Health testing methods, devices and electronic devices for vehicle gear shifting mechanisms

Abstract

This invention discloses a method, apparatus, and electronic device for health detection of a vehicle gear shift mechanism. The method includes: acquiring target detection data corresponding to a target vehicle, wherein the target detection data represents health detection data corresponding to the electronic gear shift mechanism of the target vehicle; analyzing and processing the target detection data to obtain target analysis results, wherein the target analysis results are used to predict faults in the electronic gear shift mechanism from multiple dimensions; and using the target analysis results to perform a health assessment of the electronic gear shift mechanism to obtain an assessment result, which is used to determine the health status of the electronic gear shift mechanism. This invention solves the technical problem of low vehicle safety caused by the inability of related technologies to predict faults in electronic gear shift mechanisms.

Description

Technical Field

[0001] This invention relates to the field of vehicles, and more specifically, to a method, apparatus, and electronic device for health detection of a vehicle gear shifting mechanism. Background Technology

[0002] Electronic shift mechanism is an indispensable and important component of a vehicle. It converts the driver's shifting actions into signals and transmits them to the vehicle controller, thereby realizing the switching of vehicle gears.

[0003] Currently, related technologies almost never perform health checks on electronic shift mechanisms. If a fault occurs in the electronic shift mechanism, the vehicle's instrument panel may issue error messages and alarms to alert the driver and force the car into a safe mode. However, this method can only alert and address the fault after it has occurred; it cannot predict the fault.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, and electronic device for health detection of a vehicle gear shift mechanism, in order to at least solve the technical problem of low vehicle safety caused by the inability of related technologies to predict faults in electronic gear shift mechanisms.

[0006] According to one embodiment of the present invention, a health detection method for a vehicle gear shift mechanism is provided, comprising: acquiring target detection data corresponding to a target vehicle, wherein the target detection data is used to represent health detection data corresponding to the electronic gear shift mechanism of the target vehicle; analyzing and processing the target detection data to obtain target analysis results, wherein the target analysis results are used to predict faults in the electronic gear shift mechanism from multiple dimensions; and using the target analysis results to perform a health assessment on the electronic gear shift mechanism to obtain an assessment result, wherein the assessment result is used to determine the health status of the electronic gear shift mechanism.

[0007] Optionally, the target detection data includes: first data, obtaining the first data corresponding to the target vehicle includes: statistically analyzing the normal triggering data of the electronic shift mechanism, wherein the normal triggering data includes at least one of the following: number of starts, number of times the lever unlock button is triggered, number of times the target gear button is triggered, and number of times the target gear is executed; the first data is obtained based on the normal triggering data.

[0008] Optionally, the target detection data further includes: second data, which includes: statistically analyzing the abnormal triggering data of the electronic shift mechanism, wherein the abnormal triggering data includes at least one of the following: the number of times the handle button contact fails, the number of times the handle node is lost, the number of times overvoltage alarms occur, the number of times undervoltage alarms occur, and the number of times wake-up fails; the second data is obtained based on the abnormal triggering data.

[0009] Optionally, the target detection data is analyzed and processed to obtain target analysis results, including: using a target neural network model to predict the target detection data to obtain a first analysis result, wherein the target neural network is obtained by machine learning training using multiple sets of historical detection data; performing statistical processing on the target detection data to obtain a second analysis result; performing early warning analysis on the target detection data to obtain a third analysis result; and determining the target analysis result based on the first analysis result, the second analysis result, and the third analysis result.

[0010] Optionally, the evaluation results may include at least one of the following: fault severity information, warning severity information, geographical distribution information, fault distribution information, and time distribution information of the electronic shift mechanism.

[0011] Optionally, the health detection method for the vehicle shift mechanism also includes: sending the evaluation results to the graphical user interface of the terminal device so that the graphical user interface can display the evaluation results.

[0012] According to one embodiment of the present invention, a health detection device for a vehicle gear shift mechanism is also provided, comprising: an acquisition module for acquiring target detection data corresponding to a target vehicle, wherein the target detection data represents health detection data corresponding to the electronic gear shift mechanism of the target vehicle; an analysis module for analyzing and processing the target detection data to obtain target analysis results, wherein the target analysis results are used to predict faults in the electronic gear shift mechanism from multiple dimensions; and an evaluation module for using the target analysis results to perform a health evaluation on the electronic gear shift mechanism to obtain an evaluation result, wherein the evaluation result is used to determine the health status of the electronic gear shift mechanism.

[0013] Optionally, the acquisition module is also used to collect normal trigger data of the electronic shift mechanism, wherein the shift trigger data includes at least one of the following: number of starts, number of times the handle unlock button is triggered, number of times the target gear button is triggered, and number of times the target gear is executed; and the first data is obtained based on the shift trigger data.

[0014] Optionally, the acquisition module is also used to collect abnormal trigger data of the electronic shift mechanism, wherein the abnormal trigger data includes at least one of the following: number of times the handle button contact fails, number of times the handle node is lost, number of overvoltage alarms, number of undervoltage alarms, and number of wake-up failures; and second data is obtained based on the abnormal trigger data.

[0015] Optionally, the analysis module is further configured to perform predictive processing on the target detection data using a target neural network model to obtain a first analysis result, wherein the target neural network is obtained by machine learning training using multiple sets of historical detection data; perform statistical processing on the target detection data to obtain a second analysis result; perform early warning analysis on the target detection data to obtain a third analysis result; and determine the target analysis result based on the first analysis result, the second analysis result, and the third analysis result.

[0016] Optionally, the health detection device for the vehicle shift mechanism further includes: a sending module for sending the evaluation results to the graphical user interface of the terminal device so that the graphical user interface can display the evaluation results.

[0017] According to one embodiment of the present invention, a storage medium storing a computer program is also provided, wherein the computer program is configured to execute the health detection method for the vehicle shift mechanism described in any of the above claims when running.

[0018] According to one embodiment of the present invention, a processor is also provided, the processor being used to run a program, wherein the program is configured to execute the health detection method of the vehicle shift mechanism as described above when running.

[0019] According to one embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the health detection method for the vehicle shift mechanism described in any of the preceding claims.

[0020] In this embodiment of the invention, target detection data corresponding to the target vehicle is acquired, and then the target detection data is analyzed and processed to obtain target analysis results. Finally, the target analysis results are used to perform a health assessment on the electronic shift mechanism to obtain an assessment result. The assessment result is used to determine the health status of the electronic shift mechanism, thereby achieving the purpose of predicting faults in the electronic shift mechanism and thus realizing the technical effect of improving vehicle safety. This solves the technical problem of low vehicle safety caused by the inability of related technologies to predict faults in the electronic shift mechanism. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0022] Figure 1 This is a flowchart of a health detection method for a vehicle gear shifting mechanism according to one embodiment of the present invention;

[0023] Figure 2This is a schematic diagram of a health detection system for a vehicle gear shifting mechanism according to one embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of an electronic gear shift lever according to one embodiment of the present invention;

[0025] Figure 4 This is a structural block diagram of a health detection device for a vehicle gear shifting mechanism according to one embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] According to an embodiment of the present invention, a method embodiment for health detection of a vehicle gear shifting mechanism is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This method embodiment can be executed in an electronic device or similar computing device that includes a memory and a processor. Taking operation on a vehicle terminal as an example, the vehicle terminal may include one or more processors (processors may include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), digital signal processing (DSP) chips, microcontroller units (MCUs), field-programmable gate arrays (FPGAs), neural network processors (NPUs), tensor processors (TPUs), artificial intelligence (AI) type processors, etc.) and a memory for storing data. Optionally, the vehicle terminal may also include transmission devices, input / output devices, and display devices for communication functions. Those skilled in the art will understand that the above structural description is merely illustrative and does not limit the structure of the vehicle terminal. For example, the vehicle terminal may include more or fewer components than described above, or have a different configuration than described above.

[0030] The memory can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the vehicle gear shift mechanism health detection method in this embodiment of the invention. The processor executes various functional applications and data processing by running the computer program stored in the memory, thereby realizing the aforementioned vehicle gear shift mechanism health detection method. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0031] The transmission device is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0032] Display devices can be, for example, touchscreen liquid crystal displays (LCDs) and touch displays (also referred to as "touchscreens" or "touch displays"). The LCD allows users to interact with the user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), which allows users to interact with the GUI through finger contact and / or gestures on a touch-sensitive surface. Optional human-computer interaction functions include: creating web pages, drawing, word processing, creating electronic documents, playing games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital video, playing digital music, and / or web browsing, etc. Executable instructions for performing the above human-computer interaction functions are configured / stored in one or more processor-executable computer program products or readable storage media.

[0033] Figure 1 This is a flowchart of a health detection method for a vehicle gear shifting mechanism according to one embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0034] Step S12: Obtain target detection data corresponding to the target vehicle, wherein the target detection data is used to represent the health detection data corresponding to the electronic shift mechanism of the target vehicle.

[0035] In step S12 above, target detection data corresponding to the target vehicle is obtained.

[0036] Specifically, the aforementioned target detection data can be used to represent the health detection data corresponding to the electronic shift mechanism of the target vehicle, wherein the aforementioned electronic shift mechanism may include an electronic shifter and an electronic shift actuator.

[0037] Step S14: Analyze and process the target detection data to obtain target analysis results, which are used to predict the faults of the electronic shift mechanism from multiple dimensions.

[0038] In step S14 above, after obtaining the target detection data corresponding to the target vehicle, the target detection data can be analyzed and processed to obtain the target analysis results.

[0039] Specifically, the health monitoring data corresponding to the vehicle's electronic gear shift mechanism can be analyzed and processed to obtain target analysis results. Then, the target analysis results can be used to predict the faults of the electronic gear shift mechanism from multiple dimensions.

[0040] Step S16: Use the target analysis results to perform a health assessment on the electronic shift mechanism and obtain the assessment results. The assessment results are used to determine the health status of the electronic shift mechanism.

[0041] In step S16 above, after analyzing and processing the target detection data to obtain the target analysis results, the target analysis results can be used to conduct a health assessment of the electronic shift mechanism to obtain the assessment results.

[0042] Optionally, the evaluation results may include at least one of the following: fault severity information, warning severity information, geographical distribution information, fault distribution information, and time distribution information of the electronic shift mechanism, wherein the fault severity information may include aging degree and remaining life.

[0043] Specifically, when using the results of target analysis to conduct a health assessment of the electronic shift mechanism, information on the degree of failure, warning level, geographical distribution, fault distribution, and time distribution of the electronic shift mechanism can be obtained, thereby enabling fault prediction of the electronic shift mechanism from multiple dimensions.

[0044] Based on the above steps S12 to S16, target detection data corresponding to the target vehicle is acquired, and then the target detection data is analyzed and processed to obtain target analysis results. Finally, the target analysis results are used to perform a health assessment on the electronic shift mechanism to obtain assessment results. The assessment results are used to determine the health status of the electronic shift mechanism, thereby achieving the purpose of predicting faults in the electronic shift mechanism and thus realizing the technical effect of improving vehicle safety. This solves the technical problem of low vehicle safety caused by the inability of related technologies to predict faults in the electronic shift mechanism.

[0045] Optionally, in step S12 above, the target detection data includes: first data, and obtaining the first data corresponding to the target vehicle includes:

[0046] Step S121: Calculate the normal trigger data of the electronic shift mechanism. The normal trigger data includes at least one of the following: number of starts, number of times the handle unlock button is triggered, number of times the target gear button is triggered, and number of times the target gear is executed.

[0047] In step S121 above, when obtaining the first data corresponding to the target vehicle, the normal trigger data of the electronic shift mechanism can be statistically analyzed so as to obtain the first data based on the normal trigger data. The first data is the regular data of the electronic shift mechanism.

[0048] For example, normal trigger data of the electronic shift mechanism can be collected, including at least one of the following: number of starts, number of times the lever unlock button is triggered, number of times the target gear button is triggered, and number of times the target gear is executed, so as to obtain the regular data of the electronic shift mechanism based on at least one of the normal trigger data.

[0049] Step S122: Obtain the first data based on the normal trigger data.

[0050] In step S122 above, after collecting the normal trigger data of the electronic shift mechanism, the first data can be obtained based on the normal trigger data.

[0051] Based on the above steps S121 to S122, by statistically analyzing the normal trigger data of the electronic shift mechanism, the first data can be obtained based on the normal trigger data, so as to analyze and process the first data and then predict the fault of the electronic shift mechanism from multiple dimensions.

[0052] Optionally, in step S12 above, the target detection data further includes: second data, and obtaining the second data corresponding to the target vehicle includes:

[0053] Step S123: Statistically analyze the abnormal trigger data of the electronic shift mechanism. The abnormal trigger data includes at least one of the following: number of times the handle button contact fails, number of times the handle node is lost, number of overvoltage alarms, number of undervoltage alarms, and number of wake-up failures.

[0054] In step S123 above, when obtaining the second data corresponding to the target vehicle, the abnormal trigger data of the electronic shift mechanism can be statistically analyzed, wherein the second data is the normal data of the electronic shift mechanism, which is the first data.

[0055] For example, the sequential trigger data of the electronic shift mechanism can be statistically analyzed, including at least one of the following: the number of times the handle button contact fails, the number of times the handle node is lost, the number of times the overvoltage alarm occurs, the number of times the undervoltage alarm occurs, and the number of times the wake-up fails, so as to obtain the abnormal data of the electronic shift mechanism based on at least one of the abnormal trigger data.

[0056] Step S124: Obtain the second data based on the abnormal trigger data.

[0057] In step S124 above, after statistically analyzing the abnormal triggering data of the electronic shift mechanism, the second data can be obtained based on the abnormal triggering data.

[0058] Based on the above steps S123 to S124, by statistically analyzing the abnormal triggering data of the electronic shift mechanism, second data can be obtained based on the abnormal triggering data, so as to analyze and process the second data and then predict the fault of the electronic shift mechanism from multiple dimensions.

[0059] Optionally, in step S14 above, the target detection data is analyzed and processed to obtain the target analysis results, including:

[0060] In an optional embodiment, analyzing and processing the target detection data may include predictive processing, statistical processing, and early warning analysis.

[0061] Step S141: The target detection data is predicted and processed using a target neural network model to obtain the first analysis result. The target neural network is trained by machine learning using multiple sets of historical detection data.

[0062] In step S141 above, when analyzing and processing the target detection data to obtain the target analysis result, a target neural network model can be used to predict the target detection data to obtain the first analysis result, which can then be stored.

[0063] Specifically, by using a neural network model to analyze and process the regular and abnormal data of the electronic shift mechanism, the first analysis result, namely the model analysis result, can be obtained.

[0064] Step S142: Perform statistical processing on the target detection data to obtain the second analysis result.

[0065] In step S142 above, when analyzing and processing the target detection data to obtain the target analysis result, the target detection data can also be statistically processed to obtain the second analysis result, which can then be stored.

[0066] Specifically, the system can also classify and statistically analyze the regular and abnormal data of the electronic shift mechanism. The classification categories can be set by region, time, regular data type, and abnormal data type, and the statistical data can be stored.

[0067] Step S143: Perform early warning analysis on the target detection data to obtain the third analysis result.

[0068] In step S143 above, when analyzing and processing the target detection data to obtain the target analysis result, the target detection data can also be subjected to early warning analysis to obtain a third analysis result, which can then be stored.

[0069] Specifically, early warning analysis can be performed based on the regular and abnormal data of the electronic shift mechanism to obtain early warning data, which can then be stored.

[0070] Step S144: Determine the target analysis result based on the first analysis result, the second analysis result, and the third analysis result.

[0071] In step S144 above, after obtaining the first analysis result, the second analysis result, and the third analysis result, the target analysis result can be determined based on the first analysis result, the second analysis result, and the third analysis result.

[0072] Specifically, by performing predictive processing, statistical processing, and early warning analysis on the routine and abnormal data of the electronic shift mechanism, fault prediction of the electronic shift mechanism can be carried out from multiple dimensions.

[0073] Based on the above steps S141 to S144, the target detection data is predicted and processed by a target neural network model to obtain the first analysis result. Then, the target detection data is statistically processed to obtain the second analysis result. Subsequently, the target detection data is analyzed for early warning to obtain the third analysis result. Finally, the target analysis result is determined based on the first analysis result, the second analysis result, and the third analysis result. This method can predict faults in the electronic shift mechanism from multiple dimensions and can quickly locate faults.

[0074] Optionally, the health inspection method for the vehicle's gear shifting mechanism also includes:

[0075] Step S17: Send the evaluation results to the graphical user interface of the terminal device so that the graphical user interface can display the evaluation results.

[0076] Based on step S17 above, the evaluation results are sent to the graphical user interface of the terminal device so that the graphical user interface can display the evaluation results, making it convenient for users to view the health status of the electronic gear shift mechanism.

[0077] Figure 2 This is a schematic diagram of a health detection system for an electronic shift mechanism according to one embodiment of the present invention, such as... Figure 2 As shown, the health monitoring system of the electronic shift mechanism is mainly divided into two parts: the vehicle end and the cloud end. The vehicle end consists of an electronic shift lever, an electronic shifter, an electronic shift controller, an electronic shift actuator, a memory, and a communication terminal. The cloud end consists of a data access interface, a data memory, an analysis module, a cloud data interface, and a terminal service system.

[0078] The electronic shift lever and electronic shifter serve as the interface between the driver and the vehicle, receiving the driver's shifting operations and shifting intentions. The electronic shift lever and electronic shifter communicate with each other via LIN. Figure 3 This is a schematic diagram of an electronic gear shift lever according to one embodiment of the present invention, as shown below. Figure 3 As shown, the electronic shift lever includes an unlock button and a P-gear button. The button signals are triggered by contact connections, with each button using three contacts. Each button is successfully triggered when at least one contact is connected. If one or two of the three contacts fail, the abnormal button contact data is transmitted to the electronic shifter.

[0079] The electronic shifter transmits shift signals and target detection data to the electronic shift controller via CAN communication. The electronic shift controller receives the shift signals from the electronic shifter, determines the shift signal, and outputs a shift command to the electronic shift actuator to execute the shift action. The electronic shift controller also receives target detection data from the electronic shifter and electronic shift actuator, determines the target detection data, and stores the health detection signal in its memory. The communication terminal serves as the interface between the vehicle and the cloud for data transmission, and can read, download, and transmit the health detection data from the memory.

[0080] Target detection data from the vehicle is transmitted to a cloud-based data storage and analysis module via a data access interface. The data storage receives raw data from the vehicle, including both routine and abnormal data. The analysis module includes model analysis, statistical analysis, and early warning analysis. Model analysis analyzes health monitoring data using a pre-defined model and outputs the results, which are then stored in the data storage. Statistical analysis categorizes and statistically analyzes the health monitoring data, allowing for classification by region, time, routine data type, and abnormal data type, and stores the statistical data in the data storage. Early warning analysis comprehensively assesses abnormal and routine data, outputs early warning information, makes a warning decision, and stores the warning data in the data storage.

[0081] The terminal service system reads, downloads, and receives data from the data storage through the cloud data interface, displays the results, and outputs the fault degree, warning degree, aging degree, remaining life, geographical distribution, fault distribution, and time distribution of the single vehicle's gear shift mechanism. It can complete the early warning and fault monitoring of the electronic gear shift mechanism, quickly locate the problematic vehicle, and also complete the statistical analysis of the specific information of each fault occurrence, statistical analysis of fault, frequency, geographical and time factors, which can be used for fault correlation analysis.

[0082] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0083] This embodiment also provides a health detection device for a vehicle gear shifting mechanism, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0084] Figure 4 This is a structural block diagram of a health detection device for a vehicle gear shift mechanism according to one embodiment of the present invention. As shown in the figure, the device includes an acquisition module 401, used to acquire target detection data corresponding to a target vehicle, wherein the target detection data represents the health detection data corresponding to the electronic gear shift mechanism of the target vehicle; an analysis module 402, used to analyze and process the target detection data to obtain target analysis results, wherein the target analysis results are used to predict the faults of the electronic gear shift mechanism from multiple dimensions; and an evaluation module 403, used to perform a health evaluation of the electronic gear shift mechanism using the target analysis results to obtain evaluation results, wherein the evaluation results are used to determine the health status of the electronic gear shift mechanism.

[0085] Optionally, the acquisition module 401 is also used to collect normal trigger data of the electronic shift mechanism, wherein the normal trigger data includes at least one of the following: number of starts, number of times the handle unlock button is triggered, number of times the target gear button is triggered, and number of times the target gear is executed; and the first data is obtained based on the normal trigger data.

[0086] Optionally, the acquisition module 401 is also used to collect abnormal trigger data of the electronic shift mechanism, wherein the abnormal trigger data includes at least one of the following: number of times the handle button contact fails, number of times the handle node is lost, number of times overvoltage alarms occur, number of times undervoltage alarms occur, and number of times wake-up fails; and second data is obtained based on the abnormal trigger data.

[0087] Optionally, the analysis module 402 is further configured to perform predictive processing on the target detection data using a target neural network model to obtain a first analysis result, wherein the target neural network is obtained by machine learning training using multiple sets of historical detection data; perform statistical processing on the target detection data to obtain a second analysis result; perform early warning analysis on the target detection data to obtain a third analysis result; and determine the target analysis result based on the first analysis result, the second analysis result, and the third analysis result.

[0088] Optionally, the health detection device for the vehicle shift mechanism further includes a sending module 404 for sending the evaluation results to the graphical user interface of the terminal device so that the graphical user interface can display the evaluation results.

[0089] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0090] This embodiment also provides a storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when it runs.

[0091] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0092] Step S1: Obtain target detection data corresponding to the target vehicle, wherein the target detection data is used to represent the health detection data corresponding to the electronic shift mechanism of the target vehicle;

[0093] Step S2: Analyze and process the target detection data to obtain target analysis results, wherein the target analysis results are used to predict the faults of the electronic shift mechanism from multiple dimensions.

[0094] Step S3: Use the target analysis results to perform a health assessment on the electronic shift mechanism and obtain the assessment results. The assessment results are used to determine the health status of the electronic shift mechanism.

[0095] Optionally, in this embodiment, the storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0096] This embodiment also provides a processor for running a program, wherein the program is configured to execute the steps in any of the above method embodiments during runtime.

[0097] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0098] Step S1: Obtain target detection data corresponding to the target vehicle, wherein the target detection data is used to represent the health detection data corresponding to the electronic shift mechanism of the target vehicle;

[0099] Step S2: Analyze and process the target detection data to obtain target analysis results, wherein the target analysis results are used to predict the faults of the electronic shift mechanism from multiple dimensions.

[0100] Step S3: Use the target analysis results to perform a health assessment on the electronic shift mechanism and obtain the assessment results. The assessment results are used to determine the health status of the electronic shift mechanism.

[0101] This embodiment also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0102] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0103] Step S1: Obtain target detection data corresponding to the target vehicle, wherein the target detection data is used to represent the health detection data corresponding to the electronic shift mechanism of the target vehicle;

[0104] Step S2: Analyze and process the target detection data to obtain target analysis results, wherein the target analysis results are used to predict the faults of the electronic shift mechanism from multiple dimensions.

[0105] Step S3: Use the target analysis results to perform a health assessment on the electronic shift mechanism and obtain the assessment results. The assessment results are used to determine the health status of the electronic shift mechanism.

[0106] Optionally, specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0107] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0108] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0110] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0112] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0113] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method of health detection of a vehicle shift mechanism, characterized by, The method comprises the following steps: statistic normal trigger data and abnormal trigger data of an electronic gear shifting mechanism of a target vehicle, wherein the normal trigger data comprises at least one of the following: start-up times, handle unlocking button trigger times, target gear button trigger times, target gear execution times, and the abnormal trigger data comprises at least one of the following: handle button contact failure times, handle node loss times, overvoltage alarm times, undervoltage alarm times, and wake-up failure times; obtain target detection data based on the normal trigger data and the abnormal trigger data, wherein the target detection data is used to represent health detection data corresponding to the electronic gear shifting mechanism; perform prediction processing on the target detection data by using a target neural network model to obtain a first analysis result, wherein the target neural network is obtained by machine learning training of multiple sets of historical detection data; perform statistical processing on the target detection data to obtain a second analysis result; perform early warning analysis on the target detection data to obtain a third analysis result; determine a target analysis result based on the first analysis result, the second analysis result, and the third analysis result; perform health assessment on the electronic gear shifting mechanism by using the target analysis result to obtain an assessment result, wherein the assessment result is used to determine the health condition of the electronic gear shifting mechanism.

2. The method of claim 1, wherein, The assessment result comprises at least one of the following: fault degree information, early warning degree information, regional distribution information, fault distribution information, and time distribution information of the electronic gear shifting mechanism.

3. The method of claim 1, wherein, The method further comprises the following steps: send the assessment result to a graphical user interface of a terminal device to enable the graphical user interface to display the assessment result.

4. A health detection device for a vehicle shift mechanism, characterized by, The method comprises the following steps: an acquisition module is configured to statistic normal trigger data and abnormal trigger data of an electronic gear shifting mechanism of a target vehicle, wherein the normal trigger data comprises at least one of the following: start-up times, handle unlocking button trigger times, target gear button trigger times, target gear execution times, and the abnormal trigger data comprises at least one of the following: handle button contact failure times, handle node loss times, overvoltage alarm times, undervoltage alarm times, and wake-up failure times; obtain target detection data based on the normal trigger data and the abnormal trigger data, wherein the target detection data is used to represent health detection data corresponding to the electronic gear shifting mechanism; an analysis module is configured to perform prediction processing on the target detection data by using a target neural network model to obtain a first analysis result, wherein the target neural network is obtained by machine learning training of multiple sets of historical detection data; perform statistical processing on the target detection data to obtain a second analysis result; perform early warning analysis on the target detection data to obtain a third analysis result; and determine a target analysis result based on the first analysis result, the second analysis result, and the third analysis result; an assessment module is configured to perform health assessment on the electronic gear shifting mechanism by using the target analysis result to obtain an assessment result, wherein the assessment result is used to determine the health condition of the electronic gear shifting mechanism.

5. A non-volatile storage medium, characterized by, The storage medium stores a computer program, and the computer program is configured to execute the health detection method of the vehicle gear shifting mechanism in any one of claims 1 to 3 when running.

6. A processor, comprising: The processor is configured to run a program, and the program is configured to execute the health detection method of the vehicle gear shifting mechanism in any one of claims 1 to 3 when running. 7.An electronic device comprising a memory and a processor, the electronic device characterized by, The storage medium stores a computer program, and the processor is configured to run the computer program to execute the health detection method of the vehicle gear shifting mechanism in any one of claims 1 to 3.

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