Intelligent diagnosis method and device, storage medium and program product

Abstract

The invention discloses an intelligent diagnosis method and device, a storage medium and a program product, and relates to the technical field of automobile diagnos.The method comprises the steps that vehicle fault information is recognized through a pre-constructed diagnosis model; generating diagnosis schemes according to the vehicle fault information, and performing popularity ranking of the diagnosis schemes; polling the popularity rank of the diagnosis scheme, and judging whether the currently displayed diagnosis scheme conforms to expectation or not; if the currently displayed diagnosis scheme accords with the expectation, confirming the currently displayed diagnosis scheme as a final diagnosis scheme; otherwise, acquiring matching parameters of the diagnosis scheme by guiding real-time input information so as to update the popularity rank of the diagnosis scheme until the diagnosis scheme is confirmed to serve as a final diagnosis scheme. According to the method, the vehicle fault is identified through the heat ranking of the diagnosis model and the polling diagnosis scheme, and the diagnosis scheme can be quickly found without additionally inputting information; and if the final diagnosis scheme does not exist, realizing model iteration and scheme popularity ranking dynamic updating by using real-time input information of a user.

Description

Technical Field

[0001] The present application relates to the field of automobile diagnosis technology, and in particular to an intelligent diagnosis method, device, storage medium and program product. Background Art

[0002] In the field of automotive diagnostic technology, with the increase in automotive electronics and complexity, the development of intelligent diagnostic technology is particularly important.

[0003] Existing intelligent diagnostic software usually contains a large number of menu options and complex function settings, which makes it easy for users to get lost during operation and difficult to find the required diagnostic functions quickly and accurately. In addition, due to the complexity of the diagnostic software menu, the intelligent diagnostic system often finds it difficult to give accurate diagnostic results when dealing with certain complex or rare faults, resulting in misdiagnosis or missed diagnosis.

[0004] Currently, when diagnosing automobile faults, intelligent diagnostic systems usually require users to input a large amount of data, such as vehicle model, year, fault code, etc., and cannot provide a one-step diagnostic solution. Although this form of interaction helps improve the accuracy of diagnosis, it also increases the difficulty of user operation, making the intelligent diagnostic process less "intelligent".

[0005] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The main purpose of this application is to provide an intelligent diagnostic method, device, storage medium and program product, aiming to solve the technical problem that a large amount of data needs to be input in the process of automobile diagnosis to improve accuracy, and the diagnostic solution cannot be implemented in one step.

[0007] To achieve the above objectives, the present application proposes an intelligent diagnosis method, which includes:

[0008] Identify vehicle fault information through pre-built diagnostic models;

[0009] Generating a diagnosis plan according to the vehicle fault information and ranking the popularity of the diagnosis plan;

[0010] Poll the popularity ranking of the diagnostic solution to determine whether the currently displayed diagnostic solution meets expectations;

[0011] If the currently displayed diagnostic scheme meets expectations, the currently displayed diagnostic scheme is confirmed as the final diagnostic scheme; otherwise, the matching parameters of the diagnostic scheme are obtained by guiding real-time input information to update the popularity ranking of the diagnostic scheme until the currently displayed diagnostic scheme that meets expectations is confirmed as the final diagnostic scheme.

[0012] In one embodiment, the steps of constructing the pre-designed diagnostic model include:

[0013] Collect historical diagnostic data, maintenance cases and user input information to clean and format data;

[0014] Analyze data and extract features using machine learning techniques;

[0015] Building an initial diagnostic model through machine learning algorithms;

[0016] Use the labeled training dataset for initial diagnostic model training;

[0017] Verifying and testing the initial diagnostic model through a validation set and a test set;

[0018] Design user interfaces to input user information;

[0019] The initial diagnosis model is iteratively updated according to user input information and a new diagnosis scheme to obtain a diagnosis model.

[0020] In one embodiment, the step of identifying vehicle fault information by using a pre-built diagnostic model includes:

[0021] Perform data preprocessing on vehicle fault information and extract key features related to diagnosis;

[0022] inputting the key features into a pre-built diagnostic model;

[0023] According to the input key features and the preset algorithm, the necessary matching parameters of the diagnosis scheme are obtained.

[0024] In one embodiment, the step of generating a diagnosis plan according to the vehicle fault information and ranking the popularity of the diagnosis plan includes:

[0025] Matching the necessary matching parameters with preset matching parameters in the knowledge base;

[0026] generating a set of diagnostic schemes according to the matching results of the necessary matching parameters;

[0027] Calculating the popularity of the diagnostic scheme by using the popularity ranking parameters of the diagnostic scheme, and generating a popularity ranking of the diagnostic scheme;

[0028] The most popular diagnostic solution is placed at the front of the popularity ranking for user display.

[0029] In one embodiment, the step of polling the popularity ranking of the diagnostic solutions and determining whether the currently displayed diagnostic solution meets expectations includes:

[0030] Display the top diagnostic solutions in the popularity ranking to users;

[0031] Determine whether the currently displayed diagnostic solution meets expectations;

[0032] If it meets expectations, the currently displayed diagnostic solution is displayed continuously, and the currently displayed diagnostic solution is confirmed as the final diagnostic solution; otherwise, the solution is iterated according to the popularity ranking, and the next diagnostic solution is displayed;

[0033] By polling the user review results of each displayed diagnostic solution at a preset time, it is determined whether the iteratively displayed diagnostic solution meets expectations;

[0034] If the iteratively displayed diagnostic solution does not meet expectations, the matching parameters of the diagnostic solution are obtained through real-time user input information to update the popularity ranking of the diagnostic solution until the iteratively displayed diagnostic solution is confirmed as the final diagnostic solution; otherwise, the final diagnostic solution is directly confirmed.

[0035] In one embodiment, the step of obtaining the matching parameters of the diagnostic scheme by guiding the real-time input information to update the popularity ranking of the diagnostic scheme includes:

[0036] Displaying a description of the problem and possible options related to the current diagnosis via a user interface;

[0037] Guide users to input information;

[0038] Obtain input information provided by users through the user interface in real time;

[0039] The input information is verified and input is confirmed.

[0040] In one embodiment, the step of obtaining the matching parameters of the diagnostic scheme by guiding the real-time input information to update the popularity ranking of the diagnostic scheme further includes:

[0041] Collecting real-time user input information during the diagnosis process through the user interface;

[0042] Processing and analyzing the user's real-time input information to extract key information related to diagnosis;

[0043] According to the key information, obtaining supplementary matching parameters of the diagnostic scheme;

[0044] Rematching a set of diagnostic schemes through necessary matching parameters and the supplementary matching parameters and recalculating the heat of the diagnostic schemes;

[0045] The popularity ranking of the diagnostic scheme is updated according to the recalculated popularity of the diagnostic scheme.

[0046] In addition, to achieve the above objectives, the present application also proposes an intelligent diagnostic device, which includes:

[0047] A diagnostic model recognition module, used to recognize vehicle fault information through a pre-built diagnostic model;

[0048] A diagnostic scheme ranking module, used for generating diagnostic schemes according to the vehicle fault information and ranking the popularity of the diagnostic schemes;

[0049] A first judgment module is used to poll the popularity ranking of the diagnostic scheme to determine whether the currently displayed diagnostic scheme meets expectations;

[0050] An input guidance module, if the currently displayed diagnostic solution does not meet expectations, obtains matching parameters of the diagnostic solution by guiding real-time input information to update the popularity ranking of the diagnostic solution;

[0051] The diagnosis scheme confirmation module is used to confirm the currently displayed diagnosis scheme that meets expectations as the final diagnosis scheme.

[0052] In addition, to achieve the above objectives, the present application also proposes an intelligent diagnostic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the intelligent diagnostic method described above.

[0053] In addition, to achieve the above objectives, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the intelligent diagnosis method described above are implemented.

[0054] In addition, to achieve the above objectives, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the intelligent diagnosis method described above are implemented.

[0055] One or more technical solutions proposed in this application have at least the following technical effects:

[0056] The present application identifies vehicle fault information through a pre-built diagnostic model; generates a diagnostic solution based on the vehicle fault information and ranks the popularity of the diagnostic solution; polls the popularity ranking of the diagnostic solution to determine whether the currently displayed diagnostic solution meets expectations; if the currently displayed diagnostic solution meets expectations, the currently displayed diagnostic solution is confirmed as the final diagnostic solution; otherwise, the matching parameters of the diagnostic solution are obtained by guiding real-time input information to update the popularity ranking of the diagnostic solution until it is confirmed as the final diagnostic solution. The present application identifies and diagnoses vehicle faults through a pre-built diagnostic model and polls the popularity ranking of the diagnostic solution, solving the technical problem that a large amount of data is usually required to be input in the process of automobile diagnosis to improve accuracy, and the diagnostic solution cannot be implemented in one step. Compared with the prior art, the diagnostic solution can be quickly found without additional input information. When there is no final diagnostic solution, the user's real-time input information is used to implement the iteration of the diagnostic model and the dynamic update of the solution popularity ranking until the currently displayed diagnostic solution that meets expectations is confirmed as the final diagnostic solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the description, are used to explain the principles of the present application.

[0058] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0059] Figure 1 A flowchart of the first embodiment of the intelligent diagnosis method of the present application is provided;

[0060] Figure 2 A flowchart of the second embodiment of the intelligent diagnosis method of the present application is provided;

[0061] Figure 3 A flowchart of the third embodiment of the intelligent diagnosis method of the present application is provided;

[0062] Figure 4 A flowchart of the fourth embodiment of the intelligent diagnosis method of the present application is provided;

[0063] Figure 5 A flowchart of the fifth embodiment of the intelligent diagnosis method of the present application is provided;

[0064] Figure 6 A flowchart of the sixth embodiment of the intelligent diagnosis method of the present application is provided;

[0065] Figure 7A flow chart of the seventh embodiment of the intelligent diagnosis method of the present application;

[0066] Figure 8 This is a schematic diagram of the module structure of the intelligent diagnostic device according to an embodiment of the present application;

[0067] Fig. 9 Schematic diagram of the device structure of the hardware operating environment involved in the intelligent diagnosis method in the embodiment of the present application.

[0068] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0069] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0070] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0071] Since the prior art automobile diagnosis process usually requires the input of a large amount of data to improve accuracy, it is impossible to implement a one-step diagnosis solution.

[0072] The present application provides a solution, which identifies vehicle fault information through a pre-built diagnostic model; generates a diagnostic solution based on the vehicle fault information and ranks the popularity of the diagnostic solution; polls the popularity ranking of the diagnostic solution to determine whether the currently displayed diagnostic solution meets expectations; if the currently displayed diagnostic solution meets expectations, the currently displayed diagnostic solution is confirmed as the final diagnostic solution; otherwise, the matching parameters of the diagnostic solution are obtained by guiding real-time input information to update the popularity ranking of the diagnostic solution until it is confirmed as the final diagnostic solution. The present application identifies and diagnoses vehicle faults through a pre-built diagnostic model and polls the popularity ranking of diagnostic solutions. The diagnostic solution can be quickly found without additional input information. When the final diagnostic solution does not exist, the user's real-time input information is used to implement diagnostic model iteration and dynamic update of solution popularity ranking until the currently displayed diagnostic solution that meets expectations is confirmed as the final diagnostic solution.

[0073] Based on this, the present application embodiment provides an intelligent diagnosis method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the intelligent diagnosis method of the present application.

[0074] In this embodiment, the intelligent diagnosis method includes steps S10 to S50:

[0075] Step S10, identifying vehicle fault information through a pre-built diagnostic model;

[0076] It should be noted that the vehicle fault information at least includes fault code, vehicle model and year, historical diagnostic data, sensor readings, vehicle performance data, etc.; the collected vehicle fault information is cleaned, formatted and standardized, and features related to fault diagnosis are extracted from the pre-processed data, such as combinations of fault codes, abnormal patterns of sensor readings, etc.; the pre-built diagnostic model operation adopts machine learning algorithms such as decision trees, support vector machines, neural networks, etc. for training.

[0077] Specifically, the newly collected vehicle fault information is input into a pre-built diagnostic model, and the diagnostic model outputs possible fault types and diagnostic suggestions based on the input data.

[0078] Step S20, generating a diagnosis plan according to the vehicle fault information and ranking the popularity of the diagnosis plan;

[0079] It should be noted that a series of possible diagnostic schemes are generated based on vehicle fault information using a diagnostic knowledge base and an algorithm, and each diagnostic scheme includes at least a fault description, a fault cause, recommended inspection steps, and maintenance suggestions. The popularity ranking of the diagnostic schemes is based on the evaluation results of the generated diagnostic schemes, and the popularity value of each diagnostic scheme is calculated, such as the matching degree of the fault code, the number of times the diagnostic scheme is adopted, and the success rate, etc. The diagnostic schemes are sorted according to the popularity values ​​to form a popularity ranking, and the ranking results are displayed to users, with diagnostic schemes with high popularity being ranked first.

[0080] Step S30, polling the popularity ranking of the diagnostic scheme to determine whether the currently displayed diagnostic scheme meets expectations;

[0081] It should be noted that polling the popularity ranking of the diagnostic solutions means that the system displays the generated diagnostic solutions to the user according to the popularity values ​​from high to low, and judges whether the currently displayed diagnostic solution meets expectations by checking the detailed information of the currently displayed diagnostic solution, including fault description, fault cause, recommended inspection steps and maintenance suggestions. If it does not meet expectations, the next diagnostic solution is polled according to the popularity ranking and the above steps are repeated.

[0082] Step S40, if the currently displayed diagnostic solution meets expectations, confirm the currently displayed diagnostic solution as the final diagnostic solution;

[0083] It should be noted that the user can evaluate the currently displayed diagnostic plan. If he believes that the diagnostic plan is reasonable and consistent with the actual fault condition of the vehicle, he can confirm it through the user interface. The system will record the diagnostic plan confirmed by the user as the final diagnostic plan. The confirmation can be completed by clicking a button, selecting an option, voice command or other interactive methods.

[0084] Step S50, if the currently displayed diagnostic solution does not meet expectations, then obtain matching parameters of the diagnostic solution by guiding real-time input information to update the popularity ranking of the diagnostic solution, until the currently displayed diagnostic solution that meets expectations is confirmed as the final diagnostic solution.

[0085] It should be noted that if the currently displayed diagnostic solution does not meet expectations, the system may guide the user to provide additional information through the user interface to help more accurately diagnose the vehicle fault. The guidance may include asking questions, providing input fields or options, entering voice commands, etc.; at the same time, the system uses the additional information provided by the user to update the matching parameters of the diagnostic solution, and the matching parameters may include fault code matching, vehicle and year correlation, historical success count, etc.; a series of diagnostic solutions and diagnostic solution popularity rankings are regenerated based on the updated matching parameters; the system will continue to rotate the updated diagnostic solution popularity rankings and determine whether the displayed new diagnostic solution meets expectations.

[0086] The present application identifies vehicle fault information through a pre-built diagnostic model; generates a diagnostic solution based on the vehicle fault information and ranks the popularity of the diagnostic solution; polls the popularity ranking of the diagnostic solution to determine whether the currently displayed diagnostic solution meets expectations; if the currently displayed diagnostic solution meets expectations, the currently displayed diagnostic solution is confirmed as the final diagnostic solution; otherwise, the matching parameters of the diagnostic solution are obtained by guiding real-time input information to update the popularity ranking of the diagnostic solution until it is confirmed as the final diagnostic solution. The present application identifies and diagnoses vehicle faults through a pre-built diagnostic model and polls the popularity ranking of the diagnostic solution, solving the technical problem that a large amount of data is usually required to be input in the process of automobile diagnosis to improve accuracy, and the diagnostic solution cannot be implemented in one step. Compared with the prior art, the diagnostic solution can be quickly found without additional input information. When there is no final diagnostic solution, the user's real-time input information is used to implement the iteration of the diagnostic model and the dynamic update of the solution popularity ranking until the currently displayed diagnostic solution that meets expectations is confirmed as the final diagnostic solution.

[0087] Based on this, the present application embodiment provides an intelligent diagnosis method, referring to Figure 2 , Figure 2 This is a flow chart of the second embodiment of the intelligent diagnosis method of the present application.

[0088] In this embodiment, the construction of the pre-constructed diagnostic model includes steps A101 to A107:

[0089] Step A101, collecting historical diagnostic data, maintenance cases and user input information to clean and format the data;

[0090] It should be noted that the historical diagnostic data may come from vehicle maintenance records, fault code databases, etc.; the maintenance cases may include vehicle successful maintenance records, troubleshooting processes, etc.; the user input information may come from user feedback, etc.; data cleaning includes removing irrelevant data, erroneous data, duplicate data, etc.; formatting data may include data conversion, standardization, normalization, etc., so that the data meets the requirements of the diagnostic model.

[0091] Step A102, using machine learning technology to analyze data and extract features;

[0092] It should be noted that the features include at least fault codes and their combinations, sensor readings, and user-described symptoms to reduce the dimension of the data. Cross-validation techniques can also be used to verify the importance of the extracted features to the diagnostic model.

[0093] Step A103, constructing an initial diagnosis model through a machine learning algorithm;

[0094] It should be noted that the machine learning algorithm includes at least a decision tree, a support vector machine, and a neural network.

[0095] Step A104, using the labeled training data set to perform initial diagnosis model training;

[0096] Step A105, verifying and testing the initial diagnosis model through a verification set and a test set;

[0097] It should be noted that a part of the cleaned and formatted data is selected as the training data set, where the labeled data set means that each data sample has a corresponding label or target variable, such as fault category, maintenance suggestion, etc.; the training data set is divided into smaller subsets, such as training set, validation set and test set. The training set is used to train the model, the validation set is used to adjust the model parameters, and the test set is used to evaluate the model performance.

[0098] Step A106, designing a user interface to input user information;

[0099] It should be noted that the system analyzes the needs and expectations of users, determines what information the user interface needs to collect, designs multiple interaction methods, such as clicking, input, interaction, etc., and provides clear guidance and feedback to help users enter information correctly.

[0100] Step A107, iteratively updating the initial diagnosis model according to the user input information and the new diagnosis scheme to obtain a diagnosis model.

[0101] It should be noted that the system integrates user input information and new diagnostic schemes into the existing test set, uses the integrated data set to retrain the diagnostic model, adjusts and optimizes the diagnostic model parameters, performs iterative updates, and improves the accuracy and adaptability of the diagnostic model.

[0102] Further, refer to Figure 3 The third embodiment of the intelligent diagnosis method of the present application provides a flow chart based on the above Figure 3 In the embodiment shown, the "identifying vehicle fault information by using a pre-built diagnostic model" in step S10 is further refined to include steps A301 to A303:

[0103] Step A301, preprocessing the vehicle fault information to extract key features related to diagnosis;

[0104] It should be noted that the data preprocessing includes at least data cleaning and data conversion, so as to convert the vehicle fault information into a format suitable for diagnosis model processing; the key features include at least fault codes, sensor readings, and diagnostic routes.

[0105] Step A302, inputting the key features into a pre-built diagnostic model;

[0106] It should be noted that the key features may be further subjected to feature scaling and feature encoding processing to improve the accuracy of the diagnosis model.

[0107] Step A303, obtaining necessary matching parameters of the diagnosis scheme according to the input key features and the preset algorithm.

[0108] It should be noted that the necessary matching parameters may include fault code, fault category, fault probability, confidence level, etc.

[0109] Specifically, fault information is collected through the vehicle's electronic control unit (ECU) or other data sources, cleaned and formatted, and input into the loaded diagnostic model. Key features related to fault diagnosis are extracted from the preprocessed data, and the input features are processed using the preset algorithm in the diagnostic model to calculate the necessary matching parameter fault codes for the diagnostic solution.

[0110] This embodiment pre-processes the vehicle fault information and inputs it into the loaded diagnostic model to extract key features for matching to obtain necessary matching parameters, which can improve the accuracy and efficiency of diagnosis and facilitate the generation of subsequent diagnostic solutions.

[0111] Further, refer to Figure 4 The fourth embodiment of the intelligent diagnosis method of the present application provides a flow chart based on the above Figure 4In the embodiment shown, the step S20 of "generating a diagnosis plan according to the vehicle fault information and ranking the popularity of the diagnosis plan" is further refined to include steps A401 to A404:

[0112] Step A401, matching the necessary matching parameters with preset matching parameters in the knowledge base;

[0113] It should be noted that the knowledge base may include fault codes, historical fault cases, fault types, etc.

[0114] Step A402, generating a set of diagnostic solutions according to the matching results of the necessary matching parameters;

[0115] It should be noted that each diagnostic solution may include the cause of the fault, maintenance suggestions, maintenance routes, etc.

[0116] Step A403, calculating the heat of the diagnostic scheme by using the heat ranking parameters of the diagnostic scheme, and generating the heat ranking of the diagnostic scheme;

[0117] It should be noted that the popularity ranking parameters at least include the number of times adopted, failure probability, historical maintenance records, etc.

[0118] Step A404, arranging the most popular diagnostic solution at the front of the popularity ranking for user display.

[0119] Specifically, the necessary matching parameters are extracted from the vehicle fault information as fault codes through the diagnostic model, and the fault codes are matched with the preset matching parameters in the knowledge base. A group of 5 possible diagnostic schemes are generated according to the matching results, and the popularity of each diagnostic scheme is obtained by calculating the number of times based on the popularity ranking parameters of the diagnostic scheme. The diagnostic scheme with the highest popularity is ranked at the top of the popularity ranking for user display and selection, helping users to quickly identify the most likely fault causes and solutions.

[0120] This embodiment ensures that the generated diagnostic scheme is highly relevant to the vehicle fault information through necessary matching parameters and preset matching parameters in the knowledge base, and quickly generates a set of diagnostic schemes based on the matching results, thereby reducing the time and labor cost of manual analysis of fault information. The generated diagnostic schemes are ranked based on popularity, which reflects the credibility and priority of the generated diagnostic schemes, helping users to quickly identify and select the most popular and effective diagnostic schemes and reduce waiting time.

[0121] Further, refer to Figure 5 The fifth embodiment of the intelligent diagnosis method of the present application provides a flow chart based on the above Figure 5In the embodiment shown, the step S30 of "polling the popularity ranking of the diagnostic scheme to determine whether the currently displayed diagnostic scheme meets expectations" is further refined, including steps A501 to A505:

[0122] Step A501, displaying the top diagnostic solutions in the popularity ranking to the user;

[0123] It should be noted that the display interface may include elements such as icons, lists, indicator lights, etc., which intuitively display the popularity of the diagnostic solution and related information.

[0124] Step A502, determining whether the currently displayed diagnostic solution meets expectations;

[0125] It should be noted that user feedback on the currently displayed diagnostic solution is collected through the user interface, and the feedback includes user selections, evaluations, questions, etc.; based on the nature of the diagnostic problem and user needs, standards are set to determine whether the diagnostic solution meets expectations, and the standards include the accuracy, feasibility, cost, etc. of the diagnostic solution; the collected user feedback is compared with the set expected standards, and based on the comparison results, a judgment is made and output as to whether the currently displayed diagnostic solution meets expectations.

[0126] Step A503: If the result meets expectations, the currently displayed diagnostic solution is displayed continuously, and the currently displayed diagnostic solution is confirmed as the final diagnostic solution; otherwise, the solution is iterated according to the popularity ranking, and the next diagnostic solution is displayed;

[0127] Step A504, by polling the user review results of each displayed diagnostic solution at a preset time, determining whether the iteratively displayed diagnostic solution meets expectations;

[0128] It should be noted that the user review results of the currently displayed diagnostic plan within a preset time are obtained. The user review results may include user selections, evaluations, questions, etc., and whether the currently displayed diagnostic plan meets expectations is judged based on the user review results. If it does not meet expectations, the next diagnostic plan is displayed in turn based on the popularity ranking, and whether it meets expectations is continued to be judged. The diagnostic plans are polled until no generated diagnostic plan meets expectations.

[0129] Step A505, if the iteratively displayed diagnostic solution does not meet expectations, the matching parameters of the diagnostic solution are obtained through real-time user input information to update the popularity ranking of the diagnostic solution until the iteratively displayed diagnostic solution is confirmed as the final diagnostic solution; otherwise, it is directly confirmed as the final diagnostic solution.

[0130] It should be noted that, based on user feedback and preset expected standards, if the diagnostic solution displayed in the current iteration does not meet expectations, and the diagnostic solutions displayed in the polling do not meet expectations, the user is guided to input information in real time, and the input information may include vehicle model, maintenance history, vehicle usage, etc.; based on the information input by the user in real time, the supplementary matching parameters of the diagnostic solution are obtained, and a set of diagnostic solutions are updated based on the necessary matching parameters and the supplementary matching parameters, and the popularity ranking of the updated diagnostic solution is recalculated; based on the updated popularity ranking, the next diagnostic solution is selected to continue displaying; if the iteratively displayed diagnostic solution meets expectations, it is confirmed as the final diagnostic solution. If the diagnostic solution after the polling display update iteration still does not meet expectations, the user is guided to input information again to update the iterative diagnostic solution and the popularity ranking until the final diagnostic solution is confirmed.

[0131] This embodiment determines whether the currently displayed diagnostic scheme meets expectations by polling the popularity ranking of the diagnostic schemes, wherein polling the popularity ranking of the diagnostic schemes improves the accuracy and efficiency of diagnosis by preferentially displaying the most likely diagnostic schemes; users can directly participate in the diagnostic process by inputting information and reviewing diagnostic schemes in real time, which helps to quickly locate the cause of the fault and improve user participation and satisfaction.

[0132] Further, refer to Figure 6 The fifth embodiment of the intelligent diagnosis method of the present application provides a flow chart based on the above Figure 6 In the embodiment shown, the step S50 of "obtaining matching parameters of the diagnostic scheme by guiding real-time input information to update the popularity ranking of the diagnostic scheme" is further refined, including steps A601 to A604:

[0133] Step A601, displaying a description of a problem and possible options related to the current diagnosis through a user interface;

[0134] It should be noted that the options displayed by the user interface for the user to select may include radio buttons, check boxes, etc.

[0135] Step A602, guiding the user to input information;

[0136] It should be noted that the user interface guides the user to select corresponding options or input relevant information according to the displayed problem description.

[0137] Step A603, obtaining input information provided by the user through the user interface in real time;

[0138] It should be noted that the user interface obtains and displays the information input by the user in real time, and can also be modified.

[0139] Step A604, verifying and confirming the input information.

[0140] It should be noted that the user interface has a confirmation button and a cancel button to confirm the input message or re-enter the message when the input is wrong.

[0141] Specifically, when the user interface displays a description of the problem related to the current diagnosis, for example, "Your vehicle has an engine failure. Please select the following possible symptoms: engine stalling, engine shaking, abnormal engine noise, etc.", the user interface guides the user to input information, such as "Please enter the frequency and duration of the engine shaking, etc." The user interface obtains the user input information in real time: "The user entered the engine shaking frequency as "5 times per minute"", and finally the user interface displays a confirmation button, for example: "Confirm the input information", the user clicks the confirmation button, and the user interface displays confirmation information, such as: "You have successfully entered the engine shaking frequency as 5 times per minute, please wait for the diagnosis result."

[0142] This embodiment guides users to input information by displaying diagnosis-related problem descriptions, obtains user input information in real time, and verifies and confirms the input information; it helps users to more clearly understand the context of the diagnosis and the type of information that needs to be provided, avoids misdiagnosis or missed diagnosis due to incorrect or incomplete information, and helps to update diagnostic models and popularity rankings, thereby providing more accurate diagnostic solutions.

[0143] Further, refer to Figure 7 The fifth embodiment of the intelligent diagnosis method of the present application provides a flow chart based on the above Figure 7 The embodiment shown further refines the step S50 of "obtaining matching parameters of the diagnostic scheme by guiding real-time input information to update the popularity ranking of the diagnostic scheme" and further includes steps A701 to A705:

[0144] Step A701, collecting real-time user input information during the diagnosis process through the user interface;

[0145] It should be noted that the user inputs real-time information according to the real-time input prompts during the diagnosis process displayed on the user interface.

[0146] Step A702, processing and analyzing the user's real-time input information to extract key information related to diagnosis;

[0147] It should be noted that natural language processing can be used to identify user input information in real time and extract key information.

[0148] Step A703, obtaining supplementary matching parameters of the diagnosis scheme according to the key information;

[0149] It should be noted that the key information is mapped into the parameter space of the diagnosis model, the weight is adjusted according to the importance of the feature, and the supplementary matching parameters of the diagnosis scheme are generated.

[0150] Step A704, rematching a set of diagnosis schemes by using the necessary matching parameters and the supplementary matching parameters and recalculating the heat of the diagnosis schemes;

[0151] Step A705, updating the popularity ranking of the diagnostic scheme according to the recalculated popularity of the diagnostic scheme;

[0152] It should be noted that a set of possible diagnostic schemes are re-matched from the knowledge base using necessary matching parameters and supplementary matching parameters. For the re-matched diagnostic schemes, the popularity of each scheme is recalculated based on its correlation with user input and popularity ranking parameters such as the number of historical adoptions. The popularity ranking of the diagnostic schemes is updated based on the recalculated popularity.

[0153] This embodiment generates supplementary matching parameters through real-time user input information, combines the necessary matching parameters with the preset matching parameters in the knowledge base for matching, generates a group of possible diagnostic solutions, recalculates the popularity of the diagnostic solutions, and updates the popularity rankings according to the popularity ranking parameters. When the diagnostic solutions generated according to the necessary matching parameters are not sufficient to meet expectations, the user can input in real time the key supplementary matching parameters required for diagnosing the fault information, regenerate a group of possible diagnostic solutions and update the popularity rankings of the diagnostic solutions, thereby improving the accuracy and efficiency of diagnosis and enhancing user satisfaction.

[0154] In addition, this application also provides an intelligent diagnostic device, please refer to Figure 8 , the intelligent diagnostic device comprises:

[0155] 10: Diagnostic model recognition module, used to identify vehicle fault information through a pre-built diagnostic model;

[0156] 20: a diagnostic scheme ranking module, used to generate a diagnostic scheme according to the vehicle fault information and to rank the popularity of the diagnostic scheme;

[0157] 30: A first judgment module, used to poll the popularity ranking of the diagnostic scheme to determine whether the currently displayed diagnostic scheme meets expectations;

[0158] 40: an input guidance module, if the currently displayed diagnostic solution does not meet expectations, obtain matching parameters of the diagnostic solution by guiding real-time input information to update the popularity ranking of the diagnostic solution;

[0159] 50: A diagnosis scheme confirmation module is used to confirm the currently displayed diagnosis scheme that meets expectations as the final diagnosis scheme.

[0160] The intelligent diagnostic device provided by the present application adopts the intelligent diagnostic method in the above embodiment, which can solve the technical problem that a large amount of data usually needs to be input to improve the accuracy during the automobile diagnosis process, and the diagnostic solution cannot be implemented in one step. Compared with the prior art, the beneficial effects of the intelligent diagnostic device provided by the present application are the same as the beneficial effects of the intelligent diagnostic method provided by the above embodiment, and the other technical features of the intelligent diagnostic device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0161] In addition, the present application also provides an intelligent diagnostic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein 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 so that the at least one processor can execute the intelligent diagnostic method in the above-mentioned embodiment one.

[0162] Reference below Fig. 9 , which shows a schematic diagram of the structure of an intelligent diagnostic device suitable for implementing the embodiment of the present application. The intelligent diagnostic device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Fig. 9 The intelligent diagnostic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0163] like Fig. 9As shown, the intelligent diagnostic device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the intelligent diagnostic device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the intelligent diagnostic device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows an intelligent diagnostic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided alternatively.

[0164] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0165] The intelligent diagnostic device provided by the present application adopts the intelligent diagnostic method in the above embodiment, which can solve the technical problem that a large amount of data usually needs to be input to improve the accuracy during the automobile diagnosis process, and the diagnostic solution cannot be implemented in one step. Compared with the prior art, the beneficial effects of the intelligent diagnostic device provided by the present application are the same as the beneficial effects of the intelligent diagnostic method provided by the above embodiment, and the other technical features in the intelligent diagnostic device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0166] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0167] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0168] In addition, the present application also provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the intelligent diagnosis method in the above-mentioned embodiment.

[0169] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0170] The computer-readable storage medium may be included in the intelligent diagnostic device; or may exist independently without being assembled into the intelligent diagnostic device.

[0171] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the intelligent diagnostic device, the intelligent diagnostic device: identifies vehicle fault information through a pre-built diagnostic model; generates a diagnostic solution based on the vehicle fault information and performs a popularity ranking of the diagnostic solution; polls the popularity ranking of the diagnostic solution to determine whether the currently displayed diagnostic solution meets expectations; if the currently displayed diagnostic solution meets expectations, the currently displayed diagnostic solution is confirmed as the final diagnostic solution; otherwise, the matching parameters of the diagnostic solution are obtained by guiding real-time input information to update the popularity ranking of the diagnostic solution, until the currently displayed diagnostic solution that meets expectations is confirmed as the final diagnostic solution.

[0172] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0173] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0174] The modules involved in the embodiments described in the present application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0175] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned intelligent diagnosis method, and can solve the technical problem that a large amount of data is usually required to be input in the process of automobile diagnosis to improve accuracy, and the diagnosis scheme cannot be implemented in one step. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the intelligent diagnosis method provided by the above-mentioned embodiment, and will not be repeated here.

[0176] In addition, the present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned intelligent diagnosis method when executed by a processor.

[0177] The computer program product provided by the present application can solve the technical problem that a large amount of data is usually required to be input in the process of automobile diagnosis to improve accuracy, and the diagnosis scheme cannot be implemented in one step. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the intelligent diagnosis method provided by the above embodiment, and will not be repeated here.

[0178] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. An intelligent diagnosis method, characterized in that: The method comprises: Identify vehicle fault information through pre-built diagnostic models; Generating a diagnosis plan according to the vehicle fault information and ranking the popularity of the diagnosis plan; Poll the popularity ranking of the diagnostic solution to determine whether the currently displayed diagnostic solution meets expectations; If the currently displayed diagnostic scheme meets expectations, the currently displayed diagnostic scheme is confirmed as the final diagnostic scheme; otherwise, the matching parameters of the diagnostic scheme are obtained by guiding real-time input information to update the popularity ranking of the diagnostic scheme until the currently displayed diagnostic scheme that meets expectations is confirmed as the final diagnostic scheme.

2. The method according to claim 1, characterized in that The steps of constructing the pre-built diagnostic model include: Collect historical diagnostic data, maintenance cases and user input information to clean and format data; Analyze data and extract features using machine learning techniques; Building an initial diagnostic model through machine learning algorithms; Use the labeled training dataset for initial diagnostic model training; Verifying and testing the initial diagnostic model through a validation set and a test set; Design user interfaces to input user information; The initial diagnosis model is iteratively updated according to user input information and a new diagnosis scheme to obtain a diagnosis model.

3. The method according to claim 2, characterized in that The step of identifying vehicle fault information by using a pre-built diagnostic model includes: Perform data preprocessing on vehicle fault information and extract key features related to diagnosis; inputting the key features into a pre-built diagnostic model; According to the input key features and the preset algorithm, the necessary matching parameters of the diagnosis scheme are obtained.

4. The method according to claim 3, characterized in that The step of generating a diagnosis plan according to the vehicle fault information and ranking the popularity of the diagnosis plan comprises: Matching the necessary matching parameters with preset matching parameters in the knowledge base; generating a set of diagnostic schemes according to the matching results of the necessary matching parameters; Calculating the popularity of the diagnostic scheme by using the popularity ranking parameters of the diagnostic scheme, and generating a popularity ranking of the diagnostic scheme; The most popular diagnostic solution is placed at the front of the popularity ranking for user display.

5. The method according to claim 4, characterized in that The step of polling the popularity ranking of the diagnostic scheme and determining whether the currently displayed diagnostic scheme meets expectations includes: Display the top diagnostic solutions in the popularity ranking to users; Determine whether the currently displayed diagnostic solution meets expectations; If it meets expectations, the currently displayed diagnostic solution is displayed continuously, and the currently displayed diagnostic solution is confirmed as the final diagnostic solution; otherwise, the solution is iterated according to the popularity ranking, and the next diagnostic solution is displayed; By polling the user review results of each displayed diagnostic solution at a preset time, it is determined whether the iteratively displayed diagnostic solution meets expectations; If the iteratively displayed diagnostic solution does not meet expectations, the matching parameters of the diagnostic solution are obtained through real-time user input information to update the popularity ranking of the diagnostic solution until the iteratively displayed diagnostic solution is confirmed as the final diagnostic solution; otherwise, it is directly confirmed as the final diagnostic solution.

6. The method according to claim 5, characterized in that The step of obtaining the matching parameters of the diagnostic scheme by guiding the real-time input information to update the popularity ranking of the diagnostic scheme includes: Displaying a description of the problem and possible options related to the current diagnosis via a user interface; Guide users to input information; Obtain input information provided by users through the user interface in real time; The input information is verified and input is confirmed.

7. The method according to claim 6, characterized in that The step of obtaining the matching parameters of the diagnostic scheme by guiding the real-time input information to update the popularity ranking of the diagnostic scheme also includes: Collecting real-time user input information during the diagnosis process through the user interface; Processing and analyzing the user's real-time input information to extract key information related to diagnosis; According to the key information, obtaining supplementary matching parameters of the diagnostic scheme; Rematching a set of diagnostic schemes through necessary matching parameters and the supplementary matching parameters and recalculating the heat of the diagnostic schemes; The popularity ranking of the diagnostic scheme is updated according to the recalculated popularity of the diagnostic scheme.

8. An intelligent diagnostic device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the intelligent diagnosis method according to any one of claims 1 to 7.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the intelligent diagnosis method according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the intelligent diagnosis method according to any one of claims 1 to 7 are implemented.

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