Vehicle fault detection method, device and equipment, storage medium and vehicle
By extracting the identification information of some nodes in the fault tree and looking for inference information in the inference information database, and filling the fault tree for vehicle fault detection, the problems of large amount of data and low detection efficiency in traditional methods are solved, and faster fault detection is achieved.
Patent Information
- Application Number
- CN202311253809.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-09-26
- Publication Date
- 2025-03-28
AI Technical Summary
Traditional vehicle fault detection methods require the writing of detection codes in advance and the issuance of codes containing specific reasoning information, resulting in large data transmission, long user waiting time and low detection efficiency.
By extracting the identification information of some nodes in the fault tree, searching and adding inference information in the preset inference information database, filling the fault tree with a complete inference path, and thus performing fault detection.
The amount of data received is reduced, the waiting time for users is shortened, and the overall detection speed is accelerated.
Smart Images
Figure CN119717754A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of vehicle technology, and in particular to a vehicle fault detection method, device, equipment, storage medium and vehicle. Background Art
[0002] With the development of the economy, cars have entered more and more families. In order to ensure the driving safety of vehicles, fault detection needs to be performed frequently. However, the traditional fault detection method often writes the detection code in advance based on the fault problem to be detected, and then sends the completed code to the vehicle so that the vehicle can perform fault detection based on the code. For different fault problems, it is necessary to send codes containing different detection algorithms. The data transmission volume is large, the user waiting time is long, and the overall detection efficiency of vehicle fault detection is low. Therefore, how to perform fault detection on the vehicle to improve the overall efficiency of fault detection is a technical problem that needs to be solved. Summary of the invention
[0003] In order to solve the above technical problems, the present disclosure provides a vehicle fault detection method, device, equipment, storage medium and vehicle.
[0004] A first aspect of an embodiment of the present disclosure provides a vehicle fault detection method, the method comprising:
[0005] Extracting identification information of at least part of the nodes contained in the fault tree, wherein the root node of the fault tree corresponds to a vehicle fault event, and the child nodes under the root node of the fault tree correspond to cause events leading to the vehicle fault event;
[0006] For each node in the at least some nodes, searching for reasoning information corresponding to the identification information of the node in a preset reasoning information library, and adding the reasoning information to the node, the reasoning information being used to deduce an event result corresponding to the current node based on event results corresponding to child nodes under the current node;
[0007] Based on the cause events corresponding to the child nodes in the fault tree and the reasoning information corresponding to each node, the event result of the vehicle fault event corresponding to the root node is determined, and the event result of the vehicle fault event is determined as the fault detection result.
[0008] A second aspect of an embodiment of the present disclosure provides a vehicle fault detection device, the device comprising:
[0009] A first extraction module is used to extract identification information of at least part of the nodes contained in the fault tree, wherein the root node of the fault tree corresponds to the vehicle fault event, and the child nodes under the root node of the fault tree correspond to the cause events leading to the vehicle fault event;
[0010] A search module, used for searching, for each of the at least some nodes, the reasoning information corresponding to the identification information of the node in a preset reasoning information library, and adding the reasoning information to the node, wherein the reasoning information is used to deduce the event result corresponding to the current node based on the event result corresponding to the child node under the current node;
[0011] The detection module is used to determine the event result of the vehicle fault event corresponding to the root node based on the cause events corresponding to the child nodes in the fault tree and the reasoning information corresponding to each node, and determine the event result of the vehicle fault event as the fault detection result.
[0012] A third aspect of an embodiment of the present disclosure provides a computer device, comprising a memory and a processor, and a computer program, wherein the computer program is stored in the memory, and when the computer program is executed by the processor, the vehicle fault detection method as described in the first aspect above is implemented.
[0013] A fourth aspect of an embodiment of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the vehicle fault detection method as described in the first aspect above is implemented.
[0014] The fifth aspect of an embodiment of the present disclosure provides a vehicle, comprising a memory and a processor, and a computer program, wherein the computer program is stored in the memory, and when the computer program is executed by the processor, the vehicle fault detection method as described in the first aspect above is implemented.
[0015] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:
[0016] In the vehicle fault detection method, apparatus, device, storage medium and vehicle provided by the embodiments of the present disclosure, by extracting identification information of at least some nodes contained in a fault tree, the root node of the fault tree corresponds to a vehicle fault event, and the child nodes under the root node of the fault tree correspond to the cause events that cause the vehicle fault event. For each of the at least some nodes, the reasoning information corresponding to the identification information of the node is searched in a preset reasoning information library, and the reasoning information is added to the node. The reasoning information is used to deduce the event result corresponding to the current node based on the event result corresponding to the child nodes under the current node. Fault detection is performed based on the fault tree, and the reasoning information corresponding to the identification information of each node can be searched in the reasoning information library to fill the fault tree so that the fault tree has a complete reasoning path. Based on the cause events corresponding to the child nodes in the fault tree and the reasoning information corresponding to each node, the event result of the vehicle fault event corresponding to the root node is determined, and the event result of the vehicle fault event is determined as the fault detection result. Then, when receiving the configuration file sent by the server, the configuration file does not need to contain specific reasoning information, which reduces the amount of received data, shortens the waiting time of the user, and speeds up the overall detection speed. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0019] Figure 1 is a flow chart of a vehicle fault detection method provided by an embodiment of the present disclosure;
[0020] Figure 2 is a flow chart of a method for searching for reasoning information provided by an embodiment of the present disclosure;
[0021] Figure 3 is a flow chart of a method for determining an event result of a vehicle failure event provided by an embodiment of the present disclosure;
[0022] Figure 4 is a flow chart of another method for determining an event result of a vehicle failure event provided by an embodiment of the present disclosure;
[0023] Figure 5 is a structural schematic diagram of a vehicle fault detection device provided by an embodiment of the present disclosure;
[0024] Figure 6 It is a structural diagram of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION
[0025] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.
[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.
[0027] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.
[0028] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.
[0029] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".
[0030] Figure 1 1 is a flow chart of a vehicle fault detection method provided by an embodiment of the present disclosure. The method can be executed by a vehicle fault detection device, which can be arranged on a vehicle. Figure 1 As shown, the vehicle fault detection method provided in this embodiment includes the following steps:
[0031] S101. Extract identification information of at least some nodes contained in a fault tree, wherein a root node of the fault tree corresponds to a vehicle fault event, and child nodes under the root node of the fault tree correspond to cause events leading to the vehicle fault event.
[0032] The identification information in the embodiments of the present disclosure may be understood as information used to identify a node in a fault tree and distinguish it from other nodes. For example, the identification information may be numbering information.
[0033] In the disclosed embodiment, the fault tree may include a root node for characterizing a vehicle fault event of a target detection, and child nodes of different levels under the root node, each child node corresponding to a cause event, and the cause event corresponding to each child node can directly affect the cause event corresponding to its upper parent node. For the child node directly connected to the root node, the corresponding cause event can directly affect the vehicle fault event corresponding to the root node. Optionally, the fault tree can be established based on a pre-acquired fault tree configuration file issued by the cloud, and the configuration file includes identification information of each node of the fault tree, node information and the relationship between each node. After receiving the fault tree configuration file, the vehicle can establish a fault tree based on the fault tree configuration file.
[0034] In the embodiment of the present disclosure, the vehicle fault detection device can determine at least some of the nodes that need to add reasoning information from the various nodes included in the fault tree. Specifically, the nodes that do not contain reasoning information can be determined as nodes that need to add at least some of the reasoning information, and identification information can be extracted from the information of these nodes.
[0035] Optionally, when the fault tree configuration file is sent from the cloud, for reasoning information not included in the reasoning information library, such reasoning information and the correspondence between the reasoning information and the nodes can be included in the sent configuration file, so that when extracting the identification information of at least some of the nodes included in the fault tree, only the identification information of the nodes for which the corresponding reasoning information does not exist in the configuration file is extracted.
[0036] In an exemplary implementation of the disclosed embodiment, the vehicle fault detection device may determine at least some of the nodes that do not contain reasoning information from the nodes included in the fault tree, and extract identification information from the information of these nodes.
[0037] In another exemplary implementation of the disclosed embodiment, the vehicle fault detection device can perform keyword detection on the information of at least part of the nodes, determine keywords related to the identification information, such as "id", "NO.", etc., and determine the content after the keyword as the identification information of the node. It can also extract specific types of information, such as digital information, and determine it as the identification information of these nodes.
[0038] S102. For each node among the at least some nodes, search for reasoning information corresponding to the identification information of the node in a preset reasoning information library, and add the reasoning information to the node, wherein the reasoning information is used to deduce the event result corresponding to the current node based on the event result corresponding to the child node under the current node.
[0039] The reasoning information in the disclosed embodiment may be understood as information used to infer the event result of the current node based on the event result corresponding to the subordinate child nodes. For example, the reasoning information may include calculation methods, reasoning logic, etc., which are not limited here.
[0040] The reasoning information base in the embodiment of the present disclosure may be understood as a database for storing reasoning information corresponding to a node, and the reasoning information base may be set in local storage.
[0041] In an embodiment of the present disclosure, the vehicle fault detection device can determine at least some of the nodes in the fault tree to which reasoning information needs to be added and extract the identification information of these nodes. Then, for each node, the device can search for the reasoning information corresponding to the identification information of the node in a preset reasoning information library, and after finding the corresponding reasoning information, add the reasoning information as the information of the node to the node in the fault tree.
[0042] In an exemplary implementation of the embodiment of the present disclosure, the vehicle fault detection device may use a preset search instruction to search for the representation information of each node, and add the found identification information to the corresponding node.
[0043] In another exemplary implementation of the disclosed embodiment, the vehicle fault detection device may, for each node of at least some of the nodes, compare the identification information of the node with each identification information in a preset reasoning information library until identification information in the reasoning information library that is consistent with the identification information of the node is found, and the reasoning information corresponding to the identification information is determined as the reasoning information of the node.
[0044] S103, based on the cause events corresponding to the child nodes in the fault tree and the reasoning information corresponding to each node, determine the event result of the vehicle fault event corresponding to the root node, and determine the event result of the vehicle fault event as the fault detection result.
[0045] In the disclosed embodiment, the vehicle fault detection device can determine that the fault tree has a complete reasoning path after adding the identification information corresponding to each node in at least some of the nodes to the corresponding node, and thus perform fault detection on the vehicle based on the fault tree. Specifically, the event result of whether the vehicle fault event corresponding to the root node has occurred can be inferred using the reasoning information corresponding to each node based on the event result of the cause event corresponding to each sub-node in the fault tree, and the event result of the vehicle fault event can be determined as the fault detection result, thereby realizing fault detection of the vehicle.
[0046] In an exemplary implementation of the disclosed embodiment, a vehicle fault detection device may generate a fault detection execution sequence table based on a fault tree, periodically traverse the status files of various vehicle components according to the fault detection execution sequence table, and fill the traversal results into the fault tree as event results of cause events corresponding to each node of the fault tree, deduce the event result of whether the vehicle fault event corresponding to the root node occurs based on the reasoning information of each node in the fault tree, and determine the event result of the vehicle fault event as the fault detection result.
[0047] The disclosed embodiment extracts identification information of at least some nodes contained in a fault tree, wherein the root node of the fault tree corresponds to a vehicle fault event, and the child nodes under the root node of the fault tree correspond to the cause events that cause the vehicle fault event. For each of the at least some nodes, the reasoning information corresponding to the identification information of the node is searched in a preset reasoning information library, and the reasoning information is added to the node. The reasoning information is used to deduce the event result corresponding to the current node based on the event result corresponding to the child nodes under the current node. Based on the cause events corresponding to the child nodes in the fault tree and the reasoning information corresponding to each node, the event result of the vehicle fault event corresponding to the root node is determined, and the event result of the vehicle fault event is determined as the fault detection result. The reasoning information corresponding to the identification information of each node can be searched in the reasoning information library to fill the fault tree, so that the fault tree has a complete reasoning path. Fault detection is performed based on the fault tree, and when the configuration file is sent to the vehicle, it is not necessary to send specific reasoning information, thereby reducing the amount of data sent, shortening the waiting time of the user, and accelerating the overall detection speed.
[0048] In some embodiments of the present disclosure, the reasoning information includes a calculation method and reasoning logic, and the reasoning information is used to calculate and reason the event results corresponding to the subordinate child nodes based on the calculation method and reasoning logic of the current node to obtain the event results corresponding to the current node.
[0049] Among them, the calculation methods include algorithm models in addition to traditional mathematical operations, and the reasoning logic includes membership logic, set logic, similarity logic, etc. in addition to traditional operations such as and, or, and not, which are not limited here.
[0050] Specifically, the reasoning information may include calculation methods and reasoning logic. The reasoning information is used to calculate and reason the event results corresponding to the subordinate sub-nodes based on the event results corresponding to the sub-nodes under the current node, using the calculation method and reasoning logic of the current node, to obtain the event results corresponding to the current node, thereby ultimately determining the event results of the fault event corresponding to the root node of the fault tree and completing the vehicle fault detection.
[0051] Figure 2 is a flow chart of a method for searching for reasoning information provided by an embodiment of the present disclosure, such as Figure 2 As shown, based on the above embodiment, the reasoning information can be searched by the following method, wherein the reasoning information library includes a calculation method library and a reasoning logic library.
[0052] S201: Searching for a calculation method corresponding to the identification information of the node in a preset calculation method library.
[0053] In the embodiment of the present disclosure, the reasoning information library may specifically include a calculation method library and a reasoning logic library. The calculation method library may be understood as a database for storing the calculation methods corresponding to the nodes, and the reasoning logic library may be understood as a database for storing the reasoning logic corresponding to the nodes. Similar to the reasoning information library, the calculation method library and the reasoning logic library are also set in local storage.
[0054] In the disclosed embodiment, the vehicle fault detection device can determine at least some of the nodes in the fault tree to which reasoning information needs to be added, and after extracting the identification information of these nodes, for each node, search for the calculation method corresponding to the identification information of the node in a preset calculation method library, and after finding the corresponding calculation method, add the calculation method as the information of the node to the node in the fault tree. The specific search method is similar to S102 and will not be repeated here.
[0055] S202: Searching for the reasoning logic corresponding to the identification information of the node in a preset reasoning logic library.
[0056] In the embodiment of the present disclosure, the vehicle fault detection device can search for the reasoning logic corresponding to the identification information of the node in a preset reasoning logic library after searching for the calculation method corresponding to the identification information of the node, and after finding the corresponding reasoning logic, add the reasoning logic as the information of the node to the node in the fault tree. The specific search method is similar to S102 and will not be repeated here.
[0057] In an exemplary implementation of the embodiments of the present disclosure, in at least some of the nodes to which reasoning information needs to be added, there is at least one of a first node, a second node, and a third node, and the first identification information corresponding to the first node only has a corresponding calculation method, the second identification information corresponding to the second node only has a corresponding reasoning logic, and the third identification information corresponding to the third node has a corresponding calculation method and reasoning logic.
[0058] The disclosed embodiment searches for the calculation method corresponding to the identification information of the node in a preset calculation method library, and searches for the reasoning logic corresponding to the identification information of the node in a preset reasoning logic library. The calculation method and reasoning logic corresponding to the identification information can be searched in the calculation method library and the reasoning logic library respectively, and the two searches can be performed simultaneously. Compared with the reasoning information library, the overall data volume of the calculation method library and the reasoning logic library is smaller, the search speed is faster, and the search time is shorter, which further improves the overall speed of vehicle fault detection.
[0059] Figure 3 is a flow chart of a method for determining an event result of a vehicle failure event provided by an embodiment of the present disclosure, such as Figure 3 As shown, based on the above embodiment, the event result of the vehicle failure event can be determined by the following method.
[0060] S301: Obtain an event result of a cause event corresponding to a leaf node in the fault tree, where the leaf node is a child node at the end of the fault tree.
[0061] The leaf node in the embodiment of the present disclosure can be understood as a sub-node located at the end of the fault tree, and the event result of the cause event corresponding to the leaf node can be directly obtained through a sensor or an electronic control unit (Electronic Control Unit, ECU).
[0062] In the disclosed embodiment, the vehicle fault detection device may obtain the event result of the cause event corresponding to the leaf node at the end of the fault tree in the fault tree through a sensor or an ECU.
[0063] S302. Based on the event results of the cause events corresponding to each of the leaf nodes and the reasoning information of the parent nodes of each of the leaf nodes, the event results of the cause events corresponding to the parent nodes are inferred, and so on, until the event result of the vehicle failure event corresponding to the root node is determined.
[0064] In the disclosed embodiment, the vehicle fault detection device can determine the parent node of each leaf node after determining the event result of the cause event corresponding to each leaf node, and obtain the reasoning information of the parent node of the leaf node, combine the event result of the leaf node and the reasoning information of the parent node, determine the event result of the cause event corresponding to the parent node, and then determine the parent node of the previous level corresponding to the parent node, combine the event result of the parent node and the reasoning information of the parent node of the previous level, determine the event result of the cause event corresponding to the parent node of the previous level, and so on, until the event result of the vehicle fault event corresponding to the root node is determined.
[0065] For example, the child nodes under the root node A are node B and node C, node B is a leaf node located at the end of the fault tree, and the child nodes under node C are node D and node E, both of which are leaf nodes located at the end of the fault tree. When the vehicle fault detection device detects the vehicle fault event corresponding to the root node A based on the fault tree, it can first determine the event results of the cause events corresponding to the leaf nodes B, D, and E, and then determine the event results of the cause events corresponding to node C in combination with the event results corresponding to nodes D and E and the reasoning information of node C, and determine the event results of the vehicle fault event corresponding to the root node A in combination with the event results corresponding to nodes B and C and the reasoning information of the root node A.
[0066] The disclosed embodiment obtains the event result of the cause event corresponding to the leaf node in the fault tree, where the leaf node is a child node located at the end of the fault tree. Based on the event result of the cause event corresponding to each leaf node and the reasoning information of the parent node of each leaf node, the event result of the cause event corresponding to the parent node is inferred, and so on, until the event result of the vehicle failure event corresponding to the root node is determined. The fault tree can be deduced layer by layer to determine whether a vehicle failure event occurs, and at the same time, various cause events that lead to the occurrence of the vehicle failure event are clarified to facilitate subsequent troubleshooting of the fault.
[0067] In some other embodiments of the present disclosure, the vehicle fault detection device can extract parameter information of at least some of the nodes from the fault tree, and for each of the at least some of the nodes, the parameter information includes the parameter type and parameter value of the parameter used by the reasoning information of the node.
[0068] Specifically, after determining at least some of the nodes in the fault tree to which reasoning information needs to be added, the vehicle fault detection device can extract parameter information of these nodes from the fault tree. Specifically, the parameter type and parameter value of the parameters required for the reasoning information of each of these nodes can be extracted. For example, for nodes related to vehicle speed, a vehicle speed threshold parameter is required when completing reasoning based on the reasoning information. The parameter type of the vehicle speed threshold parameter can be an integer, and the specific parameter value can be 60. Therefore, when the configuration file of the fault tree is sent down in the cloud, different parameters can be flexibly sent down according to the vehicle situation, thereby improving the reusability of the same fault tree on different vehicles.
[0069] Figure 4 is a flowchart of another method for determining the event result of a vehicle failure event provided by an embodiment of the present disclosure, such as Figure 4 As shown, based on the above embodiment, the event result of the vehicle failure event can be determined by the following method.
[0070] S401. For each node in the at least part of the nodes, substitute parameter information of the node into a calculation method and reasoning logic.
[0071] In the embodiment of the present disclosure, the vehicle fault detection device can, after determining the parameter information of each node in at least part of the nodes that need to add reasoning information, substitute the parameter information into the corresponding reasoning information, specifically substitute the corresponding calculation method and reasoning logic, to obtain a complete calculation method and reasoning logic containing actual parameters.
[0072] S402: Calculate the calculation result of the cause event corresponding to each leaf node based on the event result of the cause event corresponding to each leaf node and the calculation method of the parent node of each leaf node.
[0073] In the disclosed embodiment, the vehicle fault detection device can, after substituting specific parameters into the corresponding calculation method and reasoning logic, firstly, for the event results of the cause events corresponding to each leaf node, use the calculation method corresponding to the parent node of these leaf nodes to calculate the event results corresponding to the leaf nodes to obtain the calculation results. For example, leaf node A and leaf node B are two leaf nodes under node C, the event result corresponding to leaf node A is that the braking force provided by the first brake system is 20, and the event result corresponding to leaf node B is that the braking force provided by the second brake system is 10. The calculation method corresponding to node C is to determine the weighted average of the braking forces corresponding to leaf node A and leaf node B as the comprehensive braking force. The parameters of the calculation method corresponding to node C are that the weight corresponding to leaf node A is 0.7, and the weight corresponding to leaf node B is 0.3. Then the calculation result obtained by using the calculation method of node C is 20*0.7+10*0.3=17.
[0074] S403: Based on the calculation results of the cause events corresponding to the leaf nodes and the reasoning logic of the parent nodes of the leaf nodes, the event results of the cause events corresponding to the parent nodes are inferred.
[0075] In the disclosed embodiment, the vehicle fault detection device can, after obtaining the calculation results of the cause events corresponding to the leaf nodes, use the reasoning logic corresponding to the parent nodes of these leaf nodes to perform logical deduction on the calculation results to obtain the event results of the cause events corresponding to the parent nodes. For example, after determining that the calculation results of leaf nodes A and leaf node B calculated using the calculation method of parent node C are 17, the reasoning logic corresponding to node C is used, such as whether the calculation result is greater than a preset threshold, and the parameters of the reasoning logic corresponding to node C: the preset threshold is 15, it can be determined that the calculation result 17 is greater than the preset threshold 15, and further determine that the cause event result corresponding to node C is that the cause event has occurred, and the comprehensive braking force is 17.
[0076] The disclosed embodiment substitutes the parameter information of each node into the corresponding calculation method and reasoning logic, calculates the calculation result of the cause event corresponding to each leaf node based on the event result of the cause event corresponding to each leaf node and the calculation method of the parent node of each leaf node, and infers the event result of the cause event corresponding to the parent node based on the calculation result of the cause event corresponding to each leaf node and the reasoning logic of the parent node of each leaf node. The calculation method and reasoning logic can be comprehensively applied to determine the event result of the event of each node, and then determine the event result of the vehicle failure event of the root node, thereby improving the accuracy of the fault detection result. At the same time, when a fault occurs, it is convenient for relevant personnel to understand the cause of the fault and eliminate the fault as soon as possible.
[0077] Figure 5 is a structural diagram of a vehicle fault detection device provided by an embodiment of the present disclosure, such as Figure 5As shown, the vehicle fault detection device 500 includes: a first extraction module 510, a search module 520, and a detection module 530, wherein the first extraction module 510 is used to extract identification information of at least some nodes contained in a fault tree, the root node of the fault tree corresponds to a vehicle fault event, and the child nodes under the root node of the fault tree correspond to the cause events that cause the vehicle fault event. The search module 520 is used to search for reasoning information corresponding to the identification information of the node in a preset reasoning information library for each node in the at least some nodes, and add the reasoning information to the node, the reasoning information is used to deduce the event result corresponding to the current node based on the event result corresponding to the child nodes under the current node, and the detection module 530 is used to determine the event result of the vehicle fault event corresponding to the root node based on the cause events corresponding to the child nodes in the fault tree and the reasoning information corresponding to each node, and determine the event result of the vehicle fault event as the fault detection result.
[0078] Optionally, the reasoning information includes a calculation method and reasoning logic, and the reasoning information is used to calculate and reason the event results corresponding to the subordinate child nodes based on the calculation method and reasoning logic of the current node to obtain the event results corresponding to the current node.
[0079] Optionally, the reasoning information library includes a calculation method library and a reasoning logic library, and the search module 520 includes: a first search unit, used to search for the calculation method corresponding to the identification information of the node in the preset calculation method library; a second search unit, used to search for the reasoning logic corresponding to the identification information of the node in the preset reasoning logic library.
[0080] Optionally, the detection module 530 includes: an acquisition unit, used to obtain the event result of the cause event corresponding to the leaf node in the fault tree, wherein the leaf node is a child node located at the end of the fault tree; an inference unit, used to infer the event result of the cause event corresponding to the parent node based on the event result of the cause event corresponding to each of the leaf nodes and the inference information of the parent node of each of the leaf nodes, and so on, until the event result of the vehicle fault event corresponding to the root node is determined.
[0081] Optionally, the vehicle fault detection device 500 also includes: a second extraction module, used to extract parameter information of at least some of the nodes from the fault tree, and for each node in the at least some of the nodes, the parameter information includes the parameter type and parameter value of the parameter used by the reasoning information of the node.
[0082] Optionally, the reasoning unit includes: a substitution sub-unit, used to substitute the parameter information of the node into the calculation method and reasoning logic for each node in the at least part of the nodes; a calculation sub-unit, used to calculate the calculation result of the cause event corresponding to each leaf node based on the event result of the cause event corresponding to each leaf node and the calculation method of the parent node of each leaf node; and an inference sub-unit, used to infer the event result of the cause event corresponding to the parent node based on the calculation result of the cause event corresponding to each leaf node and the reasoning logic of the parent node of each leaf node.
[0083] The vehicle fault detection device provided in this embodiment can execute the method described in any of the above embodiments, and its execution method and beneficial effects are similar and will not be repeated here.
[0084] Figure 6 It is a structural diagram of a computer device provided in an embodiment of the present disclosure.
[0085] like Figure 6 As shown, the computer device may include a processor 610 and a memory 620 storing computer program instructions.
[0086] Specifically, the processor 610 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.
[0087] The memory 620 may include a large capacity memory for information or instructions. By way of example and not limitation, the memory 620 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 620 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 620 may be inside or outside the integrated gateway device. In a particular embodiment, the memory 620 is a non-volatile solid-state memory. In a particular embodiment, the memory 620 includes a read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (Electrically Erasable Programmable ROM, EEPROM), an electrically rewritable ROM (EAROM) or a flash memory, or a combination of two or more of these.
[0088] The processor 610 performs the steps of the vehicle fault detection method provided in the embodiment of the present disclosure by reading and executing the computer program instructions stored in the memory 620.
[0089] In one example, the computer device may further include a transceiver 630 and a bus 640. Figure 6 As shown, the processor 610, the memory 620 and the transceiver 630 are connected via a bus 640 and communicate with each other.
[0090] The bus 640 includes hardware, software, or both. For example, but not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industrial Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, the bus 640 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.
[0091] The embodiments of the present disclosure also provide a computer-readable storage medium, which may store a computer program. When the computer program is executed by a processor, the processor implements the vehicle fault detection method provided by the embodiments of the present disclosure.
[0092] The above-mentioned storage medium may, for example, include a memory 620 of computer program instructions, and the above-mentioned instructions may be executed by the processor 610 of the vehicle fault detection device to complete the vehicle fault detection method provided by the embodiment of the present disclosure. Optionally, the storage medium may be a non-temporary computer-readable storage medium, for example, the non-temporary computer-readable storage medium may be a ROM, a random access memory (Random Access Memory, RAM), a compact disc read-only memory (Compact DiscROM, CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc. The above-mentioned computer program may be written in any combination of one or more programming languages to perform the program code of the operation of the embodiment of the present disclosure, and the programming language includes an object-oriented programming language, such as Java, C++, etc., and also includes a conventional procedural programming language, such as "C" language or a similar programming language. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device, partially on the remote computing device, or completely on the remote computing device or server.
[0093] An embodiment of the present disclosure also provides a vehicle, which includes a memory, a processor, and a computer program, wherein the computer program is stored in the memory, and when the computer program is executed by the processor, the various processes and effects in the above-mentioned embodiments of the present disclosure can be implemented, which will not be elaborated here.
[0094] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A vehicle fault detection method, characterized in that: The method comprises: Extracting identification information of at least part of the nodes contained in the fault tree, wherein the root node of the fault tree corresponds to a vehicle fault event, and the child nodes under the root node of the fault tree correspond to cause events leading to the vehicle fault event; For each node in the at least some nodes, searching for reasoning information corresponding to the identification information of the node in a preset reasoning information library, and adding the reasoning information to the node, the reasoning information being used to deduce an event result corresponding to the current node based on event results corresponding to child nodes under the current node; Based on the cause events corresponding to the child nodes in the fault tree and the reasoning information corresponding to each node, the event result of the vehicle fault event corresponding to the root node is determined, and the event result of the vehicle fault event is determined as the fault detection result.
2. The method according to claim 1, characterized in that The reasoning information includes a calculation method and reasoning logic, and the reasoning information is used to calculate and reason the event results corresponding to the subordinate child nodes based on the calculation method and reasoning logic of the current node to obtain the event result corresponding to the current node.
3. The method according to claim 2, characterized in that The reasoning information library includes a calculation method library and a reasoning logic library, and searching the preset reasoning information library for the reasoning information corresponding to the identification information of the node includes: Searching for a calculation method corresponding to the identification information of the node in a preset calculation method library; The reasoning logic corresponding to the identification information of the node is searched in a preset reasoning logic library.
4. The method according to claim 2, characterized in that: The determining the event result of the vehicle fault event corresponding to the root node based on the cause events corresponding to the child nodes in the fault tree and the reasoning information corresponding to each node includes: Obtaining an event result of a cause event corresponding to a leaf node in the fault tree, wherein the leaf node is a child node located at the end of the fault tree; Based on the event results of the cause events corresponding to each of the leaf nodes and the reasoning information of the parent nodes of each of the leaf nodes, the event results of the cause events corresponding to the parent nodes are inferred, and so on, until the event results of the vehicle failure events corresponding to the root node are determined.
5. The method according to claim 4, characterized in that Before determining the event result of the vehicle fault event corresponding to the root node based on the cause events corresponding to the child nodes in the fault tree and the reasoning information corresponding to each node, the method further includes: Parameter information of at least some of the nodes is extracted from the fault tree, and for each node in the at least some of the nodes, the parameter information includes parameter types and parameter values of parameters used by reasoning information of the node.
6. The method according to claim 5, characterized in that The event result of the cause event corresponding to each leaf node and the reasoning information of the parent node of each leaf node are inferred to obtain the event result of the cause event corresponding to the parent node, including: For each node in the at least some of the nodes, substituting the parameter information of the node into the calculation method and reasoning logic; Based on the event results of the cause events corresponding to each of the leaf nodes and the calculation method of the parent node of each of the leaf nodes, the calculation results of the cause events corresponding to each of the leaf nodes are calculated; Based on the calculation results of the cause events corresponding to each of the leaf nodes and the reasoning logic of the parent nodes of each of the leaf nodes, the event results of the cause events corresponding to the parent nodes are inferred.
7. A vehicle fault detection device, characterized in that: The device comprises: A first extraction module is used to extract identification information of at least part of the nodes contained in the fault tree, wherein the root node of the fault tree corresponds to the vehicle fault event, and the child nodes under the root node of the fault tree correspond to the cause events leading to the vehicle fault event; A search module, used for searching, for each of the at least some nodes, the reasoning information corresponding to the identification information of the node in a preset reasoning information library, and adding the reasoning information to the node, wherein the reasoning information is used to deduce the event result corresponding to the current node based on the event result corresponding to the child node under the current node; The detection module is used to determine the event result of the vehicle fault event corresponding to the root node based on the cause events corresponding to the child nodes in the fault tree and the reasoning information corresponding to each node, and determine the event result of the vehicle fault event as the fault detection result.
8. A computer device, characterized in that: include: Memory; processor; and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the method according to any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that: The 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 6 is implemented.
10. A vehicle, characterized in that: include: Memory; processor; and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the method according to any one of claims 1 to 6.