Intelligent guiding type diagnosis and repair method for commercial vehicle and related device

Abstract

The invention discloses an intelligent guiding type diagnosis and repair method for a commercial vehicle and a related device, and is suitable for the fields of unmanned driving, automatic driving, unmanned vehicles and the like. The method comprises the steps that when a target vehicle breaks down, first fault information and a vehicle equipment code submitted by a user are received, and controller information is determined according to fault keywords in the first fault information; triggering the Internet of Vehicles platform to issue an acquisition instruction to the target vehicle to obtain second fault information of the corresponding controller; according to the periodically updated whole vehicle data, screening out a fault code and a freezing frame which meet a fault condition from the second fault information; determining a whole vehicle signal operation model according to the fault code, predicting a fault signal source, and determining and sequencing a plurality of potential fault reasons; and sequentially prompting and guiding the user to troubleshoot hardware related to the fault cause, and determining the fault cause according to the troubleshooting result to complete intelligent diagnosis and repair. The diagnosis and repair mode is more intelligent, accurate, rapid and efficient, the diagnosis and repair cost is reduced, and the diagnosis and repair experience of the user is improved.

Description

Technical Field

[0001] The present application relates to the field of new energy vehicle technology, specifically to the technical fields of intelligent diagnosis and repair and big data, and in particular to an intelligent guided diagnosis and repair method and related devices for commercial vehicles. Background Art

[0002] At present, the fault diagnosis and maintenance of automobiles, especially commercial vehicles, basically stays at the stage of using diagnostic instruments to read fault codes and manually troubleshoot after a fault occurs. This diagnosis and maintenance method requires experienced maintenance personnel to accurately locate the fault location and perform corresponding maintenance.

[0003] However, for maintenance personnel with less experience or new models, it is necessary to check multiple possible fault points one by one, which makes it difficult to locate the fault accurately and in a timely manner, resulting in low diagnosis and repair efficiency and increased diagnosis and repair costs. Summary of the invention

[0004] The present application provides an intelligent guided repair method and related devices for commercial vehicles to solve the problems of low repair efficiency and high repair cost of existing automobile repair solutions.

[0005] The technical solution is as follows:

[0006] In a first aspect, an intelligent guided diagnosis and repair method for a commercial vehicle is provided, comprising:

[0007] Receiving first fault information and a vehicle equipment code submitted by a user for describing a whole vehicle fault phenomenon of a target vehicle, and determining controller information related to the fault based on a fault keyword in the first fault information;

[0008] Based on the received vehicle equipment code and the determined controller information, trigger the Internet of Vehicles platform to send a collection instruction to the target vehicle, so that the on-board terminal of the target vehicle collects second fault information of the corresponding controller according to the collection instruction, wherein the second fault information at least includes a fault code and a freeze frame;

[0009] After receiving the second fault information, based on the latest vehicle data currently received, the fault code that meets the fault condition and the freeze frame of the corresponding controller are screened out;

[0010] According to the selected fault codes, the vehicle signal operation model is determined, and the source of the fault signal is predicted according to the vehicle signal operation model, and multiple potential fault causes are preliminarily determined according to the fault probability;

[0011] According to the multiple potential fault causes, the user is prompted and guided in turn to check the hardware related to each potential fault cause, and based on the troubleshooting results input by the user, the root cause of the fault is determined and the intelligent guided diagnosis and repair is completed.

[0012] In a possible implementation, after the primary cause of the fault is determined based on multiple potential fault causes, the method further includes:

[0013] According to the determined root cause of the fault, maintenance suggestions are pushed to the service end providing maintenance services; wherein the maintenance suggestions at least include: matching maintenance tools, estimated maintenance time and predicted replacement spare parts.

[0014] In a possible implementation, based on the latest vehicle data currently received, the fault codes that meet the fault conditions and the freeze frames of the corresponding controllers are screened out, specifically including:

[0015] Periodically receive the latest vehicle data collected from the vehicle terminal and forwarded by the Internet of Vehicles platform, and update the local vehicle database;

[0016] Based on the updated vehicle data in the local vehicle database, respectively verify the fault code and the corresponding freeze frame in the second fault information;

[0017] If the verification passes, it is determined that the fault code and the corresponding freeze frame meet the fault condition, and all fault codes and the freeze frames of the corresponding controller that meet the fault condition are screened out.

[0018] In a possible implementation, a vehicle signal operation model is determined based on the screened fault codes, and the source of the fault signal is predicted based on the vehicle signal operation model, and multiple potential fault causes are preliminarily determined in order of fault probability, including:

[0019] Determine the fault detection mechanism based on the screened fault codes;

[0020] Based on the determined fault detection mechanism, a matching vehicle signal operation model is selected from the fault model library;

[0021] Inputting the freeze frame of the corresponding controller into the vehicle signal calculation model to predict the source of the fault signal;

[0022] A plurality of potential fault causes are preliminarily determined based on the predicted fault signal source, and the plurality of potential fault causes are sorted according to the fault likelihood.

[0023] In a possible implementation, the fault model library includes a plurality of vehicle signal operation models, wherein each vehicle signal operation model corresponds to a fault detection mechanism;

[0024] Each vehicle signal operation model is obtained by repeatedly training the corresponding machine learning basic model based on historical fault signals.

[0025] In a second aspect, an intelligent guided repair device for a commercial vehicle is provided, comprising:

[0026] A receiving module, configured to receive first fault information and a vehicle equipment code submitted by a user for describing a whole vehicle fault phenomenon of a target vehicle, and determine controller information related to the fault based on a fault keyword in the first fault information;

[0027] A trigger module, configured to trigger the Internet of Vehicles platform to send a collection instruction to the target vehicle based on the received vehicle equipment code and the determined controller information, so that the vehicle-mounted terminal of the target vehicle collects second fault information of the corresponding controller according to the collection instruction, wherein the second fault information at least includes a fault code and a freeze frame;

[0028] A screening module, configured to screen out the fault codes meeting the fault condition and the freeze frames of the corresponding controllers based on the latest vehicle data currently received after receiving the second fault information;

[0029] The first determination module is used to determine the vehicle signal operation model according to the screened fault codes, and predict the source of the fault signal according to the vehicle signal operation model, and preliminarily determine multiple potential fault causes sorted by fault probability;

[0030] The second determination module is used to prompt and guide the user in turn according to the multiple potential fault causes, to check the hardware related to each potential fault cause, and based on the troubleshooting results input by the user, to determine the root cause of the fault and complete intelligent guided diagnosis and repair.

[0031] In a third aspect, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, and the at least one instruction is loaded and executed by a processor to implement the method of the above-mentioned aspect and any possible implementation manner.

[0032] In a fourth aspect, an electronic device is provided, including:

[0033] at least one processor; and

[0034] a memory communicatively connected to the at least one processor; wherein,

[0035] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any possible implementation manner and the aspects described above.

[0036] According to a fifth aspect, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the above-mentioned aspects and any possible implementation method.

[0037] In a sixth aspect, a diagnosis and repair platform for commercial vehicles is provided, comprising the electronic device as described above.

[0038] The beneficial effects of the technical solution provided by this application include at least:

[0039] It can be seen from the above technical solution that when a fault occurs in the target vehicle, the first fault information and vehicle equipment code submitted by the user are received, and the controller information is determined according to the fault keyword in the first fault information; then, the Internet of Vehicles platform is triggered to send a collection instruction to the target vehicle to obtain the second fault information of the corresponding controller; then, based on the latest vehicle data updated periodically, the fault code and freeze frame that meet the fault conditions are screened out from the received second fault information; then, the vehicle signal operation model is determined locally according to the fault code, the source of the fault signal is predicted, and multiple potential fault causes are preliminarily determined and sorted; the user is prompted and guided to check the hardware related to each fault cause in turn, and the root cause of the fault is finally determined according to the troubleshooting results and the intelligent guided diagnosis and repair is completed. This diagnosis and repair method roughly determines the controller related to the fault through the first fault information, and then collects the fault code and freeze frame of the relevant controller, predicts the potential fault cause through the matching operation model, and then the user intervenes to complete the troubleshooting and diagnosis in a guided manner. Compared with the existing solution that requires manual inspection one by one throughout the process, the diagnosis and repair method of this application is more intelligent, accurate, fast and efficient, especially reducing the maintenance cost to a certain extent and greatly improving the user's maintenance experience.

[0040] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 It is a schematic diagram of the steps of the intelligent guided diagnosis and repair method for commercial vehicles provided in an embodiment of the present application.

[0043] Figure 2 It is a control flow diagram of the intelligent guided diagnosis and repair method for commercial vehicles provided in an embodiment of the present application.

[0044] Figure 3This is a structural block diagram of an intelligent guided repair device for commercial vehicles provided in yet another embodiment of the present application.

[0045] Figure 4 It is a block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0047] Obviously, the described embodiments are only part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0048] It should be noted that the terminal devices involved in the embodiments of the present application may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers and other smart devices; display devices may include but are not limited to personal computers, televisions and other devices with display functions.

[0049] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0050] In view of the existing vehicle fault diagnosis, especially the diagnosis and repair of commercial vehicles with high commercial value, it is still based on the method of manually using diagnostic instruments to check one by one, which leads to the problem of high fault diagnosis location and maintenance costs and low efficiency. The embodiment of the present application proposes an intelligent guided diagnosis and repair scheme for commercial vehicles. The inventive concept is: when a fault occurs in the target vehicle, the first fault information and vehicle equipment code submitted by the user are received, and the controller information is determined according to the fault keyword in the first fault information; then, the Internet of Vehicles platform is triggered to send a collection instruction to the target vehicle to obtain the second fault information of the corresponding controller; then, based on the latest vehicle data updated periodically, the fault code and freeze frame that meet the fault conditions are screened from the received second fault information; then, the vehicle signal operation model is determined locally according to the fault code, the source of the fault signal is predicted, and multiple potential fault causes are preliminarily determined and sorted; the user is prompted and guided to check the hardware related to each fault cause in turn, and the root cause of the fault is finally determined according to the troubleshooting results and the intelligent guided diagnosis and repair is completed. This diagnostic and repair method roughly determines the controller related to the fault through the first fault information, then collects the fault code and freeze frame of the relevant controller, predicts the potential cause of the fault through the matching operation model, and then the user intervenes to complete the troubleshooting and repair in a guided manner. Compared with the existing solution that requires manual troubleshooting one by one throughout the process, the diagnostic and repair method of this application is more intelligent, accurate, fast and efficient, especially reducing the maintenance cost to a certain extent, greatly improving the user's maintenance experience.

[0051] Reference Figure 1 As shown, it is a schematic diagram of the steps of the intelligent guided diagnosis and repair method for commercial vehicles provided in an embodiment of the present application. The executor of the diagnosis and repair method may be an intelligent guided diagnosis and repair device for commercial vehicles. In the present application, the intelligent guided diagnosis and repair device for commercial vehicles may be a software module with computing and data processing capabilities, or an electronic device integrated with similar software modules. Optionally, the executor of the intelligent guided diagnosis and repair device for commercial vehicles in the present application may be a diagnosis and repair platform for commercial vehicles, which provides diagnosis and repair services for new energy vehicles, self-driving vehicles, etc., such as electric trucks, unmanned delivery vehicles, unmanned taxis, etc.

[0052] like Figure 1 As shown, the intelligent guided diagnosis and repair method for a commercial vehicle may include the following steps:

[0053] Step 102: receiving first fault information and a vehicle equipment code submitted by a user for describing a whole vehicle fault phenomenon of a target vehicle, and determining controller information related to the fault based on a fault keyword in the first fault information.

[0054] After the target vehicle breaks down, the user (owner or maintenance personnel) can input the first fault information and the vehicle equipment code of the target vehicle through the user interface of the client. The client here can be a user-side software used in conjunction with the intelligent guided diagnostic and repair device of the commercial vehicle. The client can be installed on a terminal device (for example, a mobile phone, pad, or other intelligent electronic device), and is mainly used to display the operation interface to the user, receive the input information submitted by the user, and send it to the background (i.e., the intelligent guided diagnostic and repair device of the commercial vehicle) to pull the corresponding service data.

[0055] It should be understood that the above method of inputting information in the user interface may be that the user inputs corresponding information in the input field, or selects corresponding item information through the menu bar as input.

[0056] In the present application, the first fault information may be one or more words or short sentences used to describe the whole vehicle fault phenomenon of the target vehicle, or a sequence code composed of characters such as letters, numbers, etc. The present application embodiment takes the first fault information as multiple words or short sentences as an example, and these multiple words or short sentences may be descriptions of one or more fault phenomena. For example, the whole vehicle loses high voltage, the vehicle cannot be started, the vehicle start button fails, etc.

[0057] After receiving the first fault information and vehicle equipment code submitted by the user through the client, one or more fault keywords can be extracted from the words and / or phrases in the first fault information, for example, [vehicle, unable to, start]. Then, controller information related to the fault can be initially locked based on these fault keywords, that is, some controllers related to the fault can be determined.

[0058] Step 104: Based on the received vehicle equipment code and the determined controller information, the Internet of Vehicles platform is triggered to send a collection instruction to the target vehicle, so that the on-board terminal of the target vehicle collects the second fault information of the corresponding controller according to the collection instruction, wherein the second fault information at least includes a fault code and a freeze frame.

[0059] After receiving the vehicle equipment code and controller information, a corresponding collection instruction is generated based on the vehicle equipment code and controller information, and the collection instruction is sent to the target vehicle corresponding to the vehicle equipment code through the Internet of Vehicles platform. Afterwards, the target vehicle collects the fault code and freeze frame of the corresponding controller as the second fault information according to the controller information carried in the collection instruction, and returns the second fault information to the diagnosis and repair device through the Internet of Vehicles platform.

[0060] Among them, the fault code is a serial code generated when a target vehicle fails during operation, and the freeze frame is a data frame before and after the target vehicle fails during operation. The time range of the freeze frame can be flexibly set and adjusted according to needs.

[0061] Step 106: After receiving the second fault information, based on the latest vehicle data currently received, the fault codes that meet the fault conditions and the freeze frames of the corresponding controllers are screened out.

[0062] Considering that the fault code in the second fault information forwarded by the Internet of Vehicles platform may be misidentified, that is, the target vehicle did not have a certain fault, but the controller reported an error fault code, resulting in the fault code fed back to the Internet of Vehicles platform being wrong, so it is necessary to verify the fault code and freeze frame by combining the periodically updated vehicle data.

[0063] An optional verification method is to periodically receive the latest vehicle data collected from the on-board terminal and forwarded by the Internet of Vehicles platform, and update the local vehicle database; based on the vehicle data in the updated local vehicle database, respectively verify the fault code and the corresponding freeze frame in the second fault information; if the verification passes, it is determined that the fault code and the corresponding freeze frame meet the fault conditions, and all fault codes that meet the fault conditions and the freeze frames of the corresponding controllers are screened out.

[0064] Among them, when verifying the fault code and freeze frame in the second fault information, the fault code can be used to compare one by one with the updated vehicle data to check whether the fault corresponding to the fault code has occurred and is recorded in the vehicle data. If the fault corresponding to the fault code is not recorded by the vehicle data, it is determined that the fault code and its corresponding freeze frame do not meet the fault conditions, and the error information needs to be filtered out. In this way, all fault codes and corresponding freeze frames that meet the fault conditions can be screened out, thereby effectively filtering and screening the second fault information, improving the accuracy of the data used for diagnosis and repair, so as to facilitate subsequent accurate diagnosis and repair of the fault.

[0065] In addition to the above-mentioned verification method, the second fault information can also be preliminarily screened. The fault codes in the second fault information can be preliminarily screened through the existing fault code library. If it is determined that the fault code in the second fault information is in the fault code library, the second fault information is further screened in combination with the updated vehicle data; if it is determined that there is a fault code in the second fault information that is not in the fault code library, the non-existent fault code is considered to be a false alarm information, and it is filtered out, and the fault code retained in the second fault information is screened as above in combination with the updated vehicle data.

[0066] The existing fault code library may be a relatively comprehensive fault code library compiled based on historical fault information and experience summary statistics, and the fault code library may include fault codes for frequently occurring fault information and fault codes for occasionally occurring fault information.

[0067] Step 108: Determine a vehicle signal operation model based on the screened fault codes, predict the source of the fault signal based on the vehicle signal operation model, and preliminarily determine multiple potential fault causes sorted by fault probability.

[0068] Optionally, in the present application, a fault detection mechanism can be determined based on the screened fault codes; based on the determined fault detection mechanism, a matching vehicle signal operation model can be selected from the fault model library; the freeze frame of the corresponding controller is input into the vehicle signal operation model to predict the source of the fault signal; based on the predicted source of the fault signal, multiple potential fault causes are preliminarily determined, and the multiple potential fault causes are sorted according to the probability of the fault. The fault model library contains multiple vehicle signal operation models, each of which corresponds to a fault detection mechanism; each vehicle signal operation model is obtained by repeatedly training the corresponding machine learning basic model based on historical fault signals.

[0069] It should be understood that in the present application, the basic machine learning model involved can be a neural network model or an algorithm model such as a decision tree algorithm, a logistic regression algorithm, etc.

[0070] In addition to the above-mentioned prediction of the source of the fault signal based on the machine learning model, the fault detection mechanism can also be determined based on the screened fault code, and then the whole vehicle signal transmission model can be built locally based on the fault detection mechanism. In fact, the whole vehicle signal transmission model is different from the whole vehicle signal operation model. It simulates the vehicle's signal transmission architecture through a software module, combines the acquired freeze frame of the target vehicle, performs simulated transmission on the whole vehicle signal transmission model, screens and analyzes the rationality of the relevant CAN signal and the signal source, and preliminarily determines multiple potential fault causes.

[0071] In the solution of the present application, among the multiple potential fault causes initially determined, each potential fault cause may be a specific fault signal and / or a faulty component.

[0072] It can be seen that whether predicting the source of the fault signal through the trained whole-vehicle signal operation model, or simulating the operating environment of the target vehicle through the constructed whole-vehicle signal transmission model to determine the source of the fault signal, the fault can be preliminarily located well.

[0073] Step 110: Based on the multiple potential fault causes, the user is prompted and guided in turn to troubleshoot the hardware related to each potential fault cause, and based on the troubleshooting results input by the user, the root cause of the fault is determined and the intelligent guided diagnosis and repair is completed.

[0074] In specific implementation, after the problem signal and component are locked, the device will present the most likely causes of the fault, arranged in order. According to the hardware input model, the maintenance personnel are guided to conduct hardware troubleshooting and input the troubleshooting results. According to the input troubleshooting results, the possible causes of the fault on the device side are updated in real time until the maintenance personnel finally input that the problem is solved.

[0075] Optionally, after the root cause of the fault is determined based on multiple potential causes of the fault, maintenance suggestions can be pushed to the service end providing maintenance services based on the determined root cause of the fault; wherein the maintenance suggestions include at least: matching maintenance tools, estimated maintenance time and predicted replacement parts.

[0076] Among them, the service end providing maintenance services can be a maintenance station or a service station deployed by a 4S store, thereby cooperating with the intelligent guided diagnosis and repair device to improve the maintenance service experience.

[0077] Reference Figure 2 As shown, it is a schematic diagram of the process of performing intelligent guided diagnosis and repair on commercial vehicles provided in an embodiment of the present application.

[0078] First, enter the vehicle fault phenomenon in the intelligent guided diagnosis and maintenance system (hereinafter referred to as the intelligent diagnosis and maintenance platform) interface; such as vehicle high voltage loss, no text display on the instrument, instrument text prompt high voltage fault, etc. At the same time, enter the VIN and controller information, and call the vehicle networking system to obtain fault information.

[0079] like Figure 2 In the process, the first fault information containing the fault keyword can be directly input through the UI interface on the mobile phone; or, the URL Scheme and Universal Link can be used to jump to the intelligent guided repair application, that is, a related other application can be jumped to the intelligent guided repair application through the above method, so that the first fault information containing the fault keyword can be input in the UI interface of the application. After receiving the first fault information, the intelligent guided repair system extracts the fault keyword and performs the above steps. Figure 1 The method in the above section roughly determines the controller information related to the fault.

[0080] In fact, the controller information can also be input by the user, that is, after understanding the fault phenomenon, experienced maintenance personnel or drivers and other users can roughly determine the controller information related to the fault. Then, the device code VIN and controller information of the target vehicle can be entered to facilitate calling the vehicle networking interface to obtain the fault information for positioning and repair, that is, the second fault information, etc.

[0081] At the same time, TBOX will periodically upload vehicle data to update the vehicle database, which will serve as the database for the intelligent diagnosis and repair platform to troubleshoot problems. On the other hand, after receiving the device code and controller information, the Internet of Vehicles system will send instructions to TBOX to read the fault code and freeze frame and other secondary fault information, and upload the secondary fault information to the Internet of Vehicles platform after obtaining it.

[0082] Afterwards, the intelligent diagnosis and repair platform will receive the above second fault information and database information (i.e. updated vehicle data) and proceed to the next step of analysis.

[0083] like Figure 2 As shown, the second fault information and vehicle data will be transmitted to the intelligent diagnosis and repair platform as vehicle fault analysis data. For the intelligent diagnosis and repair platform, after receiving the vehicle data, fault code DTC, freeze frame and other information, the vehicle fault signal during this period can be detected according to the vehicle data to determine whether a fault has really occurred, and compared with DTC and freeze frame to confirm whether DTC is a false alarm. This realizes the verification and screening of fault codes. Then, according to the fault detection mechanism, a vehicle signal operation model is built to screen and analyze the rationality and signal source of relevant CAN signals, and preliminarily locate the problem signal and problem components. After the problem signal and component are locked, the device side presents the most likely cause of the fault and arranges them in order. According to the hardware input model, guide and prompt the maintenance personnel to conduct hardware troubleshooting; and input the troubleshooting results. According to the input troubleshooting results, the possible fault causes on the device side are updated in real time. Until the maintenance personnel finally input the problem solution. The intelligent diagnosis and repair platform will also recommend relevant maintenance tools, estimated maintenance time, and estimated replacement spare parts to users or maintenance platforms such as service stations based on the root cause of the fault.

[0084] For example, if the fault phenomenon is: shift failure; vehicle high voltage drops; then 5 controllers are determined, and instructions are sent to all 5 controllers. The execution end is DMS to confirm whether DMS has any faults and whether it is a VCU fault. If it is a VCU fault, the fault detection mechanism can be determined based on the fault code read, and based on the established vehicle signal operation model, the problem signals and components are initially screened and located, and whether it is a charging failure, pre-charging failure, severe insulation failure, etc. This will achieve efficient and accurate positioning and repair of vehicle faults, saving costs while improving diagnosis and repair efficiency.

[0085] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0086] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0087] Figure 3 FIG. 1 shows a structural block diagram of an intelligent guided repair device for a commercial vehicle provided by an embodiment of the present application. Figure 3 As shown. The intelligent guided repair device 300 for commercial vehicles of this embodiment may include: a receiving module 301, a triggering module 302, a screening module 303, a first determination module 304, and a second determination module 305. Among them, the receiving module 301 is used to receive the first fault information and the vehicle equipment code submitted by the user to describe the whole vehicle fault phenomenon of the target vehicle, and determine the controller information related to the fault based on the fault keyword in the first fault information; the triggering module 302 is used to trigger the Internet of Vehicles platform to send a collection instruction to the target vehicle based on the received vehicle equipment code and the determined controller information, so that the on-board terminal of the target vehicle collects the second fault information of the corresponding controller according to the collection instruction, wherein the second fault information at least includes a fault code and a freeze frame; the screening module 303 is used to, upon receiving the second fault After receiving the fault information, based on the latest vehicle data currently received, the fault codes that meet the fault conditions and the freeze frames of the corresponding controllers are screened out; the first determination module 304 is used to determine the vehicle signal operation model according to the screened fault codes, and predict the source of the fault signal according to the vehicle signal operation model, and preliminarily determine a plurality of potential fault causes sorted by fault probability; the second determination module 305 is used to prompt and guide the user in turn according to the plurality of potential fault causes, to check the hardware related to each potential fault cause, and based on the troubleshooting results input by the user, to determine the root cause of the fault and complete the intelligent guided diagnosis and repair.

[0088] It should be noted that part or all of the intelligent guided diagnostic and repair device for the commercial vehicle of this embodiment may be an application located in the local terminal, or may also be a functional unit such as a plug-in or software development kit (SDK) set in the application located in the local terminal, or may also be a processing engine located in a network-side server, or may also be a distributed system located on the network side, for example, a processing engine or distributed system in an autonomous driving platform on the network side, etc. This embodiment does not specifically limit this.

[0089] It is understandable that the application may be a local program (nativeApp) installed on the local terminal, or may be a webpage program (webApp) of a browser on the local terminal, which is not limited in this embodiment.

[0090] Optionally, in a possible implementation of this embodiment, the intelligent guided diagnosis and repair device for the commercial vehicle further includes: a recommendation module, which is used to push maintenance suggestions to a service end providing maintenance services according to the determined root cause of the fault after the second determination module 305 has determined the root cause of the fault according to multiple potential fault causes; wherein the maintenance suggestions include at least: matching maintenance tools, estimated maintenance time, and predicted replacement parts.

[0091] Optionally, in a possible implementation of this embodiment, when the screening module 303 screens out fault codes and freeze frames of corresponding controllers that meet the fault conditions based on the latest vehicle data currently received, it is specifically used to periodically receive the latest vehicle data collected from the vehicle-mounted terminal and forwarded by the Internet of Vehicles platform, and update the local vehicle database; based on the vehicle data in the updated local vehicle database, the fault codes and corresponding freeze frames in the second fault information are respectively verified; if the verification passes, it is determined that the fault code and the corresponding freeze frame meet the fault conditions, and all fault codes and freeze frames of the corresponding controllers that meet the fault conditions are screened out.

[0092] Optionally, in a possible implementation of the present embodiment, the first determination module 304 determines the vehicle signal operation model based on the screened fault codes, predicts the source of the fault signal based on the vehicle signal operation model, and preliminarily determines multiple potential fault causes sorted by fault probability. It is specifically used to determine the fault detection mechanism based on the screened fault codes; select a matching vehicle signal operation model from the fault model library based on the determined fault detection mechanism; input the freeze frame of the corresponding controller into the vehicle signal operation model to predict the source of the fault signal; preliminarily determine multiple potential fault causes based on the predicted fault signal source, and sort the multiple potential fault causes according to the fault probability.

[0093] Optionally, in a possible implementation of this embodiment, the fault model library contains multiple vehicle signal operation models, wherein each vehicle signal operation model corresponds to a fault detection mechanism; each vehicle signal operation model is obtained by repeatedly training a corresponding machine learning basic model based on historical fault signals.

[0094] In this embodiment, when a target vehicle fails, the first fault information and vehicle equipment code submitted by the user can be received, and the controller information can be determined according to the fault keyword in the first fault information; then, the Internet of Vehicles platform is triggered to send a collection instruction to the target vehicle to obtain the second fault information of the corresponding controller; then, according to the latest vehicle data updated periodically, the fault code and freeze frame that meet the fault condition are screened out from the received second fault information; then, the vehicle signal operation model is determined locally according to the fault code, the source of the fault signal is predicted, and multiple potential fault causes are preliminarily determined and sorted; the user is prompted and guided to check the hardware related to each fault cause in turn, and the cause of the fault is finally determined according to the results of the investigation and the intelligent guided diagnosis and repair is completed. This diagnosis and repair method roughly determines the controller related to the fault through the first fault information, and then collects the fault code and freeze frame of the relevant controller, predicts the potential fault cause through the matching operation model, and then the user intervenes and completes the investigation and repair in a guided manner. Compared with the existing scheme that requires manual one-by-one investigation throughout the process, the diagnosis and repair method of this application is more intelligent, accurate, fast and efficient, especially to a certain extent, reducing the maintenance cost and greatly improving the user's maintenance experience.

[0095] An embodiment of the present application provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement the intelligent guided diagnosis and repair method for a commercial vehicle as described above.

[0096] An embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the intelligent guided diagnosis and repair method for commercial vehicles as described above.

[0097] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the intelligent guided diagnosis and repair method for a commercial vehicle as described above.

[0098] One embodiment of the present application provides an autonomous driving vehicle, including the electronic device as described above. Specifically, the autonomous driving vehicle may be a vehicle of level L2 or above. For example, it may be an autonomous driving commercial vehicle with the above functions.

[0099] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved are in compliance with the relevant laws and regulations and do not violate public order and good morals.

[0100] Figure 4A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0101] like Figure 4 As shown, the electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0102] Multiple components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0103] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as the intelligent guided repair method for commercial vehicles. For example, in some embodiments, the intelligent guided repair method for commercial vehicles may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the intelligent guided repair method for commercial vehicles described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to execute the intelligent guided repair method for the commercial vehicle in any other appropriate manner (for example, by means of firmware).

[0104] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0105] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0106] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0107] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0108] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0109] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0110] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps disclosed in this application can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in this application can be achieved, and this document does not limit this.

[0111] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. An intelligent guided diagnosis and repair method for a commercial vehicle, characterized in that: include: Receiving first fault information and a vehicle equipment code submitted by a user for describing a whole vehicle fault phenomenon of a target vehicle, and determining controller information related to the fault based on a fault keyword in the first fault information; Based on the received vehicle equipment code and the determined controller information, trigger the Internet of Vehicles platform to send a collection instruction to the target vehicle, so that the on-board terminal of the target vehicle collects second fault information of the corresponding controller according to the collection instruction, wherein the second fault information at least includes a fault code and a freeze frame; After receiving the second fault information, based on the latest vehicle data currently received, the fault code that meets the fault condition and the freeze frame of the corresponding controller are screened out; According to the selected fault codes, the vehicle signal operation model is determined, and the source of the fault signal is predicted according to the vehicle signal operation model, and multiple potential fault causes are preliminarily determined according to the fault probability; According to the multiple potential fault causes, the user is prompted and guided in turn to check the hardware related to each potential fault cause, and based on the troubleshooting results input by the user, the root cause of the fault is determined and the intelligent guided diagnosis and repair is completed.

2. The method according to claim 1, characterized in that After the primary cause of the fault is determined based on multiple potential causes of the fault, the method further includes: According to the determined root cause of the fault, maintenance suggestions are pushed to the service end providing maintenance services; wherein the maintenance suggestions at least include: matching maintenance tools, estimated maintenance time and predicted replacement spare parts.

3. The method according to claim 1 or 2, characterized in that Based on the latest vehicle data currently received, filter out the fault codes that meet the fault conditions and the freeze frames of the corresponding controllers, including: Periodically receive the latest vehicle data collected from the vehicle terminal and forwarded by the Internet of Vehicles platform, and update the local vehicle database; Based on the updated vehicle data in the local vehicle database, respectively verify the fault code and the corresponding freeze frame in the second fault information; If the verification passes, it is determined that the fault code and the corresponding freeze frame meet the fault condition, and all fault codes and the freeze frames of the corresponding controller that meet the fault condition are screened out.

4. The method according to claim 1 or 2, characterized in that: Based on the selected fault codes, the vehicle signal operation model is determined, and the source of the fault signal is predicted based on the vehicle signal operation model. Multiple potential fault causes are preliminarily determined in order of fault probability, including: Determine the fault detection mechanism based on the screened fault codes; Based on the determined fault detection mechanism, a matching vehicle signal operation model is selected from the fault model library; Inputting the freeze frame of the corresponding controller into the vehicle signal calculation model to predict the source of the fault signal; A plurality of potential fault causes are preliminarily determined based on the predicted fault signal source, and the plurality of potential fault causes are sorted according to the fault likelihood.

5. The method according to claim 4, characterized in that The fault model library includes a plurality of vehicle signal operation models, wherein each vehicle signal operation model corresponds to a fault detection mechanism; Each vehicle signal operation model is obtained by repeatedly training the corresponding machine learning basic model based on historical fault signals.

6. An intelligent guided diagnostic and repair device for commercial vehicles, characterized in that: include: A receiving module, configured to receive first fault information and a vehicle equipment code submitted by a user for describing a whole vehicle fault phenomenon of a target vehicle, and determine controller information related to the fault based on a fault keyword in the first fault information; A trigger module, configured to trigger the Internet of Vehicles platform to send a collection instruction to the target vehicle based on the received vehicle equipment code and the determined controller information, so that the vehicle-mounted terminal of the target vehicle collects second fault information of the corresponding controller according to the collection instruction, wherein the second fault information at least includes a fault code and a freeze frame; A screening module, configured to screen out the fault codes meeting the fault condition and the freeze frames of the corresponding controllers based on the latest vehicle data currently received after receiving the second fault information; The first determination module is used to determine the vehicle signal operation model according to the screened fault codes, and predict the source of the fault signal according to the vehicle signal operation model, and preliminarily determine multiple potential fault causes sorted by fault probability; The second determination module is used to prompt and guide the user in turn according to the multiple potential fault causes, to check the hardware related to each potential fault cause, and based on the troubleshooting results input by the user, to determine the root cause of the fault and complete intelligent guided diagnosis and repair.

7. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-5.

9. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 5.

10. A commercial vehicle diagnostic and repair platform, characterized in that: Comprising the electronic device as claimed in claim 7.

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