CN115328689B

CN115328689B - Fault diagnosis method, device, equipment and program product

Info

Publication number: CN115328689B
Application number: CN202211249173.8A
Authority: CN
Inventors: 唐钟; 徐大伟
Original assignee: Jidu Technology Co ltd
Current assignee: Beijing Jidu Technology Co Ltd
Priority date: 2022-10-12
Filing date: 2022-10-12
Publication date: 2023-06-27
Anticipated expiration: 2042-10-12
Also published as: CN115328689A

Abstract

The embodiment of the application provides a fault diagnosis method, device, equipment and program product, which relate to the technical field of fault diagnosis and are used for improving the fault detection efficiency of a vehicle so as to improve the fault maintenance efficiency. In an embodiment of the present application, the method may include: acquiring equipment information and fault information of target equipment to be maintained; determining at least one fault knowledge point matched with the equipment information and the fault information of the target equipment from an equipment fault knowledge base; the equipment fault knowledge base comprises a plurality of fault types, a test scheme and a fault repair scheme corresponding to fault reasons, wherein one fault type comprises at least one fault knowledge point, and one fault knowledge point corresponds to one or more fault reasons; determining the probability of each fault cause corresponding to the at least one fault knowledge point; and determining the fault reason of the target equipment based on the probability of each fault reason corresponding to the at least one fault knowledge point.

Description

Fault diagnosis method, device, equipment and program product

Technical Field

The present disclosure relates to the field of fault diagnosis technologies, and in particular, to a fault diagnosis method, device, apparatus, and program product.

Background

With the continuous improvement of the intelligent degree of vehicles and the improvement of the complexity of the electronic and electric architecture of vehicles, the updating of the knowledge skills of after-market technicians of vehicles often has difficulty in keeping up with the rapid iteration of the electronic technologies and software functions of vehicles. When a vehicle fails, it is often time and effort consuming for after-market technicians to troubleshoot the cause of the failure, which makes the vehicle's maintenance less efficient. Existing vehicle fault diagnosis schemes typically include the following two types: one is based on the analysis of the fault tree of the whole vehicle and the subsystem, and the other is based on the large data mining and matching of the maintenance cases.

The first vehicle fault diagnosis scheme is to build fault trees of all subsystems of a vehicle, analyze fault transfer logic among all subsystems and finally connect the fault trees of all subsystems to build a fault tree of the whole vehicle. Obviously, the fault diagnosis scheme has the problems of complex system, high development difficulty, poor usability among different vehicle types and the like, and the fault diagnosis method also needs to be established on the basis of a large number of cases of actual fault vehicles. While failure diagnosis schemes based on large data of maintenance cases also require accumulation of a large number of historical maintenance cases, it is apparent that such failure diagnosis is not suitable for vehicle maintenance of vehicle models without historical maintenance data.

Therefore, how to provide a general fault diagnosis method for various existing vehicles to improve the fault detection efficiency of the vehicles and further improve the fault maintenance efficiency is still needed to provide a further solution.

Disclosure of Invention

Aspects of the present application provide a fault diagnosis method, apparatus, device, and program product for improving the fault detection efficiency of a vehicle and thus improving the fault maintenance efficiency.

In a first aspect, an embodiment of the present application provides a fault diagnosis method, including:

acquiring equipment information and fault information of target equipment to be maintained;

determining at least one fault knowledge point matched with the equipment information and the fault information of the target equipment from an equipment fault knowledge base; the equipment fault knowledge base comprises a plurality of fault types, a test scheme and a fault repair scheme corresponding to fault reasons, wherein one fault type comprises at least one fault knowledge point, and one fault knowledge point corresponds to one or more fault reasons;

determining the probability of each fault cause corresponding to the at least one fault knowledge point;

and determining the fault reason of the target equipment based on the probability of each fault reason corresponding to the at least one fault knowledge point.

In a second aspect, embodiments of the present application further provide a computer device, including: a memory and a processor; wherein the memory is used for storing a computer program; the processor is coupled to the memory for executing the computer program for performing the steps in the method of the first aspect.

In a third aspect, embodiments of the present application also provide a computer program product comprising a computer program/instruction which, when executed by a processor, implements the steps of the method according to the first aspect.

The technical scheme provided by the embodiment of the application can be seen that the embodiment of the application has at least one of the following technical effects:

according to one or more embodiments provided by the application, a pre-constructed equipment fault knowledge base can be used as a basis for equipment fault diagnosis, corresponding fault knowledge points are matched from the equipment fault knowledge base according to equipment information and fault information of target equipment, the fault occurrence probability of each fault cause corresponding to the matched fault knowledge points is determined, and finally the fault cause of the target equipment can be diagnosed according to the fault occurrence probability corresponding to each fault cause, so that the equipment fault troubleshooting efficiency is effectively improved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:

fig. 1 is a schematic implementation flow chart of a fault diagnosis method according to an exemplary embodiment of the present application;

fig. 2 is a schematic structural diagram of an equipment fault knowledge base in a fault diagnosis method according to an exemplary embodiment of the present application;

FIG. 3 (a) is a schematic flow chart of a first part of a fault diagnosis method applied in a practical scenario according to an exemplary embodiment of the present application;

FIG. 3 (b) is a schematic flow chart of a second part of the fault diagnosis method applied in a practical scenario according to an exemplary embodiment of the present application;

fig. 4 is a schematic structural diagram of a fault diagnosis device according to an exemplary embodiment of the present application;

fig. 5 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present application.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described below with reference to specific embodiments of the present application and corresponding drawings.

As described in the background art, the existing fault diagnosis schemes generally need to be based on a large amount of historical maintenance data of the fault vehicle, and the existing fault diagnosis schemes are complex in system and difficult to develop, so that the fault diagnosis schemes cannot be used for some new vehicles without the historical maintenance data.

To this end, in order to improve the troubleshooting efficiency of the vehicle and further improve the troubleshooting efficiency, the embodiment of the application provides a solution, and the basic idea is: and according to the equipment information and the fault information of the target equipment, matching corresponding fault knowledge points from the equipment fault knowledge base, determining the fault occurrence probability of each fault cause corresponding to the matched fault knowledge points, and finally diagnosing the fault cause of the target equipment according to the fault occurrence probability corresponding to each fault cause, thereby effectively improving the equipment fault troubleshooting efficiency.

The equipment fault knowledge base can be constructed based on the system fault principle of equipment, the test of a whole machine (such as a whole vehicle) and the problem data of system integration test, and the data according to which the equipment fault knowledge base is constructed can be obtained in the production process before the equipment is marketed, so that the equipment fault diagnosis method can be realized without accumulating a large amount of historical maintenance data of fault equipment, and the time cost required for constructing the equipment fault knowledge base is saved.

The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.

Fig. 1 is a schematic flow chart of an implementation of a fault diagnosis method according to an embodiment of the present application. The method of fig. 1 may include:

step 110, obtaining device information and fault information of the target device to be repaired.

Step 120, determining at least one fault knowledge point matched with the device information and the fault information of the target device from the device fault knowledge base.

And 130, determining the probability of each fault reason corresponding to at least one fault knowledge point.

And 140, determining the fault reason of the target equipment based on the probability of each fault reason corresponding to at least one fault knowledge point.

It should be noted that, the fault diagnosis method provided in the embodiment of the present application may be suitable for fault diagnosis of vehicles such as automobiles, ships, planes, trains, and the like, and in addition, the fault diagnosis method may also be suitable for other electronic devices, for example, electronic devices such as computers, mobile phones, computers, and the like. As an example, the embodiment of the present application specifically describes the implementation process of the fault diagnosis method by taking a vehicle as an example.

The device information of the target device includes a device model number and a time of offline of the target device, or a device model number and a software version of the target device. Taking the target device as the target vehicle as an example, for step 110, the vehicle information of the target vehicle may include the vehicle identification code (Vehicle Identification Number, VIN) of the target vehicle, the vehicle model number, the off-line time, and the software version of the entire vehicle of the target vehicle. Wherein VIN is composed of 17 digits and uppercase letters, the first to third digits are identification codes of vehicle manufacturers, the fourth to ninth digits are vehicle description parts, and the tenth to seventeenth digits are vehicle indication parts. And VINs of different vehicles cannot be repeated, and the VINs can be used as unique identifications of the vehicles and are equivalent to identification card numbers of the vehicles. The vehicle model, time to offline, and software version of the entire vehicle of the target vehicle may be obtained from the vehicle center based on the VIN of the target vehicle.

The fault information of the target vehicle may include information such as fault codes (Diagnostic Trouble Code, DTC), alarm alert information (Warning Telltale Indicator, WTI), abnormal signals, and symptoms of the fault of the target vehicle. The DTC is used for positioning a part of fault parts and a part of fault reasons of the vehicle under the condition that the vehicle is not disassembled, and can be used for detecting and diagnosing the fault parts such as a vehicle engine, a vehicle chassis, a vehicle body and accessories, vehicle exhaust pollutants, noise and the like.

When the target vehicle fails, vehicle information such as the vehicle model, the off-line time, the whole vehicle software version and the like of the target vehicle can be determined based on the VIN of the target vehicle, and meanwhile, failure information such as DTC, WTI, abnormal signals, failure symptoms and the like displayed when the target vehicle fails is obtained.

For example, for step 120, the equipment fault knowledge base includes a plurality of fault types, a test scheme corresponding to a fault cause, and a fault repair scheme, where a fault type includes at least one fault knowledge point, and a fault knowledge point corresponds to one or more fault causes, and the equipment fault knowledge base is constructed based on the equipment system fault principle, the equipment test, and the problem data of the system integration test.

Taking a vehicle as an example, the device can be used for acquiring the system failure principle, the whole vehicle test and the problem data of the system integration test of one or more vehicles before the one or more vehicles are marketed. Therefore, even if the model of the target vehicle is a new model on the market, the fault cause of the target vehicle can be diagnosed based on the equipment fault knowledge base. It should be appreciated that since the cause of failure of vehicles of different vehicle types will also typically vary, the one or more vehicles may include vehicles of the same vehicle type as the target vehicle in order to achieve accurate localization of the cause of failure of the target vehicle.

As an embodiment, the fault types included in the device fault repository may include fault types such as DTCs, WTIs, exception signals, and fault symptoms. In addition, the equipment failure knowledge base may also include failure types that match the vehicle model and time of departure of the vehicle, as well as failure types that match the vehicle model and the vehicle software version of the vehicle.

Fig. 2 is a schematic structural diagram of an equipment fault knowledge base in a fault diagnosis method according to an exemplary embodiment of the present application. The equipment fault knowledge base comprises a fault code base, a fault symptom base, a fault reason base, a test scheme base, a fault repair scheme base, an alarm prompt information base and an abnormal signal base. The fault code library stores fault knowledge points of the DTC fault type, the fault symptom library stores fault knowledge points of the fault type such as fault symptoms, the alarm prompt information library stores fault knowledge points of the fault type such as alarm prompt information, and the abnormal signal library stores fault knowledge points of the fault type such as abnormal signals. The fault cause library stores fault knowledge points of fault types except a fault code library, a fault symptom library, an alarm prompt information library and an abnormal signal library. The test scheme library comprises test schemes formulated for each fault cause in the equipment fault knowledge base, and the fault repair scheme comprises fault repair schemes formulated for each fault cause in the equipment fault knowledge base. In addition, the fault symptom library comprises a whole vehicle symptom library, a system symptom library and a working condition library. The fault reason library comprises a part library, a fault part library and a failure mode library.

In addition to the basic database, because there may be an association between different fault types, there may be an association between a fault cause and each fault type, and there may also be an association between a fault cause and a test scheme, based on which, in order to improve efficiency and accuracy of vehicle fault diagnosis, the above-mentioned equipment fault knowledge base may further include a fault cause-fault code relational library, a fault cause-fault symptom relational library, a fault cause-test scheme relational library, a fault cause-fault repair scheme relational library, an alarm prompt information-abnormal signal relational library, a fault cause-alarm prompt information relational library, and a fault cause-abnormal signal relational library. Optionally, in the method shown in fig. 1, after determining the failure cause of the target device, a failure repair scheme corresponding to the failure cause may be recommended. The troubleshooting scheme may be prompted to the maintenance personnel, for example, by an output device such as a display screen or speaker.

It should be understood that vehicles can pass multiple system failure principle tests, whole vehicle tests and system integration tests during production to be marketed for users. In the process of multiple system fault principle tests, whole vehicle tests and system integration tests, the vehicle may expose some hardware problems, and the hardware problems are recorded in the equipment fault knowledge base in the form of fault knowledge points during the tests and are associated with the vehicle model and the offline time (namely the production time) of the vehicle, so that the hardware problems can be used as the basis for checking the fault cause of the vehicle when the fault occurs in the process of being used by a user after the vehicle is marketed.

Optionally, after the target vehicle fails, in order to implement comprehensive troubleshooting of the failure cause of the target vehicle, hardware and software problems generated in the production process of the target vehicle may be first troubleshooted based on the vehicle information of the target vehicle, so as to determine whether the failure cause of the target vehicle is associated with the hardware and software problems in the production process. Specifically, determining at least one fault knowledge point matched with the device information and the fault information of the target device from the device fault knowledge base includes:

determining the equipment model and the offline time of the target equipment or determining the equipment model and the software version of the target equipment based on the equipment information of the target equipment;

determining whether a first fault knowledge point matched with the equipment model and the offline time of the target equipment or a second fault knowledge point matched with the equipment model and the software version of the target equipment exists in an equipment fault knowledge base;

if the first fault knowledge point or the second fault knowledge point exists in the equipment fault knowledge base, determining whether the fault corresponding to the first fault knowledge point or the second fault knowledge point is repaired or not;

if the fault corresponding to the first fault knowledge point or the second fault knowledge point is repaired, determining at least one fault knowledge point matched with the fault information of the target equipment and the equipment model of the target equipment from the equipment fault knowledge base.

Optionally, after determining whether the first fault knowledge point matched with the vehicle model and the off-line time of the target vehicle exists in the vehicle fault knowledge base, if the first fault knowledge point matched with the vehicle model and the off-line time of the target vehicle does not exist in the vehicle fault knowledge base, it is indicated that the target vehicle has no hardware problem which can cause the fault cause of the target vehicle in the production test process, and in this case, the fault cause associated with the vehicle model and the off-line time of the target vehicle can be removed from the fault cause which causes the fault of the target vehicle.

When a first fault knowledge point matched with the vehicle model and the offline time of the target vehicle exists in the vehicle fault knowledge base, the first fault knowledge point indicates that the target vehicle has some hardware problems during the test in the production process, and the problems are usually repaired in the production or after-sale maintenance of the vehicle and then marketed for users. In this case, it may be determined whether the failure cause corresponding to the first failure knowledge point is repaired, and if the failure cause corresponding to the first failure knowledge point has been repaired, the failure cause corresponding to the first failure knowledge point may be excluded from the failure causes that cause the failure of the target vehicle.

It should be understood that the software version of the whole vehicle can also record the exposed software problems during the development, test and trial, and the software problems can also be recorded into a vehicle fault knowledge base in the form of fault knowledge points and are associated with the vehicle model of the vehicle and the software version of the whole vehicle, so that the software problems can be used as the basis for checking the cause of the vehicle fault when the vehicle is in fault in the use process of the user after the vehicle is marketed. Specifically, when the cause of the vehicle fault is checked, it may be determined whether a software problem exists in the software development test process for the whole vehicle software version matched with the vehicle model of the target vehicle, and if a second fault knowledge point corresponding to the software problem is recorded in the vehicle fault knowledge base, the cause of the fault corresponding to the second fault knowledge point may be determined as one of the causes of the fault causing the target vehicle fault. In this case, a fault repairing scheme corresponding to a fault cause caused by the whole vehicle software version matched with the vehicle model of the target vehicle can be determined from the vehicle fault knowledge base, and the target vehicle is repaired according to the fault repairing scheme. And if the second fault knowledge point corresponding to the software problem is not recorded in the vehicle fault knowledge base, the fault reason corresponding to the second fault knowledge point can be removed from the fault reasons which lead to the fault of the target vehicle.

As an embodiment, the above-described embodiment of checking the first fault knowledge point and the second fault knowledge point may be implemented as two parallel embodiments, or may be combined. When the two embodiments are combined, the sequence of the first fault knowledge point and the second fault knowledge point in the embodiment of the present application is not particularly limited, and the sequence may be selected according to actual needs.

For example, for step 130, one fault knowledge point corresponds to one or more fault reasons, and the fault repair schemes of different fault reasons are often different, so that in order to determine the fault reason that causes the fault of the target vehicle, to accurately implement fault repair on the target vehicle, an online test (or a remote test) may be performed on the target vehicle by using the test scheme of each fault reason corresponding to at least one fault knowledge point, so as to determine which fault reasons corresponding to at least one fault knowledge point are related to the test result, and which fault reasons are unrelated to the test result, so as to achieve the purpose of determining the probability that each fault reason causes the fault of the target vehicle. Specifically, determining the probability of each fault cause corresponding to at least one fault knowledge point includes:

Testing the target equipment based on an on-line test scheme of each fault cause corresponding to at least one fault knowledge point to obtain a test result of the target equipment;

based on the test result, determining a first related fault reason, a first unrelated fault reason and a first pending fault reason from the fault reasons corresponding to at least one fault knowledge point; the first related fault reason is a fault reason with the probability of 1, the first related fault reason is a fault reason with the probability of 0, and the first pending fault reason is a fault reason except the first related fault reason and the first related fault reason among the fault reasons corresponding to at least one fault knowledge point;

and determining the probability of the first to-be-confirmed fault reason based on the number of the fault reasons corresponding to the fault knowledge points to which the first to-be-confirmed fault reason belongs, wherein the first to-be-confirmed fault reason is the fault reason of the offline test scheme in the first to-be-confirmed fault reason.

The on-line test can be performed on the target vehicle in a remote test mode, and for some test schemes incapable of performing the remote test, off-line test can be performed on the target vehicle by off-line maintenance personnel to the site to obtain a test result of the target vehicle.

After determining a first relevant fault reason having an association relationship with the on-line test result of the target vehicle from the fault reasons corresponding to the at least one fault knowledge point, determining that the probability that the first relevant fault reason causes the target vehicle to fail at this time is 100%, and marking the first relevant fault reason as a 'confirmation' state to indicate that the first relevant fault reason has been determined as the fault reason that the target vehicle fails at this time. After determining a first related fault reason that has no association with the test result of the target vehicle from among the fault reasons corresponding to the at least one fault knowledge point, it may be determined that the probability that the first related fault reason causes the target vehicle to fail at this time is 0, and the first related fault reason may be deleted from the fault reasons corresponding to the at least one fault knowledge point to indicate that the first related fault reason may be excluded from the fault reasons that the target vehicle fails at this time.

When the test result obtained by testing the target vehicle based on the test scheme corresponding to the at least one fault knowledge point determines the first related fault reason and the first related fault reason, the first pending state of the fault reasons corresponding to the at least one fault knowledge point except the first related fault reason and the first related fault reason may be marked, that is, the fault reason that the first pending fault reason may cause the fault of the target vehicle at this time is indicated. The first pending fault cause may include a first pending fault cause of the in-line test scheme and a fault cause of the in-line test scheme. For the first to-be-confirmed fault reason of the on-line testing scheme, the on-line testing of the target vehicle can be carried out by the on-line maintenance personnel to the site, and the testing result of the target vehicle can be obtained. For the failure reasons of the online test scheme, maintenance personnel of the equipment failure knowledge base are required to pay important attention to the failure reasons so as to research out the corresponding online test scheme as the basis for subsequently perfecting the equipment failure knowledge base. Specifically, the first pending fault cause further includes a fault cause for which no online test scenario exists.

For the first to-be-confirmed fault reasons of the offline test scheme in the first to-be-confirmed fault reasons, the probability of the first to-be-confirmed fault reasons can be determined based on the number of fault reasons corresponding to the fault knowledge points to which the first to-be-confirmed fault reasons belong, and then the first to-be-confirmed fault reasons are ranked according to the sequence from the high probability to the low probability, wherein the fault reasons with the same probability are ranked according to the criteria that the fault reasons with low test cost are ranked at the front, and if the probability of the fault reasons is the same and the test cost is the same, the first to-be-confirmed fault reasons are ranked according to the fault identification, and then the first to-be-confirmed fault reasons are checked one by one according to the test schemes corresponding to the fault reasons in the first to-be-confirmed fault reasons until the number of the first to-be-confirmed fault reasons is 0.

The probability of the first to-be-confirmed fault cause RCn can be determined according to the formula count_n/Σcount_i, where Σcount_i is the sum of the numbers of fault causes corresponding to the fault knowledge points corresponding to the first to-be-confirmed fault cause RCn, and count_n is 1 in the initial state. In the initial state, assuming that the sum of the numbers of the fault reasons corresponding to the fault knowledge points corresponding to the first fault reason to be confirmed RCn is 3, the probability of the first fault reason to be confirmed RCn is determined to be 1/3 according to the formula count_n/Σcount_i.

Optionally, if a fault repair scheme corresponding to a certain pending fault reason in the first pending fault reasons exists in the fault repair schemes corresponding to the confirmed fault reasons (namely, the first related fault reasons), the fault repair scheme corresponding to the confirmed fault reasons can solve the vehicle fault caused by the pending fault reasons, so that the pending fault reasons can be deleted from the first pending fault reasons, thereby reducing the time and test cost of fault diagnosis and improving the fault repair efficiency. Specifically, determining the failure cause of the target vehicle based on the probability of each failure cause corresponding to at least one failure knowledge point includes:

ordering the first pending fault reasons based on at least one of the probability of the first pending fault reasons, the fault identification, the test cost and the test scheme;

for each pending fault reason in the sorted first pending fault reasons, if the fault repair scheme corresponding to the pending fault reason is consistent with the fault repair scheme corresponding to the first related fault reason, deleting the pending fault reason to obtain the rest pending fault reasons;

according to the sequence of the residual undetermined fault reasons, on the premise that the residual undetermined fault reasons exist in the on-line test scheme, the on-line test scheme corresponding to the residual undetermined fault reasons is sequentially executed;

And determining the fault reason of the target equipment based on the test result of the residual undetermined fault reason.

In general, it is necessary to confirm a more detailed test scheme to further check which are related fault causes with a probability of 100% and which are unrelated fault causes with a probability of 0, since the first pending fault cause is not confirmed whether it is the fault cause causing the target vehicle to fail. If the fault repairing schemes corresponding to some of the pending fault reasons and the fault repairing schemes corresponding to some of the confirmed first relevant fault reasons are consistent before that, the fault problems caused by the fault repairing schemes corresponding to the confirmed relevant fault reasons can be repaired by using the fault repairing schemes corresponding to the relevant fault reasons regardless of the probability of the pending fault reasons and whether the probability of the pending fault reasons is the relevant fault reasons or not.

Based on the method, the method and the device for testing the fault repair of the target vehicle can delete the undetermined fault reasons consistent with the fault repair schemes corresponding to some related fault reasons in the confirmed first related fault reasons before confirming a finer testing scheme for the undetermined fault reasons, so that the checking cost of the undetermined fault reasons can be saved, and the overall fault checking efficiency of the target vehicle is improved.

Optionally, when there are multiple test schemes corresponding to one remaining pending fault reason, the test schemes corresponding to the remaining pending fault reason are sequentially executed according to the sequence of the remaining pending fault reason, and the test scheme with lower test cost may be preferentially selected to perform on-site or remote test on the target vehicle, if the probability that the test scheme with lower test cost cannot confirm the corresponding remaining pending fault reason is 100% or 0, then the test scheme with second low test cost is selected to perform test on the target vehicle until the probability that the remaining pending fault reason corresponding to the test scheme is 100% or 0 is determined.

Optionally, the embodiments of the present application may sort the first related fault reason, the first pending fault reason, and the probability of the first pending fault reason among the first pending fault reasons after determining the fault reasons. Specifically, the first related fault reasons can be ranked first, and if a plurality of first related fault reasons exist, the first related fault reasons are ranked according to the fault identifications of the first related fault reasons. After the first relevant fault cause, the first fault cause to be confirmed is ordered. The first to-be-confirmed fault reasons can be ranked according to the probability of the first to-be-confirmed fault reasons, and if the probability of the two or more first to-be-confirmed fault reasons is the same, the first to-be-confirmed fault reasons are ranked according to the test cost. And if two or more first to-be-confirmed fault reasons are the same in test cost, sorting according to the fault identification. After the first fault cause to be confirmed, the fault causes of the offline testing scheme are ranked, and particularly the fault causes can be ranked according to the fault identification. The ordering of the fault causes can be used for reference of maintenance sequences by maintenance personnel. The fault cause is updated after the new related fault cause and the unrelated fault cause are continuously confirmed until the number of undetermined fault causes cannot be confirmed to be 0.

It should be understood that after the remaining pending fault reasons are tested according to the test schemes corresponding to the remaining pending fault reasons, the remaining pending fault reasons can be further checked according to the test results, that is, the relevant fault reasons with the association relation with the test results and the irrelevant fault reasons without the association relation with the test results are determined, so as to further confirm the fault reasons of the target vehicle. Specifically, determining the failure cause of the target vehicle based on the test result of the remaining pending failure causes includes:

determining a second related fault reason and a second unrelated fault reason from the residual undetermined fault reasons based on the test result of the residual undetermined fault reason, and deleting the second related fault reason and the second unrelated fault reason from the residual undetermined fault reason;

the above operations are repeatedly performed until the number of remaining pending fault causes is 0, and the fault cause of the target vehicle is determined based on the first related fault cause and the second related fault cause. The target fault causes include a first related fault cause and a second related fault cause.

If the number of the remaining pending fault reasons is not 0, the following steps may be continuously performed: first, determining a pending fault reason having the same fault repair scheme as the first related fault reason and/or the second related fault reason from the remaining pending fault reasons, and deleting the pending fault reason from the remaining pending fault reasons. And sequencing the residual undetermined fault reasons according to the probability, the test cost and the fault reason identification of the current residual undetermined fault reasons. And then, according to the sequence of the residual undetermined fault reasons, sequentially executing the test schemes corresponding to the residual undetermined fault reasons. And finally, determining a third related fault reason with the probability of 100% and a third unrelated fault reason with the probability of 0 from the residual undetermined fault reasons based on the test results of the residual undetermined fault reasons. Until the number of fault causes for which the pending fault causes remain is 0.

Optionally, the vehicle fault knowledge base is constructed by system fault principles, whole vehicle tests and problem data of system integration tests based on a plurality of vehicles before marketing in an initial state, so that the constructed vehicle fault knowledge base can fully cover all fault reasons corresponding to fault knowledge points of various vehicles and fault repair schemes corresponding to the fault reasons. Specifically, after determining the failure cause of the target vehicle based on the probability of each failure cause corresponding to at least one failure knowledge point, the method provided in the embodiment of the present application further includes:

repairing the target equipment based on a fault repairing scheme corresponding to the fault cause of the target equipment;

and updating the equipment fault knowledge base based on the fault cause of the target equipment and the repair result of the target equipment.

Alternatively, the embodiment of the application can iteratively update the vehicle fault knowledge base from two layers of knowledge point maturity and knowledge point heat of the fault knowledge points. Specifically, based on the failure cause of the target device and the repair result of the target device, updating the device failure knowledge base includes:

Determining a target fault knowledge point corresponding to a fault cause of target equipment;

updating the knowledge point maturity of the target fault knowledge point under the target fault type in the equipment fault knowledge base based on the target fault type corresponding to the target fault knowledge point and the repair result of the target equipment;

updating knowledge point heat of the target fault knowledge points under the target fault type in the equipment fault knowledge base based on the target fault type corresponding to the target fault knowledge points;

the knowledge point maturity of the target fault knowledge point is used for representing the success rate of matching the target fault knowledge point with the equipment fault information, and the knowledge point heat of the target fault knowledge point is used for representing the importance degree of the target fault knowledge point in the equipment fault knowledge base.

The initial value of the knowledge point heat of the target fault knowledge point in the vehicle fault knowledge base is 0, if the type of faults such as DTC, WTIinformation, abnormal signals and fault symptoms of the target vehicle are determined once the faults are matched with the target fault knowledge point in the process of checking the fault cause of the target vehicle according to the vehicle fault knowledge base, and the heat value of the knowledge point of the target fault knowledge point is +1 after the faults are matched once. Obviously, as the number of fault vehicles performing fault investigation according to the vehicle fault knowledge base increases, the knowledge point heat value of each fault knowledge point in the vehicle fault knowledge base also changes, and the higher the knowledge point heat value is, the higher the frequency of the fault knowledge point in the fault vehicle is indicated, so that higher requirements are put forward on the fault reason, the test scheme and the fault repair scheme corresponding to the fault knowledge point. Therefore, knowledge point heat of the target fault knowledge point can be used as an important basis for a technician to continuously optimize the vehicle fault knowledge base.

Specifically, the knowledge point maturity of the target fault knowledge point may be represented by the number of knowledge point successes of the target fault knowledge point, and the ratio of the number of knowledge point successes S of the target fault knowledge point to the sum (s+f) of the number of knowledge point successes S and the number of knowledge point failures F, that is, S/(s+f). In the fault checking process of the vehicle according to the vehicle fault knowledge base, the vehicle fault knowledge base is also in iterative updating. In this process, the knowledge point success number S and the knowledge point failure number F of the target failure knowledge point are also dynamically changed.

Taking a fault vehicle as an example, in the process that the fault vehicle performs fault investigation by taking a vehicle fault knowledge base as an example, if the fault corresponding to the target fault knowledge point is successfully repaired according to a test scheme and a fault repair scheme corresponding to the target fault knowledge point provided by the vehicle fault knowledge base, the successful times S+1 of the knowledge point corresponding to the target fault knowledge point are obtained. Otherwise, if the fault corresponding to the target fault knowledge point cannot be repaired according to the test scheme and the fault repair scheme corresponding to the target fault knowledge point provided by the vehicle fault knowledge base, the number of failure times F+1 of the knowledge point corresponding to the fault knowledge point. In units of percentages, the initial values of S and F are 1, so that the knowledge point maturity of the newly-built fault knowledge points in the vehicle fault knowledge base is 50%. Specifically, based on the target fault type corresponding to the target fault knowledge point and the repair result of the target device, updating the knowledge point maturity of the target fault knowledge point under the target fault type in the device fault knowledge base, including:

Determining whether a fault corresponding to a target fault knowledge point under a target fault type in the target equipment is repaired or not based on a repair result of the target equipment;

if the fault corresponding to the target fault knowledge point under the target fault type in the target equipment is restored, updating the successful times of the knowledge points of the target fault knowledge point under the target fault type, and updating the maturity of the knowledge points of the target fault knowledge point under the target fault type based on the updated successful times of the knowledge points;

if the fault corresponding to the target fault knowledge point under the target fault type in the target equipment is not repaired, updating the knowledge point failure times of the target fault knowledge point under the target fault type, and updating the knowledge point maturity of the target fault knowledge point under the target fault type based on the updated knowledge point failure times.

Obviously, as the number of fault vehicles performing fault investigation according to the vehicle fault knowledge base increases, the knowledge point maturity of each fault knowledge point in the vehicle fault knowledge base also changes, and the fault knowledge point with the larger knowledge point maturity shows that the reliability of the fault knowledge point as a fault reason for troubleshooting and repairing the fault vehicle is higher. The lower the maturity of the knowledge points is, the lower the reliability of the knowledge points as the fault reasons for troubleshooting and repairing the fault vehicle is, so that the corresponding fault reasons, test schemes and fault repairing schemes are required to be further optimized. Obviously, the knowledge point maturity provides an effective basis for technicians to continuously optimize a vehicle fault knowledge base, and also provides a reference for evaluating the reliability of each fault knowledge point.

Fig. 3 (a) and fig. 3 (b) are schematic flow diagrams of the fault diagnosis method according to an exemplary embodiment of the present application, where fig. 3 (a) is a first portion and fig. 3 (b) is a second portion. In fig. 3, the fault diagnosis method includes:

s31, acquiring vehicle information and vehicle fault information of the target vehicle.

S32, based on VIN of the target vehicle, vehicle information such as the vehicle model, the off-line time and the whole vehicle software version of the target vehicle is obtained from the vehicle center.

S33, matching the first fault knowledge point according to the vehicle model and the offline time.

S34, determining whether the first fault knowledge point is matched.

If yes, S35 is executed, otherwise S37 is executed.

S35, determining whether the first fault knowledge point is repaired in the production or after-sales maintenance of the batch of vehicles corresponding to the VIN.

If yes, S37 is executed, otherwise S36 is executed.

S36, recommending the fault maintenance scheme according to the fault maintenance scheme corresponding to the first fault knowledge point.

And S37, matching a second fault knowledge point according to the vehicle model and the whole vehicle software version.

S38, determining whether the second fault knowledge point is matched.

If yes, S39 is executed, otherwise S310 is executed.

S39, recommending the fault maintenance scheme according to the fault maintenance scheme corresponding to the second fault knowledge point.

S310, whether the characteristics of fault types such as DTC/WTIinformation/abnormal signals/fault characteristics and the like exist.

If yes, executing S311, otherwise, ending maintenance.

S311, the fault information of the target vehicle is matched with fault knowledge of several fault types in the vehicle fault knowledge base respectively.

S312, determining whether at least one fault knowledge point is matched.

If yes, then execution S313, otherwise, contact technical support.

S313, knowledge point heat of at least one fault knowledge point is +1.

And S314, remotely testing the target vehicle according to the testing scheme of each fault cause corresponding to at least one fault knowledge point.

S315, based on the test result, determining relevant fault reasons, irrelevant fault reasons and undetermined fault reasons from the fault reasons corresponding to at least one fault knowledge point.

S316, determining the probability of the undetermined fault reason.

And S317, sorting the related fault reasons and the undetermined fault reasons.

S318, determining whether the number of the undetermined fault reasons is 0.

If yes, S319 is performed, otherwise S320 is performed.

And S319, repairing the fault of the target vehicle according to a fault repairing scheme corresponding to the relevant fault reason.

And S320, if the same fault restoration scheme as the related fault reasons exists in the undetermined fault reasons, deleting the undetermined fault reasons corresponding to the same fault restoration scheme.

After S320 is performed, the updated pending fault cause may be obtained, and the process may return to step S317, where the updated pending fault cause and the related fault cause are ranked, and S321 is performed after S317 is performed.

S321, performing field test on the target vehicle according to the test scheme corresponding to the relevant fault cause.

S322, updating the related fault reasons, the unrelated fault reasons and the undetermined fault reasons according to the test result.

After S322 is performed, the process proceeds back to S318, and it is determined whether the number of pending failure causes is 0, and if 0, S319 is performed, and after S319 is performed, S323 is performed. If the number of pending failure causes is not 0, the process proceeds back to S320.

S323, determining the fault type of the fault knowledge point corresponding to the relevant fault reason.

S3241, if the target vehicle is the DTC, determining the DTC code of the fault of the target vehicle.

S3242, determining whether the DTC code still exists in the repaired target vehicle.

If yes, S328 is executed, where the number of failure times f+1 of the knowledge point of the fault knowledge point corresponding to the DTC code is calculated, and the knowledge point maturity of the fault knowledge point corresponding to the DTC code is updated.

If not, S327 is executed, where the number of times of successful knowledge points s+1 of the fault knowledge point corresponding to the DTC code is updated, and the knowledge point maturity of the fault knowledge point corresponding to the DTC code is updated.

S3251, if the WTII information is the WTII information, determining the WTII information of the fault of the target vehicle.

S3252, determining whether the WTII information still exists for the repaired target vehicle.

If yes, S328 is executed, where the number of failure times f+1 of the knowledge point of the fault knowledge point corresponding to the WTI information is calculated, and the knowledge point maturity of the fault knowledge point corresponding to the WTI information is updated.

If not, S327 is executed, where the number of times of successful knowledge point success s+1 of the fault knowledge point corresponding to the WTI information is determined, and the knowledge point maturity of the fault knowledge point corresponding to the WTI information is updated.

S3261, if the failure symptom is the failure symptom, determining the failure symptom of the failure of the target vehicle.

S3262, it is determined whether the repaired target vehicle still has the fault symptom.

If yes, S328 is executed, where the number of failure times f+1 of the knowledge points of the fault knowledge points corresponding to the fault symptom is calculated, and the knowledge point maturity of the fault knowledge points corresponding to the fault symptom is updated.

If not, S327 is executed to update the knowledge point maturity of the fault knowledge point corresponding to the fault symptom, and the number of successful knowledge point times s+1 of the fault knowledge point corresponding to the fault symptom.

S3271, if the signal is an abnormality signal, determining an abnormality signal of the current failure of the target vehicle.

S3272, it is determined whether the repaired target vehicle still presents the abnormal signal.

If yes, S328 is executed, where the number of failure times f+1 of the knowledge point of the fault knowledge point corresponding to the abnormal signal is updated, and the knowledge point maturity of the fault knowledge point corresponding to the abnormal signal is updated.

If not, S327 is executed, where the number of times of successful knowledge point success s+1 of the fault knowledge point corresponding to the abnormal signal is updated, and the knowledge point maturity of the fault knowledge point corresponding to the abnormal signal is updated.

S327, the knowledge point success times S+1 of the corresponding fault knowledge points.

S328, the failure times F+1 of the knowledge points corresponding to the fault knowledge points.

According to the fault diagnosis method provided by one or more embodiments, the pre-constructed equipment fault knowledge base can be used as the basis for equipment fault diagnosis, corresponding fault knowledge points are matched from the equipment fault knowledge base according to the equipment information and the fault information of the target equipment, the fault occurrence probability of each fault cause corresponding to the matched fault knowledge points is determined, and finally the fault cause of the target equipment can be diagnosed according to the fault occurrence probability corresponding to each fault cause, so that the equipment fault troubleshooting efficiency is effectively improved. The constructed equipment fault knowledge base is based on the system fault principle, the whole machine test and the system integration test of a plurality of equipment, and the data can be obtained in the production process before the equipment is marketed, so that the equipment fault knowledge base can be realized without accumulating a large amount of historical maintenance data of the fault equipment, thereby saving the time cost required by building the equipment fault knowledge base.

It should be noted that, in some of the above embodiments and the flows described in the drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that the operations may be performed out of the order in which they appear herein or performed in parallel, the sequence numbers of the operations, such as 110, 120, etc., are merely used to distinguish between the various operations, and the sequence numbers themselves do not represent any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.

Fig. 4 is a schematic structural diagram of a fault diagnosis apparatus according to another exemplary embodiment of the present application. The fault diagnosis apparatus is used to perform the fault diagnosis method of the embodiment shown in fig. 1 above. As shown in fig. 4, the fault diagnosis apparatus includes:

an information acquisition module 41 for acquiring device information and fault information of a target device to be repaired;

a knowledge point determining module 42, configured to determine at least one fault knowledge point that matches the device information and the fault information of the target device from a device fault knowledge base; the equipment fault knowledge base comprises a plurality of fault types, a test scheme corresponding to the fault reasons and a fault repair scheme, wherein one fault type comprises at least one fault knowledge point, one fault knowledge point corresponds to one or more fault reasons, and the equipment fault knowledge base is constructed based on the system fault principle, the equipment test and the problem data of the system integration test of equipment;

A probability determining module 43, configured to determine a probability of each fault cause corresponding to the at least one fault knowledge point;

the fault determining module 44 is configured to determine a fault cause of the target device based on probabilities of each fault cause corresponding to the at least one fault knowledge point.

According to the fault diagnosis device provided by one or more embodiments, the pre-constructed equipment fault knowledge base can be used as the basis for equipment fault diagnosis, corresponding fault knowledge points are matched from the equipment fault knowledge base according to the equipment information and the fault information of the target equipment, the fault occurrence probability of each fault cause corresponding to the matched fault knowledge points is determined, and finally the fault cause of the target equipment can be diagnosed according to the fault occurrence probability corresponding to each fault cause, so that the equipment fault troubleshooting efficiency is effectively improved. The constructed equipment fault knowledge base is based on the system fault principle, the whole machine test and the system integration test of a plurality of equipment, and the data can be obtained in the production process before the equipment is marketed, so that the equipment fault knowledge base can be realized without accumulating a large amount of historical maintenance data of the fault equipment, thereby saving the time cost required by building the equipment fault knowledge base.

The specific implementation of the fault diagnosis apparatus shown in fig. 4 has been described in detail in the embodiment of the fault diagnosis method, and will not be described in detail here.

Fig. 5 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present application. The electronic device is used to perform the fault diagnosis method of the embodiment shown in fig. 1 above. Referring to fig. 5, the electronic device includes: a memory 51 and a processor 52.

Memory 51 is used to store computer programs and may be configured to store various other data to support operations on the computing platform. Examples of such data include instructions for any application or method operating on a computing platform, contact data, phonebook data, messages, pictures, videos, and the like.

The memory 51 may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

The processor 52 is coupled to the memory 51, and is configured to execute the computer program in the memory 51, so as to perform the fault diagnosis method according to the embodiment of the present application, and the detailed description may refer to the related description in the above method embodiment, which is not repeated herein.

Further, as shown in fig. 5, the electronic device may further include: communication component 53, display 54, power component 55, audio component 56, and other components. Only some of the components are schematically shown in fig. 5, which does not mean that the electronic device only comprises the components shown in fig. 5. In addition, the components within the dashed box in fig. 5 are optional components, not necessarily optional components, depending on the product form of the electronic device. The terminal device in this embodiment may be implemented as a terminal device that can be deployed in a vehicle, such as a desktop computer, a notebook computer, a smart phone, or an IOT device, or may be a server device, such as a conventional server, a cloud server, or a server array. If the terminal device of the embodiment is implemented as a terminal device such as a desktop computer, a notebook computer, a smart phone, etc., the terminal device may include components within the dashed line frame in fig. 5; if the terminal device of the embodiment is implemented as a server device such as a conventional server, a cloud server, or a server array, the components within the dashed box in fig. 5 may not be included.

Accordingly, the present application further provides a computer readable storage medium storing a computer program, where the computer program is executed to implement the steps executable by the electronic device in the above method embodiments.

The methods in this application may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described herein are performed in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, a network device, a user device, a core network device, an OAM, or other programmable apparatus.

The computer program or instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer program or instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that integrates one or more available media. The usable medium may be a magnetic medium, e.g., floppy disk, hard disk, tape; but also optical media such as digital video discs; but also semiconductor media such as solid state disks. The computer readable storage medium may be volatile or nonvolatile storage medium, or may include both volatile and nonvolatile types of storage medium.

The communication component is configured to facilitate wired or wireless communication between the device in which the communication component is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as a mobile communication network of WiFi,2G, 3G, 4G/LTE, 5G, etc., or a combination thereof. In one exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.

The display includes a screen, which may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or sliding action, but also the duration and pressure associated with the touch or sliding operation.

The power supply component provides power for various components of equipment where the power supply component is located. The power components may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the devices in which the power components are located.

The audio component described above may be configured to output and/or input an audio signal. For example, the audio component includes a Microphone (MIC) configured to receive external audio signals when the device in which the audio component is located is in an operational mode, such as a call mode, a recording mode, and a speech recognition mode. The received audio signal may be further stored in a memory or transmitted via a communication component. In some embodiments, the audio assembly further comprises a speaker for outputting audio signals.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that 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 exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.

Claims

1. A fault diagnosis method, characterized by comprising:

Testing the target equipment based on a testing scheme of each fault cause corresponding to the at least one fault knowledge point, and determining the probability of each fault cause corresponding to the at least one fault knowledge point;

determining the fault reason of the target equipment based on the probability of each fault reason corresponding to the at least one fault knowledge point;

determining a target fault knowledge point corresponding to a fault cause of the target equipment;

updating knowledge point heat of the target fault knowledge point under the target fault type in the equipment fault knowledge base based on the target fault type corresponding to the target fault knowledge point;

the knowledge point maturity of the target fault knowledge point is used for representing the success rate of the target fault knowledge point to the equipment fault information, and the knowledge point heat of the target fault knowledge point is used for representing the importance degree of the target fault knowledge point in the equipment fault knowledge base; the knowledge point maturity of the target fault knowledge point is represented by the ratio of the number of successful knowledge points of the target fault knowledge point to the sum of the number of successful knowledge points and the number of failed knowledge points of the target fault knowledge point, the initial value of the knowledge point heat of the target fault knowledge point is 0, and when the fault of the target equipment is determined to be matched with the target fault knowledge point once, the value of the knowledge point heat of the target fault knowledge point is +1.

2. The method of claim 1, wherein the testing the target device based on the test solution for each fault cause corresponding to the at least one fault knowledge point, determining the probability of each fault cause corresponding to the at least one fault knowledge point, comprises:

testing the target equipment based on an on-line test scheme of each fault cause corresponding to the at least one fault knowledge point to obtain a test result of the target equipment;

based on the test result, determining a first related fault reason, a first related fault reason and a first to-be-determined fault reason from the fault reasons corresponding to the at least one fault knowledge point; the first related fault reason is a fault reason with the probability of 1, the first related fault reason is a fault reason with the probability of 0, and the first to-be-determined fault reason is a fault reason except for the first related fault reason and the first related fault reason among the fault reasons corresponding to the at least one fault knowledge point;

3. The method of claim 2, wherein the first pending failure cause further comprises a failure cause of an absence on-line test scenario.

4. A method according to claim 2 or 3, wherein said determining the cause of the failure of the target device based on the probability of the respective cause of the failure corresponding to the at least one point of knowledge of the failure comprises:

sorting the first pending fault reasons based on at least one of the probability of the first pending fault reason, fault identification, test cost and test scheme information;

for each pending fault reason in the ordered first pending fault reasons, if the fault repair scheme corresponding to the pending fault reason is consistent with the fault repair scheme corresponding to the first related fault reason, deleting the pending fault reason to obtain the rest pending fault reasons;

according to the sequence of the residual undetermined fault reasons, on the premise that the residual undetermined fault reasons exist on-line testing schemes, sequentially executing the on-line testing schemes corresponding to the residual undetermined fault reasons;

5. The method of claim 4, wherein the determining the failure cause of the target device based on the test results of the remaining pending failure causes comprises:

determining a second related fault reason and a second unrelated fault reason from the residual undetermined fault reasons based on the test result of the residual undetermined fault reasons, and deleting the second related fault reason and the second unrelated fault reason from the residual undetermined fault reasons;

and repeatedly executing the above operation until the number of the residual undetermined fault reasons is 0, and determining the fault reason of the target equipment based on the first related fault reason and the second related fault reason.

6. The method of claim 1, wherein the updating the knowledge point maturity of the target fault knowledge point for the target fault type in the equipment fault knowledge base based on the target fault type corresponding to the target fault knowledge point and the repair result of the target equipment comprises:

determining whether a fault corresponding to the target fault knowledge point in the target equipment under the target fault type is repaired or not based on a repair result of the target equipment;

If the fault corresponding to the target fault knowledge point under the target fault type in the target equipment is determined to be repaired, updating the knowledge point success times of the target fault knowledge point under the target fault type, and updating the knowledge point maturity of the target fault knowledge point under the target fault type based on the updated knowledge point success times;

7. A method according to any one of claims 1 to 3, wherein determining at least one fault knowledge point matching the device information and the fault information of the target device from the device fault knowledge base comprises:

determining a device model and a offline time of the target device or determining a device model and a software version of the target device based on the device information of the target device;

Determining whether a first fault knowledge point matched with the equipment model and the offline time of the target equipment or a second fault knowledge point matched with the equipment model and the software version of the target equipment exists in the equipment fault knowledge base;

and if the fault corresponding to the first fault knowledge point or the second fault knowledge point is repaired, determining at least one fault knowledge point matched with the fault information of the target equipment and the equipment model of the target equipment from the equipment fault knowledge base.

8. A computer device, comprising: a memory and a processor; wherein the memory is used for storing a computer program; the processor is coupled to the memory for executing the computer program for performing the steps in the method of any of claims 1-7.

9. A computer readable storage medium storing a computer program, characterized in that the steps in the method according to any one of claims 1-7 are implemented when the computer program is executed.