ends the downsampling method.

[0040] An example of the feature amount in this embodiment will be described with reference to Fig. 2. Fig. 2(A) is a diagram showing a first example of the overall data. In the overall data shown in Fig. 2(A), the horizontal axis represents time and the vertical axis represents the engagement torque of the friction material. In this example, the cut-out data is cut out in windows W1 to W4 shown in Fig. 2(A).

[0041] Fig. 2(B) is a diagram showing a second example of the overall data. In the overall data shown in Fig. 2(B), the horizontal axis represents time and the vertical axis represents the relative rotation speed of the friction material. In this example, the cut-out data is cut out in windows W5 to W8 shown in Fig. 2(B).

[0042] Fig. 2(C) is a diagram showing an example of the feature quantities of the entire data and the feature quantities of the cut-out data. Fig. 2(C) compares the frequency of occurrence of the feature quantities of the entire data with the frequency of occurrence of the feature quantities of the cut-out data for engagement torque, engagement torque x number of engagements, and relative rotation speed during idling. For each feature quantity, the horizontal axis represents the value of the feature quantity, and the vertical axis represents the frequency. The smaller the error in the distribution of the frequency of occurrence of the feature quantities between the entire data and the cut-out data, the more similar the features of the entire data and the cut-out data are.

[0043] <Method for detecting signs of failure> 3 is a flowchart showing an example of a failure sign detection method according to this embodiment. The failure sign detection method is an example of information processing to which the downsampling method according to this embodiment is applied.

[0044] In step S201, the information processing device acquires driving data of a vehicle to be detected from big data.

[0045] In step S202, the information processing device extracts the driving data acquired in step S201 using the downsampling method (see FIG. 1) in this embodiment.

[0046] In step S203, the information processing device calculates a predetermined damage rate Ss based on the cut-out data cut in step S202. Since the cut-out data is compact due to downsampling compared to the original driving data, the calculation of the damage rate Ss can be performed in a short time.

[0047] Next, the information processing device multiplies the damage rate Ss by a predetermined magnification to calculate the current damage rate Sr. The predetermined magnification can be calculated by (actual driving time or driving distance) / (driving time or driving distance of the cut-out data).

[0048] In step S204, the information processing device displays the current damage rate Sr calculated in step S203.

[0049] In step S205, the information processing device compares the current damage rate Sr with a specified value and determines whether the current damage rate Sr is significantly larger than the travel distance or travel time. If the damage rate Sr is equal to or greater than the specified value (YES), the information processing device proceeds to step S206. On the other hand, if the damage rate Sr is less than the specified value (NO), the information processing device skips steps S206 to S207 and ends the failure sign detection method.

[0050] In step S206, the information processing device determines that a failure symptom has been detected.

[0051] In step S207, the information processing device notifies the user of the vehicle of the malfunction symptom. Specifically, the information processing device notifies the user of the vehicle of the possibility that a malfunction will occur in the near future.

[0052] <Effects> The downsampling method in this embodiment selects the cut-out data so that the error between the frequency of occurrence of the feature of the entire driving data and the frequency of occurrence of the feature of the cut-out data is equal to or less than a predetermined value. The cut-out data selected by the downsampling method has similar characteristics to the entire driving data. Therefore, according to this embodiment, downsampling of the driving data can be performed with high accuracy. In one aspect, according to this embodiment, a failure sign can be detected with high accuracy.

[0053] [supplement] Although the embodiments of the present invention have been described in detail above, the present invention is not limited to these embodiments, and various modifications and changes are possible within the scope of the gist of the present invention described in the claims.

Claims

[Claim 1] Acquiring driving data; Calculating a predetermined feature amount for the running data; generating cut-out data from the travel data using a window; calculating an error between an appearance frequency of the feature amount of the traveling data and an appearance frequency of the feature amount of the cut-out data; setting the time length of the window and the position of the window by a random selection method so that the error is equal to or smaller than a predetermined value; The downsampling method has the following structure:

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