CN116257030A - Vehicle fault diagnosis method, device, electronic equipment and storage medium - Google Patents

Vehicle fault diagnosis method, device, electronic equipment and storage medium Download PDF

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Publication number
CN116257030A
CN116257030A CN202111499806.6A CN202111499806A CN116257030A CN 116257030 A CN116257030 A CN 116257030A CN 202111499806 A CN202111499806 A CN 202111499806A CN 116257030 A CN116257030 A CN 116257030A
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target
diagnosis result
fault
diagnosis
determining
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汪大涵
郭健
张潘
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Beijing Co Wheels Technology Co Ltd
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Beijing Co Wheels Technology Co Ltd
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Priority to CN202111499806.6A priority Critical patent/CN116257030A/en
Priority to PCT/CN2022/136651 priority patent/WO2023103984A1/en
Publication of CN116257030A publication Critical patent/CN116257030A/en
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0262—Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C5/00—Registering or indicating the working of vehicles
    • G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/24—Pc safety
    • G05B2219/24065—Real time diagnostics
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

The present disclosure relates to a vehicle fault diagnosis method, apparatus, electronic device, and storage medium. The method comprises the following steps: determining a target diagnosis result set according to a fault knowledge graph and a plurality of target fault codes, wherein the target diagnosis result set comprises a plurality of target diagnosis results, and the fault knowledge graph comprises a corresponding relation between the plurality of fault codes and the diagnosis results; and generating a target diagnosis result sequence according to the correlation degree between each target diagnosis result in the target diagnosis result set and the target fault code. The method can comprehensively diagnose the faults of the vehicle.

Description

Vehicle fault diagnosis method, device, electronic equipment and storage medium
Technical Field
The disclosure relates to the technical field of automobiles, and in particular relates to a vehicle fault method, a vehicle fault device, electronic equipment and a storage medium.
Background
The current diagnosis method for vehicle faults mainly comprises the following steps: one is a manual method of identification, i.e. a maintenance person determines the failure of the vehicle by means of looking, listening, smelling etc., by means of the maintenance person's own maintenance experience and some simple tools. The other is a simple instrument diagnosis method, namely, on the basis of a manual experience method, the fault type is judged by means of a simple instrument such as a universal meter, an oscilloscope and the like. Since both of these approaches depend to a large extent on the experience and ability of the maintenance personnel, the time taken for diagnosis is large and the accuracy of diagnosis is also poor.
Based on this, an intelligent vehicle-mounted diagnostic system has been developed, that is, data analysis is performed on automobile parts by using a vehicle controller, and then the data is displayed on a vehicle-mounted screen or the location and time of occurrence of a fault are displayed on a hand-held diagnostic apparatus.
However, although some faults with obvious performances can be obtained in time by adopting the vehicle-mounted diagnosis system, the most fundamental fault cause and potential fault problems still cannot be obtained for serious faults of the vehicle.
Disclosure of Invention
The present disclosure provides a vehicle fault diagnosis method, apparatus, electronic device, and storage medium, capable of comprehensively diagnosing a vehicle fault.
In a first aspect, the present disclosure provides a vehicle fault diagnosis method, including:
determining a target diagnosis result set according to a fault knowledge graph and a plurality of target fault codes, wherein the target diagnosis result set comprises a plurality of target diagnosis results, and the fault knowledge graph comprises a corresponding relation between the plurality of fault codes and the diagnosis results;
and generating a target diagnosis result sequence according to the correlation degree between each target diagnosis result in the target diagnosis result set and the target fault code.
Optionally, the determining the target diagnosis result set according to the fault knowledge graph and the plurality of target fault codes includes:
Determining a diagnosis result set according to the fault knowledge graph aiming at each target fault code, wherein the diagnosis result set comprises at least one diagnosis result;
and determining the target diagnosis result set according to the occurrence frequency of each diagnosis result in the diagnosis result sets of the target fault codes.
Optionally, before generating the target diagnosis result sequence according to the correlation degree between each target diagnosis result in the target diagnosis result set and the target fault code, the method further includes:
and determining the correlation degree between each target diagnosis result and the target fault code according to the frequency corresponding to each target diagnosis result in the target diagnosis result set.
Optionally, before generating the target diagnosis result sequence according to the correlation degree between each target diagnosis result in the target diagnosis result set and the target fault code, the method further includes:
and determining the correlation degree between each target diagnosis result and each target fault code according to the time sequence of reporting each target fault code.
Optionally, before determining the correlation between each target diagnosis result and the target fault code according to the time sequence of reporting each target fault code, the method further includes:
And determining that the frequency of each diagnosis result does not meet a preset condition according to the frequency of each diagnosis result in the plurality of diagnosis result sets.
Optionally, the determining the target diagnosis result set according to the occurrence frequency of each diagnosis result in the multiple diagnosis result sets of the multiple target fault codes includes:
if the frequency of each diagnosis result meets a preset condition, determining that the union set of the plurality of diagnosis result sets is the target diagnosis result set;
and if the frequency of each diagnosis result does not meet the preset condition, determining the plurality of diagnosis result sets as the target diagnosis result set.
Optionally, the preset condition is that at least one frequency of the frequencies corresponding to all the diagnostic results in the plurality of diagnostic result sets is greater than or equal to a preset frequency.
Optionally, the determining the target diagnosis result set according to the fault knowledge graph and the plurality of target fault codes includes:
determining a diagnosis result set of each target fault code according to the fault knowledge graph and the priority of diagnosis results in the fault knowledge graph, wherein the diagnosis result set comprises at least one diagnosis result;
And determining the diagnosis result set as the target diagnosis result set corresponding to each target fault code aiming at each target fault code.
Optionally, before generating the target diagnosis result sequence according to the correlation degree between each target diagnosis result in the target diagnosis result set and the target fault code, the method further includes:
and determining the correlation degree between each target diagnosis result in each target diagnosis result set and the corresponding target fault code according to the priority of all target diagnosis results in each target diagnosis result set.
Optionally, the diagnosis result in the fault knowledge graph includes: at least one of a controller, a fault set condition, a fault recovery condition, a fault cause, and a maintenance recommendation.
Optionally, the method further comprises:
inputting the target fault codes into a trained model, outputting a diagnosis result sequence of the target fault codes based on the trained model, and sequencing diagnosis results in the diagnosis result sequence according to probability values.
In a second aspect, the present disclosure provides a vehicle fault diagnosis apparatus including:
the determining module is used for determining a target diagnosis result set according to a fault knowledge graph and a plurality of target fault codes, wherein the target diagnosis result set comprises a plurality of target diagnosis results, and the fault knowledge graph comprises a corresponding relation between the plurality of fault codes and the diagnosis results;
And the sequence generation module is used for generating a target diagnosis result sequence according to the correlation degree of each target diagnosis result in the target diagnosis result set and the target fault code.
In a third aspect, the present disclosure provides an electronic device comprising: a processor for executing a computer program stored in a memory, which when executed by the processor implements the steps of any of the methods provided in the first aspect.
In a fourth aspect, the present disclosure provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of any of the methods provided in the first aspect.
In the technical scheme provided by the disclosure, a target diagnosis result set is determined according to a fault knowledge graph and a plurality of target fault codes, wherein the target diagnosis result set comprises a plurality of target diagnosis results, and the fault knowledge graph comprises a corresponding relation between the plurality of fault codes and the diagnosis results; according to the correlation degree between each target diagnosis result in the target diagnosis result set and the target fault code, a target diagnosis result sequence is generated, so that based on a plurality of fault codes generated by the vehicle fault, the direct diagnosis results and the related diagnosis results corresponding to the fault codes, namely the direct diagnosis results and the related diagnosis results of the current fault of the vehicle, can be quickly obtained, and the comprehensive diagnosis of the vehicle fault can be realized.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, the drawings that are required for the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic diagram of an application scenario provided in the present disclosure;
FIG. 2 is a schematic flow chart of a vehicle fault diagnosis method provided by the present disclosure;
FIG. 3 is a flow chart of another vehicle fault diagnosis method provided by the present disclosure;
FIG. 4 is a flow chart of yet another vehicle fault diagnosis method provided by the present disclosure;
FIG. 5 is a flow chart of yet another vehicle fault diagnosis method provided by the present disclosure;
FIG. 6 is a flow chart of yet another vehicle fault diagnosis method provided by the present disclosure;
FIG. 7 is a flow chart of yet another vehicle fault diagnosis method provided by the present disclosure;
FIG. 8 is a flow chart of yet another vehicle fault diagnosis method provided by the present disclosure;
FIG. 9 is a flow chart of yet another vehicle fault diagnosis method provided by the present disclosure;
FIG. 10 is a flow chart of yet another vehicle fault diagnosis method provided by the present disclosure;
FIG. 11 is a flow chart of yet another vehicle fault diagnosis method provided by the present disclosure;
fig. 12 is a schematic structural diagram of a vehicle fault diagnosis device provided by the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.
The present disclosure is applied to a vehicle, and fig. 1 is a schematic diagram of an application scenario provided by the present disclosure, as shown in fig. 1, including: the vehicle 110 and the cloud server 120, the vehicle 110 is in communication connection with the cloud server 120, the cloud server 120 periodically sends a fault code request to the vehicle 110, the vehicle 110 returns a fault code to the cloud server 120 when a fault exists currently based on the received fault code request, and the cloud server 120 diagnoses the fault of the vehicle based on the received fault code.
Continuing with the illustration of FIG. 1, the application scenario may further comprise: when the vehicle 110 fails, the vehicle failure diagnostic apparatus 130 is connected to the vehicle 110, the connected vehicle failure diagnostic apparatus 130 transmits a failure code request to the vehicle 110, the vehicle 110 returns a failure code generated under the current failure to the vehicle failure diagnostic apparatus 130 based on the received failure code request, and the vehicle failure diagnostic apparatus 130 diagnoses the current failure of the vehicle based on the received failure code.
When a vehicle fails, a corresponding fault code is generated, and each time the vehicle fails, at least one fault code can be generated, and particularly, when the vehicle fails seriously, a plurality of fault codes can be generated. The fault code is a character string in which the first character is a letter and the subsequent characters are a set of digits, wherein the first letter is used to identify the type of fault code, and the set of digits is used to identify at least one of a cause of a current fault, a repair suggestion, and a fault recovery condition.
The technical solution of the present disclosure may be applied to the vehicle fault diagnosis apparatus 130 and/or the cloud server 120 in the above scenario, where a target diagnosis result set is determined according to a fault knowledge graph and a plurality of target fault codes, where the target diagnosis result set includes a plurality of target diagnosis results, and the fault knowledge graph includes a correspondence between the plurality of fault codes and the diagnosis results; according to the correlation degree between each target diagnosis result in the target diagnosis result set and the target fault code, a target diagnosis result sequence is generated, so that based on a plurality of fault codes generated by the vehicle fault, the direct diagnosis results and the related diagnosis results corresponding to the fault codes, namely the direct diagnosis results and the related diagnosis results of the current fault of the vehicle, can be quickly obtained, and the comprehensive diagnosis of the vehicle fault can be realized.
The technical solutions of the present disclosure are explained in detail below by means of several specific embodiments.
Fig. 2 is a flow chart of a vehicle fault diagnosis method provided by the present disclosure, as shown in fig. 2, including:
s101, determining a target diagnosis result set according to the fault knowledge graph and a plurality of target fault codes.
The target diagnosis result set comprises a plurality of target diagnosis results, and the fault knowledge graph comprises a plurality of corresponding relations between fault codes and diagnosis results.
Fig. 3 is a schematic diagram of a fault knowledge graph provided by the present disclosure, where, as shown in fig. 3, the fault knowledge graph includes multiple different types of nodes, where the multiple different types of nodes include a fault code node 1, and further include at least one of a fault cause node 2, a maintenance suggestion node 3, a fault setting condition node 4, a fault recovery condition node 5, and a controller node 6. The nodes can be connected by connecting wires, and a corresponding relationship exists between the nodes at two ends of the connecting wires, for example, as shown in fig. 3, the two ends of the connecting wires can be a fault code node 1 and a fault reason node 2, and the two ends of the connecting wires can also be a fault code node 1 and a maintenance suggestion node 3.
The fault knowledge graph includes 6449 nodes, wherein the total number of the controller nodes 6 is 36, the total number of the fault reason nodes 2 is 1099, the total number of the fault code nodes 1 is 2066, the total number of the fault recovery condition nodes 5 is 580, the total number of the maintenance recommended nodes 3 is 839, and the total number of the fault setting condition nodes 4 is 1879.
Based on the above embodiment, the diagnosis result in the fault knowledge graph may be at least one of a fault cause, a maintenance suggestion, a fault setting condition, a fault recovery condition and a controller, and the fault knowledge graph includes correspondence between a plurality of fault codes and diagnosis results, where it is to be noted that the same fault code may correspond to a plurality of diagnosis results, and different fault codes may correspond to one diagnosis result, and the correspondence between the diagnosis results and the fault codes is determined based on historical investigation results.
When a serious fault occurs in the vehicle at present, a plurality of target fault codes can be generated, wherein the target fault code is one of all fault codes in the fault knowledge graph, and thus, according to the received target fault codes, a diagnosis result set corresponding to each target fault code is found out from the fault knowledge graph. For example, when a serious fault occurs in the vehicle currently, a target fault code B1, a target fault code B2 and a target fault code B3 are generated, wherein the target fault code B1 in the fault knowledge graph corresponds to the diagnosis result A1 and the diagnosis result A3, the target fault code B2 corresponds to the diagnosis result A1, the diagnosis result A2 and the diagnosis result A3, the diagnosis result set 1 of the target fault code B1 is { diagnosis result A1, diagnosis result A3}, the diagnosis result set 2 of the target fault code B2 is { diagnosis result A1, diagnosis result A2, diagnosis result A3}, and the diagnosis result set 3 of the target fault code B3 is { diagnosis result A1}.
And determining a target diagnosis result set according to the diagnosis result set of each target fault code, wherein the target diagnosis result set comprises a plurality of target diagnosis results, and the target diagnosis result can be one diagnosis result in all the diagnosis result sets or one diagnosis result set. The set of target diagnostic results may be one, i.e. one set of target diagnostic results is determined for a plurality of target fault codes, or the set of target diagnostic results may be a plurality, i.e. one set of target diagnostic results is determined for each target fault code. The number of target diagnosis result sets corresponding to the plurality of target fault codes and the type of the target diagnosis result in each target diagnosis result set are not particularly limited in this embodiment.
S103, generating a target diagnosis result sequence according to the correlation degree between each target diagnosis result in the target diagnosis result set and the target fault code.
For example, if the plurality of target fault codes correspond to one target diagnosis result set, and one target diagnosis result in the target diagnosis result set is one diagnosis result set, based on the correlation degree between the diagnosis result set and all the target fault codes, arranging the diagnosis result sets in the target diagnosis result set according to the sequence from the big to the small of the correlation degree, and generating a target diagnosis result sequence. For example, based on the above embodiment, the target diagnosis result sets corresponding to the target fault codes B1, B2 and B3 are { diagnosis result set 1, diagnosis result set 2, diagnosis result set 3}, where the correlation between the diagnosis result set 1 and all the target fault codes is greater than the correlation between the diagnosis result set 2 and all the target fault codes, and the correlation between the diagnosis result set 3 and all the target fault codes is greater than the correlation between the diagnosis result set 1 and all the target fault codes, and the generated target diagnosis result sequence is { diagnosis result set 3, diagnosis result set 1, diagnosis result set 2}.
For example, if the plurality of target fault codes correspond to one target diagnosis result set, and one target diagnosis result in the target diagnosis result set is one diagnosis result, based on the correlation degree between the diagnosis result and all the target fault codes, arranging the diagnosis results in the target diagnosis result set according to the sequence from the high correlation degree to the low correlation degree, and generating a target diagnosis result sequence. For example, based on the above embodiment, the set of target diagnosis results corresponding to the target fault codes B1, B2 and B3 is { diagnosis result A1, diagnosis result A2, diagnosis result A3}, where the correlation between the diagnosis result A1 and all the target fault codes is greater than the correlation between the diagnosis result A3 and all the target fault codes, and the correlation between the diagnosis result A3 and all the target fault codes is greater than the correlation between the diagnosis result A2 and all the target fault codes, and the generated target diagnosis result sequence is { diagnosis result A1, diagnosis result A3, diagnosis result A2}.
For example, if the plurality of target fault codes correspond to the plurality of target diagnosis result sets, one target diagnosis result set is one diagnosis result set, and based on the correlation between the diagnosis results in each diagnosis result set and the corresponding target fault codes, each diagnosis result in each diagnosis result set is arranged according to the sequence from the high correlation to the low correlation, and a target diagnosis result sequence corresponding to each target fault code is generated. For example, based on the implementation, the target diagnosis result sets corresponding to the target fault code B1, the target fault code B2 and the target fault code B3 are { diagnosis result set 1}, { diagnosis result set 2} and { diagnosis result set 3}, where the correlation between the diagnosis result A1 and the target fault code B1 in the diagnosis result set 1 is greater than the correlation between the diagnosis result A3 and the target fault code B1, the correlation between the diagnosis result A1 and the target fault code B2 in the diagnosis result set 2 is greater than the correlation between the diagnosis result A3 and the target fault code B2, the correlation between the diagnosis result A3 and the target fault code B2 is greater than the correlation between the diagnosis result A2 and the target fault code B2, the generated target diagnosis result sequence of the target fault code B1 is { diagnosis result A1, the generated target diagnosis result sequence of the target fault code B2 is { diagnosis result A1, the diagnosis result A3}, and the generated target diagnosis result sequence of the target fault code B3 is { diagnosis result A1}.
In this embodiment, a target diagnosis result set is determined according to a fault knowledge graph and a plurality of target fault codes, where the target diagnosis result set includes a plurality of target diagnosis results, and the fault knowledge graph includes correspondence between the plurality of fault codes and the diagnosis results; according to the correlation degree between each target diagnosis result in the target diagnosis result set and the target fault code, a target diagnosis result sequence is generated, so that based on a plurality of fault codes generated by the vehicle fault, the direct diagnosis results and the related diagnosis results corresponding to the fault codes, namely the direct diagnosis results and the related diagnosis results of the current fault of the vehicle, can be quickly obtained, and the comprehensive diagnosis of the vehicle fault can be realized.
Fig. 4 is a schematic flow chart of another vehicle fault diagnosis provided in the present disclosure, and fig. 4 is a specific description of one possible implementation manner when S101 is performed based on the embodiment shown in fig. 2, as follows:
s1011, determining a diagnosis result set according to the fault knowledge graph aiming at each target fault code.
The set of diagnostic results includes at least one diagnostic result.
The target fault code may be a non-circuit type target fault code for indicating a fault in the non-communication bus system, and the first letter of the non-circuit type target fault code is a fault code of a non-U character, for example, the first letter of the non-circuit type target fault code may be a character of B, C, P or the like. The fault knowledge graph comprises the corresponding relation between the target fault codes and the diagnosis results, at least one corresponding diagnosis result is found out from the fault knowledge graph according to the received target fault codes, namely a diagnosis result set of each target fault code is obtained, and the diagnosis result set comprises at least one diagnosis result.
For example, the target fault code C1, the target fault code C2, and the target fault code C3 are received, the target fault code C1 corresponds to the diagnosis result A1 and the diagnosis result A3 in the fault knowledge graph, the target fault code C2 corresponds to the diagnosis result A1, the diagnosis result A2, and the diagnosis result A3, and the target fault code C3 corresponds to the diagnosis result A1. Accordingly, for the target fault code C1, the determined diagnosis result set is { diagnosis result A1, diagnosis result A3}; aiming at the target fault code C2, the determined diagnosis result set is { diagnosis result A1, diagnosis result A2, diagnosis result A3}; for the target fault code C3, the determined set of diagnostic results is { diagnostic result A1}.
S1012, determining a target diagnosis result set according to the occurrence frequency of each diagnosis result in the diagnosis result sets of the target fault codes.
And determining the occurrence frequency of each diagnosis result in all diagnosis results according to all diagnosis results in all diagnosis result sets of all target fault codes. For example, based on the above embodiment, all diagnostic result sets include: the method comprises the steps of { a diagnosis result A1, a diagnosis result A3}, { a diagnosis result A1, a diagnosis result A2, a diagnosis result A3} and { a diagnosis result A1}, wherein all diagnosis result sets comprise three diagnosis results, namely a diagnosis result A1, a diagnosis result A2 and a diagnosis result A3, the frequency of occurrence of the diagnosis result A1 in all diagnosis result sets is 3, the frequency of occurrence of the diagnosis result A2 in all diagnosis result sets is 1, and the frequency of occurrence of the diagnosis result A3 in all diagnosis result sets is 2.
And determining a target diagnosis result set according to whether the occurrence frequency of each diagnosis result in all the diagnosis results meets a preset condition, wherein the target diagnosis result in the target diagnosis result set can be the diagnosis result set or the diagnosis result, and then carrying out detailed analysis.
As a specific description of one possible implementation when S1012 is performed, as shown in fig. 5:
s201, determining whether the frequency of each diagnosis result meets a preset condition.
If yes, executing S202; if not, S203 is executed.
Optionally, the preset condition may be that at least one frequency of the frequencies corresponding to all the diagnostic results in the multiple diagnostic result sets is greater than or equal to a preset frequency. If the preset condition is met, the frequency corresponding to at least one diagnosis result in all diagnosis results is larger than or equal to the preset frequency, and if the preset condition is not met, the frequency corresponding to no diagnosis result in all diagnosis results is larger than or equal to the preset frequency, namely the frequency corresponding to all diagnosis results is smaller than the preset frequency.
S202, determining a union set of the plurality of diagnosis result sets as the target diagnosis result set.
If at least one of the diagnosis results has the frequency greater than or equal to the preset frequency, determining the union of the diagnosis result sets as the target diagnosis result set. For example, the preset frequency may be 3, and based on the above embodiment, the frequency of occurrence of the diagnosis result A1 in all the diagnosis result sets is equal to the preset frequency, that is, the preset condition is satisfied, and it may be determined that the diagnosis result set { diagnosis result A1, diagnosis result A3}, the diagnosis result set { diagnosis result A1, diagnosis result A2, diagnosis result A3} and the union of the diagnosis result set { diagnosis result A1}, that is { diagnosis result A1, diagnosis result A2, diagnosis result A3} are the target diagnosis result set.
S203, determining the multiple diagnosis result sets as the target diagnosis result set.
If the frequency corresponding to each of all the diagnosis results is smaller than the preset frequency, each diagnosis result set can be used as a target diagnosis result, and all the diagnosis result sets form a target diagnosis result set. For example, the preset frequency may be 3, and all the diagnosis result sets include: the method comprises the steps of { diagnosis result A1, diagnosis result A2}, { diagnosis result A1, diagnosis result A3} and { diagnosis result A2}, wherein three diagnosis results are included in all diagnosis result sets, the occurrence frequency of the diagnosis result A1 and the diagnosis result A2 in all diagnosis result sets is 2, the occurrence frequency of the diagnosis result A3 in all diagnosis result sets is 1, namely, is less than the preset frequency, namely, the preset condition is not met, the target diagnosis result set is determined to be { { diagnosis result A1, diagnosis result A2}, { diagnosis result A1, diagnosis result A3}, { diagnosis result A2}, namely { diagnosis result A1, diagnosis result A2} is earlier in time than { diagnosis result A1, diagnosis result A3}, and { diagnosis result A1, diagnosis result A3} is earlier in time than { diagnosis result A2}.
Fig. 6 is a schematic flow chart of another vehicle fault diagnosis method provided by the present disclosure, and fig. 6 is a flowchart of another vehicle fault diagnosis method based on the embodiment shown in fig. 4, before executing S103, further including:
S102, determining the correlation degree between each target diagnosis result and the target fault code according to the frequency corresponding to each target diagnosis result in the target diagnosis result set.
If the target diagnosis result in the target diagnosis result set is a diagnosis result, the higher the frequency of occurrence of the target diagnosis result in the target diagnosis result set is, the higher the correlation degree between the target diagnosis result and the target fault code is, so that the sequence of the correlation degree of the target diagnosis results can be determined based on the frequency of occurrence of each target diagnosis result.
For example, based on the above embodiment, the target diagnosis result set is { diagnosis result A1, diagnosis result A2, diagnosis result A3}, the frequency corresponding to diagnosis result A1 is 3 times, the frequency corresponding to diagnosis result A2 is 1 time, the frequency corresponding to diagnosis result A3 is 2 times, the correlation between diagnosis result A1 and the target fault code is highest, then the target diagnosis result A3 is the diagnosis result A3, and finally the diagnosis result A2 is the diagnosis result A2, so the generated target diagnosis result sequence is { diagnosis result A1, diagnosis result A3, diagnosis result A2}.
In this embodiment, the correlation between each target diagnosis result and the target fault code is determined according to the frequency corresponding to each target diagnosis result in the target diagnosis result set, so that the diagnosis results of the current fault of the vehicle can be ordered based on the frequency corresponding to the target diagnosis result, and the direct diagnosis result and the indirect diagnosis result of the current fault of the vehicle can be obtained.
Fig. 7 is a schematic flow chart of another vehicle fault diagnosis method provided by the present disclosure, and fig. 7 is a flowchart of another vehicle fault diagnosis method based on the embodiment shown in fig. 4, before executing S103, further including:
s102', determining the correlation degree between each target diagnosis result and each target fault code according to the time sequence of reporting each target fault code.
And if the target diagnosis result in the target diagnosis result set is the diagnosis result set, acquiring the reporting time of each target fault code, and if the reporting time of the target fault code is earlier, the correlation degree between the diagnosis result set corresponding to the target fault code and the target fault code is higher, so that the correlation degree sequence between the diagnosis result set and the target fault code can be determined based on the reporting time sequence of each target fault code.
For example, based on the above embodiment, the target diagnosis result set is { { diagnosis result A1, diagnosis result A2}, { diagnosis result A1, diagnosis result A3}, { diagnosis result A2}, the time for reporting the target fault code C1 is 13:21, the time for reporting the target fault code C2 is 13:23, and the time for reporting the target fault code C3 is 13:20, and then the correlation between the target diagnosis result { diagnosis result A2} and the target fault code is highest, and then the target diagnosis result { diagnosis result A1, diagnosis result A2}, and finally the target diagnosis result { diagnosis result A1, diagnosis result A3}, so that the generated target diagnosis result sequence is { diagnosis result A2}, { diagnosis result A1, diagnosis result A2}, and { diagnosis result A1, diagnosis result A3 }.
In this embodiment, the correlation degree between each target diagnosis result and the target fault code is determined according to the time sequence of reporting each target fault code, so that the diagnosis results of the current fault of the vehicle can be ordered based on the reporting time of the target fault code, and the direct diagnosis result and the indirect diagnosis result of the current fault of the vehicle can be obtained.
Fig. 8 is a schematic flow chart of another vehicle fault diagnosis method provided by the present disclosure, and fig. 8 is a flowchart of another vehicle fault diagnosis method provided by the present disclosure, before executing S102', further including:
s1013, determining that the frequency of each diagnosis result does not meet a preset condition according to the frequency of each diagnosis result in the plurality of diagnosis result sets.
Optionally, the preset condition may be that at least one frequency of the frequencies corresponding to all the diagnostic results in the multiple diagnostic result sets is greater than or equal to a preset frequency. If the preset condition is met, the frequency corresponding to at least one diagnosis result in all diagnosis results is larger than or equal to the preset frequency, and if the preset condition is not met, the frequency corresponding to no diagnosis result in all diagnosis results is larger than or equal to the preset frequency, namely the frequency corresponding to all diagnosis results is smaller than the preset frequency.
If the preset condition is not met, determining the correlation degree between each target diagnosis result and the target fault code based on the time sequence of reporting each target fault code; if the preset condition is met, determining the correlation degree between each target diagnosis result and the target fault code based on the frequency corresponding to each target diagnosis result in the target diagnosis result set.
Fig. 9 is a flowchart of yet another vehicle fault diagnosis method provided by the present disclosure, and fig. 9 is a specific description of another possible implementation manner when S101 is performed on the basis of the embodiment shown in fig. 2, as follows:
s301, determining a diagnosis result set of each target fault code according to the fault knowledge graph and the priority of the diagnosis result in the fault knowledge graph aiming at each target fault code.
The set of diagnostic results includes at least one diagnostic result.
The target fault code may be a circuit-class target fault code for indicating a fault in the aspect of the communication bus system, where the first letter of the circuit-class target fault code is a fault code of a U-character. The diagnostic nodes in the fault knowledge graph comprise a corresponding relation between a target fault code and a diagnostic result, wherein the diagnostic result can comprise: at least one of the primary diagnosis result, the secondary diagnosis result and the tertiary diagnosis result, the priority of the primary diagnosis result is higher than the priority of the secondary diagnosis result, and the priority of the secondary diagnosis result is higher than the tertiary diagnosis result. For example, the primary diagnostic result may be a bus diagnostic result, the secondary diagnostic result may be a child node diagnostic result, and the tertiary diagnostic result may be a leaf node diagnostic result.
Determining the priority of the diagnosis results in the fault knowledge graph according to the priori knowledge of the circuit topology graph, determining the first-stage diagnosis result corresponding to the target fault code in the fault knowledge graph according to each target fault code, determining the second-stage diagnosis result corresponding to the target fault code in the fault knowledge graph after the first-stage diagnosis result is determined, and finally determining the third-stage diagnosis result corresponding to the target fault code in the fault knowledge graph after the second-stage diagnosis result is determined, so that the diagnosis result set of the target fault code can be determined, and the diagnosis result set may comprise at least one of the first-stage diagnosis result, the second-stage diagnosis result and the third-stage diagnosis result.
For example, the set of diagnostic results of the target fault code U1 is { diagnostic result L2, diagnostic result L3}, the set of diagnostic results of the target fault code U2 is { diagnostic result L1, diagnostic result L2, diagnostic result L3}, and the set of diagnostic results of the target fault code U3 is { diagnostic result L3}, where diagnostic result L1 is a primary diagnostic result, diagnostic result L2 is a secondary diagnostic result, and diagnostic result L3 is a tertiary diagnostic result.
S302, determining the diagnosis result set as the target diagnosis result set corresponding to each target fault code according to each target fault code.
Each target fault code corresponds to one diagnosis result set, and the diagnosis result set can be used as a target diagnosis result set of each target fault code, so that a plurality of target diagnosis result sets can be determined for a plurality of target fault codes, wherein each target diagnosis result set comprises at least one target diagnosis result, and the target diagnosis result is the diagnosis result.
For example, based on the above embodiment, the target diagnosis result set of the target fault code U1 is { target diagnosis result L2, target diagnosis result L3}, the target diagnosis result set of the target fault code U2 is { target diagnosis result L1, target diagnosis result L2, target diagnosis result L3}, and the target diagnosis result set of the target fault code U3 is { target diagnosis result L3}.
In this embodiment, a diagnosis result set of each target fault code is determined according to a fault knowledge graph and a priority of diagnosis results in the fault knowledge graph, where the diagnosis result set includes at least one diagnosis result; aiming at each target fault code, determining a diagnosis result set as a target diagnosis result set corresponding to each target fault code, and determining the target diagnosis result set of each target fault code based on the priority of the diagnosis results, so that not only can the direct diagnosis result of the vehicle fault be determined, but also the related diagnosis result can be determined, and the comprehensive diagnosis of the vehicle fault can be realized.
Fig. 10 is a schematic flow chart of another vehicle fault diagnosis method provided by the present disclosure, and fig. 10 is a flowchart of another vehicle fault diagnosis method based on the embodiment shown in fig. 9, before executing S103, further including:
s102', determining the correlation degree between each target diagnosis result in each target diagnosis result set and the corresponding target fault code according to the priority of all target diagnosis results in each target diagnosis result set.
The target diagnosis result set is the diagnosis result set, the target diagnosis result in the target diagnosis result set is the diagnosis result in the diagnosis result set, and the priority of the target diagnosis result is the priority of the diagnosis result in the diagnosis result set. According to the sequence of determining the diagnosis results in the diagnosis result set, the relevance of each target diagnosis result in the target diagnosis result set and the corresponding target fault code can be determined, and the content shows that the first-level diagnosis result is determined firstly, then the second-level diagnosis result is determined, and finally the third-level diagnosis result is determined, so that the relevance ranking of each target diagnosis result in the target diagnosis result set can be determined based on the sequence of determining the diagnosis results.
For example, based on the above embodiment, among the target diagnosis result set { target diagnosis result L2, target diagnosis result L3}, the target diagnosis result L2 is determined first, then the target diagnosis result L3 is determined, among the target diagnosis result set { target diagnosis result L1, target diagnosis result L2, target diagnosis result L3}, the target diagnosis result L1 is determined first, then the target diagnosis result L2 is determined, finally the target diagnosis result L3 is determined, and only one target diagnosis result L3 is determined in the target diagnosis result set { target diagnosis result L3}. From this, it can be determined that, in the target diagnosis result set of the target fault code U1, the correlation between the target diagnosis result L2 and the target fault code U1 is greater than the correlation between the target diagnosis result L3 and the target fault code U1; in the target diagnosis result set of the target fault code U2, the correlation degree between the target diagnosis result L1 and the target fault code U2 is larger than the correlation degree between the target diagnosis result L2 and the target fault code U2, and the correlation degree between the target diagnosis result L2 and the target fault code U2 is larger than the correlation degree between the target diagnosis result L3 and the target fault code U2. Thus, the determined target diagnosis result sequence corresponding to the target fault code U1 is { target diagnosis result L2, target diagnosis result L3}, the target diagnosis result sequence corresponding to the target fault code U2 is { target diagnosis result L1, target diagnosis result L2, target diagnosis result L3}, and the target diagnosis result sequence corresponding to the target fault code U3 is { target diagnosis result L3}.
In this embodiment, by determining the correlation between each target diagnostic result in each target diagnostic result set and the corresponding target fault code according to the priorities of all the target diagnostic results in each target diagnostic result set, each target diagnostic result in all the target diagnostic result sets can be ordered based on the priorities of the target diagnostic results, so that the direct diagnostic result and the indirect diagnostic result of the current fault of the vehicle can be obtained.
Fig. 11 is a schematic flow chart of another vehicle fault diagnosis method provided in the present disclosure, and fig. 11 is a schematic flow chart of the embodiment shown in fig. 2, and further includes:
s104, inputting the target fault codes into a trained model, and outputting a diagnosis result sequence of the target fault codes based on the trained model.
And sequencing the diagnosis results in the diagnosis result sequence according to the probability value.
The model has three layers, including an input layer, a hidden layer and an output layer, the number of neurons of the input layer is the total number of fault codes, the number of neurons of the output layer is the total number of diagnostic results, and the number of neurons of the hidden layer is 256. And taking the diagnosis result in the fault knowledge graph as a label, taking the fault code in the fault knowledge graph as a training feature, setting a dimension vector L of the neuron number of an input layer of the model, initializing the numerical value to 0, carrying out numerical processing on the input fault code and the diagnosis result based on a numerical dictionary, changing the corresponding position in the L from 0 to 1, and finally obtaining a vector L1, thereby training the model to obtain a trained model.
The method comprises the steps of inputting a plurality of target fault codes into a trained model, and outputting a plurality of diagnosis results by the trained model, wherein the plurality of diagnosis results respectively correspond to different probability values, and based on the sequence from the large probability value to the small probability value, the sequence of the plurality of diagnosis results can be determined, and the diagnosis results which are ranked at the front in the sequence are the direct diagnosis result and the related diagnosis result of the current fault of the vehicle.
In this embodiment, a plurality of target fault codes are input into a trained model, a diagnosis result sequence of the plurality of target fault codes is output based on the trained model, diagnosis results in the diagnosis result sequence are ordered according to probability values, direct diagnosis result faults and related diagnosis results of the current fault of the vehicle can be obtained, and the target diagnosis result sequence obtained in the embodiment can be verified.
The present disclosure also provides a vehicle fault diagnosis apparatus, fig. 12 is a schematic structural diagram of the vehicle fault diagnosis apparatus provided by the present disclosure, and as shown in fig. 12, the vehicle fault diagnosis apparatus includes:
the determining module 210 is configured to determine a target diagnosis result set according to a fault knowledge graph and a plurality of target fault codes, where the target diagnosis result set includes a plurality of target diagnosis results, and the fault knowledge graph includes correspondence between the plurality of fault codes and the diagnosis results.
The sequence generating module 220 is configured to generate a target diagnosis result sequence according to the correlation degree between each target diagnosis result in the target diagnosis result set and the target fault code.
Optionally, the determining module 210 is further configured to determine, for each target fault code, a set of diagnostic results according to the fault knowledge graph, where the set of diagnostic results includes at least one diagnostic result; and determining the target diagnosis result set according to the occurrence frequency of each diagnosis result in the diagnosis result sets of the target fault codes.
Optionally, the determining module 210 is further configured to determine, according to the frequency corresponding to each target diagnosis result in the target diagnosis result set, a correlation degree between each target diagnosis result and the target fault code.
Optionally, the determining module 210 is further configured to determine a correlation degree between each target diagnosis result and the target fault code according to a time sequence of reporting each target fault code.
Optionally, the determining module 210 is further configured to determine, according to the frequency of occurrence of each diagnostic result in the plurality of diagnostic result sets, that the frequency of each diagnostic result does not meet a preset condition.
Optionally, the determining module 210 is further configured to determine that the union of the multiple diagnostic result sets is the target diagnostic result set if the frequency of each diagnostic result meets a preset condition; and if the frequency of each diagnosis result does not meet the preset condition, determining the plurality of diagnosis result sets as the target diagnosis result set.
Optionally, the preset condition is that at least one frequency of the frequencies corresponding to all the diagnostic results in the plurality of diagnostic result sets is greater than or equal to a preset frequency.
Optionally, the determining module 210 is further configured to determine, for each target fault code, a set of diagnostic results of each target fault code according to the fault knowledge graph and the priority of diagnostic results in the fault knowledge graph, where the set of diagnostic results includes at least one diagnostic result; and determining the diagnosis result set as the target diagnosis result set corresponding to each target fault code aiming at each target fault code.
Optionally, the determining module 210 is further configured to determine a correlation degree between each target diagnostic result in each target diagnostic result set and a corresponding target fault code according to priorities of all target diagnostic results in each target diagnostic result set.
Optionally, the diagnosis result in the fault knowledge graph includes: at least one of a controller, a fault set condition, a fault recovery condition, a fault cause, and a maintenance recommendation.
Optionally, the vehicle fault diagnosis device further includes:
And the result sequence module is used for inputting the plurality of target fault codes into the trained model, outputting a diagnosis result sequence of the plurality of target fault codes based on the trained model, and sequencing the diagnosis results in the diagnosis result sequence according to probability values.
The vehicle fault diagnosis device provided by the disclosure may be used to execute the steps of the above method embodiments, and its implementation principle and technical effects are similar, and will not be described herein.
The present disclosure also provides an electronic device, including: and a processor for executing a computer program stored in a memory, which when executed by the processor implements the steps of the method embodiments described above.
The present disclosure also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the above-described method embodiments.
The present disclosure also provides a computer program product which, when run on a computer, causes the computer to perform the steps of implementing the method embodiments described above.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is merely a specific embodiment of the disclosure to enable one skilled in the art to understand or practice the disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown and described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (14)

1. A vehicle fault diagnosis method, characterized by comprising:
determining a target diagnosis result set according to a fault knowledge graph and a plurality of target fault codes, wherein the target diagnosis result set comprises a plurality of target diagnosis results, and the fault knowledge graph comprises a corresponding relation between the plurality of fault codes and the diagnosis results;
and generating a target diagnosis result sequence according to the correlation degree between each target diagnosis result in the target diagnosis result set and the target fault code.
2. The method of claim 1, wherein determining the set of target diagnostic results based on the fault knowledge-graph and the plurality of target fault codes comprises:
Determining a diagnosis result set according to the fault knowledge graph aiming at each target fault code, wherein the diagnosis result set comprises at least one diagnosis result;
and determining the target diagnosis result set according to the occurrence frequency of each diagnosis result in the diagnosis result sets of the target fault codes.
3. The method of claim 2, wherein before generating the sequence of target diagnostic results according to the correlation between each target diagnostic result in the set of target diagnostic results and the target fault code, further comprising:
and determining the correlation degree between each target diagnosis result and the target fault code according to the frequency corresponding to each target diagnosis result in the target diagnosis result set.
4. The method of claim 2, wherein before generating the sequence of target diagnostic results according to the correlation between each target diagnostic result in the set of target diagnostic results and the target fault code, further comprising:
and determining the correlation degree between each target diagnosis result and each target fault code according to the time sequence of reporting each target fault code.
5. The method of claim 4, wherein before determining the correlation between each target diagnosis result and the target fault code according to the time sequence in which each target fault code is reported, further comprises:
And determining that the frequency of each diagnosis result does not meet a preset condition according to the frequency of each diagnosis result in the plurality of diagnosis result sets.
6. The method of claim 2, wherein determining the target set of diagnostic results based on the frequency of occurrence of each diagnostic result in the plurality of sets of diagnostic results for the plurality of target fault codes comprises:
if the frequency of each diagnosis result meets a preset condition, determining that the union set of the plurality of diagnosis result sets is the target diagnosis result set;
and if the frequency of each diagnosis result does not meet the preset condition, determining the plurality of diagnosis result sets as the target diagnosis result set.
7. The method according to claim 5 or 6, wherein the preset condition is that at least one frequency of the frequencies corresponding to all the diagnostic results in the plurality of diagnostic result sets is greater than or equal to a preset frequency.
8. The method of claim 1, wherein determining the set of target diagnostic results based on the fault knowledge-graph and the plurality of target fault codes comprises:
determining a diagnosis result set of each target fault code according to the fault knowledge graph and the priority of diagnosis results in the fault knowledge graph, wherein the diagnosis result set comprises at least one diagnosis result;
And determining the diagnosis result set as the target diagnosis result set corresponding to each target fault code aiming at each target fault code.
9. The method of claim 8, wherein prior to generating the sequence of target diagnostic results based on the correlation of each target diagnostic result in the set of target diagnostic results with the target fault code, further comprising:
and determining the correlation degree between each target diagnosis result in each target diagnosis result set and the corresponding target fault code according to the priority of all target diagnosis results in each target diagnosis result set.
10. The method of claim 1, wherein the diagnostic result in the fault knowledge-graph comprises: at least one of a controller, a fault set condition, a fault recovery condition, a fault cause, and a maintenance recommendation.
11. The method as recited in claim 1, further comprising:
inputting the target fault codes into a trained model, outputting a diagnosis result sequence of the target fault codes based on the trained model, and sequencing diagnosis results in the diagnosis result sequence according to probability values.
12. A vehicle failure diagnosis apparatus, characterized by comprising:
the determining module is used for determining a target diagnosis result set according to a fault knowledge graph and a plurality of target fault codes, wherein the target diagnosis result set comprises a plurality of target diagnosis results, and the fault knowledge graph comprises a corresponding relation between the plurality of fault codes and the diagnosis results;
and the sequence generation module is used for generating a target diagnosis result sequence according to the correlation degree of each target diagnosis result in the target diagnosis result set and the target fault code.
13. An electronic device, comprising: a processor for executing a computer program stored in a memory, which when executed by the processor performs the steps of the method of any of claims 1-11.
14. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1-11.
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