CN114842573A

CN114842573A - Vehicle test data processing method and system and cloud platform

Info

Publication number: CN114842573A
Application number: CN202210343713.2A
Authority: CN
Inventors: 李茂生; 蓝超浩; 邓晓仪; 郭杰军; 李芷筠; 黄润鑫
Original assignee: Zongshen Piaggio Foshan Motorcycle Co Ltd
Current assignee: Zongshen Piaggio Foshan Motorcycle Co Ltd
Priority date: 2022-04-02
Filing date: 2022-04-02
Publication date: 2022-08-02
Anticipated expiration: 2042-04-02
Also published as: CN114842573B

Abstract

According to the vehicle test data processing method, the vehicle test data processing system and the cloud platform, the first maintenance description of the previously set sample in the data and the first correlation condition of the first maintenance description of the derived sample in the derived vehicle working state test set are obtained firstly, identifying remaining maintenance descriptions in the collected vehicle operating condition test set and/or the derivative vehicle operating condition test set after determining the first correlation condition to optimize the collected vehicle operating condition test set, that is, the remaining maintenance descriptions in the vehicle operating condition test set that are not relevant to obtaining the first correlation condition can be identified after performing obtaining the first correlation condition, the problem of inaccurate remaining maintenance descriptions is improved, therefore, the accuracy of the vehicle working state test set can be effectively improved, and the possibility of vehicle failure can be reduced.

Description

Vehicle test data processing method and system and cloud platform

Technical Field

The application relates to the technical field of data processing, in particular to a vehicle test data processing method and system and a cloud platform.

Background

Data processing is the basic link of system engineering and automatic control. Data processing is throughout various fields of social production and social life. The development of data processing technology and the breadth and depth of its application have greatly influenced the progress of human society development.

At present, the technology applied to data processing is more and more extensive, so that the efficiency can be effectively improved, and the labor cost can be reduced. For example, in a vehicle test, the data types of the vehicle test are various (for example, vehicle brake data, vehicle engine operating data, and the like), a lot of time is consumed by performing analysis manually, and inaccurate data analysis or data omission may occur to cause failure of analysis. Therefore, it is difficult to guarantee the accuracy of the vehicle operating state test set.

Disclosure of Invention

In view of this, the application provides a vehicle test data processing method, a system and a cloud platform.

In a first aspect, a vehicle test data processing method is provided, including: identifying a first maintenance description of a previously set sample in the collected vehicle working state test set and a first maintenance description of a derivative sample in the derivative vehicle working state test set; obtaining a first association between a first maintenance description of a previously set sample and a first maintenance description of a derived sample; optimizing the collected vehicle working state test set through the first maintenance description of the derivative sample in combination with the first correlation condition; wherein one or more of the remaining maintenance descriptions of the previously set samples in the collected vehicle working state test set and the remaining maintenance descriptions of the derived samples in the derived vehicle working state test set are identified after determining the first correlation condition for optimizing the collected vehicle working state test set.

It can be understood that, by obtaining a first association condition of a first maintenance description of a previously set sample in the data and a first maintenance description of a derivative sample in the derivative vehicle operating state test set, the remaining maintenance descriptions in the collected vehicle operating state test set and/or the derivative vehicle operating state test set are identified after determining the first association condition, so as to optimize the collected vehicle operating state test set, that is, the remaining maintenance descriptions in the vehicle operating state test set that are not relevant to obtaining the first association condition can be identified after obtaining the first association condition, so as to improve the problem that the remaining maintenance descriptions are inaccurate, thereby effectively improving the accuracy of the vehicle operating state test set, and therefore, reducing the possibility of vehicle failure.

In a separately implemented embodiment, prior to obtaining the first association between the first maintenance description of the previously set sample and the first maintenance description of the derivative sample, the method further comprises: obtaining a second correlation condition between the initial maintenance description in the initial vehicle working state test set and the first maintenance description of the previously set sample in the collected vehicle working state test set; the first maintenance description of the derivative sample is determined by performing feature selection on the initial maintenance description in the initial vehicle working state test set and combining the initial maintenance description in the corresponding feature selection set; detecting that the second correlation condition meets the initial maintenance description and the prior setting sample of the first prior setting requirement; and optimizing the first maintenance description of the corresponding previously set sample in the collected vehicle working state test set through the detected initial maintenance description, and rejecting the first maintenance description of the derivative sample corresponding to the detected initial maintenance description in the derivative vehicle working state test set.

It can be understood that, before the first correlation condition is processed, the collected vehicle working state test set is optimized for the first time through the initial vehicle working state test set, and the derived vehicle working state test set and the collected vehicle working state test set after optimization are optimized, so that the possibility that the initial maintenance description in the initial vehicle working state test set can be added into the collected vehicle working state test set is improved.

In a separately implemented embodiment, after removing the first maintenance description of the derivative sample corresponding to the detected initial maintenance description in the test set of operating conditions of the derivative vehicle, the method further comprises: marking the derived samples with the first maintenance descriptions removed as samples to be removed so as to remove the second maintenance descriptions of the samples to be removed after the second maintenance descriptions of the derived samples are identified subsequently.

It can be understood that, instead of simultaneously removing the second maintenance description of the derived samples when the first maintenance description content is removed, the derived samples from which the first maintenance description has been removed are marked, and when the second maintenance description needs to be used, the second maintenance description is identified and a step of removing the second maintenance description of the derived samples is performed, so that the difficulty in identifying the second maintenance description is reduced.

In a separately implemented embodiment, prior to obtaining the first association between the first maintenance description of the previously set sample and the first maintenance description of the derivative sample, the method further comprises: identifying a first maintenance description of a candidate sample in the candidate vehicle working state test set, and obtaining a third correlation condition between the first maintenance description of the derived sample and the first maintenance description of the candidate sample; and optimizing the derived vehicle working state test set by combining the third correlation condition.

The method has the advantages that the derived vehicle working state test set is optimized through the candidate vehicle working state test set, so that the sample data volume of the derived vehicle working state test set is increased, more and more vehicle abnormal data can be loaded into the collected vehicle working state test set, and the optimization possibility of the collected vehicle working state test set is improved.

In a separately implemented embodiment, the optimization of the derived vehicle operating condition test set in conjunction with the third correlation scenario comprises: identifying a second maintenance description of the derived samples in the derived vehicle operating condition test set and a second maintenance description of the candidate samples in the candidate vehicle operating condition test set; optimizing a working state test set of the derivative vehicle by combining the third correlation condition, the second maintenance description of the derivative sample and the second maintenance description of the candidate sample; and/or, optimizing a derived vehicle operating condition test set, comprising: splicing the maintenance description content of the candidate sample to the maintenance description content of the derivative sample, or taking the maintenance description content of the candidate sample as the updated maintenance description content of the derivative sample.

It can be understood that the maintenance description contents of the candidate samples are spliced to the maintenance description contents of the derived samples or used as the updated maintenance description contents of the derived samples by respectively identifying the derived vehicle working state test set and the second maintenance description of the candidate vehicle working state test set and then giving a third correlation condition, the second maintenance description of the derived samples and the second maintenance description of the candidate samples, so that when the number of sample data in the derived vehicle working state test set is increased, the data in the derived vehicle working state test set can be continuously updated, and the normal work of the vehicle can be guaranteed as much as possible.

In a separately implemented embodiment, optimizing a test set of operating conditions of a derivative vehicle in conjunction with a third correlation, a second maintenance description of a derivative sample, and a second maintenance description of a candidate sample, comprises: selecting candidate samples and derivative samples of which the third correlation condition meets the second prior setting requirement as first prior configuration samples and first prior derivative samples; determining a first maintenance description and a second maintenance description of the spliced first prior configuration sample and a second prior derivative sample, and obtaining a fourth correlation condition between the spliced first maintenance description and the spliced second maintenance description; selecting a first pre-configuration sample and a first pre-derivative sample of which the fourth correlation condition meets a third pre-setting requirement as a first reference sample and a first derivative sample; stitching the maintenance description of the first reference sample to the maintenance description of the first derivative sample; and taking the remaining candidate samples except the first reference sample as the updated maintenance description of the derivative sample.

It can be understood that the third preset requirement is configured, so that the correlation condition between the data spliced with each other meets the requirement, the probability that the candidate samples spliced to the derivative samples belong to the same kind of data is high, and the condition that the derivative samples after splicing cover a plurality of data with differences is weakened, so that the normal operation of the vehicle cannot be accurately guaranteed.

In a separately implemented embodiment, optimizing the collected vehicle operating condition test set by deriving a first maintenance description of the sample in conjunction with the first correlation case includes at least one of the following steps: selecting a prior setting sample and a derivative sample of which the first correlation condition meets a fourth prior setting requirement to obtain a first prior setting sample and a second derivative sample, and splicing the maintenance description content of the second derivative sample to the maintenance description content of the first prior setting sample; and selecting a derivative sample with the first correlation condition meeting the fifth prior setting requirement as a third derivative sample, and taking the maintenance description content of the third derivative sample as the updated maintenance description content of the prior setting sample on the basis that the feature selection set where the third derivative sample is located meets the prior setting template.

It can be understood that, by configuring the fourth prior setting requirement and the fifth prior setting requirement, the derived samples can be arranged by the collected vehicle working state test set when the association condition meets the requirement, or can become updated prior setting samples in the collected vehicle working state test set, so that the prior setting samples which can be continuously updated in the collected vehicle working state test set are ensured to a certain extent, and the normal operation of the vehicle can be ensured.

In a separately implemented embodiment, selecting the first set sample and the derivative sample for which the first correlation condition meets the fourth prior setting requirement to obtain the first prior set sample and the second derivative sample comprises: selecting a prior setting sample and a derivative sample of which the first correlation condition meets a sixth prior setting requirement, and determining a first prior setting sample and a second derivative sample; or selecting a prior setting sample and a derivative sample of which the first correlation condition meets a seventh prior setting requirement to serve as a first reference prior setting sample and a second prior derivative sample, identifying a third nursing description of the first reference prior setting sample, and taking the first reference prior setting sample and the second prior derivative sample as the first reference prior setting sample and the second derivative sample on the basis that a fifth correlation condition between the first nursing description of the second prior derivative sample and the third nursing description of the first reference prior setting sample meets an eighth prior setting requirement.

It will be appreciated that by configuring the sixth and seventh prior setting requirements, more derivative data can be used to optimize the test set of vehicle operating conditions that have been collected.

In an independently implemented embodiment, the splicing the maintenance description of the candidate sample to the maintenance description of the derivative sample, or splicing the maintenance description of the second derivative sample to the maintenance description of the first previously set sample, includes: correspondingly taking the candidate sample and the derivative sample as a first sample to be spliced and a second sample to be spliced, or correspondingly taking the second derivative sample and the first prior set sample as the first sample to be spliced and the second sample to be spliced; weighting the first maintenance description of the first sample to be spliced and the first maintenance description of the second sample to be spliced to obtain the first maintenance description of the second sample to be spliced again; and comparing the ranges of the feature selection sets of the first sample to be spliced and the second sample to be spliced, and selecting the second maintenance description bound by the larger feature selection set as the second maintenance description of the second sample to be spliced.

It can be understood that the maintenance description content of the derived sample is spliced with the maintenance description content of the previously set sample, so that the first maintenance description of the spliced previously set sample is changed, and the second maintenance description is changed into the optimal second maintenance description, so that the spliced previously set sample can cover the maintenance description content of the original derived data, the first maintenance description can represent the maintenance description of the global data in the previously set sample, and the reliability of the maintenance data is improved.

In a separately implemented embodiment, prior to optimizing the derived vehicle operating condition test set, or optimizing an already collected vehicle operating condition test set, the method further comprises: identifying a second maintenance description of the derivative samples in the derivative vehicle operating condition test set; and rejecting a second maintenance description of the sample to be rejected on the basis of verifying that the derived sample is marked as the sample to be rejected.

It can be understood that the frequency of identifying the content of the second maintenance description can be reduced by eliminating the abnormal data of the second maintenance description once before the vehicle working state test set is optimized or the collected vehicle working state test set is optimized, so that the workload is reduced.

In an independently implemented embodiment, at least one of the first association condition, the second association condition, the third association condition, the fourth association condition, and the fifth association condition corresponds to a matching relationship between two maintenance descriptions.

It can be understood that the matching relationship is used for judging whether the derived data can be spliced by the previously set samples in the collected vehicle working state test set or continuously optimizing the data in the collected vehicle working state test set in the mode of the updated previously set samples, so that the data can be continuously updated, and the normal work of the vehicle is ensured to the maximum extent.

In an independently implemented embodiment, the first maintenance description is a focused maintenance description of a feature selection set of a sample bound with the first maintenance description, wherein the feature selection set is obtained by performing feature selection on a plurality of overhaul data according to the covered sample; the residual maintenance description comprises at least one of a second maintenance description and a third maintenance description of the sample bound with the residual maintenance description, the second maintenance description is used for limiting the spliced feature selection set according to a specific instruction when the feature selection set where the sample bound with the second maintenance description is located is spliced, and the third maintenance description is the first maintenance description when the sample bound with the third maintenance description is recorded as the sample set in advance.

It can be understood that by dividing the maintenance description content into the first maintenance description, the second maintenance description and the third maintenance description, the splicing of the feature selection set can be limited by a plurality of maintenance descriptions, so that the accuracy of the spliced two maintenance descriptions is improved.

In a second aspect, a vehicle test data processing system is provided, comprising a processor and a memory in communication with each other, the processor being configured to retrieve a computer program from the memory and to implement the above-mentioned method by running the computer program.

In a third aspect, a cloud platform is provided, comprising: a memory for storing a computer program; a processor coupled to the memory for executing the computer program stored by the memory to implement the above-described method.

According to the vehicle test data processing method, the vehicle test data processing system and the cloud platform provided by the embodiment of the application, by first obtaining the first maintenance description of the previously set sample in the data and the first correlation condition of the first maintenance description of the derivative sample in the derivative vehicle working state test set, identifying remaining maintenance descriptions in the collected vehicle operating state test set and/or the derived vehicle operating state test set after determining the first correlation condition to optimize the collected vehicle operating state test set, that is, the remaining maintenance descriptions in the vehicle operating condition test set that are not relevant to obtaining the first correlation condition can be identified after performing obtaining the first correlation condition, the problem of inaccurate remaining maintenance descriptions is improved, therefore, the accuracy of the vehicle working state test set can be effectively improved, and the possibility of vehicle failure can be reduced.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a flowchart of a vehicle test data processing method according to an embodiment of the present disclosure.

Fig. 2 is a block diagram of a vehicle test data processing apparatus according to an embodiment of the present application.

Fig. 3 is an architecture diagram of a vehicle test data processing system according to an embodiment of the present application.

Detailed Description

In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.

Referring to fig. 1, a method for processing vehicle test data is shown, which may include the following steps S11-S13.

Step S11: first maintenance descriptions of previously set samples in the collected vehicle operating condition test set and first maintenance descriptions of derived samples in the derived vehicle operating condition test set are identified.

In this embodiment, before identifying the first maintenance description of the previous set sample in the collected vehicle working state test set and the first maintenance description of the derivative sample in the derivative vehicle working state test set, the derivative vehicle working state test set needs to be obtained first.

Further, the already collected vehicle operating condition test set may be constructed in a cloud platform or remaining manner for storing maintenance description content regarding some previously set samples.

In a possible implementation example, the maintenance description content of each previous setting sample in the collected vehicle working state test set may be obtained by performing feature selection on the maintenance description related to the previous setting sample, that is, the maintenance description content of each previous setting sample is the maintenance description content of the feature selection set bound to the previous setting sample. For example, different repair data covering a previously set sample may be extracted according to the initial maintenance description of the covered previously set sample, and then the extracted initial maintenance description is subjected to feature selection, so as to obtain the maintenance description content of the feature selection set (also understood as the feature selection set where the previously set sample is located) bound to the previously set sample. In a specific implementation process, the samples are set as local samples, initial maintenance descriptions of the local samples can be extracted from different local sample overhaul data, feature selection is performed on the initial maintenance descriptions of the local samples, a feature selection set bound to each local sample is obtained, and then the maintenance description of the feature selection set bound to each local sample is obtained and serves as the maintenance description content of the local sample. Further, the maintenance description content of the previously set sample may include a first maintenance description, a second maintenance description, and a third maintenance description. The first maintenance description of the previous setting sample may represent a focused maintenance description of a feature selection set where the previous setting sample bound by the first maintenance description is located. The feature selection set is obtained by performing feature selection on a plurality of overhaul data according to covered samples. The second maintenance description of the previously set sample is used for limiting the spliced feature selection set according to a specific instruction when the feature selection set where the previously set sample bound with the second maintenance description is located is spliced, so that the second maintenance description can be called as a focused maintenance description. And limiting the spliced feature selection sets according to a specific indication, namely judging whether the two feature selection sets can be spliced or not. The third maintenance description of the first set sample is the first maintenance description when the sample record bound by the third maintenance description is the first maintenance description of the first set sample, that is, the first maintenance description of the first set sample when the feature selection set where the first set sample bound by the third maintenance description exists in the collected vehicle working state test set for the first time, that is, the first maintenance description of the first set sample when the first set sample file generated newly in the collected vehicle working state test set exists, may be referred to as the third maintenance description as the original focus maintenance description.

Further, the candidate vehicle working state test set related to the derived vehicle working state test set has higher similarity with the collected vehicle working state test set. The samples in the derived vehicle operating state test set and the candidate vehicle operating state test set, such as the derived samples and the candidate samples, may refer to the related maintenance description of the previously set samples, and may be local samples, residual samples, and the like. The maintenance description content of the derived sample and the candidate sample in the derived vehicle working state test set or the candidate vehicle working state test set can also be as follows: the maintenance description of the feature selection set where the sample bound with the maintenance description content is located, for example, the maintenance description contents of the derivative sample and the candidate sample include a first maintenance description and a second maintenance description, and the definitions of the first maintenance description and the second maintenance description may refer to the above related maintenance description, for example, the first maintenance description of the derivative sample is a focus maintenance description of the feature selection set where the derivative sample is located, and the second maintenance description of the derivative sample is a second maintenance description of the feature selection set where the derivative sample is located. In the derived vehicle operating condition test set and the candidate vehicle operating condition test set, the first maintenance description of the sample may or may not be consistent with the second maintenance description. For example, when the derived vehicle operating condition test set is formed first, the first maintenance description of the derived sample is the same as the second maintenance description, and if the derived sample in the derived vehicle operating condition test set is optimized, the first maintenance description may be different from the second maintenance description (the range of the second maintenance description exceeds the first maintenance description). The derived vehicle working state testing set can cover a plurality of derived samples, and certainly, can also cover an independent derived sample. Only one individual candidate sample may also be covered in the candidate vehicle operating condition test set. When the derived vehicle working state test set only covers one derived sample, and the range of a plurality of feature selection sets exceeds a, the derived sample has no bound feature selection set, and the derived sample at this time is only the initial maintenance description in the initial vehicle working state test set described in the following embodiment, and does not include the first maintenance description and the second maintenance description, but can still participate in the optimization process of the collected vehicle working state test set. And if the range of the specified feature selection set is not less than a, the result is consistent with the result of a plurality of derivative samples in the derivative vehicle working state test set.

In one possible implementation, the derived vehicle operating state test set is used as a derived maintenance description in addition to the collected vehicle operating state test set to optimize the collected vehicle operating state test set. In this embodiment, the derived vehicle operating state test set is obtained by performing feature selection on the initial maintenance description in the most recently collected initial vehicle operating state test set, and steps S11-S13 may be directly performed through the obtained derived vehicle operating state test set to optimize the collected vehicle operating state test set. In an alternative embodiment, in order to achieve more accurate optimization of the collected vehicle operating state test set, before performing step S11, the collected vehicle operating state test set is optimized by generating an initial vehicle operating state test set of derivative vehicle operating state test sets, which may be specifically as follows; in addition, on the basis that there is a candidate vehicle operating state test set that is not spliced to the already collected vehicle operating state test set, before step S11 is executed, the derived vehicle operating state test set is optimized through the candidate vehicle operating state test set, so that the candidate vehicle operating state test set is spliced to the derived vehicle operating state test set, and the already collected vehicle operating state test set is optimized through the optimized derived vehicle operating state test set, which may be referred to specifically as follows. It should be understood that the above at least one of optimizing the collected vehicle operating state test set by generating the initial vehicle operating state test set of the derived vehicle operating state test set and optimizing the derived vehicle operating state test set by generating the candidate vehicle operating state test set is performed.

In one possible implementation, prior to obtaining the first association between the first maintenance description of the previously set sample and the first maintenance description of the derived sample, a second association between the initial maintenance description of the initial vehicle operating condition test set and the first maintenance description of the previously set sample of the already collected vehicle operating condition test set may be obtained. Wherein, obtaining the second association between the initial maintenance description in the initial vehicle working state test set and the first maintenance description of the previously set sample may also be understood as comparing the initial maintenance description with the first maintenance description. The following association between the first maintenance description of the processing candidate sample and the first maintenance description of the derived sample and the association between the first maintenance description of the processing derived sample and the first maintenance description of the previously set sample may also be understood as comparing the corresponding two maintenance descriptions.

In one possible implementation, the initial data in the initial vehicle operating state testing set and the maintenance description of the previously set sample in the collected vehicle operating state testing set may be combined with the maintenance description of the service data obtained from the service data. The initial maintenance description in the initial vehicle working state test set can represent the maintenance description of each initial data in the initial vehicle working state test set, compared with a feature selection set covering a plurality of initial data, one of the initial data in the initial vehicle working state test set is equivalent to an individual unit, the maintenance description of the initial data is equivalent to the maintenance description of the unit, and the first maintenance description of the derivative sample in the derivative vehicle working state test set can be obtained according to the initial vehicle working state test set. Preferably, the initial maintenance description of the corresponding sample in the initial vehicle working state test set is obtained, the initial maintenance description is subjected to feature selection according to a matching relationship to form a plurality of feature selection sets, and then the first maintenance description of the derivative sample is determined by combining the initial maintenance description in the corresponding feature selection set. For example, when the matching relationship between the initial maintenance descriptions exceeds the previously set feature selection judgment value, the initial data where a plurality of initial maintenance descriptions whose matching relationship meets the requirement are located is formed into a feature selection set, wherein the formed feature selection set comprises a first maintenance description and a second maintenance description, and the first maintenance description of the derivative sample is the first maintenance description of the feature selection set where the derivative sample is located. The first maintenance description may represent a maintenance description that is capable of representing a feature pick set by processing an initial maintenance description for each local data in the feature pick set. Wherein the second association case may be a matching relationship. When the feature selection set where the derivative sample is located is obtained by performing feature selection through the initial vehicle working state test set, the first maintenance description and the second maintenance description of the derivative sample are consistent.

Further, feature selection is carried out through the initial vehicle working state test set, wherein a part of data may form the feature selection set to become a derivative sample, but a part of data in the initial vehicle working state test set does not form the derivative sample. Preferably, the requirement for enabling feature selection as a derivative sample is the size of initial data having a matching relationship exceeding a previously set feature selection judgment value, and if the number of initial inspection data having a matching relationship meeting the requirement is too small and does not satisfy the previously set minimum range of the generated feature selection set, the part of initial inspection data is not feature-selected to form a derivative sample.

After obtaining a second association condition between the initial maintenance description in the initial vehicle working state test set and the first maintenance description of the previously set sample in the collected vehicle working state test set, detecting that the second association condition meets the initial maintenance description and the previously set sample which meet the first previously set requirement. The first maintenance description of the corresponding previously set sample in the collected vehicle operating state test set is then optimized by the detected initial maintenance description. And then rejecting the first maintenance description of the derivative sample bound with the detected initial maintenance description in the test set of the working state of the derivative vehicle. And the second association condition comprises a matching relation between the two corresponding first maintenance descriptions. The first prior setting requirement may be that the second association condition is not lower than the first prior setting determination value, for example, the first prior setting determination value is 1.6, and when the second association condition is not lower than 1.6, the second association condition is determined to meet the second prior setting requirement.

Further, the form of the first maintenance description of the corresponding previously set sample in the collected vehicle working state test set is optimized through the detected initial maintenance description, and the detected initial maintenance description may be used to find the first maintenance description of the corresponding derived sample in the derived vehicle working state test set, and then the first maintenance description of the previously set sample is optimized through the first maintenance description of the corresponding derived sample. Preferably, when the initial maintenance description corresponding to the derived sample and the first maintenance description corresponding to the previously set sample are weighted, the previously set sample having the updated first maintenance description is obtained. Because the first maintenance description corresponding to the derived sample is weighted with the first maintenance description corresponding to the previously set sample, the data repetition is improved, and the first maintenance description bound in the derived vehicle working state testing set can be removed. After the first maintenance description of the derived samples corresponding to the detected initial maintenance description in the derived vehicle working state test set is removed, marking the derived samples from which the first maintenance description is removed as samples to be removed, and removing the second maintenance description of the samples to be removed after the second maintenance description of the derived samples is subsequently identified.

Therefore, before the first correlation condition is processed, the collected vehicle working state test set is optimized for the first time through the initial vehicle working state test set, and the collected vehicle working state test set after optimization is optimized through the derivative vehicle working state test set, so that the possibility that the initial maintenance description in the initial vehicle working state test set can be added into the collected vehicle working state test set is improved.

By the design, when the first maintenance description content is removed, the derivative sample from which the first maintenance description is removed is marked first, and when the second maintenance description needs to be used, the second maintenance description is identified and the removal step of the second maintenance description of the derivative sample is executed, so that the identification difficulty of the second maintenance description is reduced.

In an alternative embodiment, prior to obtaining a first association between the first maintenance description of a previously set sample and the first maintenance description of a derived sample, the first maintenance description of the candidate samples in the test set of candidate vehicle operating conditions may be identified, and a third association between the first maintenance description of the derived sample and the first maintenance description of the candidate sample may be obtained. And optimizing the working state test set of the derivative vehicle by combining the third correlation condition. The candidate vehicle working state test set may be formed by residual derivative samples which are not successfully optimized in the derivative vehicle working state test set to the collected vehicle working state test set in the previous sample data optimization process. Then, in the process, the candidate vehicle working state test set can be understood, wherein the candidate samples in the candidate vehicle working state test set also comprise the first maintenance description and the second maintenance description.

Further, the process of optimizing the derived vehicle operating state test set in combination with the third correlation case may be to identify a second maintenance description of the derived sample in the derived vehicle operating state test set and a second maintenance description of the candidate sample in the candidate vehicle operating state test set, and then optimize the derived vehicle operating state test set in combination with the third correlation case between the first maintenance description of the candidate sample and the first maintenance description of the derived sample and the second maintenance descriptions of the candidate sample and the derived sample. A third correlation case may be a matching relationship between the two corresponding first maintenance descriptions. The second prior setting requirement herein may mean that the third association condition is not lower than the second prior setting determination value, for example, when the second prior setting determination value is 1.6, then when the third association condition is not lower than 1.6, then the third association condition is deemed to meet the second prior setting requirement.

For example, candidate samples and derivative samples for which the third correlation case meets the second prior setting requirement may be selected as the first prior configuration sample and the first prior derivative sample. And determining a first maintenance description and a second maintenance description of the first prior configuration sample and the second prior derivative sample after splicing, and obtaining a fourth correlation condition between the first maintenance description and the second maintenance description after splicing. Wherein, the fourth association case here may be a matching relationship. The first pre-configuration sample and the first pre-derivative sample for which the fourth correlation case meets the third pre-setting requirement are chosen as the first reference sample and the first derivative sample. The third prior setting requirement may indicate that the fourth association condition is not lower than the third prior setting determination value, for example, when the third prior setting determination value is 1.4, and when the fourth association condition is not lower than 1.4, the fourth association condition is determined to meet the third prior setting requirement.

The method for optimizing the derived vehicle working state test set may be to splice the maintenance description contents of the candidate samples to the maintenance description contents of the derived samples, or to use the maintenance description contents of the candidate samples as the updated maintenance description contents of the derived samples. The method for splicing the maintenance description contents of the candidate samples to the maintenance description contents of the derivative samples may be that the candidate samples and the derivative samples are correspondingly used as a first sample to be spliced and a second sample to be spliced, and then the first maintenance descriptions of the first sample to be spliced and the second sample to be spliced are weighted to obtain the first maintenance description of the first sample to be spliced again. And comparing the ranges of the feature selection sets of the first sample to be spliced and the second sample to be spliced, and selecting the second maintenance description bound by the feature selection set as the second maintenance description of the second sample to be spliced. Preferably, if the fourth correlation condition meets the third prior setting requirement, the maintenance description content of the first reference sample is spliced to the maintenance description content of the first derivative sample. And the updated first maintenance description of the derived sample obtained after the splicing of the two is obtained by processing the first maintenance descriptions corresponding to the candidate sample and the derived sample again, and the updated second maintenance description of the derived sample is one of the second maintenance description of the selected candidate sample and the second maintenance description of the derived sample. Specifically, the selecting step is to judge the range of the feature selection set where the candidate sample and the derivative sample are located, and select the second maintenance description as the updated second maintenance description of the derivative sample after splicing.

And if the fourth correlation condition does not meet the third prior setting requirement, taking the rest candidate samples except the first reference sample as the updated maintenance description content of the derivative sample. The maintenance description content herein may be only the first maintenance description, or may include both the first maintenance description and the second maintenance description. Therefore, by configuring the fourth prior setting requirement and the fifth prior setting requirement, the derived samples can be arranged by the collected vehicle working state test set when the association condition meets the requirement, or the derived samples can become updated prior setting samples in the collected vehicle working state test set, so that the continuously updated prior setting samples in the collected vehicle working state test set are ensured to a certain extent, and the normal work of the vehicle can be ensured.

In a possible implementation embodiment, after the second maintenance description of the derived sample in the derived vehicle working state test set and the second maintenance description of the candidate sample in the candidate vehicle working state test set are identified, whether the derived sample can be spliced with the candidate sample is directly judged according to the method, if yes, the first maintenance descriptions of the derived sample and the candidate sample are spliced, and the splicing operation of the second maintenance description of the candidate sample and the derived sample is marked. In some possible embodiments, after identifying the second maintenance descriptions of the derived samples in the derived vehicle working state test set, before optimizing the derived vehicle working state test set, the above-mentioned labeled second maintenance description removing operation of removing the derived samples of the first maintenance description as samples to be removed may be performed. And then judging candidate samples in the candidate vehicle working state test set through the second maintenance description of the rest derived samples to splice. Certainly, in other possible embodiments, after identifying the second maintenance description of the derived sample in the derived vehicle operating state testing set and the second maintenance description of the candidate sample in the candidate vehicle operating state testing set, the removing operation of the second maintenance description of the sample to be removed marked before is not performed, whether the maintenance description content of the derived sample and the maintenance description content between the candidate samples can be spliced or not is directly judged, if the splicing can be performed, the first maintenance description and the second maintenance description of the candidate sample are weighted to the corresponding derived sample, and the removing operation of the second maintenance description of the sample to be removed marked before is performed.

Step S12: a first correlation between the first maintenance description of the previously set sample and the first maintenance description of the derived sample is obtained.

For example, the first association case here may be a matching relationship between two first maintenance descriptions. Specifically, the method may include processing a matching relationship between the first maintenance descriptions of all the derived samples in the derived vehicle working state test set and the first maintenance descriptions of all the previously set samples in the collected vehicle working state test set to obtain a matching relationship between each derived sample and the previously set sample having the best matching relationship in the collected vehicle working state test set, where the matching relationship is the first association condition. That is, the first correlation case is to select the one with the best matching relationship between the derived sample and the previously set sample.

Step S13: optimizing the collected vehicle working state test set through the first maintenance description of the derivative sample in combination with the first correlation condition; wherein one or more of the remaining maintenance descriptions of the previously set samples in the collected vehicle working state test set and the remaining maintenance descriptions of the derived samples in the derived vehicle working state test set are identified after determining the first correlation condition for optimizing the collected vehicle working state test set.

In one possible embodiment, the optimization of the collected vehicle operating state test set may include splicing the maintenance description of the derived sample into the maintenance description of the bound previous set sample, and/or adding the derived sample as an updated previous set sample, i.e., adding the maintenance description of the derived sample to the stored data as the updated maintenance description of the previous set sample.

The above is a specific optimization of the collected vehicle working state test set, and may be determined in combination with the first association condition, for example, if the first association condition is large, the maintenance description content of the derivative sample is spliced into the maintenance description content of the bound previous setting sample, and if the first association condition is small, the maintenance description content of the derivative sample is added into the stored data to serve as the updated maintenance description content of the previous setting sample. For example, a first set sample and a derivative sample of which the first association meets the fourth previous setting requirement may be selected to obtain a first previous set sample and a second derivative sample, and the maintenance description content of the second derivative sample may be spliced to the maintenance description content of the first previous set sample. The second derivative sample and the first previously set sample can be used as the first sample to be spliced and the second sample to be spliced. And weighting the first maintenance description of the first sample to be spliced and the first maintenance description of the second sample to be spliced so as to obtain the first maintenance description of the second sample to be spliced again. And then comparing the ranges of the feature selection sets of the first sample to be spliced and the second sample to be spliced, and selecting the second maintenance description bound by the feature selection set as the second maintenance description of the second sample to be spliced.

In the present embodiment, the fourth previous setting request may be divided into a sixth previous setting request and a seventh previous setting request. That is, the first prior setting sample and the second derivative sample are determined by selecting the prior setting sample and the derivative sample of which the first correlation condition meets the sixth prior setting requirement. The maintenance description of the second derivative sample is then spliced to the maintenance description of the first previously set sample. The first maintenance descriptions of the two first maintenance descriptions are spliced, and the derivative sample and the prior setting sample which are respectively bound by the two first maintenance descriptions are marked so as to carry out splicing operation on the second maintenance descriptions of the derivative sample and the prior setting sample in the future. Here, the sixth prior setting request may be identical to the first prior setting request. If the matching relationship between the first-set sample and the derivative sample is large enough, the probability that the first-set sample and the derivative sample belong to the same data is high, and therefore, when the matching relationship meets the first-set requirement or the sixth first-set requirement, the first-set sample and the derivative sample can be directly spliced. By configuring the sixth prior setting requirement and the seventh prior setting requirement, more derivative data can be used to optimize the already collected vehicle operating condition test set.

The first correlation case is selected as a first reference first set sample and a second prior derivative sample, which meet the seventh prior setting requirement. Here, the seventh prior setting requirement may indicate that the matching relationship exceeds the lowest determination value spliced by the prior setting samples, and then the second prior derived sample may obtain the possibility of the second splicing. And identifying a third nursing description of the first reference prior setting sample, and taking the first reference prior setting sample and the second prior derivation sample as the first prior setting sample and the second derivation sample on the basis that a fifth correlation condition between the first nursing description of the second prior derivation sample and the third nursing description of the first reference prior setting sample meets an eighth prior setting requirement. For example, the possibility of re-stitching is to obtain a matching relationship between the first maintenance description of the second pre-derived sample and the third maintenance description of the first reference pre-set sample, and if the matching relationship between the first maintenance description and the third maintenance description meets the eighth pre-set requirement, the second pre-derived sample may be stitched into the first reference pre-set sample. Here, the eighth prior setting request may be to exceed the lowest determination value spliced by the prior setting samples. The seventh prior setting requirement and the eighth prior setting requirement in the present application coincide.

Further, in the stitching of the derivative sample and the prior setting sample, the first maintenance description of the stitched prior setting sample is obtained by performing weighting processing on the first maintenance description of the derivative sample and the first maintenance description of the prior setting sample. The specific implementation method comprises the steps of taking a derivative sample to be spliced and a preset sample as a first sample to be spliced and a second sample to be spliced, and carrying out weighting processing on first maintenance descriptions of the first sample to be spliced and the second sample to be spliced so as to obtain a first maintenance description of the second sample to be spliced again. The derived sample can be the first sample to be spliced or the second sample to be spliced. And comparing the range of the derived sample to be spliced with the range of the previously set sample, namely the range of the feature selection set where the derived sample and the previously set sample are located, and selecting the second maintenance description bound by the larger feature selection set as the second maintenance description of the second sample to be spliced after splicing.

In a possible implementation embodiment, after the splicing requirement between the derived sample and the preset sample is judged, before the vehicle working state test set is optimized or in the process of optimizing the collected vehicle working state test set, the second maintenance description of the derived sample in the derived vehicle working state test set is identified, and on the basis that the sample marked as the sample to be removed exists, the second maintenance description of the sample to be removed is removed. For example, the elimination steps of the derived vehicle working state test set left in the collected vehicle working state test set and the second maintenance description in the collected vehicle working state test set are optimized through the initial vehicle working state test set.

In connection with the first correlation case, optimizing the already collected vehicle operating state test set by the first maintenance description of the derivative sample may further include: and selecting the derivative sample with the first correlation condition meeting the fifth prior setting requirement as a third derivative sample, and taking the maintenance description content of the third derivative sample as the updated maintenance description content of the prior setting sample on the basis that the feature selection set where the third derivative sample is located meets the prior setting template. The splicing requirement may be that the first association condition meets a fourth prior setting requirement, and at this time, the fourth prior setting requirement is configured such that the first association condition is not lower than a fourth prior setting determination value. Preferably, the first prior setting request and the fourth prior setting request may coincide, that is, the first prior setting determination value and the fourth prior setting determination value coincide.

In a possible implementation example, the derived sample of which the first correlation condition meets the fifth prior setting requirement is selected as the third derived sample, and on the basis that the feature selection set in which the third derived sample is located meets the prior setting template, the maintenance description content of the third derived sample is used as the maintenance description content of the updated prior setting sample.

The fifth prior setting request may be that the first correlation condition is lower than the fifth prior setting determination value. The fifth prior setting determination value may be a maximum determination value for previously setting the generation of the prior setting sample, and therefore, the fifth prior setting determination value here is lower than the seventh prior setting determination value in the seventh prior setting request. If the first correlation condition is lower than the fifth previously set determination value, it may indicate that the derived sample has a low matching relationship with all previously set samples in the collected vehicle operating state test set, and there is no previously set sample in the collected vehicle operating state test set that belongs to the same sample as the derived sample. For example, when the matching relationship between a derivative sample and all the previously set samples in the collected vehicle operating condition test set is lower than the fifth previously set determination value, and the derivative sample contains a plurality of contents, that is, contains a plurality of overhaul data, and conforms to the lowest range for generating the previously set samples, the derivative sample can be added to the collected vehicle operating condition test set as the updated previously set sample. And a part of the final derived vehicle working state test set which is not added into the collected vehicle working state test set forms a candidate vehicle working state test set.

The method comprises the steps of firstly optimizing an acquired vehicle working state test set once through an initial vehicle working state test set, carrying out feature selection on the initial vehicle working state test set to obtain a derivative vehicle working state test set, then optimizing the derivative vehicle working state test set through a candidate vehicle working state test set to obtain an updated derivative vehicle working state test set, and then using the updated derivative vehicle working state test set to optimize the acquired vehicle working state test set. It should be understood that, some ways for optimizing the collected vehicle operating state test set by the maintenance description content of the derived sample in the derived vehicle operating state test set are provided above, in the embodiments of the present disclosure, the optimization of the collected vehicle operating state test set by the maintenance description content of the derived sample in the derived vehicle operating state test set may also be implemented in a similar way for optimizing the derived vehicle operating state test set by the candidate vehicle operating state test set, for example, a previous setting sample and a derived sample whose first correlation condition meets the second previous setting requirement are selected to be used as a previous setting sample and a previous derived sample to be selected; determining a first maintenance description and a second maintenance description of a pre-selected set sample and a pre-derived sample after splicing, and obtaining a fourth correlation condition between the spliced first maintenance description and the spliced second maintenance description; selecting the prior setting sample to be selected and the derivative sample to be spliced, of which the fourth correlation condition meets the third prior setting requirement, as the prior setting sample to be spliced and the derivative sample to be spliced; splicing the maintenance description content of the derived sample to be spliced to the maintenance description content of the previously set sample to be spliced; and taking the rest derived samples except the sample to be subjected to splicing derivation as the updated maintenance description content of the prior set sample.

According to the above, by obtaining the first association condition of the first maintenance description of the previously set sample in the data and the first maintenance description of the derived sample in the derived vehicle working state test set, the collected vehicle working state test set and/or the remaining maintenance descriptions in the derived vehicle working state test set are identified after the first association condition is determined, so as to optimize the collected vehicle working state test set, that is, the remaining maintenance descriptions in the vehicle working state test set that are irrelevant to obtaining the first association condition can be identified after the first association condition is obtained, so that the problem of inaccurate remaining maintenance descriptions is solved, thereby effectively improving the accuracy of the vehicle working state test set, and therefore, the possibility of vehicle failure can be reduced.

On the basis of the above, please refer to fig. 2 in combination, there is provided a vehicle test data processing apparatus 200, applied to a vehicle test data processing system, the apparatus comprising:

the description identification module 210 is configured to identify a first maintenance description of a previously set sample in the collected vehicle working state test set and a first maintenance description of a derived sample in the derived vehicle working state test set;

a situation obtaining module 220, configured to obtain a first association situation between the first maintenance description of the previous set sample and the first maintenance description of the derivative sample;

a vehicle testing module 230, configured to optimize the collected vehicle operating state test set through the first maintenance description of the derived sample in combination with the first correlation condition; wherein one or more of the remaining maintenance descriptions of the previously set samples in the collected vehicle operating condition test set and the remaining maintenance descriptions of the derived samples in the derived vehicle operating condition test set are identified after determining the first correlation for optimizing the collected vehicle operating condition test set.

On the basis of the above, please refer to fig. 3, which shows a vehicle test data processing system 300, which includes a processor 310 and a memory 320, which are communicated with each other, wherein the processor 310 is configured to read a computer program from the memory 320 and execute the computer program to implement the above method.

On the basis of the above, there is also provided a computer-readable storage medium on which a computer program is stored, which when executed implements the above-described method.

In summary, based on the above scheme, by obtaining the first association condition of the first maintenance description of the previously set sample in the data and the first maintenance description of the derivative sample in the derivative vehicle operating state test set, the remaining maintenance descriptions in the collected vehicle operating state test set and/or the derivative vehicle operating state test set are identified after determining the first association condition, so as to optimize the collected vehicle operating state test set, that is, the remaining maintenance descriptions in the vehicle operating state test set that are irrelevant to obtaining the first association condition can be identified after performing obtaining the first association condition, thereby improving the problem that the remaining maintenance descriptions are inaccurate, and thus effectively improving the accuracy of the vehicle operating state test set, and therefore, reducing the possibility of vehicle failure.

It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).

It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, the present application uses specific words to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.

The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A vehicle test data processing method applied to a data processing system, the method comprising at least:

identifying a first maintenance description of a previously set sample in the collected vehicle working state test set and a first maintenance description of a derivative sample in the derivative vehicle working state test set;

obtaining a first association between a first maintenance description of the previously set sample and a first maintenance description of the derivative sample;

optimizing the collected vehicle working state test set through a first maintenance description of the derivative sample in combination with the first correlation condition; wherein one or more of the remaining maintenance descriptions of the previously set samples in the collected vehicle operating condition test set and the remaining maintenance descriptions of the derived samples in the derived vehicle operating condition test set are identified after determining the first correlation for optimizing the collected vehicle operating condition test set.

2. The method of claim 1, wherein prior to said obtaining a first association between the first maintenance description of the previously set sample and the first maintenance description of the derived sample, the method further comprises:

Obtaining a second association condition between the initial maintenance description in the initial vehicle working state test set and the first maintenance description of the previously set sample in the collected vehicle working state test set; the first maintenance description of the derived sample is determined by performing feature selection on the initial maintenance description in the initial vehicle working state test set and combining the initial maintenance description in the corresponding feature selection set;

detecting that the second correlation condition meets an initial maintenance description and a prior setting sample of a first prior setting requirement; and optimizing the first maintenance description of the corresponding previously set sample in the collected vehicle working state test set through the detected initial maintenance description, and rejecting the first maintenance description of the derived sample corresponding to the detected initial maintenance description in the derived vehicle working state test set.

3. The method of claim 2, wherein after said culling the first maintenance description of the derived sample corresponding to the detected initial maintenance description in the derived vehicle operating state test set, the method further comprises: marking the derived samples with the first maintenance description removed as samples to be removed so as to remove the second maintenance description of the samples to be removed after subsequently identifying the second maintenance description of the derived samples.

4. The method of claim 3, wherein prior to said obtaining a first association between the first maintenance description of the previously set sample and the first maintenance description of the derived sample, the method further comprises: identifying a first maintenance description of a candidate sample in a candidate vehicle working state test set, and obtaining a third correlation condition between the first maintenance description of the derived sample and the first maintenance description of the candidate sample; and optimizing the derived vehicle working state test set by combining the third correlation condition.

5. The method of claim 4, wherein said optimizing said derived vehicle operating state test set in conjunction with said third correlation case comprises:

identifying a second maintenance description of a derived sample in the derived vehicle operating condition test set and a second maintenance description of a candidate sample in the candidate vehicle operating condition test set; optimizing the derived vehicle working state test set by combining the third correlation condition, the second maintenance description of the derived sample and the second maintenance description of the candidate sample;

and/or, said optimizing said derived vehicle operating condition test set, comprising: splicing the maintenance description content of the candidate sample to the maintenance description content of the derivative sample, or using the maintenance description content of the candidate sample as the updated maintenance description content of the derivative sample.

6. The method of claim 5, wherein optimizing the derived vehicle operating condition test set in conjunction with the third correlation scenario, the second maintenance description of the derived sample, and the second maintenance description of the candidate sample comprises:

selecting candidate samples and derivative samples of which the third correlation condition meets a second prior setting requirement as a first prior configuration sample and a first prior derivative sample;

determining a first maintenance description and a second maintenance description of the first prior configuration sample and a second prior derivative sample after splicing, and obtaining a fourth correlation condition between the first maintenance description and the second maintenance description after splicing;

selecting a first pre-configuration sample and a first pre-derivative sample of which the fourth correlation condition meets a third pre-setting requirement as a first reference sample and a first derivative sample;

stitching the maintenance description of the first reference sample to the maintenance description of the first derivative sample; and taking the remaining candidate samples except the first reference sample as the updated maintenance description of the derived sample.

7. The method of any one of claims 1 to 6, wherein said optimizing said collected vehicle operating state test set by said first maintenance description of said derived sample in combination with said first correlation case comprises at least one of the following steps:

Selecting a prior setting sample and a derivative sample of which the first correlation condition meets a fourth prior setting requirement to obtain a first prior setting sample and a second derivative sample, and splicing the maintenance description content of the second derivative sample to the maintenance description content of the first prior setting sample;

and selecting a derivative sample with the first correlation condition meeting a fifth prior setting requirement as a third derivative sample, and taking the maintenance description content of the third derivative sample as the updated maintenance description content of the prior setting sample on the basis that the feature selection set where the third derivative sample is located meets a prior setting template.

8. The method of claim 7, wherein the selecting the first correlation case for the first predetermined sample and the derivative sample meeting the fourth predetermined requirement to obtain the first predetermined sample and the second derivative sample comprises:

selecting a prior setting sample and a derivative sample of which the first correlation condition meets a sixth prior setting requirement, and determining a first prior setting sample and a second derivative sample;

or selecting a prior setting sample and a derivative sample of which the first correlation condition meets a seventh prior setting requirement as a first reference prior setting sample and a second prior derivative sample, identifying a third care description of the first reference prior setting sample, and taking the first reference prior setting sample and the second prior derivative sample as the first prior setting sample and the second derivative sample on the basis that a fifth correlation condition between a first care description of the second prior derivative sample and the third care description of the first reference prior setting sample meets an eighth prior setting requirement;

Wherein the splicing of the maintenance description of the candidate sample to the maintenance description of the derivative sample, or the splicing of the maintenance description of the second derivative sample to the maintenance description of the first previously set sample, comprises:

correspondingly taking the candidate sample and the derivative sample as a first sample to be spliced and a second sample to be spliced, or taking the second derivative sample and the first previously set sample as the first sample to be spliced and the second sample to be spliced;

weighting the first maintenance descriptions of the first sample to be spliced and the second sample to be spliced to obtain the first maintenance description of the second sample to be spliced again;

comparing the ranges of the feature selection sets of the first sample to be spliced and the second sample to be spliced, and selecting a second maintenance description bound by a larger feature selection set as a second maintenance description of the second sample to be spliced;

wherein prior to said optimizing said derived vehicle operating condition test set, or said optimizing said already collected vehicle operating condition test set, said method further comprises: identifying a second maintenance description of a derivative sample in the derivative vehicle operating condition test set; on the basis that the samples marked as samples to be removed exist in the derived samples, second maintenance descriptions of the samples to be removed are removed;

Wherein at least one of the first association condition, the second association condition, the third association condition, the fourth association condition and the fifth association condition corresponds to a matching relationship between two maintenance descriptions;

the first maintenance description is a focused maintenance description of a feature selection set of a sample bound with the first maintenance description, wherein the feature selection set is obtained by performing feature selection on a plurality of overhaul data according to the covered sample; the residual maintenance description comprises at least one of a second maintenance description and a third maintenance description of a sample bound with the residual maintenance description, the second maintenance description is used for limiting a spliced feature selection set according to a specific instruction when the feature selection set where the sample bound with the second maintenance description is located is spliced, and the third maintenance description is recorded as the first maintenance description when the sample is set in advance.

9. A vehicle test data processing system comprising a processor and a memory in communication with each other, the processor being configured to retrieve a computer program from the memory and to implement the method of any one of claims 1 to 8 by running the computer program.

10. A cloud platform, comprising:

a memory for storing a computer program;

a processor coupled to the memory for executing the computer program stored by the memory to implement the method of any of claims 1-8.