Remote diagnosis method and device, electronic equipment and storage medium

Abstract

The invention discloses a remote diagnosis method and device, electronic equipment and a storage medium, and the method comprises the steps: firstly obtaining a remote diagnosis request of a target vehicle, then broadcasting the remote diagnosis request to n clients, and then determining k clients responding to the remote diagnosis request in the n clients, the method comprises the following steps: acquiring a remote diagnosis request from k clients, acquiring k response instructions of the k clients for the remote diagnosis request, determining a remote diagnosis capability value corresponding to each client in the k clients based on the k response instructions to obtain k remote diagnosis capability values, acquiring the maximum remote diagnosis capability value in the k remote diagnosis capability values, and determining the maximum remote diagnosis capability value in the k remote diagnosis capability values. Then determining the client corresponding to the maximum remote diagnosis capability value to obtain a target client, and finally performing remote diagnosis operation on the target vehicle based on the target client to obtain a target diagnosis result. By adopting the embodiment of the invention, the remote diagnosis efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle remote diagnosis, and in particular to a remote diagnosis method, device, electronic equipment and storage medium. Background Art

[0002] With the continuous development of vehicle technology and the increasing degree of vehicle intelligence, vehicle fault diagnosis is also facing new challenges. In recent years, with the rapid progress of communication technology and network technology, remote diagnosis has gradually become an important means of vehicle fault diagnosis. With the emergence of remote diagnosis technology, although the diagnostic efficiency has been improved to a certain extent, due to the unreasonable allocation of diagnostic resources, the current remote diagnosis efficiency is still low, so how to improve the efficiency of remote diagnosis is an urgent problem to be solved. Summary of the invention

[0003] The embodiments of the present application provide a remote diagnosis method, device, electronic device and storage medium, which improve the efficiency of remote diagnosis.

[0004] In a first aspect, an embodiment of the present application provides a remote diagnosis method, which is applied to a diagnostic device, wherein the diagnostic device includes a device connector; a communication connection is established between the diagnostic device and the target vehicle through the device connector and the target vehicle connector of the target vehicle, and the method includes:

[0005] Obtaining a remote diagnosis request of a target vehicle;

[0006] Broadcasting the remote diagnosis request to n clients; n is a positive integer greater than 1;

[0007] Determine k clients among the n clients that respond to the remote diagnosis request, and obtain response instructions of the k clients to the remote diagnosis request to obtain k response instructions; k is an integer less than or equal to n;

[0008] Determine the remote diagnosis capability value corresponding to each of the k clients based on the k response instructions to obtain k remote diagnosis capability values;

[0009] Obtaining a maximum remote diagnosis capability value among the k remote diagnosis capability values;

[0010] Determine the client corresponding to the maximum remote diagnosis capability value to obtain a target client;

[0011] A remote diagnosis operation is performed on the target vehicle based on the target client to obtain a target diagnosis result.

[0012] In a second aspect, an embodiment of the present application provides a remote diagnosis device, the remote diagnosis device comprising: an acquisition unit and a processing unit;

[0013] The acquisition unit is used to acquire a remote diagnosis request of a target vehicle;

[0014] The processing unit is used to broadcast the remote diagnosis request to n clients; n is a positive integer greater than 1;

[0015] Determine k clients among the n clients that respond to the remote diagnosis request, and obtain response instructions of the k clients to the remote diagnosis request to obtain k response instructions; k is an integer less than or equal to n;

[0016] Determine the remote diagnosis capability value corresponding to each of the k clients based on the k response instructions to obtain k remote diagnosis capability values;

[0017] Obtaining a maximum remote diagnosis capability value among the k remote diagnosis capability values;

[0018] Determine the client corresponding to the maximum remote diagnosis capability value to obtain a target client;

[0019] A remote diagnosis operation is performed on the target vehicle based on the target client to obtain a target diagnosis result.

[0020] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor so that the electronic device executes the method of the first aspect.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0022] In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, so that a computer executes the method of the first aspect.

[0023] The implementation of the present invention has the following beneficial effects:

[0024] It can be seen that the remote diagnosis method described in the embodiment of the present invention is applied to a diagnostic device, wherein the diagnostic device includes a device connector, and a communication connection is established between the diagnostic device and the target vehicle through the device connector and the target vehicle connector of the target vehicle. The method includes: first, obtaining a remote diagnosis request of the target vehicle, and then broadcasting the remote diagnosis request to n clients, wherein n is a positive integer greater than 1, and then determining k clients among the n clients that respond to the remote diagnosis request, and obtaining response instructions of the k clients to the remote diagnosis request to obtain k response instructions, wherein k is an integer less than or equal to n, and then determining a remote diagnosis capability value corresponding to each of the k clients based on the k response instructions to obtain k remote diagnosis capability values, and then obtaining a maximum remote diagnosis capability value among the k remote diagnosis capability values, and then determining a client corresponding to the maximum remote diagnosis capability value to obtain a target client, and finally performing a remote diagnosis operation on the target vehicle based on the target client to obtain a target diagnosis result, thereby improving remote diagnosis efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the implementation methods of the present application or the background technology, the drawings required for use in the implementation methods of the present application or the background technology will be described below.

[0026] Figure 1 It is a structural schematic diagram of a remote diagnosis system provided by an embodiment of the present application;

[0027] Figure 2 This is an application example diagram of a remote diagnosis method provided by an embodiment of the present application;

[0028] Figure 3 is a flow chart of a remote diagnosis method provided by an embodiment of the present application;

[0029] Figure 4 is a flow chart for determining a remote diagnostic capability value provided by an embodiment of the present application;

[0030] Figure 5 It is a flow chart for determining a target diagnosis result provided by an embodiment of the present application;

[0031] Figure 6 is a schematic diagram of a target diagnosis result display interface provided in an embodiment of the present application;

[0032] Figure 7 It is a structural schematic diagram of a remote diagnosis device provided in an embodiment of the present application;

[0033] Figure 8 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the implementation mode of the present application will be clearly and completely described below in conjunction with the drawings in the implementation mode of the present application. Obviously, the described implementation mode is only a part of the implementation mode of the present application, not all the implementation modes. Based on the implementation mode in the present application, all other implementation modes obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0035] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0036] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0037] See also Figure 1 , Figure 1 1 is a schematic diagram of a remote diagnosis system provided in an embodiment of the present application. The remote diagnosis system 10 includes a client 101 , a diagnosis device 102 and a vehicle end 103 .

[0038] In this embodiment, after obtaining the remote diagnosis request of the target vehicle, the vehicle end 103 broadcasts the remote diagnosis request to n clients 101, and the diagnostic device 102 determines k clients 101 among the n clients 101 that respond to the remote diagnosis request, and obtains the response instructions of the k clients 101 to the remote diagnosis request, and obtains k response instructions, and determines the remote diagnosis capability value corresponding to each client 101 among the k clients 101 based on the k response instructions, and obtains k remote diagnosis capability values, and obtains the maximum remote diagnosis capability value among the k remote diagnosis capability values, determines the client 101 corresponding to the maximum remote diagnosis capability value, and obtains the target client, and performs a remote diagnosis operation on the target vehicle based on the target client to obtain a target diagnosis result.

[0039] In this embodiment, the client 101 may include multiple user interfaces, multiple communication modules and multiple data processing modules. The user interface is used for the user to interact with the system and input vehicle information, fault description, etc. The communication module is used to transmit data with the diagnostic device 102, such as sending requests and receiving diagnostic results through the network. The data processing module is used to perform preliminary processing and sorting of the information input by the user. The diagnostic device 102 may include multiple diagnostic instruments and multiple device connectors, each of which corresponds to a device connector. The diagnostic instrument collects vehicle operation data from the electronic control unit and various sensors of the vehicle, and analyzes these data to detect potential faults or abnormalities. It can identify specific faults in the vehicle based on the collected data and the built-in diagnostic algorithm, and generate corresponding fault codes and diagnostic reports. It can also perform comprehensive performance testing and evaluation on various subsystems of the vehicle, such as the engine, transmission, and brake system. The device connector establishes a physical interface between the diagnostic instrument and the vehicle end to ensure a stable data transmission channel, and converts the communication signal of the diagnostic instrument into a signal format that can be recognized and processed by the vehicle end to achieve effective communication between the two. The vehicle end 103 may include multiple vehicle connectors and multiple vehicles, each vehicle corresponds to a vehicle connector, the vehicle connector realizes data exchange and communication between the electronic control units inside the vehicle and with external diagnostic equipment, provides appropriate power for the connected external equipment (such as diagnostic equipment) to ensure its normal operation, transmits various control signals and sensor signals, enables the vehicle's status information to be read and understood by external equipment, and ensures that the vehicle can reliably connect and interact with external equipment of different types and standards (such as diagnostic instruments from different manufacturers). Please refer to Figure 2 , Figure 2 This is an application example diagram of a remote diagnosis method provided in an embodiment of the present application, in which a diagnostic instrument 202 obtains a remote diagnosis request from a target vehicle 203, and then determines a target computer 201 with a maximum remote diagnosis capability value, performs a remote diagnosis operation on the target vehicle 203 based on the target computer 201, and obtains a target diagnosis result.

[0040] See also Figure 3 , Figure 3 This is a flowchart of a remote diagnosis method provided by an embodiment of the present application, including but not limited to the following steps:

[0041] S301: Obtain a remote diagnosis request of a target vehicle.

[0042] In this embodiment, when the target vehicle detects an abnormality or fault, a diagnostic request is sent to the server where the diagnostic device is located through the built-in communication module. The user can also manually trigger the diagnostic request in the relevant application on the vehicle side and send the request to the diagnostic device or the relevant server through the network. The remote diagnostic request may include vehicle identification information: such as vehicle model, frame number, engine number, etc., for accurate identification of the vehicle; fault code or abnormal description: the fault code detected by the vehicle itself, or the user's text description of the abnormal condition of the vehicle; vehicle operation data: including real-time or recent data of key parameters such as vehicle speed, mileage, engine speed, oil temperature, etc.; sensor data: detection data from various sensors (such as oxygen sensors, pressure sensors, etc.); vehicle configuration information: the vehicle's optional configuration, software version, etc., which helps to make a more accurate diagnosis; user information: such as the owner's name, contact information, etc., so as to communicate with the user when necessary during the diagnosis process, etc.

[0043] S302: Broadcast the remote diagnosis request to n clients.

[0044] In this embodiment, n is a positive integer greater than 1. When broadcasting the remote diagnosis request to n clients, the remote diagnosis request can be pushed as a message to the registered and connected clients through the server of the diagnostic device. The server will maintain a client list and send the request data to multiple clients at the same time through the network protocol. The message queue service can also be used to put the remote diagnosis request into the message queue and set it to broadcast mode so that all clients subscribed to the relevant topic can receive the request. The network multicast function can also be used to group the clients, and the sent diagnosis request will be received by all clients in the group. In this embodiment, the client generally refers to a terminal device or application that interacts with the server. When the client is a computer, the client includes the following parts: user interface: used to display detailed information, diagnosis results, etc. of the diagnosis request, which may include a graphical interface and a data table; communication module: maintains connection with the server, receives broadcast diagnosis requests, and can send responses or feedback to the server; data processing module: parses and processes the received diagnosis request, and may perform local data storage and analysis; report generation module: can generate detailed reports based on the diagnosis results and processed data for users to view and print.

[0045] It can be seen that when multiple clients receive requests at the same time, more resources and expertise can be quickly mobilized to process the diagnostic requests, thereby speeding up the overall response speed. Even if some clients are temporarily unavailable or fail, other clients can still receive and process requests, reducing the impact of single point failures on diagnostic services. This allows more professionals or institutions to participate in the diagnostic process, regardless of their geographical location or organization, thereby expanding the coverage of the service. Different clients can also learn from each other and exchange experiences when processing the same diagnostic request, thereby improving the overall technical level and service quality.

[0046] S303: Determine k clients among the n clients that respond to the remote diagnosis request, and obtain response instructions of the k clients to the remote diagnosis request to obtain k response instructions.

[0047] In this embodiment, k is an integer less than or equal to n. First, after broadcasting a remote diagnosis request to n clients, the diagnostic device needs to inquire about the network connection status of the n clients to obtain n network connection statuses, wherein the network connection statuses include online status and offline status. Specifically, the diagnostic device periodically sends a small detection data packet to each client, and the client responds immediately after receiving it. If the diagnostic device receives a response within a specified time, it is considered that the client is online. If no response is received, it is considered that the client is offline or there is a problem with the network connection. The client can also actively send its own network connection status information to the diagnostic device at regular intervals, including online or offline. The client's connection status can also be obtained through existing network communication protocols, for example, by checking the status flag of the TCP connection to detect the reachability of the client. An intermediate server can also be used to collect and summarize the client's network connection status. The client reports to the intermediate server, and the diagnostic device then obtains this status information from the intermediate server.

[0048] Exemplarily, p clients in the online state among the n online states are determined, where p is an integer less than or equal to n and greater than or equal to k. Specifically, when the number of clients in the online state is to be determined, the diagnostic device traverses the state identifications of all clients, and counts each client identified as online. Through this one-by-one traversal and counting method, it is ultimately determined that the number of clients in the online state is p, thereby obtaining p clients in the online state among the n online states.

[0049] Exemplarily, the workload corresponding to each of the p clients is obtained to obtain p workloads. Specifically, the client itself monitors its current workload status (such as the number of tasks being processed, CPU usage, memory occupancy, etc.), and then actively sends this information to the diagnostic device on a regular basis. The diagnostic device sends a query request to each client. After receiving the request, the client immediately obtains and returns its current workload-related data. The client updates its workload data in real time to a shared database, and the diagnostic device reads the workload information of the p clients from the database to obtain p workloads.

[0050] Exemplarily, k workloads smaller than the preset workload among the p workloads are determined, and the clients corresponding to the k workloads are determined to obtain the k clients. First, the workload data of the p clients have been obtained, and the preset workload is a pre-set measurement standard value. Then, the p workloads are compared with the preset workload one by one. In the comparison process, workloads smaller than the preset workload are screened out, and the number of these workloads is k. Next, the clients corresponding to the k workloads are found through the previously recorded correspondence between the workloads and the clients, and the k clients are obtained. For example, assuming that the preset workload is a CPU usage rate of 50%, the workload of client A is 40%, the workload of client B is 60%, and the workload of client C is 30%. Then, the workloads smaller than the preset workload are the workloads of client A and client C, and the corresponding clients are A and C, and the number k is 2.

[0051] It can be seen that by screening out online clients with low workloads, diagnostic tasks can be allocated more reasonably, avoiding allocating tasks to overloaded or offline clients, thereby improving diagnostic efficiency and response speed. Clients with light workloads are given priority for diagnosis, which helps to ensure that more focused and high-quality diagnostic services are provided to target vehicles, reduce diagnostic errors or delays caused by busy clients, avoid allocating tasks to clients that may have network connection problems or are offline, reduce the risk of diagnostic failure due to client unavailability, enhance the stability and reliability of the entire diagnostic system, balance the workload of each client, prevent some clients from being overworked and reducing performance, and make full use of online and idle client resources to improve the overall operating efficiency of the diagnostic system.

[0052] After determining the k clients of the remote diagnosis request, the response instructions of the k clients to the remote diagnosis request are obtained to obtain k response instructions. Specifically, an independent and secure communication channel can be created for each determined client, for example, through a specific network protocol or interface, so that the client can send a response instruction through the channel. The diagnostic device sends clear requests to the k clients respectively, requiring them to submit a response instruction to the remote diagnosis request as soon as possible, and notifying the client in advance of the format, content requirements and submission method of the response instruction so that the client can reply according to a unified specification. After the response instruction is prepared, the client can actively push it to the diagnostic device through the established communication channel, or the diagnostic device can actively query whether the client has submitted the response instruction at a certain time interval, using a message queue system, or the client can put the response instruction into a specified queue, and the diagnostic device can obtain it from the queue.

[0053] S304: Determine a remote diagnosis capability value corresponding to each of the k clients based on the k response instructions to obtain k remote diagnosis capability values.

[0054] In this implementation, see Figure 4 , Figure 4 A flowchart for determining a remote diagnosis capability value provided by an embodiment of the present application includes but is not limited to the following steps:

[0055] S401: Determine a first diagnosis response time and a first diagnosis accuracy rate of a client corresponding to a first response instruction.

[0056] In this embodiment, the first response instruction is any one of the k response instructions. Since the first response instruction is any one of the k response instructions, the remote diagnosis capability value corresponding to each of the k clients can be determined according to the determination method of the remote diagnosis capability value corresponding to the first response instruction, and k remote diagnosis capability values ​​are obtained. First, the first diagnosis response time and the first diagnosis accuracy rate of the client corresponding to the first response instruction are determined. Specifically, when the client corresponding to the first response instruction receives the remote diagnosis request at different times in the historical time period, the diagnostic device automatically records the time point of receiving. When the client completes the diagnosis and submits the diagnosis result, the time point is recorded again. The time point at which the client corresponding to the first response instruction submits the diagnosis result at different times is subtracted from the time point at which the request is received to obtain multiple time differences, thereby determining the first diagnosis response time of the client corresponding to the first response instruction according to the multiple time differences, and then determining the diagnosis results submitted by the client corresponding to the first response instruction at different times in the historical time period. Compare with the set standard diagnosis results, calculate the accuracy rate according to the consistent part proportion, obtain multiple accuracy rates, and then determine the first diagnosis accuracy rate of the client corresponding to the first response instruction according to the multiple accuracy rates.

[0057] S402: Determine a first mapping relationship between a diagnosis response time and the remote diagnosis capability value, and a second mapping relationship between a diagnosis accuracy rate and the remote diagnosis capability value.

[0058] In this embodiment, the client's diagnostic response time and diagnostic accuracy have an important impact on the remote diagnostic capability value corresponding to each client. The shorter the diagnostic response time, the more quickly the client can respond to the diagnostic request and provide diagnostic results in a timely manner. This reflects that the client has high work efficiency and can meet the user's timeliness requirements for diagnosis more quickly. When determining the remote diagnostic capability value, a shorter diagnostic response time will be given a higher weight or extra points, thereby improving its remote diagnostic capability value. The diagnostic accuracy is directly related to the reliability and effectiveness of the diagnostic results. The higher the accuracy, the more reliable the client's diagnostic results are, and the problem can be found more accurately, providing correct guidance for subsequent maintenance and processing. When evaluating the remote diagnostic capability value, a high diagnostic accuracy will significantly increase the client's capability value, because accurate diagnosis is the core goal of the entire remote diagnostic service. The diagnostic response time and diagnostic accuracy work together to comprehensively measure the client's performance in speed and quality to determine the level of its remote diagnostic capability value.

[0059] Exemplarily, a first mapping relationship between the diagnostic response time and the remote diagnostic capability value, and a second mapping relationship between the diagnostic accuracy and the remote diagnostic capability value are determined. Specifically, a large amount of historical diagnostic data is first collected, including the diagnostic response time and diagnostic accuracy of different clients and the final diagnostic effect evaluation. By analyzing these data, the correlation between the diagnostic response time and the accuracy and the diagnostic effect is observed, thereby establishing a preliminary mapping relationship. The mapping relationship may exist in the form of a mathematical formula, a chart, or a rule description, which is not limited here. Determining the first mapping relationship between the diagnostic response time and the remote diagnostic capability value, and the second mapping relationship between the diagnostic accuracy and the remote diagnostic capability value helps to more reasonably allocate diagnostic tasks according to the actual capabilities of the client, allocate important or urgent diagnostic requests to clients with stronger capabilities, and improve overall diagnostic efficiency. The client can clearly understand how its own diagnostic response time and accuracy affect its capability value, thereby improving the workflow and methods in a targeted manner to improve the capability value and service quality, and can better meet customers' expectations for diagnostic timeliness and accuracy, thereby improving customer satisfaction and loyalty.

[0060] S403: Determine a first remote diagnosis capability value corresponding to the first diagnosis response time based on the first mapping relationship.

[0061] In this embodiment, exemplarily, the first remote diagnostic capability value corresponding to the first diagnostic response time is determined based on the first mapping relationship. Specifically, if the first mapping relationship is a mathematical formula, the value of the first diagnostic response time is substituted into the formula for calculation, and the result is the first remote diagnostic capability value. If the first mapping relationship is a graph, the position corresponding to the first diagnostic response time in the graph is found, and then the corresponding remote diagnostic capability value is read. If the first mapping relationship is a rule description, the range to which the first diagnostic response time belongs is determined according to the capability value provisions corresponding to different diagnostic response time ranges in the rule, thereby obtaining the corresponding first remote diagnostic capability value.

[0062] S404: Determine a second remote diagnosis capability value corresponding to the first diagnosis accuracy based on the second mapping relationship.

[0063] In this embodiment, exemplarily, the second remote diagnostic capability value corresponding to the first diagnostic accuracy is determined based on the second mapping relationship. Specifically, the specific form of the established second mapping relationship is first clarified, which may be a function expression, a corresponding table or a set of clear rules. If the second mapping relationship is a mathematical formula, the value of the second diagnostic accuracy is substituted into the formula for calculation, and the result is the second remote diagnostic capability value. If the second mapping relationship is a chart, the position corresponding to the second diagnostic accuracy in the chart is found, and then the corresponding remote diagnostic capability value is read. If the second mapping relationship is a rule description, according to the capability value provisions corresponding to different diagnostic accuracy ranges in the rule, the range to which the second diagnostic accuracy belongs is determined, thereby obtaining the corresponding second remote diagnostic capability value.

[0064] It can be seen that considering the diagnostic response time and diagnostic accuracy separately and establishing a mapping relationship between them and the remote diagnostic capability value can more finely and accurately evaluate the client's diagnostic capability, avoid the limitations and one-sidedness of single indicator evaluation, and through a clear mapping relationship, convert the originally abstract and subjective diagnostic performance into a specific value, thereby realizing the quantitative measurement of diagnostic capability and making the evaluation results more objective and comparable. The client can clearly understand how its performance in diagnostic response time and accuracy affects its remote diagnostic capability value, so as to improve the deficiencies in a targeted manner and improve service quality and efficiency. It provides a scientific basis for the system to allocate tasks and schedule resources according to the actual diagnostic capabilities of the client, helps to achieve optimal allocation of resources, and improves the operating efficiency of the entire remote diagnosis system. More accurate and efficient diagnostic capability evaluation helps to provide customers with better quality, timely and accurate diagnostic services, thereby improving customer satisfaction and trust in remote diagnosis services.

[0065] S405: Determine the remote diagnosis capability value corresponding to the first response instruction based on the first remote diagnosis capability value and the second remote diagnosis capability value.

[0066] In this embodiment, exemplarily, a first reference weight corresponding to the first remote diagnostic capability value and a second reference weight corresponding to the second remote diagnostic capability value are determined, wherein the sum of the first reference weight and the second reference weight is 1. Specifically, it may be a preset mapping relationship between remote diagnostic capability values ​​and reference weights, based on which the first reference weight corresponding to the first remote diagnostic capability value may be determined, and then the second reference weight corresponding to the second remote diagnostic capability value may be determined based on the sum of the first reference weight and the second reference weight being 1. Alternatively, the second reference weight corresponding to the second remote diagnostic capability value may be determined based on the mapping relationship, and then the first reference weight corresponding to the first remote diagnostic capability value may be determined based on the sum of the first reference weight and the second reference weight being 1.

[0067] Exemplarily, the usage time of the target vehicle is obtained. Specifically, if the target vehicle is used for a long time, it may mean that the vehicle's components are aged and worn, and the complexity and uncertainty of the fault are increased, which may require more time to troubleshoot various possible problems during the diagnosis process, thereby extending the diagnostic response time. The long usage time may also cause the vehicle to accumulate more historical fault data and maintenance records. These large amounts of data need to be analyzed and integrated during diagnosis, which will also increase the time cost of diagnosis. In addition, the technology of old vehicles may be relatively old, and their compatibility with modern diagnostic equipment and technologies may be poor. There may be obstacles in data transmission and communication, which in turn affects the speed of diagnostic response. However, if the vehicle is well maintained, although it has been used for a long time, due to regular inspections and maintenance, the vehicle's state may be relatively stable and predictable, thereby reducing the complexity of diagnosis to a certain extent and shortening the diagnostic response time. Therefore, the usage time of the target vehicle will affect the size of the first remote diagnostic capability value, thereby affecting the first reference weight corresponding to the first remote diagnostic capability value.

[0068] Exemplarily, the target fine-tuning parameter corresponding to the usage time is determined. Specifically, it may be a mapping relationship between a preset usage time and a fine-tuning parameter. Based on the mapping relationship, the target fine-tuning parameter corresponding to the usage time may be determined.

[0069] Exemplarily, the first reference weight is adjusted according to the target fine-tuning parameter to obtain a first target weight. Specifically, the first reference weight can be adjusted according to the target fine-tuning parameter. The specific calculation formula is as follows:

[0070] First target weight = first reference weight × (1 + target fine-tuning parameter);

[0071] The first target weight can be obtained according to the above formula.

[0072] Exemplarily, the second reference weight is adjusted according to the first target weight to obtain the second target weight, wherein the sum of the first target weight and the second target weight is 1. Specifically, since the sum of the first target weight and the second target weight is 1, after determining the first target weight, the second reference weight can be adjusted according to the first target weight to obtain the second target weight.

[0073] It can be seen that considering the usage time of the target vehicle and adjusting the weight accordingly can enable personalized diagnostic capability evaluation according to the specific situation of the vehicle, so that the evaluation results are more in line with actual needs. By introducing target fine-tuning parameters to adjust the reference weights, the evaluation system can be dynamically optimized according to different vehicle conditions, improving the adaptability and accuracy of the evaluation, and reasonably allocating the weights of the first remote diagnostic capability value and the second remote diagnostic capability value. The comprehensive consideration of different aspects of diagnostic capabilities makes the final remote diagnostic capability value more comprehensive and reliable. Fine-tuning of weights helps to more accurately measure the client's diagnostic capabilities and reduce evaluation biases caused by fixed weights. Taking the vehicle usage time into consideration can better cope with complex diagnostic situations that may exist in vehicles with different usage levels, improve the pertinence and effectiveness of diagnostic results, and ensure that the sum of the adjusted first target weight and second target weight is always 1, maintaining the integrity and logical consistency of the weight system.

[0074] Exemplarily, the remote diagnostic capability value corresponding to the first response instruction is calculated based on the first remote diagnostic capability value, the first target weight, the second remote diagnostic capability value, and the second target weight. Specifically, the reference remote diagnostic capability value is first calculated based on the first remote diagnostic capability value, the first target weight, the second remote diagnostic capability value, and the second target weight. The specific calculation formula is as follows:

[0075] Reference remote diagnosis capability value = first remote diagnosis capability value × first target weight + second remote diagnosis capability value × second target weight;

[0076] According to the above formula, the reference remote diagnosis capability value can be obtained.

[0077] Exemplarily, historical diagnostic data of the client corresponding to the first response instruction is obtained. Specifically, if there is a database specifically used to store diagnostic data, its historical diagnostic records can be retrieved according to the client's identification or related information through a specific query statement. If there is a data interface in the system, the historical diagnostic data returned can be obtained by calling the corresponding interface and passing in parameters such as the client's identification. If the historical diagnostic data is stored in the form of a file, the corresponding file can be found according to the relevant information of the client, and the data therein can be read.

[0078] Exemplarily, the customer satisfaction evaluation score of the client corresponding to the first response instruction is determined based on the historical diagnostic data. Specifically, indicators related to customer satisfaction are extracted from the historical diagnostic data, such as diagnostic accuracy, diagnostic response time, thoroughness of problem solving, etc., and corresponding weights are set for each key indicator to reflect its importance to customer satisfaction. Each key indicator is quantitatively scored. For example, a high diagnostic accuracy can be given a high score, a short response time can be given a high score, etc. According to the set weights, the scores of each indicator are weighted and summed to obtain a comprehensive customer satisfaction evaluation score.

[0079] Exemplarily, a target optimization factor corresponding to the customer satisfaction evaluation score is determined. Specifically, it may be a mapping relationship between a preset customer satisfaction evaluation score and an optimization factor, and based on the mapping relationship, the target optimization factor corresponding to the customer satisfaction evaluation score may be determined.

[0080] Exemplarily, the reference remote diagnosis capability value is adjusted according to the target optimization factor to obtain the remote diagnosis capability value corresponding to the first response instruction. Specifically, the specific calculation formula is as follows:

[0081] The remote diagnosis capability value corresponding to the first response instruction=reference remote diagnosis capability value×(1+target optimization factor);

[0082] According to the above formula, the remote diagnosis capability value corresponding to the first response instruction can be obtained.

[0083] It can be seen that by combining the first and second remote diagnosis capability values ​​and considering different weights, the client's comprehensive diagnostic capability can be evaluated more comprehensively and accurately, avoiding a single factor dominating the evaluation results. Obtaining the client's historical diagnostic data and determining the customer satisfaction evaluation score based on this can help incorporate the client's long-term performance and service quality, making the evaluation more continuous and stable. Determining the target optimization factor to adjust the reference remote diagnosis capability value can motivate the client to continuously improve the service quality to obtain better evaluation results, promote its continuous improvement and optimization of diagnostic services, and make adjustments based on customer satisfaction, which is more in line with the customer's actual needs and feelings, and improve the customer's overall satisfaction and trust in diagnostic services. Comprehensive evaluation and adjustment of multiple factors can obtain more accurate and objective remote diagnosis capability values, providing a more reliable basis for subsequent task allocation, resource allocation and other decisions.

[0084] S305: Obtain a maximum remote diagnosis capability value among the k remote diagnosis capability values.

[0085] In this implementation, the remote diagnosis capability values ​​corresponding to the k clients that have been obtained are compared and screened to find the value with the largest value, thereby obtaining the maximum remote diagnosis capability value.

[0086] S306: Determine the client corresponding to the maximum remote diagnosis capability value to obtain the target client.

[0087] In this embodiment, after the maximum remote diagnosis capability value is found, it is determined which client the maximum value corresponds to based on the previously established correspondence relationship, and this client is identified as the target client.

[0088] It can be seen that by determining the client corresponding to the maximum remote diagnosis capability value, complex diagnostic tasks can be preferentially assigned to the most capable client, thereby improving the efficiency and accuracy of diagnosis, avoiding wasting resources on clients with insufficient capabilities, and ensuring to the greatest extent that the target vehicle receives the most professional and efficient diagnostic service, reducing the possibility of misdiagnosis and missed diagnosis, providing customers with high-quality diagnostic results, and helping to improve customer satisfaction and trust in remote diagnosis services, prompting each client to strive to improve its remote diagnosis capabilities in order to obtain more diagnostic tasks and opportunities, quickly locating the most capable client, reducing the time and energy spent on selecting clients, and making the entire remote diagnosis process smoother and more efficient.

[0089] S307: Performing a remote diagnosis operation on the target vehicle based on the target client to obtain a target diagnosis result.

[0090] In this implementation, see Figure 5 , Figure 5 This is a flow chart for determining a target diagnostic result provided by an embodiment of the present application, including but not limited to the following steps:

[0091] S501: Acquire target vehicle attribute parameters of the target vehicle.

[0092] In this embodiment, the relevant data of the target vehicle can be queried from a pre-established vehicle information database. This may require inputting the vehicle identification number (such as the frame number, license plate number, etc.) to obtain detailed attribute parameters, and the relevant parameters and information stored in the vehicle can be read by connecting to the vehicle's own on-board diagnostic system interface, and the attribute parameters of a specific vehicle can be obtained from the vehicle manufacturer's vehicle management system. The target vehicle attribute parameters may include the following: vehicle brand and model: specify the manufacturer and specific style of the vehicle; year of production: the manufacturing time of the vehicle; engine type and specifications: such as displacement, fuel type, turbocharged or not, etc.; transmission type: manual, automatic, continuously variable transmission, etc.; vehicle configuration: such as safety configuration, comfort configuration, etc.; mileage: the total distance the vehicle has traveled; vehicle fault history: previous faults and repairs, etc.

[0093] S502: Determine a target diagnostic instrument corresponding to the target vehicle according to the attribute parameters of the target vehicle.

[0094] In this embodiment, first, a mapping database containing various vehicle attribute parameters and applicable diagnostic instruments is established. This database stores vehicle attributes such as different brands, models, years, engine types, and corresponding adapted diagnostic instrument information. Then, the acquired target vehicle attribute parameters are matched with the information in the database. For example, if the target vehicle is a car of a certain brand and model, produced in a certain year, and has a certain engine type, the database is searched for a diagnostic instrument record that fully matches or most closely matches it. During the matching process, priority rules can be set. For example, for newer models, more advanced diagnostic instruments may be required. For vehicles with certain specific technical configurations, only specific models of diagnostic instruments may be fully compatible. The functional coverage of the diagnostic instrument can also be considered to select a diagnostic instrument that can comprehensively detect and diagnose various systems of the target vehicle (such as the engine, transmission, electronic system, etc.). In addition, the communication protocol compatibility of the diagnostic instrument is also very important to ensure that the selected diagnostic instrument can match the communication protocol of the target vehicle to achieve accurate data reading and diagnostic operations.

[0095] It can be seen that determining the target diagnostic instrument corresponding to the target vehicle according to the target vehicle attribute parameters can ensure that the selected diagnostic instrument has detection functions and parameter settings that match the specific attributes of the target vehicle, so that vehicle problems can be detected and diagnosed more accurately. Using a suitable diagnostic instrument can reduce unnecessary detection steps and time waste, and quickly obtain the required diagnostic data. An inappropriate diagnostic instrument may not be able to read certain key data or may be incompatible with the special configuration of the vehicle, resulting in erroneous diagnostic results. Choosing according to vehicle attributes can effectively avoid this situation, avoid using overly high-end or complex diagnostic instruments for unnecessary detection, save costs, make the communication and data interaction between the diagnostic instrument and the vehicle more stable and reliable, and reduce data loss or errors caused by compatibility issues.

[0096] S503: remotely connect the target client to the target diagnostic instrument.

[0097] In this embodiment, a stable data transmission channel can be established between the target client and the target diagnostic instrument by utilizing a specific network communication protocol. Data can also be coordinated and forwarded through an intermediate server. The target client and the diagnostic instrument respectively establish connections with the server, which is responsible for data transfer and management. Specially designed software or applications can also be installed on the target client and the diagnostic instrument to perform identity authentication, connection establishment, and data interaction. Encryption technology and identity authentication mechanisms can also be used during the connection process to ensure the security and legitimacy of the connection.

[0098] The target client is remotely connected to the target diagnostic instrument, eliminating the need for on-site operation, saving time and labor costs, enabling rapid diagnosis without geographical distance restrictions, and providing diagnostic services for vehicles in a wider area. Diagnostic data can be acquired and transmitted in real time, facilitating timely and accurate diagnosis and decision-making, making full use of the functions of the diagnostic instrument, improving equipment utilization, and avoiding duplicate investment. The software and parameters of the diagnostic instrument can be remotely updated and maintained to maintain its advanced performance and functions.

[0099] S504: Based on the target client, the target diagnostic instrument is controlled to perform a remote diagnostic operation on the target vehicle to obtain the target diagnostic result.

[0100] In this embodiment, exemplarily, a diagnostic instruction is sent to the target diagnostic instrument through the target client, so that the target diagnostic instrument scans multiple systems in the target vehicle to obtain multiple diagnostic data, wherein each diagnostic data corresponds to a system. Specifically, the multiple systems in the target vehicle usually include but are not limited to the following main parts: Engine system: covering fuel supply, ignition, intake and exhaust, cooling and other aspects; Transmission system: such as transmission (manual, automatic, dual clutch, etc.), drive shaft, differential, etc.; Braking system: including brake discs, brake pads, brake fluid, brake booster and anti-lock brake. Automatic braking system (ABS), etc.; electronic control system: such as engine control unit (ECU), body control module (BCM), airbag control unit, etc.; suspension and steering system: suspension components (shock absorbers, springs, etc.), steering mechanism (steering column, steering rod, etc.); air conditioning and ventilation system: components related to functions such as refrigeration, heating, air circulation and filtration; power supply and charging system: batteries, generators, starters and related circuits; fuel and emission system: fuel tanks, fuel pumps, fuel injectors, exhaust treatment devices, etc.; lighting and signal system: headlights, turn signals, brake lights, fog lights and other lamps and related control circuits.

[0101] First, there will be a user interface or operation module on the target client for inputting and sending diagnostic instructions. The user (such as a technician) selects or inputs scanning instructions for multiple systems of the target vehicle through this interface. After clicking the operation of sending the instruction, the target client will transmit the instruction data to the target diagnostic instrument through the previously established remote connection channel (such as a network connection) in a specific data format and communication protocol. After receiving the instruction from the target client, the target diagnostic instrument will interpret the content and requirements of the instruction. Then, according to the instruction, the scanning operation of multiple systems in the target vehicle (such as the engine system, brake system, electronic control system, etc.) is started. During the scanning process, the target diagnostic instrument will communicate and interact with each system of the vehicle, read relevant sensor data, fault codes, operating parameters and other information, and for each scanned system, the target diagnostic instrument will collect and organize the corresponding data, thereby forming multiple diagnostic data corresponding to different systems.

[0102] Exemplarily, after sending the multiple diagnostic data to the target client, the multiple diagnostic data are compared with the preset data ranges corresponding to the multiple systems to obtain at least one abnormal diagnostic data. Specifically, first, it is necessary to obtain the preset data range corresponding to each system. These preset data ranges are usually determined based on the technical specifications provided by the vehicle manufacturer, previous diagnostic experience or industry standards. Then, for each diagnostic data, it is compared with the corresponding system preset data range. If the diagnostic data exceeds the upper or lower limit of the preset data range, it is marked as abnormal diagnostic data. This can be achieved through programming to achieve automatic comparison. For example, using conditional judgment statements in a programming language, when the diagnostic data is greater than a preset maximum value or less than a preset minimum value, it is identified as abnormal. During the comparison process, different comparison methods and logics may need to be used for different types of data (such as numeric, Boolean, string, etc.). For continuous numeric data, the degree of deviation from the preset range can be calculated, and whether it is abnormal can be determined based on the set threshold, and finally at least one abnormal diagnostic data is obtained.

[0103] Exemplarily, the target diagnostic result is determined based on the at least one abnormal diagnostic data. Specifically, first, each abnormal diagnostic data is analyzed in detail. Check the system and specific parameters corresponding to the abnormal data, as well as the degree and nature of the abnormality. Then, comprehensively consider the relationship between multiple abnormal diagnostic data to determine whether these abnormalities are isolated individual phenomena, or are interrelated and jointly point to a systemic problem. Next, refer to the vehicle's maintenance history and common failure modes. If similar abnormal data in the past are usually associated with specific failures, then it can be used as a reference to infer the current possibility of failure. In addition, a comprehensive judgment can be made based on factors such as the vehicle's usage environment and driving habits. For example, certain components of a vehicle that has been driving on harsh road conditions for a long time may be more prone to failure. Based on the above analysis and comprehensive judgment, conclusions are drawn about the possible fault types, fault locations, and fault severity of the target vehicle, that is, the target diagnostic result is determined, and the target diagnostic result is presented on the client's display interface. For example, please refer to Figure 6 , Figure 6 It is a schematic diagram of a target diagnosis result display interface provided in an embodiment of the present application. The target diagnosis result display interface 60 includes "abnormal system: engine system", "abnormal data: exceeding the normal operating temperature range, deviating from the normal intake pressure range", and also includes clickable modules, such as "re-diagnosis", "view diagnosis report", "end diagnosis", etc.

[0104] It can be seen that scanning multiple systems and comparing them with the preset data range can more accurately detect anomalies and improve the accuracy and reliability of diagnosis. The corresponding diagnostic instrument is determined according to the attribute parameters of the target vehicle, which fully considers the particularity and differences of the vehicle and makes the diagnosis more targeted. Remote connection and control of the diagnostic instrument can achieve flexible allocation of resources and improve the efficiency of the use of the diagnostic instrument. The data from multiple diagnoses can be accumulated to form a database, which is conducive to in-depth analysis and prediction of vehicle failure modes and provides a reference for subsequent maintenance and improvement.

[0105] In summary, the implementation of the present invention has the following beneficial effects:

[0106] It can be seen that the remote diagnosis method described in the embodiment of the present invention is applied to a diagnostic device, wherein the diagnostic device includes a device connector, and a communication connection is established between the diagnostic device and the target vehicle through the device connector and the target vehicle connector of the target vehicle. The method includes: first, obtaining a remote diagnosis request of the target vehicle, and then broadcasting the remote diagnosis request to n clients, wherein n is a positive integer greater than 1, and then determining k clients among the n clients that respond to the remote diagnosis request, and obtaining response instructions of the k clients to the remote diagnosis request to obtain k response instructions, wherein k is an integer less than or equal to n, and then determining a remote diagnosis capability value corresponding to each of the k clients based on the k response instructions to obtain k remote diagnosis capability values, and then obtaining a maximum remote diagnosis capability value among the k remote diagnosis capability values, and then determining a client corresponding to the maximum remote diagnosis capability value to obtain a target client, and finally performing a remote diagnosis operation on the target vehicle based on the target client to obtain a target diagnosis result, thereby improving remote diagnosis efficiency.

[0107] See also Figure 7 , Figure 7 It is a structural diagram of a remote diagnosis device provided in an embodiment of the present application. The remote diagnosis device 700 includes: an acquisition unit 701 and a processing unit 702;

[0108] The acquisition unit 701 is used to acquire a remote diagnosis request of a target vehicle;

[0109] The processing unit 702 is used to broadcast the remote diagnosis request to n clients; n is a positive integer greater than 1;

[0110] Determine k clients among the n clients that respond to the remote diagnosis request, and obtain response instructions of the k clients to the remote diagnosis request to obtain k response instructions; k is an integer less than or equal to n;

[0111] Determine the remote diagnosis capability value corresponding to each of the k clients based on the k response instructions to obtain k remote diagnosis capability values;

[0112] Obtaining a maximum remote diagnosis capability value among the k remote diagnosis capability values;

[0113] Determine the client corresponding to the maximum remote diagnosis capability value to obtain a target client;

[0114] A remote diagnosis operation is performed on the target vehicle based on the target client to obtain a target diagnosis result.

[0115] In some possible implementations, in determining the remote diagnosis capability value corresponding to each of the k clients based on the k response instructions to obtain the k remote diagnosis capability values, the processing unit 702 is specifically configured to:

[0116] Determine a first diagnosis response time and a first diagnosis accuracy rate of the client corresponding to a first response instruction; the first response instruction is any one of the k response instructions;

[0117] Determine a first mapping relationship between a diagnostic response time and the remote diagnostic capability value, and a second mapping relationship between a diagnostic accuracy rate and the remote diagnostic capability value;

[0118] Determine a first remote diagnosis capability value corresponding to the first diagnosis response time based on the first mapping relationship;

[0119] Determine a second remote diagnosis capability value corresponding to the first diagnosis accuracy based on the second mapping relationship;

[0120] The remote diagnosis capability value corresponding to the first response instruction is determined based on the first remote diagnosis capability value and the second remote diagnosis capability value.

[0121] In some possible implementations, in determining the remote diagnosis capability value corresponding to the first response instruction based on the first remote diagnosis capability value and the second remote diagnosis capability value, the processing unit 702 is specifically configured to:

[0122] Determine a first reference weight corresponding to the first remote diagnosis capability value and a second reference weight corresponding to the second remote diagnosis capability value; the sum of the first reference weight and the second reference weight is 1;

[0123] Obtaining the usage time of the target vehicle;

[0124] Determining a target fine-tuning parameter corresponding to the usage duration;

[0125] Adjust the first reference weight according to the target fine-tuning parameter to obtain a first target weight;

[0126] adjusting the second reference weight according to the first target weight to obtain a second target weight; the sum of the first target weight and the second target weight is 1;

[0127] The remote diagnosis capability value corresponding to the first response instruction is obtained by performing calculation based on the first remote diagnosis capability value, the first target weight, the second remote diagnosis capability value, and the second target weight.

[0128] In some possible implementations, in terms of obtaining the remote diagnosis capability value corresponding to the first response instruction by calculating based on the first remote diagnosis capability value, the first target weight, the second remote diagnosis capability value, and the second target weight, the processing unit 702 is specifically configured to:

[0129] Calculating based on the first remote diagnosis capability value, the first target weight, the second remote diagnosis capability value, and the second target weight to obtain a reference remote diagnosis capability value;

[0130] Obtaining historical diagnostic data of the client corresponding to the first response instruction;

[0131] Determine, based on the historical diagnostic data, a customer satisfaction evaluation score of the client corresponding to the first response instruction;

[0132] Determining a target optimization factor corresponding to the customer satisfaction evaluation score;

[0133] The reference remote diagnosis capability value is adjusted according to the target optimization factor to obtain the remote diagnosis capability value corresponding to the first response instruction.

[0134] In some possible implementations, the processing unit 702 is further specifically configured to:

[0135] Inquiring the network connection status of the n clients to obtain n network connection statuses; the network connection statuses include online status and offline status;

[0136] Determine p clients in the online state among the n online states; p is an integer less than or equal to n and greater than or equal to k;

[0137] Obtaining a workload corresponding to each of the p clients to obtain p workloads;

[0138] Determining k workloads among the p workloads that are smaller than a preset workload;

[0139] Clients corresponding to the k workloads are determined to obtain the k clients.

[0140] In some possible implementations, in terms of performing a remote diagnosis operation on the target vehicle based on the target client to obtain a target diagnosis result, the processing unit 702 is specifically configured to:

[0141] Obtaining target vehicle attribute parameters of the target vehicle;

[0142] Determining a target diagnostic instrument corresponding to the target vehicle according to the attribute parameters of the target vehicle;

[0143] Remotely connecting the target client to the target diagnostic instrument;

[0144] The target diagnostic instrument is controlled based on the target client to perform remote diagnostic operations on the target vehicle to obtain the target diagnostic result.

[0145] In some possible implementations, in terms of obtaining the target diagnostic result by controlling the target diagnostic instrument to perform a remote diagnostic operation on the target vehicle based on the target client, the processing unit 702 is specifically configured to:

[0146] Sending a diagnostic instruction to the target diagnostic instrument through the target client, so that the target diagnostic instrument scans multiple systems in the target vehicle to obtain multiple diagnostic data; each diagnostic data corresponds to one system;

[0147] After sending the plurality of diagnostic data to the target client, comparing the plurality of diagnostic data with preset data ranges corresponding to the plurality of systems to obtain at least one abnormal diagnostic data;

[0148] The target diagnosis result is determined based on the at least one abnormal diagnosis data.

[0149] See also Figure 8 , Figure 8 Schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 8 As shown, the electronic device 800 includes a transceiver 801, a processor 802 and a memory 803. They are connected via a bus 804. The memory 803 is used to store computer programs and data, and the transceiver 801 can transmit the data stored in the memory 803 to the processor 802. The above program includes instructions for executing the following steps:

[0150] Obtaining a remote diagnosis request for a target vehicle;

[0151] Broadcasting the remote diagnosis request to n clients; n is a positive integer greater than 1;

[0152] Determine k clients among the n clients that respond to the remote diagnosis request, and obtain response instructions of the k clients to the remote diagnosis request to obtain k response instructions; k is an integer less than or equal to n;

[0153] Determine the remote diagnosis capability value corresponding to each of the k clients based on the k response instructions to obtain k remote diagnosis capability values;

[0154] Obtaining a maximum remote diagnosis capability value among the k remote diagnosis capability values;

[0155] Determine the client corresponding to the maximum remote diagnosis capability value to obtain a target client;

[0156] A remote diagnosis operation is performed on the target vehicle based on the target client to obtain a target diagnosis result.

[0157] In some possible implementations, in terms of determining the remote diagnosis capability value corresponding to each of the k clients based on the k response instructions to obtain the k remote diagnosis capability values, the program includes instructions for executing the following steps:

[0158] Determine a first diagnosis response time and a first diagnosis accuracy rate of the client corresponding to a first response instruction; the first response instruction is any one of the k response instructions;

[0159] Determine a first mapping relationship between a diagnostic response time and the remote diagnostic capability value, and a second mapping relationship between a diagnostic accuracy rate and the remote diagnostic capability value;

[0160] Determine a first remote diagnosis capability value corresponding to the first diagnosis response time based on the first mapping relationship;

[0161] Determine a second remote diagnosis capability value corresponding to the first diagnosis accuracy based on the second mapping relationship;

[0162] The remote diagnosis capability value corresponding to the first response instruction is determined based on the first remote diagnosis capability value and the second remote diagnosis capability value.

[0163] In some possible implementations, in terms of determining the remote diagnosis capability value corresponding to the first response instruction based on the first remote diagnosis capability value and the second remote diagnosis capability value, the program includes instructions for executing the following steps:

[0164] Determine a first reference weight corresponding to the first remote diagnosis capability value and a second reference weight corresponding to the second remote diagnosis capability value; the sum of the first reference weight and the second reference weight is 1;

[0165] Obtaining the usage time of the target vehicle;

[0166] Determining a target fine-tuning parameter corresponding to the usage duration;

[0167] Adjust the first reference weight according to the target fine-tuning parameter to obtain a first target weight;

[0168] adjusting the second reference weight according to the first target weight to obtain a second target weight; the sum of the first target weight and the second target weight is 1;

[0169] The remote diagnosis capability value corresponding to the first response instruction is obtained by performing calculation based on the first remote diagnosis capability value, the first target weight, the second remote diagnosis capability value, and the second target weight.

[0170] In some possible implementations, in terms of obtaining the remote diagnosis capability value corresponding to the first response instruction by calculation based on the first remote diagnosis capability value, the first target weight, the second remote diagnosis capability value, and the second target weight, the program includes instructions for performing the following steps:

[0171] Calculating based on the first remote diagnosis capability value, the first target weight, the second remote diagnosis capability value, and the second target weight to obtain a reference remote diagnosis capability value;

[0172] Obtaining historical diagnostic data of the client corresponding to the first response instruction;

[0173] Determine a customer satisfaction evaluation score of the client corresponding to the first response instruction based on the historical diagnostic data;

[0174] Determining a target optimization factor corresponding to the customer satisfaction evaluation score;

[0175] The reference remote diagnosis capability value is adjusted according to the target optimization factor to obtain the remote diagnosis capability value corresponding to the first response instruction.

[0176] In some possible implementations, the above program includes instructions for performing the following steps:

[0177] Inquiring the network connection status of the n clients to obtain n network connection statuses; the network connection statuses include online status and offline status;

[0178] Determine p clients in the online state among the n online states; p is an integer less than or equal to n and greater than or equal to k;

[0179] Obtaining a workload corresponding to each of the p clients to obtain p workloads;

[0180] Determining k workloads among the p workloads that are smaller than a preset workload;

[0181] Clients corresponding to the k workloads are determined to obtain the k clients.

[0182] In some possible implementations, in terms of performing a remote diagnostic operation on the target vehicle based on the target client to obtain a target diagnostic result, the program includes instructions for executing the following steps:

[0183] Obtaining target vehicle attribute parameters of the target vehicle;

[0184] Determining a target diagnostic instrument corresponding to the target vehicle according to the attribute parameters of the target vehicle;

[0185] Remotely connecting the target client to the target diagnostic instrument;

[0186] The target diagnostic instrument is controlled based on the target client to perform remote diagnostic operations on the target vehicle to obtain the target diagnostic result.

[0187] In some possible implementations, in terms of obtaining the target diagnostic result by controlling the target diagnostic instrument based on the target client to perform a remote diagnostic operation on the target vehicle, the above program includes instructions for executing the following steps:

[0188] Sending a diagnostic instruction to the target diagnostic instrument through the target client, so that the target diagnostic instrument scans multiple systems in the target vehicle to obtain multiple diagnostic data; each diagnostic data corresponds to one system;

[0189] After sending the plurality of diagnostic data to the target client, comparing the plurality of diagnostic data with preset data ranges corresponding to the plurality of systems to obtain at least one abnormal diagnostic data;

[0190] The target diagnosis result is determined based on the at least one abnormal diagnosis data.

[0191] It should be understood that the electronic devices in this application may include remote diagnostic devices, smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, laptop computers, mobile Internet devices MID (Mobile Internet Devices, referred to as: MID) or wearable devices or servers, edge computing nodes, etc. The above electronic devices are only examples, not exhaustive, and include but are not limited to the above electronic devices.

[0192] The present application also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement part or all of the steps of any remote diagnosis method described in the above method implementation.

[0193] The present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any remote diagnosis method described in the above method implementation.

[0194] It should be noted that, for the above-mentioned various method implementations, 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 this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the implementations described in the specification are all optional implementations, and the actions and modules involved are not necessarily required by this application.

[0195] In the above-mentioned 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.

[0196] In the several embodiments provided in this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device implementation described above is only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0197] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0198] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software program module.

[0199] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of each implementation method of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0200] A person skilled in the art can understand that all or part of the steps in the various methods of the above-mentioned embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0201] The above is a detailed introduction to the implementation methods of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above implementation methods is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A remote diagnosis method, characterized in that: Applied to a diagnostic device, the diagnostic device includes a device connector; a communication connection is established between the diagnostic device and the target vehicle through the device connector and the target vehicle connector of the target vehicle, the method includes: Obtaining a remote diagnosis request for a target vehicle; Broadcasting the remote diagnosis request to n clients; n is a positive integer greater than 1; Determine k clients among the n clients that respond to the remote diagnosis request, and obtain response instructions of the k clients to the remote diagnosis request to obtain k response instructions; k is an integer less than or equal to n; Determine the remote diagnosis capability value corresponding to each of the k clients based on the k response instructions to obtain k remote diagnosis capability values; Obtaining a maximum remote diagnosis capability value among the k remote diagnosis capability values; Determine the client corresponding to the maximum remote diagnosis capability value to obtain a target client; A remote diagnosis operation is performed on the target vehicle based on the target client to obtain a target diagnosis result.

2. The method according to claim 1, characterized in that The step of determining the remote diagnosis capability value corresponding to each of the k clients based on the k response instructions to obtain k remote diagnosis capability values ​​includes: Determine a first diagnosis response time and a first diagnosis accuracy rate of the client corresponding to a first response instruction; the first response instruction is any one of the k response instructions; Determine a first mapping relationship between a diagnostic response time and the remote diagnostic capability value, and a second mapping relationship between a diagnostic accuracy rate and the remote diagnostic capability value; Determine a first remote diagnosis capability value corresponding to the first diagnosis response time based on the first mapping relationship; Determine a second remote diagnosis capability value corresponding to the first diagnosis accuracy based on the second mapping relationship; The remote diagnosis capability value corresponding to the first response instruction is determined based on the first remote diagnosis capability value and the second remote diagnosis capability value.

3. The method according to claim 2, characterized in that The determining the remote diagnosis capability value corresponding to the first response instruction based on the first remote diagnosis capability value and the second remote diagnosis capability value includes: Determine a first reference weight corresponding to the first remote diagnosis capability value and a second reference weight corresponding to the second remote diagnosis capability value; the sum of the first reference weight and the second reference weight is 1; Obtaining the usage time of the target vehicle; Determining a target fine-tuning parameter corresponding to the usage duration; Adjust the first reference weight according to the target fine-tuning parameter to obtain a first target weight; adjusting the second reference weight according to the first target weight to obtain a second target weight; the sum of the first target weight and the second target weight is 1; The remote diagnosis capability value corresponding to the first response instruction is obtained by performing calculation based on the first remote diagnosis capability value, the first target weight, the second remote diagnosis capability value, and the second target weight.

4. The method according to claim 3, characterized in that The calculating based on the first remote diagnosis capability value, the first target weight, the second remote diagnosis capability value, and the second target weight to obtain the remote diagnosis capability value corresponding to the first response instruction includes: Calculating based on the first remote diagnosis capability value, the first target weight, the second remote diagnosis capability value, and the second target weight to obtain a reference remote diagnosis capability value; Obtaining historical diagnostic data of the client corresponding to the first response instruction; Determine, based on the historical diagnostic data, a customer satisfaction evaluation score of the client corresponding to the first response instruction; Determining a target optimization factor corresponding to the customer satisfaction evaluation score; The reference remote diagnosis capability value is adjusted according to the target optimization factor to obtain the remote diagnosis capability value corresponding to the first response instruction.

5. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: Inquiring the network connection status of the n clients to obtain n network connection statuses; the network connection statuses include online status and offline status; Determine p clients in the online state among the n online states; p is an integer less than or equal to n and greater than or equal to k; Obtaining a workload corresponding to each of the p clients to obtain p workloads; Determining k workloads among the p workloads that are smaller than a preset workload; Clients corresponding to the k workloads are determined to obtain the k clients.

6. The method according to any one of claims 1 to 4, characterized in that: The remote diagnosis operation on the target vehicle based on the target client to obtain a target diagnosis result includes: Obtaining target vehicle attribute parameters of the target vehicle; Determining a target diagnostic instrument corresponding to the target vehicle according to the attribute parameters of the target vehicle; Remotely connecting the target client to the target diagnostic instrument; The target diagnostic instrument is controlled based on the target client to perform remote diagnostic operations on the target vehicle to obtain the target diagnostic result.

7. The method according to claim 6, characterized in that The step of controlling the target diagnostic instrument to perform a remote diagnostic operation on the target vehicle based on the target client to obtain the target diagnostic result includes: Sending a diagnostic instruction to the target diagnostic instrument through the target client, so that the target diagnostic instrument scans multiple systems in the target vehicle to obtain multiple diagnostic data; each diagnostic data corresponds to one system; After sending the plurality of diagnostic data to the target client, comparing the plurality of diagnostic data with preset data ranges corresponding to the plurality of systems to obtain at least one abnormal diagnostic data; The target diagnosis result is determined based on the at least one abnormal diagnosis data.

8. A remote diagnostic device, characterized in that: The remote diagnosis device comprises: an acquisition unit and a processing unit; The acquisition unit is used to acquire a remote diagnosis request of a target vehicle; The processing unit is used to broadcast the remote diagnosis request to n clients; n is a positive integer greater than 1; Determine k clients among the n clients that respond to the remote diagnosis request, and obtain response instructions of the k clients to the remote diagnosis request to obtain k response instructions; k is an integer less than or equal to n; Determine the remote diagnosis capability value corresponding to each of the k clients based on the k response instructions to obtain k remote diagnosis capability values; Obtaining a maximum remote diagnosis capability value among the k remote diagnosis capability values; Determine the client corresponding to the maximum remote diagnosis capability value to obtain a target client; A remote diagnosis operation is performed on the target vehicle based on the target client to obtain a target diagnosis result.

9. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the one or more programs include instructions for executing the steps in the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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