Vehicle remote diagnosis method, device, server and storage medium

Abstract

The present application relates to the field of vehicle technology, and in particular to a remote diagnosis method, device, server and storage medium for a vehicle, wherein the method is applied to a server, and the method includes: obtaining the whole vehicle data uploaded when the vehicle fails; diagnosing the status of the whole vehicle components according to the whole vehicle data, obtaining the whole vehicle diagnosis result, locating the source and cause of the fault according to the diagnosis result, and generating a fault solution based on the source and cause of the fault; generating a diagnosis report according to the source, cause and solution of the fault, and sending the diagnosis report to a preset terminal to solve the vehicle fault based on the diagnosis report. Thus, the problem that the fault algorithm model in the related technology is backward and single, resulting in poor accuracy of the fault diagnosis result, and the inability to perform server remote diagnosis on the whole vehicle data, resulting in high difficulty in fault diagnosis, and poor human-computer interaction experience in fault diagnosis is solved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a remote diagnosis method, device, server and storage medium for a vehicle. Background Art

[0002] With the continuous development of the automobile industry, the intelligence and automation of automobiles are gradually improving, and fault repair is becoming more and more difficult. When a car breaks down during driving and cannot continue driving, due to factors such as the geographical environment or weather, maintenance personnel cannot immediately arrive at the fault site for repair. In addition, there is a relative shortage of maintenance personnel who can quickly and accurately locate and diagnose faults. Therefore, the current fault repair mechanism can no longer meet the needs of the increasingly developed automobile technology.

[0003] In the related art, it is proposed to use the vehicle's controller to perform self-diagnosis of faults separately and send the diagnosis results to a mobile terminal. However, the fault diagnosis algorithm model in the existing technology is too old and single, and it is impossible to remotely diagnose the entire vehicle data, resulting in poor accuracy of the fault diagnosis results. In addition, users cannot get a friendly human-computer interaction experience when obtaining the fault diagnosis results. Summary of the invention

[0004] The present application provides a vehicle remote diagnosis method, device, server and storage medium to solve the problems in the related technology that the fault algorithm model is backward and single, resulting in poor accuracy of fault diagnosis results, inability to perform server remote diagnosis of the entire vehicle data, resulting in high difficulty in fault diagnosis, and poor human-computer interaction experience in fault diagnosis.

[0005] The first aspect of the present application provides a remote diagnosis method for a vehicle, which is applied to a server and includes the following steps: obtaining vehicle data uploaded when a vehicle fails; diagnosing the status of the vehicle components based on the vehicle data to obtain a vehicle diagnosis result, locating the source and cause of the fault based on the diagnosis result, and generating a fault solution based on the source and cause of the fault; generating a diagnostic report based on the source, cause and solution, and sending the diagnostic report to a preset terminal to solve the vehicle fault based on the diagnostic report.

[0006] According to the above-mentioned technical means, the embodiments of the present application can remotely diagnose the vehicle when a vehicle fault occurs, which can effectively reduce the difficulty of fault diagnosis. At the same time, the diagnosis results can be analyzed by improving the algorithm dimension of the fault algorithm model to improve the accuracy of the fault diagnosis results. The user experience of obtaining fault diagnosis reports can also be improved by selecting or setting a preset terminal.

[0007] Optionally, in one embodiment of the present application, the status of the vehicle components is diagnosed according to the vehicle data to obtain a vehicle diagnosis result, including: identifying the fault diagnosis alarm flag of the preset core component of the intelligent vehicle control domain, if the fault diagnosis alarm flag is identified, then the preset core component is determined to be in a fault state, otherwise the intelligent vehicle control domain is determined to be in a normal state; using the remaining wear algorithm of the consumable parts to calculate the remaining wear level of the consumable parts of the intelligent vehicle control domain, if the wear level is less than the preset level, then the consumable parts of the intelligent vehicle control domain are determined to be in a wear fault state, otherwise the consumable parts are determined to be in a normal state; identifying whether any component of the smart cockpit domain triggers an alarm, if so, then the smart cockpit domain is determined to be in a fault state, otherwise the smart cockpit domain is determined to be in a normal state.

[0008] According to the above-mentioned technical means, the embodiments of the present application can determine whether their respective areas are in a fault state by identifying the fault diagnosis alarm flags of the core components of the intelligent vehicle control domain, calculating the remaining wear level of the wearing parts and comparing it with the preset level, and identifying the alarm status of the intelligent cockpit domain. It uses a multi-dimensional fault algorithm to perform specific fault diagnosis on the status of the vehicle components, thereby improving the accuracy of diagnosing whether it is in a fault state.

[0009] Optionally, in one embodiment of the present application, locating the fault source and cause according to the diagnostic result includes: using a fault detection and identification algorithm engine to identify the diagnostic result, and determining whether a fault is identified; if a fault is identified, locating the fault source and cause, otherwise entering a manual diagnosis mode, and determining the fault source and cause based on manual diagnosis.

[0010] According to the above-mentioned technical means, the embodiment of the present application can identify the fault results and locate the source and cause of the fault through two methods: fault detection and identification algorithm engine and manual diagnosis, avoiding the singleness and limitation of the diagnostic mode, which may lead to the possibility of failure in identifying the diagnostic results, and improving the success rate of fault diagnosis.

[0011] Optionally, in one embodiment of the present application, the diagnostic report includes one or more of basic vehicle information, number of fault diagnoses, fault information, fault handling suggestions and fault solutions.

[0012] The second aspect of the present application provides a remote diagnostic device for a vehicle, which is applied to a server and includes: an acquisition module for acquiring vehicle data uploaded when a vehicle fails; a generation module for diagnosing the status of the vehicle components based on the vehicle data to obtain a vehicle diagnosis result, locating the source and cause of the fault based on the diagnosis result, and generating a fault solution based on the source and cause of the fault; a sending module for generating a diagnostic report based on the source, cause and solution, and sending the diagnostic report to a preset terminal to solve the vehicle fault based on the diagnostic report.

[0013] Optionally, in one embodiment of the present application, the generation module is further used to: identify the fault diagnosis alarm flag of a preset core component of the intelligent vehicle control domain, if the fault diagnosis alarm flag is identified, determine that the preset core component is in a fault state, otherwise determine that the intelligent vehicle control domain is in a normal state; calculate the remaining wear level of the consumables of the intelligent vehicle control domain using a consumable parts remaining wear algorithm, if the wear level is less than a preset level, determine that the consumables of the intelligent vehicle control domain are in a wear fault state, otherwise determine that the consumables are in a normal state; identify whether any component of the intelligent cockpit domain triggers an alarm, if so, determine that the smart cockpit domain is in a fault state, otherwise determine that the smart cockpit domain is in a normal state.

[0014] Optionally, in one embodiment of the present application, the generation module is further used to: use a fault detection and identification algorithm engine to identify the diagnosis result, and determine whether a fault is identified; if a fault is identified, locate the source and cause of the fault, otherwise enter a manual diagnosis mode to determine the source and cause of the fault based on manual diagnosis.

[0015] Optionally, in one embodiment of the present application, the diagnostic report includes one or more of basic vehicle information, number of fault diagnoses, fault information, fault handling suggestions and fault solutions.

[0016] The third aspect of the present application provides a server, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the remote diagnosis method of the vehicle as described in the above embodiment.

[0017] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the remote diagnosis method of a vehicle as described in the above embodiment.

[0018] Therefore, this application has at least the following beneficial effects:

[0019] 1. The embodiments of the present application can remotely diagnose the vehicle when a vehicle failure occurs, which can effectively reduce the difficulty of fault diagnosis. At the same time, the diagnosis results can be analyzed by improving the algorithm dimension of the fault algorithm model to improve the accuracy of the fault diagnosis results. The user experience of obtaining fault diagnosis reports can also be improved by selecting or setting a preset terminal.

[0020] 2. The embodiments of the present application can determine whether their respective areas are in a fault state by identifying the fault diagnosis alarm flags of the core components of the intelligent vehicle control domain, calculating the remaining wear level of the wearing parts and comparing it with the preset level, and identifying the alarm status of the intelligent cockpit domain. It uses a multi-dimensional fault algorithm to perform specific fault diagnosis on the status of the vehicle components, thereby improving the accuracy of diagnosing whether it is in a fault state.

[0021] 3. The embodiments of the present application can identify fault results and locate the source and cause of the fault through two methods: a fault detection and identification algorithm engine and manual diagnosis, thereby avoiding the singleness and limitations of the diagnostic mode, which may lead to the possibility of failure in identifying the diagnostic results, and improving the success rate of fault diagnosis.

[0022] As a result, technical problems such as the backward and single fault algorithm model in related technologies, which leads to poor accuracy of fault diagnosis results, the inability to perform remote server diagnosis of vehicle data, resulting in high difficulty in fault diagnosis, and poor human-computer interaction experience in fault diagnosis, are solved.

[0023] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0025] Figure 1 A structural diagram of a fault diagnosis system provided according to an embodiment of the present application;

[0026] Figure 2 A flowchart of a remote diagnosis method for a vehicle provided according to an embodiment of the present application;

[0027] Figure 3 A flowchart of a fault diagnosis service provided according to an embodiment of the present application;

[0028] Figure 4 An exemplary diagram of a remote diagnostic device for a vehicle provided according to an embodiment of the present application;

[0029] Figure 5 A schematic diagram of the structure of a server provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0031] The following describes the remote diagnosis method, device, server and storage medium of the vehicle of the embodiment of the present application with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, the present application provides a remote diagnosis method for a vehicle, in which the whole vehicle data uploaded when the vehicle fails is obtained, the status of the whole vehicle components is diagnosed according to the whole vehicle data, and the whole vehicle diagnosis result is obtained. The source and cause of the fault are located according to the diagnosis result, and a fault solution is generated based on the source and cause of the fault. A diagnosis report is generated according to the source, cause and solution of the fault, and the diagnosis report is sent to a preset terminal to solve the vehicle fault based on the diagnosis report. As a result, the problems of the backward and single fault algorithm model in the related technology, which leads to poor accuracy of the fault diagnosis result, the inability to perform remote server diagnosis on the whole vehicle data, the high difficulty of fault diagnosis, and the poor human-computer interaction experience of fault diagnosis are solved.

[0032] like Figure 1 As shown, the fault diagnosis system structure includes: a car, an on-board information collection module, and a fault diagnosis system.

[0033] Among them, the vehicle data and fault information reported by the vehicle are analyzed through the cloud, the vehicle's component status, fault source, fault cause and specific solution suggestions are remotely judged, and a fault diagnosis report is provided to users and 4S stores as a recommended reference for the diagnosis results. Among them, the car, as the signal generating end of the system, will generate various data and messages both in normal conditions and when the vehicle fails; the on-board information acquisition module is used for the storage and conversion of data and messages, and transmits the data and messages to the server of the fault diagnosis system through a wireless network; the fault diagnosis system is used to push the fault diagnosis results (report) to users and 4S as a reference through cloud diagnosis, to help them quickly locate the problem.

[0034] Based on the fault diagnosis system described in the above embodiment, the remote diagnosis method of the vehicle proposed in the embodiment of the present application is explained below, wherein the method is applied to a server.

[0035] like Figure 2 As shown, the remote diagnosis method of the vehicle includes the following steps:

[0036] In step S101, the whole vehicle data uploaded when the vehicle breaks down is obtained.

[0037] It is understandable that, before diagnosing a vehicle fault, the embodiment of the present application needs to obtain the entire vehicle data when the vehicle fails, wherein the data can be uploaded to the server through the vehicle.

[0038] Specifically, if Figure 3 As shown, after the vehicle is powered on, before the vehicle fault is identified, the "vehicle health detection engine" is used to detect the vehicle health status. The detection content includes: maintenance reminders based on time, mileage and user habits; display of remaining wear of wearing parts based on time, mileage, frequency of use, duration of use, usage scenarios and other dimensions; status detection of important components such as cameras and radars. Among them, the embodiment of the present application can obtain the whole vehicle data when the vehicle fails through the server by performing a health detection on the vehicle.

[0039] In step S102, the status of vehicle components is diagnosed based on the vehicle data to obtain a vehicle diagnosis result, the source and cause of the fault are located based on the diagnosis result, and a fault solution is generated based on the source and cause of the fault.

[0040] It can be understood that the embodiments of the present application can remotely diagnose the vehicle when a vehicle fault occurs, which can effectively reduce the difficulty of fault diagnosis. At the same time, by improving the algorithm dimension of the fault algorithm model, the diagnosis results can be analyzed to improve the accuracy of the fault diagnosis results.

[0041] Optionally, in one embodiment of the present application, the status of vehicle components is diagnosed according to the vehicle data to obtain a vehicle diagnosis result, including: identifying the fault diagnosis alarm flag of a preset core component of the intelligent vehicle control domain, if the fault diagnosis alarm flag is identified, then the preset core component is determined to be in a faulty state, otherwise the intelligent vehicle control domain is determined to be in a normal state; using a consumables remaining wear algorithm to calculate the remaining wear level of the consumables in the intelligent vehicle control domain, if the wear level is less than the preset level, then the consumables in the intelligent vehicle control domain are determined to be in a wear fault state, otherwise the consumables are determined to be in a normal state; identifying whether any component in the intelligent cockpit domain triggers an alarm, if so, then the intelligent cockpit domain is determined to be in a faulty state, otherwise the intelligent cockpit domain is determined to be in a normal state.

[0042] Among them, the preset core components may include batteries, on-board energy storage devices, SOC (State of Charge, remaining capacity), DC-DC (Data Controller-Data Controller, converter), braking systems, drive motor controllers, etc., without specific limitations.

[0043] Among them, the preset level can be specifically set according to actual conditions and is not specifically limited.

[0044] It can be understood that the embodiments of the present application can determine whether their respective areas are in a fault state by identifying the fault diagnosis alarm flags of the core components of the intelligent vehicle control domain, calculating the remaining wear level of the wearing parts and comparing it with the preset level and identifying the alarm status of the intelligent cockpit domain, and use multi-dimensional fault algorithms to perform specific fault diagnosis on the status of vehicle components, thereby improving the accuracy of fault diagnosis.

[0045] Specifically, the embodiment of the present application performs fault diagnosis on the whole vehicle through a fault detection and identification algorithm engine, wherein the algorithm dimension of the fault detection and identification algorithm engine is multi-dimensional component fault diagnosis of the whole vehicle based on the intelligent vehicle control domain, the intelligent driving domain, and the intelligent cockpit domain. Any abnormality or fault in the intelligent vehicle control domain, the intelligent driving domain, and the intelligent cockpit domain will trigger the fault detection and identification algorithm engine to perform fault diagnosis, wherein the fault diagnosis algorithm dimensions are as follows:

[0046] Among them, the diagnosis of the three-electric core components in the intelligent vehicle control domain can be based on GB / T 32960.3-2016 "Technical Specifications for Electric Vehicle Remote Service and Management System Part 3: Communication Protocol and Data Format", among which the fault diagnosis alarm flags include: temperature difference alarm, battery high temperature alarm, vehicle-mounted energy storage device type overvoltage alarm, vehicle-mounted energy storage device type undervoltage alarm, SOC low alarm, single cell overvoltage alarm, single cell undervoltage alarm, SOC high alarm, SOC jump alarm, rechargeable energy storage system mismatch alarm, battery cell consistency difference alarm, insulation alarm, DC-DC temperature alarm, braking system alarm, DC-DC status alarm, drive motor controller temperature alarm, high-voltage interlock status alarm, drive motor temperature alarm, among which, any component alarm can trigger the status of the three-electric to be a fault state.

[0047] The calculation rules of the remaining wear degree algorithm of the wearing parts in the intelligent vehicle control domain are as follows:

[0048] (1) Remaining mileage percentage = 100% - (current cumulative mileage - mileage of last maintenance) / replacement interval mileage * 100%.

[0049] (2) Remaining time percentage = 100% - (current date - date of last maintenance) / interval replacement time * 100%.

[0050] (3) Remaining wear percentage = 100% - current usage time / interval replacement time * 100%.

[0051] (4) Remaining number of uses: wear percentage = 100% - current number of uses / number of replacement intervals * 100%.

[0052] (5) The health percentage of the air conditioning filter is the minimum value among the remaining mileage percentage, remaining time percentage, and remaining wear percentage.

[0053] (6) The health percentage of antifreeze and brake fluid is the minimum value of the remaining mileage percentage and the remaining time percentage.

[0054] (7) The health percentage of wiper blades and brake pads is the minimum value among the remaining mileage percentage, remaining time percentage and remaining usage times wear percentage.

[0055] Among them, the status standards of wearing parts are as follows:

[0056] The condition of the wearing parts is normal: the remaining wear percentage is greater than or equal to a certain percentage, such as 30%.

[0057] The state of wearing parts is abnormal: the remaining wear percentage is within a certain range, such as 5% to 30%.

[0058] Status failure of wearing parts: the remaining wear percentage is less than a certain percentage, such as 5%.

[0059] Among them, the fault diagnosis algorithm of the IMS (IP Multimedia Subsystem) camera, power amplifier, central control screen, instrument screen, electronic rearview mirror screen, left transparent A-pillar screen and right transparent A-pillar screen in the intelligent cockpit domain is a multi-dimensional fault diagnosis algorithm based on high voltage fault alarm, low voltage fault alarm, overtemperature alarm, undertemperature alarm, network communication abnormality, network timeout and functional abnormality. The alarm of any component can trigger the status of the cockpit domain to be a faulty state.

[0060] Among them, the fault diagnosis algorithm of the millimeter-wave radar, lidar, and DMS (Digital Media Storage) camera in the intelligent driving domain is a multi-dimensional fault diagnosis algorithm based on high voltage fault alarm, low voltage fault alarm, high temperature alarm, low temperature alarm, network communication abnormality, network timeout, and functional abnormality. The alarm of any component can trigger the status of the driving domain to a fault state.

[0061] Optionally, in one embodiment of the present application, locating the source and cause of the fault according to the diagnosis result includes: using a fault detection and identification algorithm engine to identify the diagnosis result, and determining whether the fault is identified; if the fault is identified, locating the source and cause of the fault, otherwise entering the manual diagnosis mode, and determining the source and cause of the fault based on manual diagnosis.

[0062] It can be understood that the embodiments of the present application can identify fault results and locate the source and cause of the fault through both fault detection and identification algorithm engine and manual diagnosis, avoiding the singleness and limitations of the diagnostic mode, which may lead to the possibility of failure in identifying diagnostic results, and improving the success rate of fault diagnosis.

[0063] Specifically, the fault detection and identification algorithm engine is used to automatically analyze the reported faults and notify the user with an alarm. If the fault detection and identification algorithm engine automatically identifies the fault and diagnoses it, the fault detection and identification algorithm engine automatically pushes the diagnosis report and corresponding strategies to the user. The strategies include making appointments for remote over-the-air upgrades and making appointments for in-store repairs. If the engine fails to identify the fault, it will enter the manual diagnosis mode, and a professional technician will perform remote diagnosis and analysis for the user, and push the diagnosis report and corresponding strategies to the user. The strategies can be configured, including making appointments for remote over-the-air upgrades and making appointments for in-store repairs.

[0064] In step S103, a diagnosis report is generated according to the fault source, fault cause and fault solution, and the diagnosis report is sent to a preset terminal to solve the vehicle fault based on the diagnosis report.

[0065] It is understandable that the embodiments of the present application can generate a diagnostic report through the fault diagnosis results and solutions, and send it to the user, and the user can use the diagnostic report to solve the vehicle fault.

[0066] Specifically, when a fault occurs, a friendly human-computer interaction method can be used to display different fault contents to users for different levels of faults. The human-computer interaction method takes the intelligent voice assistant as an example, and provides different solution strategies for users' reference. When the cloud receives a serious fault signal, the intelligent voice assistant will prompt the fault content and broadcast the fault voice prompt, and recommend fault solutions to users in the form of cards. Among them, when a fault exists, after the vehicle is powered on, the fault prompt will be repeatedly broadcast until the user turns off the fault prompt by voice or manually.

[0067] When the fault diagnosis is completed, the user will receive a fault diagnosis report provided by the fault detection and identification algorithm engine. The report includes global diagnostic results for the intelligent driving domain, intelligent vehicle control domain, and intelligent cockpit domain. If a fault is found, the diagnostic report will display the analyzed fault cause and fault handling suggestions in detail.

[0068] Optionally, in one embodiment of the present application, the diagnostic report includes one or more of basic vehicle information, number of fault diagnoses, fault information, fault handling suggestions and fault solutions.

[0069] Specifically, the diagnostic report includes: basic vehicle information, number of fault diagnoses, fault overview, fault handling suggestions, handling suggestion display rules and fault solutions, as follows:

[0070] (1) Basic vehicle information: diagnosis time, vehicle frame number, and version.

[0071] (2) Number of fault diagnoses: planned diagnosis items, completed diagnosis items, and number of faults.

[0072] (3) Fault overview: diagnostic domain and total number of faults in the domain, detailed fault description and handling suggestions for the domain, and components involved in the fault. The fault display content includes the total number of faults and system risk level of intelligent driving, intelligent cockpit, and intelligent vehicle control, detailed component fault description, risk level, and handling suggestions under each section, and related components involved in the fault, such as the data source diagnostic background. The risk level is healthy < warning < fault.

[0073] (4) Troubleshooting suggestions: automatically generated by the backend system or manually entered by engineers.

[0074] (5) Rules for displaying processing suggestions: Processing suggestions are divided into three categories based on industry suggestions, and the rules and content are filled in by backend engineers.

[0075] (6) Troubleshooting solution: It is recommended to schedule a remote over-the-air upgrade or maintenance appointment.

[0076] According to the remote vehicle diagnosis method proposed in the embodiment of the present application, by obtaining the whole vehicle data uploaded when the vehicle fails, the status of the whole vehicle parts is diagnosed according to the whole vehicle data, and the whole vehicle diagnosis result is obtained. The source and cause of the fault are located according to the diagnosis result, and a fault solution is generated based on the source and cause of the fault. A diagnosis report is generated according to the source, cause and solution of the fault, and the diagnosis report is sent to a preset terminal to solve the vehicle fault based on the diagnosis report. In this way, the problem of backward and single fault algorithm model in related technologies, resulting in poor accuracy of fault diagnosis results, inability to perform server remote diagnosis of whole vehicle data, resulting in high difficulty in fault diagnosis, and poor human-computer interaction experience in fault diagnosis is solved.

[0077] Next, a remote diagnosis device for a vehicle according to an embodiment of the present application is described with reference to the accompanying drawings, wherein the device is applied to a server.

[0078] Figure 4 It is a block diagram of a remote diagnostic device for a vehicle according to an embodiment of the present application.

[0079] like Figure 4 As shown, the vehicle remote diagnosis device 10 includes: an acquisition module 100 , a generation module 200 and a sending module 300 .

[0080] Among them, the acquisition module 100 is used to obtain the whole vehicle data uploaded when the vehicle fails; the generation module 200 is used to diagnose the status of the whole vehicle parts according to the whole vehicle data, obtain the whole vehicle diagnosis result, locate the source and cause of the fault according to the diagnosis result, and generate a fault solution based on the source and cause of the fault; the sending module 300 is used to generate a diagnostic report according to the source, cause and solution of the fault, and send the diagnostic report to the preset terminal to solve the vehicle fault based on the diagnostic report.

[0081] Optionally, in one embodiment of the present application, the generation module 200 is further used to: identify the fault diagnosis alarm flag of a preset core component of the intelligent vehicle control domain, if the fault diagnosis alarm flag is identified, it is determined that the preset core component is in a fault state, otherwise it is determined that the intelligent vehicle control domain is in a normal state; calculate the remaining wear level of the consumables of the intelligent vehicle control domain using the remaining wear algorithm of the consumables, if the wear level is less than the preset level, it is determined that the consumables of the intelligent vehicle control domain are in a wear fault state, otherwise it is determined that the consumables are in a normal state; identify whether any component of the intelligent cockpit domain triggers an alarm, if so, it is determined that the smart cockpit domain is in a fault state, otherwise it is determined that the smart cockpit domain is in a normal state.

[0082] Optionally, in one embodiment of the present application, the generation module 200 is further used to: use the fault detection and identification algorithm engine to identify the diagnosis results, and determine whether the fault is identified; if the fault is identified, locate the source and cause of the fault, otherwise enter the manual diagnosis mode to determine the source and cause of the fault based on manual diagnosis.

[0083] Optionally, in one embodiment of the present application, the diagnostic report includes one or more of basic vehicle information, number of fault diagnoses, fault information, fault handling suggestions and fault solutions.

[0084] It should be noted that the above explanation of the embodiment of the remote diagnosis method for a vehicle is also applicable to the remote diagnosis device for a vehicle in this embodiment, and will not be repeated here.

[0085] According to the vehicle remote diagnosis device proposed in the embodiment of the present application, by obtaining the whole vehicle data uploaded when the vehicle fails, the status of the whole vehicle parts is diagnosed according to the whole vehicle data, and the whole vehicle diagnosis result is obtained. The source and cause of the fault are located according to the diagnosis result, and a fault solution is generated based on the source and cause of the fault. A diagnosis report is generated according to the source, cause and solution of the fault, and the diagnosis report is sent to a preset terminal to solve the vehicle fault based on the diagnosis report. In this way, the problem of backward and single fault algorithm model in related technologies, resulting in poor accuracy of fault diagnosis results, inability to perform server remote diagnosis of whole vehicle data, resulting in high difficulty in fault diagnosis, and poor human-computer interaction experience in fault diagnosis is solved.

[0086] Figure 5 A schematic diagram of the structure of a server provided in an embodiment of the present application. The server may include:

[0087] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0088] When the processor 502 executes the program, the remote diagnosis method for the vehicle provided in the above embodiment is implemented.

[0089] Furthermore, the server also includes:

[0090] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0091] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0092] The memory 501 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0093] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0094] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0095] The processor 502 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0096] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned remote diagnosis method for a vehicle.

[0097] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. 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 application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0098] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0099] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0100] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N 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, a field programmable gate array, etc.

[0101] A person skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0102] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A remote diagnosis method for a vehicle, characterized in that: The method is applied to a server and comprises the following steps: Obtain the vehicle data uploaded when the vehicle fails; Diagnose the status of vehicle components according to the vehicle data to obtain a vehicle diagnosis result, locate the source and cause of the fault according to the diagnosis result, and generate a fault solution based on the source and cause of the fault; Generate a diagnostic report according to the fault source, the fault cause and the fault solution, and send the diagnostic report to a preset terminal to solve the vehicle fault based on the diagnostic report; After the vehicle is powered on, before a vehicle fault is identified, the vehicle health detection engine is used to detect the vehicle health status. The detection content includes: maintenance reminders based on time, mileage and user habits; display of the remaining wear of vulnerable parts based on time, mileage, usage frequency, usage duration and usage scenarios; camera and radar status detection; The locating the fault source and the fault cause according to the diagnosis result includes: using a fault detection and recognition algorithm engine to identify the diagnosis result and determine whether a fault is identified; if a fault is identified, locating the fault source and the fault cause, otherwise entering a manual diagnosis mode to determine the fault source and the fault cause based on manual diagnosis; Among them, the algorithm dimension of the fault detection and identification algorithm engine is based on the multi-dimensional component fault diagnosis of the whole vehicle in the intelligent vehicle control domain, the intelligent driving domain, and the intelligent cockpit domain. Any abnormality or fault in the intelligent vehicle control domain, the intelligent driving domain, and the intelligent cockpit domain will trigger the fault detection and identification algorithm engine to perform fault diagnosis. Among them, the fault diagnosis algorithm dimensions are as follows: Among them, the fault diagnosis alarm flags of the intelligent vehicle control domain include: temperature difference alarm, battery high temperature alarm, vehicle energy storage device type overvoltage alarm, vehicle energy storage device type undervoltage alarm, SOC low alarm, single cell overvoltage alarm, single cell undervoltage alarm, SOC high alarm, SOC jump alarm, rechargeable energy storage system mismatch alarm, battery cell consistency difference alarm, insulation alarm, DC-DC temperature alarm, brake system alarm, DC-DC status alarm, drive motor controller temperature alarm, high voltage interlock status alarm, drive motor temperature alarm, among which, any component alarm can trigger the status of the three electrics to be a fault state; The calculation rules of the remaining wear degree algorithm of the wearing parts in the intelligent vehicle control domain are as follows: Remaining mileage percentage = 100% - (current cumulative mileage - mileage of last maintenance) / interval replacement mileage * 100%; Remaining time percentage = 100% - (current date - date of last maintenance) / interval replacement time * 100%; Remaining wear percentage = 100% - current usage time / interval replacement time * 100%; Remaining usage wear percentage = 100% - current usage / interval replacement times * 100%; The remaining wear degree of the air conditioning filter is the minimum value among the remaining mileage percentage, remaining time percentage, and remaining wear degree percentage. The remaining wear of antifreeze and brake fluid is the minimum of the remaining mileage percentage and the remaining time percentage; The remaining wear of wiper blades and brake pads is the minimum value among the remaining mileage percentage, remaining time percentage and remaining number of uses wear percentage; Among them, the status standards of wearing parts are as follows: The condition of the wearing parts is normal: the remaining wear percentage is greater than or equal to a certain upper limit value; The state of the wearing parts is abnormal: the remaining wear percentage is between a certain upper limit and a lower limit; Status failure of wearing parts: the remaining wear percentage is less than a certain lower limit; Among them, the fault diagnosis algorithm of the IP multimedia system camera, power amplifier, central control screen, instrument screen, electronic rearview mirror screen, left transparent A-pillar screen and right transparent A-pillar screen in the intelligent cockpit domain is a multi-dimensional fault diagnosis algorithm based on high voltage fault alarm, low voltage fault alarm, over-temperature alarm, under-temperature alarm, network communication abnormality, network timeout and function abnormality. The alarm of any component can trigger the cockpit domain to be in a fault state. Among them, the fault diagnosis algorithm of the millimeter-wave radar, lidar, and digital media camera in the intelligent driving domain is a multi-dimensional fault diagnosis algorithm based on high voltage fault alarm, low voltage fault alarm, high temperature alarm, low temperature alarm, network communication abnormality, network timeout, and functional abnormality. The alarm of any component can trigger the status of the driving domain to a fault state.

2. The method according to claim 1, characterized in that: The step of diagnosing the status of the vehicle components according to the vehicle data to obtain a vehicle diagnosis result includes: Identify a fault diagnosis alarm flag of a preset core component of the intelligent vehicle control domain, and if the fault diagnosis alarm flag is identified, determine that the preset core component is in a fault state, otherwise determine that the intelligent vehicle control domain is in a normal state; Identify whether any component of the smart cockpit domain triggers an alarm. If so, determine that the smart cockpit domain is in a fault state; otherwise, determine that the smart cockpit domain is in a normal state.

3. The method according to any one of claims 1 to 2, characterized in that: The diagnostic report includes one or more of basic vehicle information, fault diagnosis quantity, fault information, fault handling suggestions and fault solutions.

4. A remote diagnostic device for a vehicle, characterized in that: The device is applied to a server and includes: The acquisition module is used to obtain the vehicle data uploaded when the vehicle fails; A generation module, used to diagnose the status of vehicle components according to vehicle data, obtain vehicle diagnosis results, locate the source and cause of the fault according to the diagnosis results, and generate a fault solution based on the source and cause of the fault; A sending module, used for generating a diagnosis report according to the fault source, the fault cause and the fault solution, and sending the diagnosis report to a preset terminal to solve the fault of the vehicle based on the diagnosis report; The generating module is further used to: identify the diagnosis result by using the fault detection and recognition algorithm engine, and determine whether a fault is identified; if a fault is identified, locate the source and cause of the fault, otherwise enter the manual diagnosis mode, and determine the source and cause of the fault based on manual diagnosis; Among them, the algorithm dimension of the fault detection and identification algorithm engine is based on the multi-dimensional component fault diagnosis of the whole vehicle in the intelligent vehicle control domain, the intelligent driving domain, and the intelligent cockpit domain. Any abnormality or fault in the intelligent vehicle control domain, the intelligent driving domain, and the intelligent cockpit domain will trigger the fault detection and identification algorithm engine to perform fault diagnosis. Among them, the fault diagnosis algorithm dimensions are as follows: Among them, the fault diagnosis alarm flags of the intelligent vehicle control domain include: temperature difference alarm, battery high temperature alarm, vehicle energy storage device type overvoltage alarm, vehicle energy storage device type undervoltage alarm, SOC low alarm, single cell overvoltage alarm, single cell undervoltage alarm, SOC high alarm, SOC jump alarm, rechargeable energy storage system mismatch alarm, battery cell consistency difference alarm, insulation alarm, DC-DC temperature alarm, brake system alarm, DC-DC status alarm, drive motor controller temperature alarm, high voltage interlock status alarm, drive motor temperature alarm, among which, any component alarm can trigger the status of the three electrics to be a fault state; The calculation rules of the remaining wear degree algorithm of the wearing parts in the intelligent vehicle control domain are as follows: Remaining mileage percentage = 100% - (current cumulative mileage - mileage of last maintenance) / interval replacement mileage * 100%; Remaining time percentage = 100% - (current date - date of last maintenance) / interval replacement time * 100%; Remaining wear percentage = 100% - current usage time / interval replacement time * 100%; Remaining usage wear percentage = 100% - current usage / interval replacement times * 100%; The remaining wear degree of the air conditioning filter is the minimum value among the remaining mileage percentage, remaining time percentage, and remaining wear degree percentage. The remaining wear of antifreeze and brake fluid is the minimum of the remaining mileage percentage and the remaining time percentage; The remaining wear of wiper blades and brake pads is the minimum value among the remaining mileage percentage, remaining time percentage and remaining number of uses wear percentage; Among them, the status standards of wearing parts are as follows: The condition of the wearing parts is normal: the remaining wear percentage is greater than or equal to a certain upper limit value; The state of the wearing parts is abnormal: the remaining wear percentage is between a certain upper limit and a lower limit; Status failure of wearing parts: the remaining wear percentage is less than a certain lower limit; Among them, the fault diagnosis algorithm of the IP multimedia system camera, power amplifier, central control screen, instrument screen, electronic rearview mirror screen, left transparent A-pillar screen and right transparent A-pillar screen in the intelligent cockpit domain is a multi-dimensional fault diagnosis algorithm based on high voltage fault alarm, low voltage fault alarm, over-temperature alarm, under-temperature alarm, network communication abnormality, network timeout and function abnormality. The alarm of any component can trigger the cockpit domain to be in a fault state. Among them, the fault diagnosis algorithm of the millimeter-wave radar, lidar, and digital media camera in the intelligent driving domain is a multi-dimensional fault diagnosis algorithm based on high voltage fault alarm, low voltage fault alarm, high temperature alarm, low temperature alarm, network communication abnormality, network timeout, and functional abnormality. The alarm of any component can trigger the status of the driving domain to a fault state.

5. The device according to claim 4, characterized in that The generating module is further used for: Identify a fault diagnosis alarm flag of a preset core component of the intelligent vehicle control domain, and if the fault diagnosis alarm flag is identified, determine that the preset core component is in a fault state, otherwise determine that the intelligent vehicle control domain is in a normal state; Identify whether any component of the smart cockpit domain triggers an alarm. If so, determine that the smart cockpit domain is in a fault state; otherwise, determine that the smart cockpit domain is in a normal state.

6. The device according to any one of claims 4 to 5, characterized in that: The diagnostic report includes one or more of basic vehicle information, fault diagnosis quantity, fault information, fault handling suggestions and fault solutions.

7. A server, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the remote diagnosis method for a vehicle as described in any one of claims 1 to 3.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the remote diagnosis method for a vehicle as described in any one of claims 1 to 3.

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