Vehicle maintenance method, device and equipment and storage medium
By inserting characteristic coding and broad binary tree in the maintenance case of historical vehicle maintenance cases, the vehicle failure retrieval tree is formed, solving the problem of low efficiency of traditional search methods, and realizing fast and accurate maintenance case search and efficient management.
Patent Information
- Application Number
- CN202411492811.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-23
- Publication Date
- 2025-01-21
AI Technical Summary
Traditional vehicle maintenance search methods are inefficient and cannot quickly and accurately obtain specific maintenance case data, resulting in inefficient vehicle maintenance.
By collecting historical vehicle maintenance cases, the digital encoding processing of feature information is performed. Based on the preset vehicle failure information level, the coding features are encoded into the broader binary tree to form a vehicle fault retrieval tree, and the fast search and positioning target cases are achieved.
It greatly improves the efficiency and accuracy of vehicle repair search, and enables efficient management and rapid search of large-scale vehicle repair case data.
Smart Images
Figure CN119338441A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of vehicle detection technology, and in particular to a vehicle maintenance method, device, equipment and storage medium. Background Art
[0002] In the automobile maintenance industry, a large amount of vehicle maintenance case data has been accumulated. However, when it is necessary to find a specific maintenance case, the traditional search method is inefficient and cannot meet the needs of quickly and accurately obtaining information, resulting in low vehicle maintenance efficiency. Therefore, how to improve vehicle maintenance efficiency needs to be solved. Summary of the invention
[0003] In view of this, the purpose of the present invention is to provide a vehicle maintenance method, device, equipment and storage medium, which can efficiently manage and quickly search large-scale automobile maintenance case data. The specific scheme is as follows:
[0004] In a first aspect, the present application discloses a vehicle maintenance method, comprising:
[0005] Collecting historical vehicle maintenance cases, and performing digital coding processing on feature information in the historical vehicle maintenance cases to obtain coded features;
[0006] Inserting the encoded features into a preset generalized binary tree based on a preset vehicle fault information level to obtain a vehicle fault retrieval tree;
[0007] Obtain fault search conditions corresponding to a target vehicle, and search the vehicle fault retrieval tree based on the fault search conditions to determine corresponding target encoded features, and then generate a target vehicle maintenance case corresponding to the target vehicle based on the target encoded features, so as to perform vehicle maintenance on the target vehicle based on the target vehicle maintenance case.
[0008] Optionally, the collecting of historical vehicle maintenance cases and digitally encoding characteristic information in the historical vehicle maintenance cases to obtain encoded characteristics includes:
[0009] Collecting historical vehicle maintenance cases, and extracting vehicle features in the historical vehicle maintenance cases based on vehicle type, fault type, and maintenance method to obtain feature information;
[0010] The feature information is subjected to a data encoding conversion operation to obtain the encoded features corresponding to the historical vehicle maintenance cases.
[0011] Optionally, inserting the encoded features into a preset generalized binary tree based on a preset vehicle fault information level to obtain a vehicle fault retrieval tree includes:
[0012] Defining a node structure of a balanced binary tree based on the characteristic information to obtain a preset generalized binary tree;
[0013] The insertion order of the encoded features is determined based on a preset vehicle fault information level, and the encoded features are inserted into the preset generalized binary tree according to the insertion order to obtain a vehicle fault retrieval tree.
[0014] Optionally, determining the insertion order of the encoded features based on a preset vehicle fault information level, and inserting the encoded features into the preset generalized binary tree according to the insertion order to obtain a vehicle fault retrieval tree includes:
[0015] Determining a first target feature from the encoded features based on the vehicle type, and determining the target feature as a root node of the preset generalized binary tree and inserting it into the preset generalized binary tree;
[0016] Based on a preset vehicle fault information level, a node level corresponding to a second target feature is determined, and based on the node level and the coding value corresponding to the second target feature, the second target feature is sequentially inserted into the preset generalized binary tree to obtain a vehicle fault retrieval tree; the second target feature is other encoded features among the encoded features except the first target feature.
[0017] Optionally, the acquiring a fault search condition corresponding to the target vehicle, and searching the vehicle fault retrieval tree based on the fault search condition to determine the corresponding target encoded feature, includes:
[0018] The fault search conditions corresponding to the target vehicle are acquired and digitally coded to obtain coded conditions, and the vehicle fault retrieval tree is searched based on the coded conditions to determine the corresponding target coded features.
[0019] Optionally, searching the vehicle fault retrieval tree based on the encoded condition to determine a corresponding target encoded feature includes:
[0020] The coding value corresponding to the post-coding condition is compared with the coding value corresponding to each node in the vehicle fault retrieval tree, and the corresponding target post-coding feature is determined according to the comparison result.
[0021] In a second aspect, the present application discloses a vehicle maintenance device, comprising:
[0022] A feature information encoding module, used to collect historical vehicle maintenance cases, and digitally encode the feature information in the historical vehicle maintenance cases to obtain encoded features;
[0023] A fault tree creation module, used for inserting the encoded features into a preset generalized binary tree based on a preset vehicle fault information level to obtain a vehicle fault retrieval tree;
[0024] A fault case determination module is used to obtain the fault search conditions corresponding to the target vehicle, and search the vehicle fault retrieval tree based on the fault search conditions to determine the corresponding target encoded features, and then generate a target vehicle maintenance case corresponding to the target vehicle based on the target encoded features, so as to perform vehicle maintenance on the target vehicle based on the target vehicle maintenance case.
[0025] Optionally, the feature information encoding module includes:
[0026] A feature information extraction unit, used to collect historical vehicle maintenance cases, and extract vehicle features in the historical vehicle maintenance cases based on vehicle type and fault type to obtain feature information;
[0027] The coding conversion unit is used to perform a data coding conversion operation on the feature information to obtain the coded features corresponding to the historical vehicle maintenance cases.
[0028] In a third aspect, the present application discloses an electronic device, comprising:
[0029] Memory, used to store computer programs;
[0030] A processor is used to execute the computer program to implement the aforementioned vehicle maintenance method.
[0031] In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program, wherein the computer program implements the aforementioned vehicle maintenance method when executed by a processor.
[0032] It can be seen that in this application, historical vehicle maintenance cases are collected, and the characteristic information in the historical vehicle maintenance cases is digitally encoded to obtain encoded features; the encoded features are inserted into a preset generalized binary tree based on the preset vehicle fault information level to obtain a vehicle fault retrieval tree; the fault search conditions corresponding to the target vehicle are obtained, and the vehicle fault retrieval tree is searched based on the fault search conditions to determine the corresponding target encoded features, and then the target vehicle maintenance case corresponding to the target vehicle is generated and determined based on the target encoded features, so as to perform vehicle maintenance on the target vehicle based on the target vehicle maintenance case. In this way, the target case can be quickly and accurately located in the vehicle fault retrieval tree according to the fault search conditions, which greatly improves the efficiency and accuracy of the search and realizes efficient management and rapid search of large-scale automobile maintenance case data. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] 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, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
[0034] Figure 1 A flow chart of a vehicle maintenance method disclosed in this application;
[0035] Figure 2 A specific vehicle maintenance method flow chart disclosed in this application;
[0036] Figure 3 This is a schematic diagram of the structure of a vehicle maintenance device disclosed in this application;
[0037] Figure 4 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION
[0038] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0039] When a specific maintenance case needs to be found, the traditional search method is inefficient and cannot meet the demand for fast and accurate information acquisition, resulting in low vehicle maintenance efficiency. This application will specifically introduce a vehicle maintenance method that can quickly search for maintenance cases to improve search efficiency and accuracy.
[0040] See also Figure 1 As shown, the embodiment of the present application discloses a vehicle maintenance method, comprising:
[0041] Step S11: collecting historical vehicle maintenance cases, and performing digital coding processing on feature information in the historical vehicle maintenance cases to obtain coded features.
[0042] In this embodiment, the collecting of historical vehicle maintenance cases and digitally encoding the characteristic information in the historical vehicle maintenance cases to obtain the encoded characteristics include: collecting historical vehicle maintenance cases and extracting the vehicle characteristics in the historical vehicle maintenance cases based on the vehicle type, fault type and maintenance method to obtain characteristic information; performing data encoding conversion operation on the characteristic information to obtain the encoded characteristics corresponding to the historical vehicle maintenance cases. Specifically, the automobile maintenance cases in the collected historical data are sorted and classified, and key information in the automobile maintenance cases, such as vehicle type, fault type, maintenance method, etc., is extracted, and then the extracted key information is digitized and standardized to obtain the encoded characteristics corresponding to the historical vehicle maintenance cases. Assume that there are 1,000 automobile maintenance cases, covering a variety of vehicle models and different types of faults. Each maintenance case contains key information such as vehicle model (such as A, B, C, etc.), fault type (such as engine fault, electrical system fault, chassis fault, etc.), maintenance time, maintenance location, etc. First, data preprocessing is performed on these maintenance cases. The vehicle model is converted into a digital code, for example, the A vehicle model is coded as 1, the B vehicle model is coded as 2, and so on. The fault type is also coded in a similar manner.
[0043] Step S12: inserting the encoded features into a preset generalized binary tree based on a preset vehicle fault information level to obtain a vehicle fault retrieval tree.
[0044] In this embodiment, the encoded features are inserted into a preset generalized binary tree based on a preset vehicle fault information level to obtain a vehicle fault retrieval tree, including: defining a node structure of a balanced binary tree based on the feature information to obtain a preset generalized binary tree; determining an insertion order of the encoded features based on a preset vehicle fault information level, and inserting the encoded features into the preset generalized binary tree according to the insertion order to obtain a vehicle fault retrieval tree. Specifically, the insertion order of the encoded features is determined based on a preset vehicle fault information level, and the encoded features are inserted into the preset generalized binary tree according to the insertion order to obtain a vehicle fault retrieval tree, including: determining a first target feature from the encoded features based on the vehicle type, and inserting the target feature into the preset generalized binary tree as the root node of the preset generalized binary tree; determining a node level corresponding to a second target feature based on a preset vehicle fault information level, and inserting the second target feature into the preset generalized binary tree in sequence based on the node level and the code value corresponding to the second target feature to obtain a vehicle fault retrieval tree; the second target feature is other encoded features among the encoded features except the first target feature. Figure 2As shown, the node structure of the balanced binary tree is defined, including case key information and left and right subtree pointers, etc. Then, the processed maintenance cases are inserted into the balanced binary tree one by one in the order of the key information, and the balance adjustment is performed during the insertion process. For example, the vehicle model is used as the primary keyword and the fault type is used as the secondary keyword. First insert the first case, assuming it is the engine failure case of vehicle model A, as the root node. Then insert the second case, such as the electrical system failure case of vehicle model B, compare the vehicle model code size with the root node, and determine the insertion position. If the tree is unbalanced during the insertion process, make corresponding rotation adjustments to maintain balance.
[0045] Step S13: Obtain the fault search conditions corresponding to the target vehicle, and search the vehicle fault retrieval tree based on the fault search conditions to determine the corresponding target encoded features, and then generate a target vehicle maintenance case corresponding to the target vehicle based on the target encoded features, so as to perform vehicle maintenance on the target vehicle based on the target vehicle maintenance case.
[0046] In this embodiment, the method of obtaining the fault search condition corresponding to the target vehicle and searching the vehicle fault retrieval tree based on the fault search condition to determine the corresponding target encoded feature includes: obtaining the fault search condition corresponding to the target vehicle and performing digital encoding processing to obtain the encoded condition, and searching the vehicle fault retrieval tree based on the encoded condition to determine the corresponding target encoded feature. Then, the encoding value corresponding to the encoded condition is compared with the encoding value corresponding to each node in the vehicle fault retrieval tree, and the corresponding target encoded feature is determined according to the comparison result. Specifically, the search condition is input, such as a specific fault type of a specific vehicle model. Starting from the root node of the balanced binary tree, the search condition is compared with the key information of the current node. According to the comparison result, a recursive search is performed in the left subtree or the right subtree until a matching case is found or it is determined that it does not exist. Finally, the searched maintenance case is output, or a prompt is given that no relevant case is found. When a specific maintenance case needs to be searched, for example, a chassis fault maintenance case of vehicle model A is searched. Starting from the root node, the input vehicle model code 1 is compared with the vehicle model code of the current node. If they are equal, the fault type code is compared again. If the current node model code is greater than the input 1, continue searching in the left subtree; otherwise, search in the right subtree. In this way, the target case can be quickly located or determined to be non-existent. After several comparisons and subtree traversals, a matching chassis failure case of model A is found, and detailed information of the case can be output, including maintenance time, maintenance location, etc., to provide reference for maintenance technicians.
[0047] It should be noted that each node of a binary tree usually has at most two child nodes, but in practical applications, it can be expanded and used flexibly. In a specific embodiment, taking a certain brand of engine failure as an example, Figure 2 In the example, the root node can be "×× vehicle engine failure". The first-level branches can include: electrical system failure; mechanical system failure; fuel system failure; intake system failure. The second-level branches (taking electrical system failure as an example) can include: ignition system failure; spark plug problem; ignition coil failure; sensor failure; oxygen sensor failure; crankshaft position sensor failure. The second-level branches (taking mechanical system failure as an example): piston ring wear; improper valve clearance. The second-level branches (taking fuel system failure as an example) can include: fuel pump problem; injector blockage or failure. The second-level branches (taking intake system failure as an example) can include: air filter blockage; intake duct carbon deposition. In the actual operation process, "×× vehicle engine failure" is taken as the root node, and then the four first-level branches are regarded as the four main classification directions under the root node. From the perspective of diagnostic logic, when facing specific fault phenomena, these four main classifications can be excluded one by one, and each exclusion process can be approximately regarded as a judgment branch in the binary tree. For example, when a fault occurs, first determine whether it is an electrical system fault. If not, then determine whether it is a mechanical system fault, and so on. Although it is not a strict binary tree structure, this method can still provide a more systematic idea and method for fault diagnosis. Therefore, although the generalized binary tree in this application does not fully comply with the traditional binary tree rules, it can be adjusted and adapted according to specific circumstances in actual applications to meet the needs of analyzing and diagnosing BMW engine faults.
[0048] It can be seen that in this embodiment, historical vehicle maintenance cases are collected, and the characteristic information in the historical vehicle maintenance cases is digitally encoded to obtain encoded features; the encoded features are inserted into a preset generalized binary tree based on the preset vehicle fault information level to obtain a vehicle fault retrieval tree; the fault search conditions corresponding to the target vehicle are obtained, and the vehicle fault retrieval tree is searched based on the fault search conditions to determine the corresponding target encoded features, and then the target vehicle maintenance case corresponding to the target vehicle is generated and determined based on the target encoded features, so that the target vehicle can be repaired based on the target vehicle maintenance case. In this way, the target case can be quickly and accurately located in the vehicle fault retrieval tree according to the fault search conditions, which greatly improves the efficiency and accuracy of the search and realizes efficient management and rapid search of large-scale automobile maintenance case data.
[0049] refer to Figure 3 The embodiment of the present application also discloses a vehicle maintenance device, including:
[0050] The feature information encoding module 11 is used to collect historical vehicle maintenance cases and perform digital encoding processing on the feature information in the historical vehicle maintenance cases to obtain encoded features;
[0051] A fault tree creation module 12, for inserting the encoded features into a preset generalized binary tree based on a preset vehicle fault information level to obtain a vehicle fault retrieval tree;
[0052] The fault case determination module 13 is used to obtain the fault search conditions corresponding to the target vehicle, and search the vehicle fault retrieval tree based on the fault search conditions to determine the corresponding target encoded features, and then generate and determine the target vehicle maintenance case corresponding to the target vehicle according to the target encoded features, so as to perform vehicle maintenance on the target vehicle based on the target vehicle maintenance case.
[0053] It can be seen that in this embodiment, the target case can be quickly and accurately located in the vehicle fault retrieval tree according to the fault search conditions, which greatly improves the efficiency and accuracy of the search and realizes efficient management and rapid search of large-scale automobile repair case data.
[0054] In some specific embodiments, the feature information encoding module 11 includes:
[0055] A feature information extraction unit, used to collect historical vehicle maintenance cases, and extract vehicle features in the historical vehicle maintenance cases based on vehicle type and fault type to obtain feature information;
[0056] The coding conversion unit is used to perform a data coding conversion operation on the feature information to obtain the coded features corresponding to the historical vehicle maintenance cases.
[0057] In some specific embodiments, the fault tree creation module 12 includes:
[0058] A binary tree definition unit, used to define a node structure of a balanced binary tree based on the characteristic information to obtain a preset generalized binary tree;
[0059] The binary tree creation submodule is used to determine the insertion order of the encoded features based on the preset vehicle fault information level, and insert the encoded features into the preset generalized binary tree according to the insertion order to obtain a vehicle fault retrieval tree.
[0060] In some specific embodiments, the binary tree creation submodule includes:
[0061] A node insertion unit, configured to determine a first target feature from the encoded features based on the vehicle type, and insert the target feature as a root node of the preset generalized binary tree into the preset generalized binary tree;
[0062] A node hierarchy determination unit is used to determine the node hierarchy corresponding to the second target feature based on a preset vehicle fault information level, and to insert the second target feature into the preset generalized binary tree in sequence based on the node hierarchy and the coding value corresponding to the second target feature to obtain a vehicle fault retrieval tree; the second target feature is other encoded features among the encoded features except the first target feature.
[0063] In some specific embodiments, the fault case determination module 13 can be specifically used to obtain the fault search conditions corresponding to the target vehicle and perform digital coding processing to obtain the encoded conditions, and search the vehicle fault retrieval tree based on the encoded conditions to determine the corresponding target encoded features.
[0064] In some specific embodiments, the fault case determination module 13 can be specifically used to compare the coding value corresponding to the post-coding condition with the coding value corresponding to each node in the vehicle fault retrieval tree, and determine the corresponding target post-coding feature based on the comparison result.
[0065] Furthermore, the present application also discloses an electronic device. Figure 4 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the diagram cannot be regarded as any limitation on the scope of use of the present application.
[0066] Figure 4 The present invention provides a schematic diagram of the structure of an electronic device 20 provided in an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 is used to store a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the vehicle maintenance method disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.
[0067] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.
[0068] In addition, the memory 22, as a carrier for storing resources, can be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage method can be temporary storage or permanent storage.
[0069] The operating system 221 is used to manage and control the hardware devices and computer programs 222 on the electronic device 20, and can be Windows Server, Netware, Unix, Linux, etc. In addition to computer programs that can be used to complete the vehicle maintenance method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include computer programs that can be used to complete other specific tasks.
[0070] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the vehicle maintenance method disclosed above. The specific steps of the method can refer to the corresponding contents disclosed in the above embodiments, and will not be repeated here.
[0071] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.
[0072] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0073] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
[0074] Finally, it should be noted that, in this article, relational terms such as first and second, etc. 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 presence of other identical elements in the process, method, article or device including the elements.
[0075] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technical personnel in this field, according to the idea of the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.
Claims
1. A vehicle maintenance method, characterized in that: include: Collecting historical vehicle maintenance cases, and performing digital coding processing on feature information in the historical vehicle maintenance cases to obtain coded features; Inserting the encoded features into a preset generalized binary tree based on a preset vehicle fault information level to obtain a vehicle fault retrieval tree; Obtain fault search conditions corresponding to a target vehicle, and search the vehicle fault retrieval tree based on the fault search conditions to determine corresponding target encoded features, and then generate a target vehicle maintenance case corresponding to the target vehicle based on the target encoded features, so as to perform vehicle maintenance on the target vehicle based on the target vehicle maintenance case.
2. The vehicle maintenance method according to claim 1, characterized in that: The collecting of historical vehicle maintenance cases and digitally encoding characteristic information in the historical vehicle maintenance cases to obtain encoded characteristics include: Collecting historical vehicle maintenance cases, and extracting vehicle features in the historical vehicle maintenance cases based on vehicle type, fault type, and maintenance method to obtain feature information; The feature information is subjected to a data encoding conversion operation to obtain the encoded features corresponding to the historical vehicle maintenance cases.
3. The vehicle maintenance method according to claim 2, characterized in that: The step of inserting the encoded features into a preset generalized binary tree based on a preset vehicle fault information level to obtain a vehicle fault retrieval tree comprises: Defining a node structure of a balanced binary tree based on the characteristic information to obtain a preset generalized binary tree; The insertion order of the encoded features is determined based on a preset vehicle fault information level, and the encoded features are inserted into the preset generalized binary tree according to the insertion order to obtain a vehicle fault retrieval tree.
4. The vehicle maintenance method according to claim 3, characterized in that: The step of determining the insertion order of the encoded features based on the preset vehicle fault information level, and inserting the encoded features into the preset generalized binary tree according to the insertion order to obtain a vehicle fault retrieval tree includes: Determining a first target feature from the encoded features based on the vehicle type, and determining the target feature as a root node of the preset generalized binary tree and inserting it into the preset generalized binary tree; Based on a preset vehicle fault information level, a node level corresponding to a second target feature is determined, and based on the node level and the coding value corresponding to the second target feature, the second target feature is sequentially inserted into the preset generalized binary tree to obtain a vehicle fault retrieval tree; the second target feature is other encoded features among the encoded features except the first target feature.
5. The vehicle maintenance method according to claim 1, characterized in that: The acquiring of the fault search condition corresponding to the target vehicle, and searching the vehicle fault retrieval tree based on the fault search condition to determine the corresponding target encoded feature, comprises: The fault search conditions corresponding to the target vehicle are acquired and digitally coded to obtain coded conditions, and the vehicle fault retrieval tree is searched based on the coded conditions to determine the corresponding target coded features.
6. The vehicle maintenance method according to claim 5, characterized in that: The searching the vehicle fault retrieval tree based on the encoded condition to determine the corresponding target encoded feature includes: The coding value corresponding to the post-coding condition is compared with the coding value corresponding to each node in the vehicle fault retrieval tree, and the corresponding target post-coding feature is determined according to the comparison result.
7. A vehicle maintenance device, characterized in that: include: A feature information encoding module, used to collect historical vehicle maintenance cases, and digitally encode the feature information in the historical vehicle maintenance cases to obtain encoded features; A fault tree creation module, used for inserting the encoded features into a preset generalized binary tree based on a preset vehicle fault information level to obtain a vehicle fault retrieval tree; A fault case determination module is used to obtain the fault search conditions corresponding to the target vehicle, and search the vehicle fault retrieval tree based on the fault search conditions to determine the corresponding target encoded features, and then generate a target vehicle maintenance case corresponding to the target vehicle based on the target encoded features, so as to perform vehicle maintenance on the target vehicle based on the target vehicle maintenance case.
8. The vehicle maintenance device according to claim 7, characterized in that: The feature information encoding module includes: A feature information extraction unit, used to collect historical vehicle maintenance cases, and extract vehicle features in the historical vehicle maintenance cases based on vehicle type and fault type to obtain feature information; The coding conversion unit is used to perform a data coding conversion operation on the feature information to obtain the coded features corresponding to the historical vehicle maintenance cases.
9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the vehicle maintenance method according to any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that: Used to store a computer program, which, when executed by a processor, implements the vehicle maintenance method according to any one of claims 1 to 6.