Vehicle maintenance method and system

Abstract

The invention provides a vehicle maintenance method and system, and the method comprises the steps: collecting original operation parameters corresponding to a vehicle in real time when the vehicle is detected to be in an operation state in real time; preprocessing the original operation parameters through a preset Mamba model to correspondingly generate required standard operation parameters, and extracting corresponding key feature parameters from the standard operation parameters in real time based on a preset rule; a vehicle state prediction model matched with the vehicle is constructed in real time according to the key characteristic parameters through a Mama model, and a vehicle state prediction result corresponding to the original operation parameters is output in real time through the vehicle state prediction model; and corresponding maintenance prompt information is generated in real time according to the vehicle state prediction result, so that a user can maintain the vehicle according to the maintenance prompt information. The maintenance prompt information required by the user can be accurately generated, and the use experience of the user is correspondingly improved.

Description

Technical Field

[0001] The present invention relates to the field of automobile technology, and in particular to a vehicle maintenance method and system. Background Art

[0002] With the advancement of science and technology and the rapid development of productivity, cars have become popular in people's daily lives and have become one of the indispensable means of transportation for people's daily travel, greatly facilitating people's lives.

[0003] Among them, new energy electric vehicles have also been rapidly popularized. Specifically, in order to enable new energy electric vehicles to continue to be in a stable working state, corresponding health inspections and maintenance are required for the vehicles during daily use of the vehicles to keep the vehicles in a healthy state.

[0004] Furthermore, most of the existing technologies will install various sensors inside the vehicle and collect corresponding data through the sensors to further perform corresponding training and maintenance through the corresponding learning algorithms. However, in the actual application process, various sensors will generate a large amount of historical data, which greatly increases the calculation difficulty of the algorithm, resulting in a long calculation time and a slow response speed, which correspondingly reduces the accuracy of vehicle maintenance. Summary of the invention

[0005] Based on this, the purpose of the present invention is to provide a vehicle maintenance method and system to solve the problem that a large amount of historical data will be generated in the process of vehicle maintenance in the prior art, which greatly increases the calculation difficulty of the algorithm and requires a long calculation time.

[0006] The first aspect of the embodiment of the present invention proposes:

[0007] A vehicle maintenance method, wherein the method comprises:

[0008] When it is detected in real time that the vehicle is in a running state, original running parameters corresponding to the vehicle are collected in real time;

[0009] Preprocessing the original operating parameters through a preset Mamba model to generate required standard operating parameters, and extracting corresponding key characteristic parameters from the standard operating parameters in real time based on preset rules;

[0010] A vehicle state prediction model adapted to the vehicle is constructed in real time according to the key characteristic parameters by the Mamba model, and a vehicle state prediction result corresponding to the original operating parameters is output in real time by the vehicle state prediction model;

[0011] Corresponding maintenance reminder information is generated in real time according to the vehicle state prediction result, so that the user can maintain the vehicle according to the maintenance reminder information.

[0012] The beneficial effect of the present invention is that by collecting the original operating parameters of the vehicle in real time, the operating status of the vehicle can be obtained accordingly. Based on this, in order to improve the subsequent prediction accuracy, further preprocessing will be performed, and the required vehicle state prediction model will be obtained. Furthermore, the required vehicle state prediction result will eventually be obtained, and the corresponding maintenance prompt information can be generated according to the vehicle state prediction result, which correspondingly improves the user experience.

[0013] Furthermore, the step of preprocessing the original operating parameters by using the preset Mamba model to generate the required standard operating parameters includes:

[0014] When the original operating parameters are acquired in real time, several data types included in the original operating parameters are detected in real time;

[0015] Filtering in real time from the original operating parameters a target data set corresponding to each of the data types;

[0016] The standard operating parameters are generated according to the Mamba model and each of the target data sets, and each of the target data sets is unique.

[0017] Furthermore, the step of generating the standard operating parameters according to the Mamba model and each target data set includes:

[0018] When the target data set is acquired in real time, a corresponding target data chain is constructed in real time according to the target data set;

[0019] Detecting the starting point and the ending point corresponding to the target data link in real time, and detecting the abnormal values ​​and missing values ​​corresponding to the target data link in real time within the range of the starting point and the ending point;

[0020] The standard operating parameters are correspondingly generated according to the Mamba model, the abnormal values ​​and the missing values, and both the abnormal values ​​and the missing values ​​contain specific numerical values.

[0021] Furthermore, the step of generating the standard operating parameters according to the Mamba model, the abnormal values ​​and the missing values ​​includes:

[0022] Using the Mamba model, outliers corresponding to the target data chain are eliminated in real time according to a preset statistical method;

[0023] The Mamba model is used to fill in missing values ​​in the target data chain in real time according to a preset interpolation method to generate the standard operating parameters accordingly.

[0024] Furthermore, the step of generating corresponding maintenance prompt information in real time according to the vehicle state prediction result includes:

[0025] When the vehicle state prediction result is obtained in real time, a number of prediction values ​​corresponding to the vehicle state prediction result are extracted in real time;

[0026] A number of the predicted values ​​are quantized to generate the maintenance prompt information accordingly.

[0027] Furthermore, the step of quantizing the plurality of predicted values ​​to generate the maintenance prompt information accordingly includes:

[0028] When a plurality of the predicted values ​​are acquired in real time, a fusion process is performed on the plurality of the predicted values ​​to output a vehicle state score corresponding to the vehicle;

[0029] The change trend of the vehicle status score is calculated in real time, and the maintenance reminder information is correspondingly generated according to the vehicle status score and the change trend.

[0030] Furthermore, the step of generating the maintenance reminder information according to the vehicle status score and the change trend includes:

[0031] Determine in real time whether the vehicle status score is greater than a preset score threshold;

[0032] If it is determined in real time that the vehicle status score is greater than the preset score threshold, then determining in real time whether the change trend is an upward trend;

[0033] If it is determined in real time that the change trend is the upward trend, it is correspondingly determined that the vehicle does not need maintenance within a preset time threshold, and the maintenance reminder information is correspondingly generated.

[0034] The second aspect of the embodiment of the present invention proposes:

[0035] A vehicle maintenance system, wherein the system comprises:

[0036] A collection module, used for collecting original operating parameters corresponding to the vehicle in real time when it is detected in real time that the vehicle is in a running state;

[0037] An extraction module, used for preprocessing the original operating parameters through a preset Mamba model to generate required standard operating parameters, and extracting corresponding key characteristic parameters from the standard operating parameters in real time based on preset rules;

[0038] An output module, used to construct a vehicle state prediction model adapted to the vehicle in real time according to the key characteristic parameters through the Mamba model, and output a vehicle state prediction result corresponding to the original operating parameters in real time through the vehicle state prediction model;

[0039] The prompt module is used to generate corresponding maintenance prompt information in real time according to the vehicle state prediction result, so that the user can maintain the vehicle according to the maintenance prompt information.

[0040] Furthermore, the extraction module is specifically used for:

[0041] When the original operating parameters are acquired in real time, several data types included in the original operating parameters are detected in real time;

[0042] Filtering in real time from the original operating parameters a target data set corresponding to each of the data types;

[0043] The standard operating parameters are generated according to the Mamba model and each of the target data sets, and each of the target data sets is unique.

[0044] Furthermore, the extraction module is specifically used for:

[0045] When the target data set is acquired in real time, a corresponding target data chain is constructed in real time according to the target data set;

[0046] Detecting the starting point and the ending point corresponding to the target data link in real time, and detecting the abnormal values ​​and missing values ​​corresponding to the target data link in real time within the range of the starting point and the ending point;

[0047] The standard operating parameters are correspondingly generated according to the Mamba model, the abnormal values ​​and the missing values, and both the abnormal values ​​and the missing values ​​contain specific numerical values.

[0048] Furthermore, the extraction module is specifically used for:

[0049] Using the Mamba model, outliers corresponding to the target data chain are eliminated in real time according to a preset statistical method;

[0050] The Mamba model is used to fill in missing values ​​in the target data chain in real time according to a preset interpolation method to generate the standard operating parameters accordingly.

[0051] Furthermore, the prompt module is specifically used for:

[0052] When the vehicle state prediction result is obtained in real time, a number of prediction values ​​corresponding to the vehicle state prediction result are extracted in real time;

[0053] A number of the predicted values ​​are quantized to generate the maintenance prompt information accordingly.

[0054] Furthermore, the prompt module is specifically used for:

[0055] When a plurality of the predicted values ​​are acquired in real time, a fusion process is performed on the plurality of the predicted values ​​to output a vehicle state score corresponding to the vehicle;

[0056] The change trend of the vehicle status score is calculated in real time, and the maintenance reminder information is correspondingly generated according to the vehicle status score and the change trend.

[0057] Furthermore, the prompt module is specifically used for:

[0058] Determine in real time whether the vehicle status score is greater than a preset score threshold;

[0059] If it is determined in real time that the vehicle status score is greater than the preset score threshold, then determining in real time whether the change trend is an upward trend;

[0060] If it is determined in real time that the change trend is the upward trend, it is correspondingly determined that the vehicle does not need maintenance within a preset time threshold, and the maintenance reminder information is correspondingly generated.

[0061] The third aspect of the embodiment of the present invention proposes:

[0062] A computer comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the vehicle maintenance method as described above is implemented when the processor executes the computer program.

[0063] The fourth aspect of the embodiments of the present invention proposes:

[0064] A readable storage medium stores a computer program, wherein the program, when executed by a processor, implements the vehicle maintenance method as described above.

[0065] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A flow chart of a vehicle maintenance method provided by a first embodiment of the present invention;

[0067] Figure 2This is a structural block diagram of a vehicle maintenance system provided in the third embodiment of the present invention.

[0068] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0069] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0070] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0071] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0072] See also Figure 1 , which shows the vehicle maintenance method provided by the first embodiment of the present invention. The vehicle maintenance method provided by this embodiment can generate corresponding maintenance prompt information according to the vehicle state prediction result, thereby correspondingly improving the user experience.

[0073] Specifically, this embodiment provides:

[0074] A vehicle maintenance method comprises the following steps:

[0075] Step S10, when it is detected in real time that the vehicle is in a running state, original running parameters corresponding to the vehicle are collected in real time;

[0076] Step S20, preprocessing the original operating parameters by using a preset Mamba model to generate required standard operating parameters, and extracting corresponding key characteristic parameters from the standard operating parameters in real time based on preset rules;

[0077] Step S30, constructing a vehicle state prediction model adapted to the vehicle in real time according to the key characteristic parameters through the Mamba model, and outputting a vehicle state prediction result corresponding to the original operating parameters in real time through the vehicle state prediction model;

[0078] Step S40: generating corresponding maintenance reminder information in real time according to the vehicle state prediction result, so that the user can maintain the vehicle according to the maintenance reminder information.

[0079] Specifically, in this embodiment, it should be noted that in order to objectively and accurately complete vehicle maintenance, it is necessary to obtain the usage status of the vehicle in real time. Based on this, it is necessary to first distinguish whether the vehicle is in an off state or in a driving state. When the vehicle controller arranged inside the vehicle detects in real time that the vehicle is in a running state, subsequent processing and judgment are required. Preferably, the present invention will collect the original operating parameters corresponding to the current vehicle in real time through pre-set sensors. Furthermore, in order to objectively and accurately obtain the corresponding content contained in the current original operating parameters, the present invention will further pre-process the currently obtained original operating parameters immediately through the existing Mamba (language) model, and generate the required standard operating parameters accordingly.

[0080] Furthermore, since the current standard operating parameters contain both useful data and useless data, based on this, the present invention will further extract the key characteristic parameters contained in the current standard operating parameters, that is, some characteristic values. Furthermore, the above-mentioned Mamba model is further used to immediately generate a vehicle state prediction model adapted to the current vehicle based on the current key characteristic parameters, and the vehicle state prediction model is further used to output the vehicle state prediction results corresponding to the current original operating parameters in real time. On this basis, the corresponding maintenance reminder information can be finally generated in real time based on the vehicle state prediction results, so that the user can accurately complete the maintenance of the vehicle according to the maintenance reminder information, thereby enabling the vehicle to continue to be in a high-performance state, which correspondingly improves the user experience.

[0081] Second embodiment

[0082] Furthermore, the step of preprocessing the original operating parameters by using the preset Mamba model to generate the required standard operating parameters includes:

[0083] When the original operating parameters are acquired in real time, several data types included in the original operating parameters are detected in real time;

[0084] Filtering in real time from the original operating parameters a target data set corresponding to each of the data types;

[0085] The standard operating parameters are generated according to the Mamba model and each of the target data sets, and each of the target data sets is unique.

[0086] Furthermore, the step of generating the standard operating parameters according to the Mamba model and each target data set includes:

[0087] When the target data set is acquired in real time, a corresponding target data chain is constructed in real time according to the target data set;

[0088] Detecting the starting point and the ending point corresponding to the target data link in real time, and detecting the abnormal values ​​and missing values ​​corresponding to the target data link in real time within the range of the starting point and the ending point;

[0089] The standard operating parameters are correspondingly generated according to the Mamba model, the abnormal values ​​and the missing values, and both the abnormal values ​​and the missing values ​​contain specific numerical values.

[0090] Furthermore, the step of generating the standard operating parameters according to the Mamba model, the abnormal values ​​and the missing values ​​includes:

[0091] Using the Mamba model, outliers corresponding to the target data chain are eliminated in real time according to a preset statistical method;

[0092] The Mamba model is used to fill in missing values ​​in the target data chain in real time according to a preset interpolation method to generate the standard operating parameters accordingly.

[0093] Furthermore, the step of generating corresponding maintenance prompt information in real time according to the vehicle state prediction result includes:

[0094] When the vehicle state prediction result is obtained in real time, a number of prediction values ​​corresponding to the vehicle state prediction result are extracted in real time;

[0095] A number of the predicted values ​​are quantized to generate the maintenance prompt information accordingly.

[0096] Furthermore, the step of quantizing the plurality of predicted values ​​to generate the maintenance prompt information accordingly includes:

[0097] When a plurality of the predicted values ​​are acquired in real time, a fusion process is performed on the plurality of the predicted values ​​to output a vehicle state score corresponding to the vehicle;

[0098] The change trend of the vehicle status score is calculated in real time, and the maintenance reminder information is correspondingly generated according to the vehicle status score and the change trend.

[0099] Furthermore, the step of generating the maintenance reminder information according to the vehicle status score and the change trend includes:

[0100] Determine in real time whether the vehicle status score is greater than a preset score threshold;

[0101] If it is determined in real time that the vehicle status score is greater than the preset score threshold, then determining in real time whether the change trend is an upward trend;

[0102] If it is determined in real time that the change trend is the upward trend, it is correspondingly determined that the vehicle does not need maintenance within a preset time threshold, and the maintenance reminder information is correspondingly generated.

[0103] In addition, in the present embodiment, it is also necessary to explain that after the required original operating parameters are acquired in real time through the above steps, in order to be able to objectively and accurately perform the corresponding preprocessing, the present invention will first detect several data types contained in the current original operating parameters, and at the same time, further screen out the target data sets corresponding to each data type, based on which, the required target data chain is further constructed according to the content contained in the current target data set.

[0104] Further, since the target data link contains the required information, based on this, in order to obtain the corresponding processing range, the present invention will further detect the starting point and the end point of the current target data link, and further detect the corresponding abnormal values ​​and missing values ​​within the current starting point and the end point. Based on this, the current abnormal value is immediately eliminated through the above-mentioned Mamba model, and correspondingly, the current missing value is filled, and a complete standard operating parameter can be obtained accordingly. Further, after the required vehicle state prediction result is obtained in real time through the above steps, in order to be able to objectively and accurately generate the maintenance prompt information required by the user, preferably, the present invention will first detect several predicted values ​​contained in the current vehicle state prediction result, and at the same time, further merge into the required vehicle state score, based on this, further calculate the change trend of the subsequent score, specifically, if it is judged that the current change trend is rising, it indicates that the vehicle is in good condition, and correspondingly, if the change trend is falling, it indicates that the performance of the vehicle is declining, and corresponding maintenance is required, and the above-mentioned maintenance prompt information is generated accordingly, thereby greatly improving the user experience.

[0105] See also Figure 2 , the third embodiment of the present invention provides:

[0106] A vehicle maintenance system, wherein the system comprises:

[0107] A collection module, used for collecting original operating parameters corresponding to the vehicle in real time when it is detected in real time that the vehicle is in a running state;

[0108] An extraction module, used for preprocessing the original operating parameters through a preset Mamba model to generate required standard operating parameters, and extracting corresponding key characteristic parameters from the standard operating parameters in real time based on preset rules;

[0109] An output module, used to construct a vehicle state prediction model adapted to the vehicle in real time according to the key characteristic parameters through the Mamba model, and output a vehicle state prediction result corresponding to the original operating parameters in real time through the vehicle state prediction model;

[0110] The prompt module is used to generate corresponding maintenance prompt information in real time according to the vehicle state prediction result, so that the user can maintain the vehicle according to the maintenance prompt information.

[0111] Furthermore, the extraction module is specifically used for:

[0112] When the original operating parameters are acquired in real time, several data types included in the original operating parameters are detected in real time;

[0113] Filtering in real time from the original operating parameters a target data set corresponding to each of the data types;

[0114] The standard operating parameters are generated according to the Mamba model and each of the target data sets, and each of the target data sets is unique.

[0115] Furthermore, the extraction module is specifically used for:

[0116] When the target data set is acquired in real time, a corresponding target data chain is constructed in real time according to the target data set;

[0117] Detecting the starting point and the ending point corresponding to the target data link in real time, and detecting the abnormal values ​​and missing values ​​corresponding to the target data link in real time within the range of the starting point and the ending point;

[0118] The standard operating parameters are correspondingly generated according to the Mamba model, the abnormal values ​​and the missing values, and both the abnormal values ​​and the missing values ​​contain specific numerical values.

[0119] Furthermore, the extraction module is specifically used for:

[0120] Using the Mamba model, outliers corresponding to the target data chain are eliminated in real time according to a preset statistical method;

[0121] The Mamba model is used to fill in missing values ​​in the target data chain in real time according to a preset interpolation method to generate the standard operating parameters accordingly.

[0122] Furthermore, the prompt module is specifically used for:

[0123] When the vehicle state prediction result is obtained in real time, a number of prediction values ​​corresponding to the vehicle state prediction result are extracted in real time;

[0124] A number of the predicted values ​​are quantized to generate the maintenance prompt information accordingly.

[0125] Furthermore, the prompt module is specifically used for:

[0126] When a plurality of the predicted values ​​are acquired in real time, a fusion process is performed on the plurality of the predicted values ​​to output a vehicle state score corresponding to the vehicle;

[0127] The change trend of the vehicle status score is calculated in real time, and the maintenance reminder information is correspondingly generated according to the vehicle status score and the change trend.

[0128] Furthermore, the prompt module is specifically used for:

[0129] Determine in real time whether the vehicle status score is greater than a preset score threshold;

[0130] If it is determined in real time that the vehicle status score is greater than the preset score threshold, then determining in real time whether the change trend is an upward trend;

[0131] If it is determined in real time that the change trend is the upward trend, it is correspondingly determined that the vehicle does not need maintenance within a preset time threshold, and the maintenance reminder information is correspondingly generated.

[0132] A fourth embodiment of the present invention provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the vehicle maintenance method as described above when executing the computer program.

[0133] A fifth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the vehicle maintenance method as described above.

[0134] In summary, the vehicle maintenance method and system provided by the above embodiments of the present invention can generate corresponding maintenance reminder information according to the vehicle status prediction results, thereby improving the user experience accordingly.

[0135] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0136] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0137] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0138] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0139] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0140] The above-described embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A vehicle maintenance method, characterized in that: The method comprises: When it is detected in real time that the vehicle is in a running state, original running parameters corresponding to the vehicle are collected in real time; Preprocessing the original operating parameters through a preset Mamba model to generate required standard operating parameters, and extracting corresponding key characteristic parameters from the standard operating parameters in real time based on preset rules; A vehicle state prediction model adapted to the vehicle is constructed in real time according to the key characteristic parameters by the Mamba model, and a vehicle state prediction result corresponding to the original operating parameters is output in real time by the vehicle state prediction model; Corresponding maintenance reminder information is generated in real time according to the vehicle state prediction result, so that the user can maintain the vehicle according to the maintenance reminder information.

2. The vehicle maintenance method according to claim 1, characterized in that: The step of preprocessing the original operating parameters by presetting the Mamba model to generate the required standard operating parameters includes: When the original operating parameters are acquired in real time, several data types included in the original operating parameters are detected in real time; Filtering in real time from the original operating parameters a target data set corresponding to each of the data types; The standard operating parameters are generated according to the Mamba model and each of the target data sets, and each of the target data sets is unique.

3. The vehicle maintenance method according to claim 2, characterized in that: The step of generating the standard operating parameters according to the Mamba model and each target data set includes: When the target data set is acquired in real time, a corresponding target data chain is constructed in real time according to the target data set; Detecting the starting point and the ending point corresponding to the target data link in real time, and detecting the abnormal values ​​and missing values ​​corresponding to the target data link in real time within the range of the starting point and the ending point; The standard operating parameters are correspondingly generated according to the Mamba model, the abnormal values ​​and the missing values, and both the abnormal values ​​and the missing values ​​contain specific numerical values.

4. The vehicle maintenance method according to claim 3, characterized in that: The step of correspondingly generating the standard operating parameters according to the Mamba model, the abnormal values ​​and the missing values ​​comprises: Using the Mamba model, outliers corresponding to the target data chain are eliminated in real time according to a preset statistical method; The Mamba model is used to fill in missing values ​​in the target data chain in real time according to a preset interpolation method to generate the standard operating parameters accordingly.

5. The vehicle maintenance method according to claim 1, characterized in that: The step of generating corresponding maintenance prompt information in real time according to the vehicle state prediction result comprises: When the vehicle state prediction result is obtained in real time, a number of prediction values ​​corresponding to the vehicle state prediction result are extracted in real time; A number of the predicted values ​​are quantized to generate the maintenance prompt information accordingly.

6. The vehicle maintenance method according to claim 5, characterized in that: The step of quantizing the plurality of predicted values ​​to generate the maintenance prompt information accordingly includes: When a plurality of the predicted values ​​are acquired in real time, a fusion process is performed on the plurality of the predicted values ​​to output a vehicle state score corresponding to the vehicle; The change trend of the vehicle status score is calculated in real time, and the maintenance reminder information is correspondingly generated according to the vehicle status score and the change trend.

7. The vehicle maintenance method according to claim 6, characterized in that: The step of generating the maintenance reminder information according to the vehicle status score and the change trend includes: Determine in real time whether the vehicle status score is greater than a preset score threshold; If it is determined in real time that the vehicle status score is greater than the preset score threshold, then determining in real time whether the change trend is an upward trend; If it is determined in real time that the change trend is the upward trend, it is correspondingly determined that the vehicle does not need maintenance within a preset time threshold, and the maintenance reminder information is correspondingly generated.

8. A vehicle maintenance system, characterized in that: The system comprises: A collection module, used for collecting original operating parameters corresponding to the vehicle in real time when it is detected in real time that the vehicle is in a running state; An extraction module, used for preprocessing the original operating parameters through a preset Mamba model to generate required standard operating parameters, and extracting corresponding key characteristic parameters from the standard operating parameters in real time based on preset rules; An output module, used to construct a vehicle state prediction model adapted to the vehicle in real time according to the key characteristic parameters through the Mamba model, and output a vehicle state prediction result corresponding to the original operating parameters in real time through the vehicle state prediction model; The prompt module is used to generate corresponding maintenance prompt information in real time according to the vehicle state prediction result, so that the user can maintain the vehicle according to the maintenance prompt information.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the vehicle maintenance method according to any one of claims 1 to 7 is implemented.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the vehicle maintenance method according to any one of claims 1 to 7 is implemented.

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