Fault analysis method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a fault analysis method and device, electronic equipment and a storage medium, and relates to the technical field of fault analysis, and the method comprises the steps: determining the number of fault alarm phenomena in response to the fault alarm phenomena of a vehicle; then, in response to the fact that the number of the fault alarm phenomena is multiple, the multiple fault alarm phenomena are classified and analyzed, at least one target fault alarm phenomenon set is obtained, and all the fault alarm phenomena in the same target fault alarm phenomenon set correspond to the same fault event; all fault alarm phenomena in any target fault alarm phenomenon set meet at least one of the following classification conditions: in this way, the accuracy of fault alarm phenomenon classification can be improved, and then the accuracy of fault analysis is improved.

Description

Technical Field

[0001] The present application relates to the technical field of fault analysis, and more specifically, to a fault analysis method, device, electronic device, and storage medium. Background Art

[0002] When a vehicle experiences a complex fault, it can manifest as multiple fault warnings, such as multiple fault lights lighting up or flashing sporadically. Currently, when multiple fault lights are lit or flashing sporadically, maintenance personnel, based on experience, attempt to troubleshoot each light individually until a common fault point is identified, at which point they are treated as a single fault.

[0003] However, this manual troubleshooting method is time-consuming and labor-intensive, resulting in low efficiency in fault analysis. Summary of the Invention

[0004] The embodiments of the present application provide a fault analysis method, device, electronic device, and storage medium, which can automatically classify fault alarm phenomena belonging to the same fault event, thereby improving the efficiency of fault analysis.

[0005] In a first aspect, an embodiment of the present application provides a fault analysis method, comprising:

[0006] In response to a fault alarm phenomenon occurring in a vehicle, the number of fault alarm phenomena is determined; in response to the number of fault alarm phenomena being multiple, the multiple fault alarm phenomena are classified and analyzed to obtain at least one target fault alarm phenomenon set, wherein all fault alarm phenomena in the same target fault alarm phenomenon set correspond to the same fault event, and all fault alarm phenomena in any target fault alarm phenomenon set satisfy at least one of the following classification conditions: a first classification condition: the interval between the occurrence times of any two fault alarm phenomena is less than an interval time threshold; or, a second classification condition: any two fault alarm phenomena correspond to the same fault code in a fault alarm phenomenon mapping table, and the fault alarm phenomenon mapping table is used to represent the association between the fault code and the fault alarm phenomenon; or, a third classification condition: the source node of the triggering node corresponding to any two fault alarm phenomena is the same, and the triggering node is the node that triggers the fault alarm phenomenon.

[0007] In this embodiment, in response to a vehicle fault alarm phenomenon, the number of fault alarm phenomena is determined. Then, in response to the number of fault alarm phenomena being multiple, the multiple fault alarm phenomena are classified and analyzed to obtain at least one target fault alarm phenomenon set, wherein all fault alarm phenomena in the same target fault alarm phenomenon set correspond to the same fault event, and all fault alarm phenomena in any target fault alarm phenomenon set satisfy at least one of the following classification conditions: a first classification condition: the interval between the occurrence times of any two fault alarm phenomena is less than an interval time threshold; or a second classification condition: any two fault alarm phenomena correspond to the same fault code in a fault alarm phenomenon mapping table, where the fault alarm phenomenon mapping table is used to represent the association between the fault code and the fault alarm phenomenon; or a third classification condition: the source node of the triggering node corresponding to any two fault alarm phenomena is the same, where the triggering node is the node that triggers the fault alarm phenomenon. In this way, fault alarm phenomena belonging to the same fault event can be classified from at least one dimension, thereby improving the efficiency of fault alarm phenomenon classification and thus improving the efficiency of fault analysis. It should be understood that if the classification conditions include at least two of the first classification conditions, the second classification conditions or the third classification conditions, that is, the fault alarm phenomena belonging to the same fault event are classified from multiple dimensions, this can improve the accuracy of classifying the fault alarm phenomena and thereby improve the accuracy of fault analysis.

[0008] In a possible implementation, multiple fault alarm phenomena are classified and analyzed to obtain at least one target fault alarm phenomenon set, including:

[0009] Multiple fault alarm phenomena are classified and analyzed step by step to obtain at least one target fault alarm phenomenon set, wherein each level of classification analysis uses the first classification condition, the second classification condition or the third classification condition for analysis, and different levels of classification analysis use different classification conditions.

[0010] In an embodiment of the present application, at least one target fault alarm phenomenon set is obtained by performing a level-by-level classification analysis on multiple fault alarm phenomena, wherein each level of classification analysis uses a first classification condition, a second classification condition, or a third classification condition for analysis, and different levels of classification analysis use different classification conditions. In this way, the classification analysis results of each level can be used as the basis for the classification analysis of the next level. Since the classification analysis results of each level are at most the entire classification basis of the current level, the computing power resources required for the classification analysis can be reduced, while maintaining classification analysis in multiple dimensions and taking into account the accuracy of the fault analysis.

[0011] In a possible implementation, multiple fault alarm phenomena are classified and analyzed step by step to obtain at least one target fault alarm phenomenon set, including:

[0012] A first-level classification analysis is performed on multiple fault alarm phenomena using a first classification condition to obtain at least one first fault alarm phenomenon set, and all fault alarm phenomena in the same first fault alarm phenomenon set meet the first classification condition; for each first fault alarm phenomenon set, a second-level classification analysis is performed on the first fault alarm phenomenon set using a second classification condition to obtain at least one second fault alarm phenomenon set, and all fault alarm phenomena in the same second fault alarm phenomenon set meet the second classification condition; for each second fault alarm phenomenon set, a third-level classification analysis is performed on the second fault alarm phenomenon set using a third classification condition to obtain at least one target fault alarm phenomenon set, and all fault alarm phenomena in the same target fault alarm phenomenon set meet the third classification condition.

[0013] In an embodiment of the present application, a first classification condition is first used to perform a first-level classification analysis on multiple fault alarm phenomena to obtain at least one first fault alarm phenomenon set. Then, the number of fault alarm phenomena in the first fault alarm phenomenon set in the first fault alarm phenomenon set can be less than the number of multiple fault alarm phenomena. In this way, when a second-level classification analysis is performed using a second classification condition, the number of fault alarm phenomena classified and analyzed can be less than the total number of multiple fault alarm phenomena. Then, when a third-level classification analysis is performed using a third classification condition, the number of fault alarm phenomena classified and analyzed is also less than the number of fault alarm phenomena in the second-level classification analysis. The computing power resources required for the classification analysis using the first classification condition, the second classification condition, and the third classification condition are not gradually increased. That is, the classification analysis is first performed in a manner with less computing power resources, thereby minimizing the computing power resources required for the classification analysis as much as possible.

[0014] In a possible implementation, a first-level classification analysis is performed on multiple fault alarm phenomena using a first classification condition to obtain at least one first fault alarm phenomenon set, including:

[0015] Obtain the occurrence time of each fault alarm phenomenon in multiple fault alarm phenomena; if the interval between the occurrence times of any two fault alarm phenomena in at least two fault alarm phenomena is less than the interval time threshold, then classify the at least two fault alarm phenomena into the same first fault alarm phenomenon set.

[0016] In this embodiment, by obtaining the occurrence time of each fault alarm phenomenon among multiple fault alarm phenomena; if the interval time between the occurrence time of any two fault alarm phenomena among at least two fault alarm phenomena is less than the interval time threshold, the at least two fault alarm phenomena are divided into the same first fault alarm phenomenon set, that is, at least two fault alarm phenomena that meet the time dimension are divided into the same first fault alarm phenomenon set, which is conducive to the second-level classification analysis, which is carried out according to the first fault alarm phenomenon set, thereby improving the efficiency of the classification analysis.

[0017] In a possible implementation, the first fault alarm phenomenon set includes multiple first fault alarm phenomena, and a second-level classification analysis is performed on the first fault alarm phenomenon set using the second classification condition to obtain at least one second fault alarm phenomenon set, including:

[0018] Obtain a target fault code and a fault warning phenomenon mapping table obtained by analyzing a fault occurring in the vehicle; and classify at least two first fault warning phenomena among a plurality of first fault warning phenomena into the same second fault warning phenomenon set, wherein the at least two first fault warning phenomena correspond to the same target fault code in the fault warning phenomenon mapping table.

[0019] In this embodiment, the target fault code and the fault alarm phenomenon mapping table are obtained by analyzing the fault occurring in the vehicle; at least two first fault alarm phenomena among the multiple first fault alarm phenomena are divided into the same second fault alarm phenomenon set, and at least two first fault alarm phenomena correspond to the same target fault code in the fault alarm phenomenon mapping table, that is, at least two first fault alarm phenomena that meet the fault code dimension are divided into the same second fault alarm phenomenon set, which is conducive to the third-level classification analysis, which is carried out according to the second fault alarm phenomenon set, thereby improving the efficiency of the classification analysis.

[0020] In one possible implementation, the second fault alarm phenomenon set includes multiple second fault alarm phenomena. A third-level classification analysis is performed on the second fault alarm phenomenon set using the third classification condition to obtain at least one target fault alarm phenomenon set, including:

[0021] For each second fault alarm phenomenon, determine the alarm trigger characteristic source signal that triggers the second fault alarm phenomenon, and identify the trigger node corresponding to the alarm trigger characteristic source signal; obtain a topology map, which is used to represent the connection relationship between each node of the vehicle; if the trigger nodes corresponding to at least two second fault alarm phenomena are the same as the source nodes corresponding to the topology map, then the at least two second fault alarm phenomena are divided into the same target fault alarm phenomenon set.

[0022] In this embodiment, for each second fault alarm phenomenon, the alarm trigger characteristic source signal that triggers the second fault alarm phenomenon is determined, and the trigger node corresponding to the alarm trigger characteristic source signal is identified; a topology map is obtained, and the topology map is used to represent the connection relationship between each node of the vehicle; if the trigger nodes corresponding to at least two second fault alarm phenomena are the same in the source nodes corresponding to the topology map, then the at least two second fault alarm phenomena are divided into the same target fault alarm phenomenon set. Since the alarm trigger characteristic source signal that triggers the second fault alarm phenomenon is determined, and the trigger node corresponding to the alarm trigger characteristic source signal is identified, the recognition accuracy of the trigger node can be improved, and the accuracy of the third-level classification analysis can be improved. In addition, since the topology map is used to identify whether the source node is the same, the efficiency of determining the same source node can be improved, thereby improving the efficiency of the third-level classification analysis.

[0023] In one possible implementation, the method further includes:

[0024] For each target fault alarm phenomenon in the target fault alarm phenomenon set, determine the alarm trigger characteristic source signal that triggers the target fault alarm phenomenon; obtain a parts mapping table, the parts mapping table is used to represent the association relationship between the alarm trigger characteristic source signal and the part; obtain the target part corresponding to the alarm trigger characteristic source signal of each target fault alarm phenomenon from the parts mapping table, and obtain a target parts list.

[0025] In this embodiment, the alarm trigger characteristic source signal that triggers the target fault alarm phenomenon is determined; a parts mapping table is obtained, and the parts mapping table is used to represent the association relationship between the alarm trigger characteristic source signal and the parts; the target parts corresponding to the alarm trigger characteristic source signal of each target fault alarm phenomenon are obtained from the parts mapping table to obtain a target parts list. In this way, the target parts corresponding to the same fault event can be known, which can help maintenance personnel quickly locate the target parts corresponding to the same fault event to check for abnormalities.

[0026] In a second aspect, an embodiment of the present application provides a fault analysis device, comprising:

[0027] An alarm module is used to determine the number of fault alarm phenomena in response to a fault alarm phenomenon occurring in the vehicle; an analysis module is used to classify and analyze the multiple fault alarm phenomena in response to the number of fault alarm phenomena being multiple, and obtain at least one target fault alarm phenomenon set, wherein all fault alarm phenomena in the same target fault alarm phenomenon set correspond to the same fault event, and all fault alarm phenomena in any target fault alarm phenomenon set meet at least one of the following classification conditions: a first classification condition: the interval time between the respective occurrence times of any two fault alarm phenomena is less than the interval time threshold; or, a second classification condition: any two fault alarm phenomena correspond to the same fault code in a fault alarm phenomenon mapping table, and the fault alarm phenomenon mapping table is used to represent the association relationship between the fault code and the fault alarm phenomenon; or, a third classification condition: the source node of the triggering node corresponding to any two fault alarm phenomena is the same, and the triggering node is the node that triggers the fault alarm phenomenon.

[0028] In a third aspect, an embodiment of the present application provides an electronic device comprising a processor and a memory, wherein: the memory is used to store computer programs; and the processor is used to execute the programs stored in the memory to implement the above method.

[0029] In a fourth aspect, the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the above method is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram of a system framework for fault analysis provided in an embodiment of the present application;

[0031] Figure 2 A flowchart of a fault analysis method provided in an embodiment of the present application;

[0032] Figure 3 A flowchart of another fault analysis method provided in an embodiment of the present application;

[0033] Figure 4 A schematic diagram of the structure of a fault analysis device provided in an embodiment of the present application;

[0034] Figure 5 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the technical problems, technical solutions and beneficial effects solved by this application more clearly understood, this application is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0036] When a vehicle experiences a complex fault, it may manifest as multiple fault warnings, such as multiple fault lights lighting up or occasionally flashing. In principle, this is a fault event. However, different fault warnings do not necessarily correspond to different fault causes. It is possible that one fault cause will trigger multiple fault lights to light up simultaneously. An independent fault event generally corresponds to one fault cause and can be analyzed together. In other words, how to quickly and efficiently identify a limited number of independent fault events from multiple fault light warnings is an important step in efficiently and accurately troubleshooting electrical problems and guiding the next work direction.

[0037] However, most maintenance personnel will try to troubleshoot each fault light one by one based on their experience, and only treat them as a single fault when they find a common fault point. This troubleshooting method is time-consuming and labor-intensive.

[0038] In view of this, the embodiments of the present application propose a fault analysis method, device, electronic device and storage medium, which can automatically classify fault alarm phenomena belonging to the same fault event, thereby improving the efficiency of fault analysis.

[0039] In general, the embodiments of the present application can quickly identify multiple independent fault events through automatic intelligent analysis and processing of the software layer and physical logic, providing an efficient analysis method for troubleshooting. The software layer may include but is not limited to signals, Diagnostic Trouble Code (DTC) fault data. The physical logic may include but is not limited to the connection between controllers and wiring harnesses.

[0040] First, the system framework of the fault analysis in the embodiment of the present application is described.

[0041] See also Figure 1 , Figure 1 This is a schematic diagram of a system framework for fault analysis provided in an embodiment of the present application. Figure 1 The illustrated system framework may include a vehicle 102 and a cloud 104. Vehicle 102 communicates with cloud 104 via a network. A data storage system may store data that cloud 104 needs to process. The data storage system may be integrated with cloud 104 or located on the cloud or other network cloud. Cloud 104 may be implemented using a standalone server or a server cluster consisting of multiple servers.

[0042] In this embodiment, the cloud 104 may monitor the vehicle's fault lights, and then, after detecting the vehicle's fault lights, classify the fault lights that belong to the same independent fault event.

[0043] In another possible implementation, the vehicle's fault lights may be monitored by the vehicle's processor, and after detecting the vehicle's fault lights, the fault lights belonging to the same independent fault event may be classified, which is not limited here.

[0044] The following describes the fault analysis method according to an embodiment of the present application.

[0045] See also Figure 2 , Figure 2 This is a flow chart of a fault analysis method provided in an embodiment of the present application. Figure 2 The method shown can be executed by an electronic device, which can be, for example, a cloud or a vehicle. Figure 2 The method shown may include:

[0046] S210 : In response to a vehicle fault warning phenomenon occurring, determine the number of fault warning phenomena.

[0047] A fault warning phenomenon can be a mechanism by which a vehicle's electronic system communicates an abnormality or fault through specific means (such as the illumination of a fault light or occasional flashing). These fault warnings can be part of the vehicle's on-board diagnostics (OBD) system, designed to help promptly identify and address potential problems and ensure driving safety. Generally, when a vehicle system or component malfunctions, the corresponding fault light, such as the engine fault light, the anti-lock braking system (ABS) fault light, or the airbag fault light, illuminates on the instrument panel. The fault light can remain on steadily, indicating that the system has detected a fault and stored the corresponding fault code (DTC). Causes of fault warnings generally include, but are not limited to: unstable sensor signals, such as those caused by interference or aging; poor wiring connections, such as loose connectors, corrosion, or faulty wiring; and system control unit software issues, such as vulnerabilities in the Electronic Control Unit (ECU) software or the need for an update.

[0048] In this embodiment, the number of fault alarm phenomena may be one or more, determined according to actual conditions and not limited herein. Multiple may mean no less than 2.

[0049] S220. In response to the number of multiple fault alarm phenomena, the multiple fault alarm phenomena are classified and analyzed to obtain at least one target fault alarm phenomenon set, wherein all fault alarm phenomena in the same target fault alarm phenomenon set correspond to the same fault event, and all fault alarm phenomena in any target fault alarm phenomenon set satisfy at least one of the following classification conditions: a first classification condition: the interval between the occurrence times of any two fault alarm phenomena is less than an interval time threshold; or, a second classification condition: any two fault alarm phenomena correspond to the same fault code in a fault alarm phenomenon mapping table, and the fault alarm phenomenon mapping table is used to indicate the association between the fault code and the fault alarm phenomenon; or, a third classification condition: the source node of the triggering node corresponding to any two fault alarm phenomena is the same, and the triggering node is the node that triggers the fault alarm phenomenon.

[0050] In this embodiment, when there are multiple fault alarm phenomena, the multiple fault alarm phenomena are classified and analyzed, or when the number of fault alarm phenomena is not less than a quantity threshold, the multiple fault alarm phenomena are classified and analyzed. The quantity threshold can be, for example, an integer not less than 2, and can be set as needed, without any restriction here.

[0051] In this embodiment, the first classification condition can be understood as classification based on the time dimension. Generally speaking, after a fault event occurs, multiple fault alarm phenomena related to the same fault event will also occur. The intervals between multiple fault alarm phenomena related to the same fault event will also be short. Therefore, by setting an interval time threshold, fault alarm phenomena with an interval time less than the interval time threshold can be classified. In this embodiment, the interval time threshold can be, for example, 10 seconds, which can be set as needed and is not limited here.

[0052] In this embodiment, the second classification condition can be understood as classification based on the fault code. Generally speaking, the fault warning phenomenon associated with a certain fault code may be fixed. Therefore, the mapping relationship between the fault code and the fault warning phenomenon can be determined through experience, and then this mapping relationship can be recorded in a fault warning phenomenon mapping table. Then, multiple fault warning phenomena with the same fault code in the corresponding fault warning phenomenon mapping table can be classified. In general, the first and second classification conditions can be understood as classification conditions used in software-level analysis and processing.

[0053] In this embodiment, the third classification condition can be understood as classification based on physical logic. Generally speaking, when a node fails, the child nodes connected to it will also fail accordingly. Therefore, if multiple nodes related to multiple fault alarm phenomena correspond to the same source node, then the multiple fault alarm phenomena corresponding to these multiple nodes may also correspond to the same fault event. Therefore, classification can be performed based on whether the source node of the triggering node corresponding to any two fault alarm phenomena is the same. The third classification condition can also be understood as a classification condition utilized in the physical logic analysis process.

[0054] It should be noted that, as needed, at least one of the first classification condition, the second classification condition or the third classification condition can be set as a classification condition for classification, and other classification conditions can also be set for classification, which is not limited here.

[0055] In this embodiment, in response to a vehicle fault alarm phenomenon, the number of fault alarm phenomena is determined. Then, in response to the number of fault alarm phenomena being multiple, the multiple fault alarm phenomena are classified and analyzed to obtain at least one target fault alarm phenomenon set, wherein all fault alarm phenomena in the same target fault alarm phenomenon set correspond to the same fault event, and all fault alarm phenomena in any target fault alarm phenomenon set satisfy at least one of the following classification conditions: a first classification condition: the interval between the occurrence times of any two fault alarm phenomena is less than an interval time threshold; or a second classification condition: any two fault alarm phenomena correspond to the same fault code in a fault alarm phenomenon mapping table, where the fault alarm phenomenon mapping table is used to represent the association between the fault code and the fault alarm phenomenon; or a third classification condition: the source node of the triggering node corresponding to any two fault alarm phenomena is the same, where the triggering node is the node that triggers the fault alarm phenomenon. In this way, fault alarm phenomena belonging to the same fault event can be classified from at least one dimension, thereby improving the efficiency of fault alarm phenomenon classification and thus improving the efficiency of fault analysis. It should be understood that if the classification conditions include at least two of the first classification conditions, the second classification conditions or the third classification conditions, that is, the fault alarm phenomena belonging to the same fault event are classified from multiple dimensions, this can improve the accuracy of classifying the fault alarm phenomena and thereby improve the accuracy of fault analysis.

[0056] It should be noted that if the interval between a fault alarm phenomenon and any of the other fault alarm phenomena is not less than the interval threshold, the fault code corresponding to the fault alarm phenomenon and any of the other fault alarm phenomena in the fault alarm phenomenon mapping table is different, and / or the trigger node corresponding to the fault alarm phenomenon and the source node corresponding to the trigger node of any of the other fault alarm phenomena are different, then the fault alarm phenomenon corresponds to a single fault event. The fault alarm phenomenon is one of multiple fault alarm phenomena, and the other fault alarm phenomena may be phenomena other than the fault alarm phenomenon among the multiple fault alarm phenomena.

[0057] In a possible implementation, multiple fault alarm phenomena are classified and analyzed to obtain at least one target fault alarm phenomenon set, including:

[0058] Multiple fault alarm phenomena are classified and analyzed step by step to obtain at least one target fault alarm phenomenon set, wherein each level of classification analysis uses the first classification condition, the second classification condition or the third classification condition for analysis, and different levels of classification analysis use different classification conditions.

[0059] In this embodiment, each level of classification analysis is performed using a first classification condition, a second classification condition, or a third classification condition. Different levels of classification analysis use different classification conditions. For example, a first-level classification analysis may be performed using a first-level classification condition on multiple fault alarm phenomena to obtain a first classification result. A second-level classification analysis may then be performed using a second-level classification condition on the first classification result to obtain a second classification result. A third-level classification analysis may then be performed using a third-level classification condition on the second classification result to obtain a third classification result. Alternatively, a first-level classification analysis may be performed using a third-level classification condition on multiple fault alarm phenomena to obtain a first classification result. A second-level classification analysis may then be performed using a second-level classification condition on the first classification result to obtain a second classification result. A third-level classification analysis may then be performed using the first-level classification condition on the second classification result to obtain a third classification result.

[0060] It should be understood that the classification conditions used in each level of classification analysis can be set as needed, and no specific limitation is made here.

[0061] Generally speaking, when performing classification analysis using the first classification condition, the second classification condition, or the third classification condition, a certain amount of computing power is required.

[0062] In an embodiment of the present application, at least one target fault alarm phenomenon set is obtained by performing a level-by-level classification analysis on multiple fault alarm phenomena, wherein each level of classification analysis uses a first classification condition, a second classification condition, or a third classification condition for analysis, and different levels of classification analysis use different classification conditions. In this way, the classification analysis results of each level can be used as the basis for the classification analysis of the next level. Since the classification analysis results of each level are at most the entire classification basis of the current level, the computing power resources required for the classification analysis can be reduced, while maintaining classification analysis in multiple dimensions and taking into account the accuracy of the fault analysis.

[0063] In another possible implementation, a first-level classification analysis may also be performed, that is, at least two of the first classification condition, the second classification condition, or the third classification condition may be used simultaneously during the first-level classification analysis, thereby improving the efficiency of the classification analysis and, in turn, improving the efficiency of the fault analysis.

[0064] In a possible implementation, multiple fault alarm phenomena are classified and analyzed step by step to obtain at least one target fault alarm phenomenon set, including:

[0065] A first-level classification analysis is performed on multiple fault alarm phenomena using a first classification condition to obtain at least one first fault alarm phenomenon set, and all fault alarm phenomena in the same first fault alarm phenomenon set meet the first classification condition; for each first fault alarm phenomenon set, a second-level classification analysis is performed on the first fault alarm phenomenon set using a second classification condition to obtain at least one second fault alarm phenomenon set, and all fault alarm phenomena in the same second fault alarm phenomenon set meet the second classification condition; for each second fault alarm phenomenon set, a third-level classification analysis is performed on the second fault alarm phenomenon set using a third classification condition to obtain at least one target fault alarm phenomenon set, and all fault alarm phenomena in the same target fault alarm phenomenon set meet the third classification condition.

[0066] Among them, at least one first fault alarm phenomenon set can be understood as a first classification result, at least one second fault alarm phenomenon set can be understood as a second classification result, and at least one target fault alarm phenomenon set can be understood as a third classification result.

[0067] In this embodiment, the computing power resources required for classification analysis using the first classification condition are less than the computing power resources required for analysis using the second classification condition, and the computing power resources required for classification using the second classification condition are less than the computing power resources required for analysis using the third classification condition.

[0068] In an embodiment of the present application, a first classification condition is first used to perform a first-level classification analysis on multiple fault alarm phenomena to obtain at least one first fault alarm phenomenon set. Then, the number of fault alarm phenomena in the first fault alarm phenomenon set in the first fault alarm phenomenon set can be less than the number of multiple fault alarm phenomena. In this way, when a second-level classification analysis is performed using a second classification condition, the number of fault alarm phenomena classified and analyzed can be less than the total number of multiple fault alarm phenomena. Then, when a third-level classification analysis is performed using a third classification condition, the number of fault alarm phenomena classified and analyzed is also less than the number of fault alarm phenomena in the second-level classification analysis. The computing power resources required for the classification analysis using the first classification condition, the second classification condition, and the third classification condition are not gradually increased. That is, the classification analysis is first performed in a manner with less computing power resources, thereby minimizing the computing power resources required for the classification analysis as much as possible.

[0069] It should be noted that, in this embodiment, the first level, the second level and the third level are only relative concepts, and do not mean that there is no classification analysis before the first level classification analysis. For example, pre-classification processing may be performed before the first level classification analysis, and the method of pre-classification processing is not limited here.

[0070] The following embodiments, based on the above embodiments, respectively illustrate how to perform the first-level classification analysis, how to perform the second-level classification analysis, and how to perform the third-level classification analysis.

[0071] First, how to conduct the first-level classification analysis is explained.

[0072] In a possible implementation, a first-level classification analysis is performed on multiple fault alarm phenomena using a first classification condition to obtain at least one first fault alarm phenomenon set, including:

[0073] Obtain the occurrence time of each fault alarm phenomenon in multiple fault alarm phenomena; if the interval between the occurrence times of any two fault alarm phenomena in at least two fault alarm phenomena is less than the interval time threshold, then classify the at least two fault alarm phenomena into the same first fault alarm phenomenon set.

[0074] In this embodiment, when a fault warning phenomenon occurs, the vehicle's software layer can record the occurrence time of the fault warning phenomenon. Therefore, the occurrence time of each fault warning phenomenon can be obtained from the vehicle's software layer. If the interval between the occurrence times of any two fault warning phenomena among at least two fault warning phenomena is less than the interval time threshold, it indicates that the at least two fault warning phenomena meet the classification conditions of the time dimension. Therefore, the at least two fault warning phenomena can be classified into the same first fault warning phenomenon set.

[0075] In this embodiment, it should be noted that if the interval time between a fault alarm phenomenon and any other fault alarm phenomenon is not less than the interval time threshold, then the fault alarm phenomenon can be considered to correspond to a single fault event, and the fault alarm phenomenon can be separate.

[0076] In this embodiment, by obtaining the occurrence time of each fault alarm phenomenon among multiple fault alarm phenomena; if the interval time between the occurrence time of any two fault alarm phenomena among at least two fault alarm phenomena is less than the interval time threshold, the at least two fault alarm phenomena are divided into the same first fault alarm phenomenon set, that is, at least two fault alarm phenomena that meet the time dimension are divided into the same first fault alarm phenomenon set, which is conducive to the second-level classification analysis, which is carried out according to the first fault alarm phenomenon set, thereby improving the efficiency of the classification analysis.

[0077] Next, we will explain how to conduct the second-level classification analysis.

[0078] In a possible implementation, the first fault alarm phenomenon set includes multiple first fault alarm phenomena, and a second-level classification analysis is performed on the first fault alarm phenomenon set using the second classification condition to obtain at least one second fault alarm phenomenon set, including:

[0079] Obtain a target fault code and a fault warning phenomenon mapping table obtained by analyzing a fault occurring in the vehicle; and classify at least two first fault warning phenomena among a plurality of first fault warning phenomena into the same second fault warning phenomenon set, wherein the at least two first fault warning phenomena correspond to the same target fault code in the fault warning phenomenon mapping table.

[0080] In this embodiment, when a fault alarm phenomenon occurs, the vehicle fault is generally identified, a fault code (DTC) is obtained, and the fault code is stored in the vehicle's software layer. Therefore, the fault code can be obtained from the vehicle's software layer. In this embodiment, the fault alarm phenomenon mapping table can be a mapping table set according to experience, which records one or more fault codes. The fault alarm phenomenon mapping table also records one or more fault alarm phenomena associated with each fault code. If the fault code corresponding to at least two first fault alarm phenomena in the fault alarm phenomenon mapping table is the target fault code, it means that the at least two first fault alarm phenomena may correspond to the same fault event. Therefore, the at least two first fault alarm phenomena are classified into the same second fault alarm phenomenon set.

[0081] In this embodiment, the target fault code and the fault alarm phenomenon mapping table are obtained by analyzing the fault occurring in the vehicle; at least two first fault alarm phenomena among the multiple first fault alarm phenomena are divided into the same second fault alarm phenomenon set, and at least two first fault alarm phenomena correspond to the same target fault code in the fault alarm phenomenon mapping table, that is, at least two first fault alarm phenomena that meet the fault code dimension are divided into the same second fault alarm phenomenon set, which is conducive to the third-level classification analysis, which is carried out according to the second fault alarm phenomenon set, thereby improving the efficiency of the classification analysis.

[0082] It should be noted that if a first fault alarm phenomenon has a different fault code from any other first fault alarm phenomenon in the fault alarm phenomenon mapping table, then the first fault alarm phenomenon corresponds to a single fault event.

[0083] Next, we will explain how to conduct the third-level classification analysis.

[0084] In one possible implementation, the second fault alarm phenomenon set includes multiple second fault alarm phenomena. A third-level classification analysis is performed on the second fault alarm phenomenon set using the third classification condition to obtain at least one target fault alarm phenomenon set, including:

[0085] For each second fault alarm phenomenon, determine the alarm trigger characteristic source signal that triggers the second fault alarm phenomenon, and identify the trigger node corresponding to the alarm trigger characteristic source signal; obtain a topology map, which is used to represent the connection relationship between each node of the vehicle; if the trigger nodes corresponding to at least two second fault alarm phenomena are the same as the source nodes corresponding to the topology map, then the at least two second fault alarm phenomena are divided into the same target fault alarm phenomenon set.

[0086] Among them, the topology diagram can be pre-configured according to the connection relationship between each node of the vehicle. The source nodes corresponding to the topology diagram are the same, and can be, for example, common crimping points, pin loops, etc. The topology diagram can also be called a wiring harness topology diagram. The wiring harness topology diagram is a graphical representation that describes the connection relationship of the wiring harness inside a vehicle or equipment. It shows the physical connection and signal transmission path between each node (such as sensors, actuators, ECU, etc.). Through the wiring harness topology diagram, maintenance personnel can intuitively understand the structure and signal flow of the system, providing an important basis for fault diagnosis. The trigger node is the node that triggers the fault alarm phenomenon, which can be understood as the trigger node being the fault node. The alarm trigger characteristic source signal can be understood as a fault characteristic signal, that is, the fault alarm phenomenon is triggered by the alarm trigger characteristic source signal.

[0087] For example, assuming that a vehicle has multiple fault alarm phenomena, including the engine fault light on, the ABS fault light on, and an inaccurate speedometer, the alarm trigger characteristic source signals of the engine control unit (ECU), the ABS control unit, and the speed sensor are collected. It is then identified that the trigger unit that triggers the engine fault light to light up is the ECU, the trigger unit that triggers the ABS fault light to light up is the ABS control unit, and the trigger unit that triggers the inaccurate speedometer is the speed sensor. Then, based on the wiring harness topology diagram, it is found that these alarm trigger characteristic source signals all pass through the same node: the body control module (BCM). In other words, it is found that these trigger nodes are all connected to the BCM, so it can be known that the phenomena of the engine fault light on, the ABS fault light on, and the speedometer inaccurate meet the third classification condition.

[0088] In this embodiment, for each second fault alarm phenomenon, the alarm trigger characteristic source signal that triggers the second fault alarm phenomenon is determined, and the trigger node corresponding to the alarm trigger characteristic source signal is identified; a topology map is obtained, and the topology map is used to represent the connection relationship between each node of the vehicle; if the trigger nodes corresponding to at least two second fault alarm phenomena are the same in the source nodes corresponding to the topology map, then the at least two second fault alarm phenomena are divided into the same target fault alarm phenomenon set. Since the alarm trigger characteristic source signal that triggers the second fault alarm phenomenon is determined, and the trigger node corresponding to the alarm trigger characteristic source signal is identified, the recognition accuracy of the trigger node can be improved, and the accuracy of the third-level classification analysis can be improved. In addition, since the topology map is used to identify whether the source node is the same, the efficiency of determining the same source node can be improved, thereby improving the efficiency of the third-level classification analysis.

[0089] It should be noted that if the triggering node corresponding to a second fault alarm phenomenon is different from the source node of the triggering node corresponding to any other second fault alarm phenomenon, then the second fault alarm phenomenon corresponds to a single fault event.

[0090] In one possible implementation, a target fault alarm phenomenon set can be displayed so that maintenance personnel can learn about multiple target fault alarm phenomena belonging to the same fault event through the target fault alarm phenomenon set. This can help maintenance personnel quickly locate the fault alarm phenomena of the same fault to troubleshoot anomalies.

[0091] In another possible implementation, the method further includes:

[0092] For each target fault alarm phenomenon in the target fault alarm phenomenon set, determine the alarm trigger characteristic source signal that triggers the target fault alarm phenomenon; obtain a parts mapping table, the parts mapping table is used to represent the association relationship between the alarm trigger characteristic source signal and the part; obtain the target part corresponding to the alarm trigger characteristic source signal of each target fault alarm phenomenon from the parts mapping table, and obtain a target parts list.

[0093] The part mapping table may be a pre-defined table based on the association between the alarm triggering characteristic source signal and the parts. In this embodiment, the target parts list may include the target parts corresponding to the alarm triggering characteristic source signal for each target fault alarm phenomenon. The target parts can be understood as the parts related to the fault event.

[0094] In this embodiment, the alarm trigger characteristic source signal that triggers the target fault alarm phenomenon is determined; a parts mapping table is obtained, and the parts mapping table is used to represent the association relationship between the alarm trigger characteristic source signal and the parts; the target parts corresponding to the alarm trigger characteristic source signal of each target fault alarm phenomenon are obtained from the parts mapping table to obtain a target parts list. In this way, the target parts corresponding to the same fault event can be known, which can help maintenance personnel quickly locate the target parts corresponding to the same fault event to check for abnormalities.

[0095] For ease of understanding, the following embodiment describes how to perform fault analysis using a three-level classification analysis and a fault alarm phenomenon where a fault light is on.

[0096] See also Figure 3 , Figure 3 This is a schematic diagram of another fault analysis process provided by an embodiment of the present application. The method of this embodiment can be applied to an intelligent diagnosis and analysis system, which can be deployed in the cloud.

[0097] S301. Remote monitoring of fault lights.

[0098] This step may be the starting point of the process, indicating the start of remote monitoring of the fault light.

[0099] S302: Fault light type.

[0100] In this step, the type of the fault light can be determined, and one fault light type corresponds to one fault alarm phenomenon.

[0101] S303: Determine whether multiple fault lights are on (≥2).

[0102] In this embodiment, if multiple fault lights are on (≥2), the process proceeds to S304; otherwise, the process proceeds to S309.

[0103] S304: Determine time dependency consistency (10S).

[0104] In this embodiment, when multiple fault lamps are on, it is further determined whether the time dependencies are consistent and the time difference is within 10 seconds. If the time dependencies are consistent, the process proceeds to S305; otherwise, the process proceeds to S309.

[0105] Among them, the judgment of time subordination consistency can refer to the description of the first-level classification analysis and will not be elaborated here.

[0106] S305: Determine whether the DCT logic directions within the time slave are consistent.

[0107] In this embodiment, based on the consistency of the time slaves, it is determined whether the DTC logic directions within the time slaves are consistent.

[0108] If the logic points to the same, proceed to S306; otherwise, proceed to S309.

[0109] Among them, whether the DCT logic pointing is consistent can refer to the description of the second-level classification analysis, which will not be elaborated here.

[0110] S306: Determine whether the physical layer (wiring harness node, ECU) is associated.

[0111] In this embodiment, based on the consistency of both time dependency and DTC logic orientation, a determination is made as to whether the physical layer (including the harness node and ECU) is associated. If so, S307 is executed; otherwise, the process proceeds to S309. These events are then determined to be the same independent event, and a conclusion is drawn based on the three-layer logic. If not, these events are determined to be a single independent event, and a conclusion is drawn based on the single event logic.

[0112] In this embodiment, to determine whether the physical layers are associated, reference may be made to the description of the third-level classification analysis, which will not be elaborated here.

[0113] S307, the same independent event.

[0114] In this embodiment, if at least two fault lights of the multiple fault lights have consistent time dependencies, consistent DTC logic directions and physical layer association, it means that the at least two fault lights belong to the same independent event.

[0115] S308: Integrate the three levels of logical judgment and draw conclusions.

[0116] In this embodiment, the conclusion drawn may be, for example, at least two faulty lamps belonging to the same fault event, or components belonging to the same fault event.

[0117] S309. Single independent event.

[0118] In this example, if one of the fault lights has inconsistent time dependencies, inconsistent DTC logic pointing, or unrelated physical layers, then the fault light corresponds to a separate fault event.

[0119] S310. Draw conclusions based on single event logic.

[0120] In this embodiment, the conclusion drawn may be a specific fault event of the single independent event.

[0121] In this embodiment, if the physical layer is correlated, it is determined to be the same independent event and a conclusion is drawn based on the three-layer logic judgment. If the physical layer is not correlated, it is determined to be a single independent event and a conclusion is drawn based on the single event logic.

[0122] In this embodiment, the cloud platform can specifically read the characteristic bus data of the vehicle's instrument fault lights (also known as fault warning phenomena) through vehicle-to-cloud remote communication technology. By analyzing the characteristic bus data corresponding to each instrument fault light mapping, the cloud platform can obtain the specific status of each instrument fault light on the vehicle. The fault light status can be, for example, the time when the fault light is illuminated and the fault light phenomenon occurs. The intelligent diagnostic analysis system then filters the information of the illuminated instrument fault lights to form an instrument fault light illumination list. The instrument fault light illumination list is then categorized by time, forming a first-level screening result information list (the first fault warning phenomenon set).

[0123] Furthermore, based on the Vehicle Identification Number (VIN), the cloud platform can call the remote diagnostic system to read the vehicle's bus data and vehicle fault codes. The vehicle's bus data can include bus signals, which can include fault signature signals, thereby revealing the cause of the fault light.

[0124] Then, because of the fault code diagnosis mechanism, the lighting status of the fault lights corresponding to each fault code can be known. The intelligent diagnosis and analysis system has a fault code and fault light association mapping table (also known as a fault alarm phenomenon mapping table) preset by technicians. Through this table, the association between each fault code and each fault light can be known. Therefore, the intelligent diagnosis and analysis system can quickly classify multiple fault lights that are lit according to the fault code based on the read vehicle fault code and the fault code and fault light association mapping table. For multiple fault lights that can be associated with the same fault code, the analysis system classifies these fault light information into the same independent fault event set; at the same time, multiple independent fault event sets are summarized into the same information list to form the second-level screening result information list (the second fault alarm phenomenon set).

[0125] The intelligent analysis system further analyzes the second-level screening result information list. First, according to the system's preset fault light analysis decision tree, from the characteristic bus data corresponding to the mapping of the fault light, the bus data of the vehicle is analyzed layer by layer to the source to obtain the alarm trigger characteristic source signal of each fault event. Then, the intelligent diagnosis and analysis system analyzes each alarm trigger characteristic source signal and extracts the node information of the signal. The node information can represent the trigger node corresponding to the signal. Then, the intelligent diagnosis and analysis system indexes all signal node information according to the preset wiring harness topology association table (also known as the topology diagram or wiring harness topology diagram) to find the common points of the wiring harness topology diagram, such as common crimping points, pin loops, etc. If the same wiring harness topology diagram common points can be found, the analysis system will classify them as the same independent fault event. Multiple independent fault event sets are aggregated into the same information list to form the third-level screening result information list (target fault alarm phenomenon set). If no common points in the same harness topology are found, the analysis system may assume that these nodes are not harness-associated fault points, and the independent fault event set cannot be further filtered through the harness topology association table. Therefore, the second-level screening result information list is used as the third-level screening result information list.

[0126] In this embodiment, the intelligent diagnosis screens out multiple independent fault event sets based on the screening result information list of the third layer, and displays the information on the display screen to assist after-sales personnel in diagnosing and analyzing the independent fault events of the vehicle.

[0127] The intelligent diagnostic analysis system then aggregates the fault codes, fault signature data, and bus-related data associated with each individual fault event into a single set. It then performs precise analysis based on a pre-set diagnostic logic decision tree. Finally, it deduces and analyzes a limited number of independent fault source signatures. Based on the fault source signatures and a parts mapping table, it generates a corresponding list of faulty parts. The intelligent diagnostic analysis system displays the list of faulty parts on the display, guiding after-sales personnel in accurately inspecting and repairing the corresponding faulty parts. This results in precise diagnosis and repair.

[0128] In general, the intelligent diagnostic analysis system obtains fault signature data, bus data, fault codes, and other data from the actual vehicle through a cloud platform and remote diagnosis. Using this data, the intelligent diagnostic analysis system can map the actual vehicle's fault light illumination warning status. The system then analyzes the characteristic bus signals and abnormal characteristic signals step by step through a pre-set diagnostic analysis decision tree to derive the triggering condition signal (also known as the alarm triggering characteristic source signal) that triggers the fault light illumination warning.

[0129] According to fault generation principles, only the same fault event (or the same fault cause) can trigger the simultaneous illumination of the fault light alarm within a short period of time. Therefore, within a limited time window T (T is in seconds, generally recommended to be within 10 seconds), fault lights that illuminate at the same time can be considered as the same independent fault event. This facilitates the intelligent analysis system to analyze and classify independent fault events based on the time window conditions. Therefore, based on the time window conditions, the first independent fault event classification conditions for multiple fault light fault phenomena can be obtained.

[0130] Furthermore, since multiple fault lights represent a single fault phenomenon, each individual fault phenomenon may represent an independent fault event; multiple fault phenomena may also be classified as the same independent fault event. Therefore, an intelligent analysis system is required to classify the fault signature signals based on a specific logical mechanism and summarize the possible independent fault events. Because the designed fault code mechanism can determine the corresponding fault light illumination status for each fault code, the intelligent diagnostic analysis system includes a fault code-fault light association table pre-compiled by technicians. This table allows the system to determine the association between each fault code and each fault light.

[0131] Therefore, the intelligent diagnostic analysis system, based on the vehicle fault code and the fault code-fault light association mapping table, can quickly categorize multiple fault lights that are illuminated by fault code. For multiple fault lights associated with the same fault code, the analysis system classifies these fault light information as a single independent fault event set. This allows the system to derive a second independent fault event classification condition for multiple fault light fault phenomena based on the fault code.

[0132] On the other hand, a common fault point on the wiring harness topology can trigger fault anomalies in multiple wiring harness pin circuits and corresponding signals associated with that common fault point. Therefore, using the wiring harness topology and fault signature signals, the intelligent diagnostic analysis system can analyze the wiring harness topology based on the nodes corresponding to the fault signature signals and identify the possible common fault points. This allows the third independent fault event classification criteria for multiple fault light fault phenomena to be derived based on the wiring harness topology associations.

[0133] Based on the three independent fault event classification criteria, the system can accurately analyze and compare the fault symptoms of multiple fault lights, generating a list of independent fault events. The number of independent fault events corresponds to the number of independent fault sources for the vehicle. Next, the intelligent diagnostic analysis system conducts a step-by-step reasoning analysis of each independent fault event based on the fault decision tree, obtaining accurate and precise fault source information. This helps after-sales personnel precisely identify the fault source signal and the corresponding faulty part information.

[0134] In this embodiment, the fault light characteristic data can be accurately, efficiently and rigorously classified and analyzed through three layers of conditional information, such as the fault light occurrence time window, the fault code generation mechanism and the wiring harness topology association table. The screening and classification analysis of multiple fault data can be creatively and quickly realized, the essence can be understood through the phenomenon, and the corresponding independent fault event information list can be obtained. Further analysis can be carried out according to the fault decision tree to help after-sales personnel accurately and efficiently find the fault source signal and faulty parts.

[0135] The following is an exemplary description of the device embodiment of this embodiment.

[0136] See also Figure 4 , Figure 4 This is a structural diagram of a fault analysis device provided in an embodiment of the present application. Figure 4 The device shown can be applied to an electronic device, and includes an alarm module 410 and an analysis module 420, wherein:

[0137] The alarm module 410 is used to determine the number of fault alarm phenomena in response to a fault alarm phenomenon occurring in the vehicle; the analysis module 420 is used to classify and analyze the multiple fault alarm phenomena in response to the number of fault alarm phenomena being multiple, and obtain at least one target fault alarm phenomenon set, wherein all fault alarm phenomena in the same target fault alarm phenomenon set correspond to the same fault event, and all fault alarm phenomena in any target fault alarm phenomenon set meet at least one of the following classification conditions: a first classification condition: the interval time between the respective occurrence times of any two fault alarm phenomena is less than the interval time threshold; or, a second classification condition: any two fault alarm phenomena correspond to the same fault code in the fault alarm phenomenon mapping table, and the fault alarm phenomenon mapping table is used to represent the association relationship between the fault code and the fault alarm phenomenon; or, a third classification condition: the source node of the triggering node corresponding to any two fault alarm phenomena is the same, and the triggering node is the node that triggers the fault alarm phenomenon.

[0138] In a possible implementation, when the analysis module 420 classifies and analyzes multiple fault alarm phenomena and obtains at least one target fault alarm phenomenon set, it can be used to:

[0139] Multiple fault alarm phenomena are classified and analyzed step by step to obtain at least one target fault alarm phenomenon set, wherein each level of classification analysis uses the first classification condition, the second classification condition or the third classification condition for analysis, and different levels of classification analysis use different classification conditions.

[0140] In a possible implementation, the analysis module 420 performs a level-by-level classification analysis on multiple fault alarm phenomena to obtain at least one target fault alarm phenomenon set, which can be used to:

[0141] A first-level classification analysis is performed on multiple fault alarm phenomena using a first classification condition to obtain at least one first fault alarm phenomenon set, and all fault alarm phenomena in the same first fault alarm phenomenon set meet the first classification condition; for each first fault alarm phenomenon set, a second-level classification analysis is performed on the first fault alarm phenomenon set using a second classification condition to obtain at least one second fault alarm phenomenon set, and all fault alarm phenomena in the same second fault alarm phenomenon set meet the second classification condition; for each second fault alarm phenomenon set, a third-level classification analysis is performed on the second fault alarm phenomenon set using a third classification condition to obtain at least one target fault alarm phenomenon set, and all fault alarm phenomena in the same target fault alarm phenomenon set meet the third classification condition.

[0142] In a possible implementation, the analysis module 420 performs a first-level classification analysis on multiple fault alarm phenomena using the first classification condition to obtain at least one first fault alarm phenomenon set, which can be used to:

[0143] Obtain the occurrence time of each fault alarm phenomenon in multiple fault alarm phenomena; if the interval between the occurrence times of any two fault alarm phenomena in at least two fault alarm phenomena is less than the interval time threshold, then classify the at least two fault alarm phenomena into the same first fault alarm phenomenon set.

[0144] In one possible implementation, the first fault alarm phenomenon set includes multiple first fault alarm phenomena. When the analysis module 420 performs a second-level classification analysis on the first fault alarm phenomenon set using the second classification condition and obtains at least one second fault alarm phenomenon set, the analysis module 420 may use the following method:

[0145] Obtain a target fault code and a fault warning phenomenon mapping table obtained by analyzing a fault occurring in the vehicle; and classify at least two first fault warning phenomena among a plurality of first fault warning phenomena into the same second fault warning phenomenon set, wherein the at least two first fault warning phenomena correspond to the same target fault code in the fault warning phenomenon mapping table.

[0146] In one possible implementation, the second fault alarm phenomenon set includes multiple second fault alarm phenomena. When the analysis module 420 performs a third-level classification analysis on the second fault alarm phenomenon set using the third classification condition and obtains at least one target fault alarm phenomenon set, it can be used to:

[0147] For each second fault alarm phenomenon, determine the alarm trigger characteristic source signal that triggers the second fault alarm phenomenon, and identify the trigger node corresponding to the alarm trigger characteristic source signal; obtain a topology map, which is used to represent the connection relationship between each node of the vehicle; if the trigger nodes corresponding to at least two second fault alarm phenomena are the same as the source nodes corresponding to the topology map, then the at least two second fault alarm phenomena are divided into the same target fault alarm phenomenon set.

[0148] In a possible implementation, the analysis module 420 is further configured to:

[0149] For each target fault alarm phenomenon in the target fault alarm phenomenon set, determine the alarm trigger characteristic source signal that triggers the target fault alarm phenomenon; obtain a parts mapping table, the parts mapping table is used to represent the association relationship between the alarm trigger characteristic source signal and the part; obtain the target part corresponding to the alarm trigger characteristic source signal of each target fault alarm phenomenon from the parts mapping table, and obtain a target parts list.

[0150] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. In the several embodiments provided in the present application, the coupling between modules can be electrical. In addition, the various functional modules in the various embodiments of the present application can be integrated into a processing module, or each module can exist physically alone, or two or more modules can be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules.

[0151] The present application also provides an electronic device 50, please refer to Figure 5 , including a processor 510 and a memory 520, wherein the memory 510 is used to store computer programs; the processor 520 is used to execute the programs stored in the memory 510 to implement the fault analysis method described in any embodiment of the present application. The electronic device 50 of this embodiment can be, for example, a vehicle or a cloud.

[0152] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the fault analysis method introduced in any embodiment of the present application is implemented.

[0153] In this application, a plurality refers to two or more.

[0154] In this application, unless otherwise expressly defined, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they can refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; and internal communication between two components. A person of ordinary skill in the art will understand the specific meanings of these terms in this application.

[0155] The terms "first," "second," "third," "fourth," etc. (if any) in this application are used to distinguish similar objects and are not necessarily used to describe a particular sequential order.

[0156] The term "and / or" in this application simply describes an association between related objects, indicating that three possible relationships exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this application generally indicates that the related objects are in an "or" relationship.

[0157] Unless otherwise specified, all steps of this application may be performed sequentially or randomly. For example, a statement that a method includes steps A and B indicates that the method may include steps A and B performed sequentially, or steps B and A performed sequentially. For example, a statement that a method may also include step C indicates that step C may be added to the method in any order. For example, a method may include steps A, B, and C, or steps A, C, and B, or steps C, A, and B, etc.

[0158] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A fault analysis method, characterized in that: include: In response to a fault warning phenomenon occurring in the vehicle, determining the number of fault warning phenomena; In response to the number of fault alarm phenomena being multiple, the multiple fault alarm phenomena are classified and analyzed to obtain at least one target fault alarm phenomenon set, wherein all fault alarm phenomena in the same target fault alarm phenomenon set correspond to the same fault event, and all fault alarm phenomena in any target fault alarm phenomenon set meet at least one of the following classification conditions: First classification condition: the interval between the occurrence times of any two fault alarm phenomena is less than the interval time threshold; or Second classification condition: any two fault alarm phenomena correspond to the same fault code in the fault alarm phenomenon mapping table, where the fault alarm phenomenon mapping table is used to indicate the association between the fault code and the fault alarm phenomenon; or The third classification condition: the source nodes of the triggering nodes corresponding to any two fault alarm phenomena are the same, and the triggering node is the node that triggers the fault alarm phenomenon.

2. The method according to claim 1, characterized in that The classifying and analyzing the multiple fault alarm phenomena to obtain at least one target fault alarm phenomenon set includes: A plurality of fault alarm phenomena are classified and analyzed level by level to obtain at least one target fault alarm phenomenon set, wherein each level of classification analysis uses the first classification condition, the second classification condition, or the third classification condition for analysis, and different levels of classification analysis use different classification conditions.

3. The method according to claim 2, characterized in that The step-by-step classification analysis of the multiple fault alarm phenomena to obtain at least one target fault alarm phenomenon set includes: Performing a first-level classification analysis on multiple fault alarm phenomena using the first classification condition to obtain at least one first fault alarm phenomenon set, wherein all fault alarm phenomena in the same first fault alarm phenomenon set meet the first classification condition; For each of the first fault alarm phenomenon sets, performing a second-level classification analysis on the first fault alarm phenomenon set using the second classification condition to obtain at least one second fault alarm phenomenon set, wherein all fault alarm phenomena in the same second fault alarm phenomenon set meet the second classification condition; For each of the second fault alarm phenomenon sets, a third-level classification analysis is performed on the second fault alarm phenomenon set using the third classification condition to obtain at least one target fault alarm phenomenon set, and all fault alarm phenomena in the same target fault alarm phenomenon set meet the third classification condition.

4. The method according to claim 3, characterized in that The first classification condition is used to perform a first-level classification analysis on the multiple fault alarm phenomena to obtain at least one first fault alarm phenomenon set, including: Obtain the occurrence time of each fault alarm phenomenon among multiple fault alarm phenomena; If the interval between the occurrence times of any two fault alarm phenomena among the at least two fault alarm phenomena is less than the interval time threshold, the at least two fault alarm phenomena are classified into the same first fault alarm phenomenon set.

5. The method according to claim 3, characterized in that The first fault alarm phenomenon set includes multiple first fault alarm phenomena, and the second classification condition is used to perform a second-level classification analysis on the first fault alarm phenomenon set to obtain at least one second fault alarm phenomenon set, including: Obtaining a target fault code and a fault warning phenomenon mapping table obtained by analyzing the fault occurring in the vehicle; At least two first fault warning phenomena among the multiple first fault warning phenomena are divided into the same second fault warning phenomenon set, and the at least two first fault warning phenomena correspond to the same target fault code in the fault warning phenomenon mapping table.

6. The method according to claim 3, characterized in that The second fault alarm phenomenon set includes multiple second fault alarm phenomena, and the third classification condition is used to perform a third-level classification analysis on the second fault alarm phenomenon set to obtain at least one target fault alarm phenomenon set, including: For each second fault alarm phenomenon, determining an alarm triggering characteristic source signal that triggers the second fault alarm phenomenon, and identifying a triggering node corresponding to the alarm triggering characteristic source signal; Obtaining a topology map, wherein the topology map is used to represent the connection relationship between nodes of the vehicle; If the triggering nodes corresponding to the at least two second fault alarm phenomena are the same as the source node corresponding to the topology graph, the at least two second fault alarm phenomena are grouped into the same target fault alarm phenomenon set.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: For each target fault alarm phenomenon in the target fault alarm phenomenon set, determining an alarm triggering characteristic source signal that triggers the target fault alarm phenomenon; Obtaining a parts mapping table, wherein the parts mapping table is used to represent the association relationship between the alarm triggering feature source signal and the parts; The target parts corresponding to the alarm triggering characteristic source signal of each target fault alarm phenomenon are obtained from the parts mapping table to obtain a target parts list.

8. A fault analysis device, characterized in that: include: an alarm module, configured to determine the number of fault alarm phenomena in response to a fault alarm phenomenon occurring in the vehicle; An analysis module is configured to, in response to a plurality of fault alarm phenomena, classify and analyze the plurality of fault alarm phenomena to obtain at least one target fault alarm phenomenon set, wherein all fault alarm phenomena in the same target fault alarm phenomenon set correspond to the same fault event, and all fault alarm phenomena in any target fault alarm phenomenon set satisfy at least one of the following classification conditions: First classification condition: the interval between the occurrence times of any two fault alarm phenomena is less than the interval time threshold; or Second classification condition: any two fault alarm phenomena correspond to the same fault code in the fault alarm phenomenon mapping table, where the fault alarm phenomenon mapping table is used to indicate the association between the fault code and the fault alarm phenomenon; or The third classification condition: the source nodes of the triggering nodes corresponding to any two fault alarm phenomena are the same, and the triggering node is the node that triggers the fault alarm phenomenon.

9. An electronic device, characterized in that: comprising a processor and a memory, wherein: Memory for storing computer programs; A processor, configured to execute a program stored in a memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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