Automobile after-sales intelligent enabling system and method

Abstract

This invention discloses an intelligent empowerment system for automotive after-sales service and its implementation method. The system includes a case collection module, a case review module, a knowledge sharing module, an intelligent recommendation module, a mutual assistance and communication module, a backend data analysis module, and a business empowerment module. Dealer repair personnel can submit standardized fault cases to the platform. After review, a case database is formed. The system calculates recommendation weights based on user profiles, case quality, and interaction data, achieving personalized distribution and precise matching of cases. Users can browse cases, seek help, and engage in interactive learning within the platform, forming a technical mutual assistance community. The backend data analysis module structurally extracts the collected cases and interaction information, generating standardized technical intelligence, and pushes it to business systems such as technical support, warranty management, spare parts planning, and training management, building a two-way empowerment mechanism between OEMs and dealers, and achieving closed-loop management from fault cases to business optimization.

Description

Technical Field

[0001] This invention belongs to the field of automotive aftermarket maintenance technology. Specifically, this invention relates to an intelligent aftermarket empowerment system and method for automobiles. Background Technology

[0002] With the rapid development of China's automotive industry and the continuous improvement of people's living standards, the per capita car ownership has steadily increased. This rapid increase in the number of cars has also led to a rise in the number of malfunctions. In the after-sales service of authorized dealerships, a large number of vehicles require diagnosis and repair every day—some of which can be handled by ordinary repair technicians, but a considerable number require experienced and skilled senior technicians. The efficiency of resolving automotive malfunctions is often affected by multiple factors, including the experience level of repair personnel, their technical capabilities, tools and equipment, and the on-site environment. This can easily lead to problems such as extended repair cycles, low first-time repair rates, and decreased customer satisfaction, which in turn directly affect the safe operation and brand image of authorized dealerships.

[0003] Therefore, effectively helping dealers improve repair efficiency and diagnostic accuracy has become a major focus for automakers; simultaneously, how to systematically and quickly improve the professional capabilities of repair technicians has become a crucial issue that OEMs' after-sales service systems urgently need to address. In relevant technical practices, case retrieval often relies on fault codes, which are then matched with preset fault information. After finding relevant cases, repair personnel compare the case content with the current vehicle's fault symptoms. If the main assemblies (such as engine, transmission, power battery, electric drive system, etc.) are the same, and the historical case background is similar to the current fault, then the root cause and solution can be referenced to conduct targeted testing on the vehicle to verify whether a similar fault source exists.

[0004] This method represents the current technological standard widely adopted in the industry, but several significant problems remain in practical application: First, the reliance on pre-entered and maintained fault cases by OEMs makes it difficult to comprehensively cover the large number of real, diverse, and dynamically changing fault scenarios constantly emerging in the market, resulting in significant limitations in the representativeness and timeliness of the case library. Second, due to the limited number of available reference cases, repair technicians' willingness to actively use the system gradually decreases after multiple unsuccessful searches, leading to a negative cycle of "reduced usage – insufficient case updates – further decline in usability," the so-called "flywheel effect." If this trend continues, it will result in a decrease in the frequency of technical consultations, untimely responses to user inquiries, and difficulty in verifying and iterating answers in actual repair scenarios, ultimately hindering the improvement of the overall system efficiency and service quality.

[0005] In addition, the current approach has also revealed shortcomings such as the lack of a self-updating mechanism for case studies, insufficient support for complex and frequently occurring faults, and failure to effectively utilize the practical experience of front-line maintenance personnel. These shortcomings limit its potential value in improving after-sales service efficiency and technician capabilities.

[0006] This invention provides an intelligent empowerment system for automotive after-sales service, specifically addressing how to effectively achieve efficient collaboration and knowledge sharing among automotive brand dealership repair personnel, optimize the accuracy of case matching, and enhance the level of intelligence in knowledge sharing. Summary of the Invention

[0007] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention provides an intelligent after-sales service system for automobiles, with the purpose of achieving efficient collaboration and knowledge sharing among automotive brand dealership repair personnel, optimizing the accuracy of case matching, and improving the level of intelligence in knowledge sharing.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: an intelligent empowerment system for automotive after-sales service, comprising:

[0009] The case collection module is used to receive resolved fault cases submitted by dealer repair technicians and to input the cases in a structured manner. The structured information input includes at least vehicle model information, fault codes, symptom descriptions, diagnostic procedures, solutions, and related spare parts.

[0010] The case review module is used to review the validity and standardization of the cases, select representative or valuable cases, and generate review results.

[0011] The knowledge sharing module is used to store cases in a case database and make them available for retrieval and access by authorized users;

[0012] The intelligent recommendation module is used to calculate recommendation weights based on user profiles, case quality indicators, and multi-dimensional interactive data, and to personalize the sorting and distribution of cases in the case database.

[0013] The mutual help and communication module is used to receive unresolved problems submitted by users and to support other users in replying and discussing them;

[0014] The backend data analysis module is used to analyze the case database and user interaction data, extract fault patterns and knowledge elements, and generate structured technical intelligence; and

[0015] The business empowerment module is used to distribute the technical information to at least one of the following business systems: technical support, warranty management, spare parts planning, and training management.

[0016] When calculating recommendation weights, the intelligent recommendation module considers the following factors comprehensively:

[0017] (1) User profile factors, including user identity role, preferred car models and preferred systems;

[0018] (2) Case quality factors, including review level, technical difficulty, and completeness;

[0019] (3) Interaction popularity factors, including the number of likes, the number of favorites, the number of comments, and the number of views;

[0020] (4) Time decay factor, used to make the case popularity decrease over time;

[0021] (5) Negative feedback factor, used to reduce the recommendation weight based on downvote or report behavior.

[0022] The intelligent recommendation module includes a dynamic decay unit for popularity values, used to perform exponential decay calculations on the popularity values ​​of cases over time, where the popularity value H... t Determined by the following formula:

[0023] H t =H0×(1-r) t ×F neg

[0024] Where H0 is the initial popularity value when the case is uploaded, r is the daily decay rate with a value of 0.015, and t is the number of days after upload; F neg The adjustment coefficient for negative feedback is F; when there is no negative feedback... neg =1, when a case is downvoted or reported, the value is 0 according to the rules. <F neg <1, if recommended freeze is triggered, then F neg =0.

[0025] The initial heat value H0 is determined by the following formula:

[0026] H0=(A×1)+(B×3)+(C×2)+(D×0.1)

[0027] Where A represents the number of likes, B represents the number of favorites, C represents the number of comments, and D represents the number of views.

[0028] The case review module classifies and labels cases based on their technical difficulty, innovativeness of solutions, customer value, educational value, completeness of records, and format compliance.

[0029] The business empowerment module includes:

[0030] The technical support submodule is used to build a standardized diagnostic process based on the knowledge units;

[0031] The warranty management submodule is used to identify abnormal claim patterns and adjust the review strategy.

[0032] The spare parts planning submodule is used to predict spare parts demand based on the frequency of failures and the correlation between components.

[0033] The training management submodule is used to generate a training course library based on typical cases and push personalized learning content.

[0034] The background data analysis module can identify high-frequency failure modes based on historical case data and push the results to the quality management department of the automobile manufacturer.

[0035] The background data analysis module extracts the correlation between fault codes, symptoms and components through pattern recognition and cluster analysis, forming standardized knowledge units.

[0036] The present invention also provides a method for intelligent empowerment of automotive after-sales service based on the aforementioned intelligent empowerment system, comprising the following steps:

[0037] S1. Receive fault cases uploaded by dealer repair personnel and input them in a structured format;

[0038] Recommended content is generated based on user profiles and interaction data;

[0039] Receive questions submitted by users and organize experts or maintenance personnel to answer them;

[0040] S2. Review, classify, and label the cases;

[0041] Cluster analysis and knowledge extraction were performed on case studies and interactive data.

[0042] S3. Distribute the extraction results to the relevant business systems of automobile manufacturers to achieve business collaboration and empowerment.

[0043] This invention, an intelligent after-sales empowerment system for automobiles, aims to provide comprehensive and efficient information and knowledge support to the after-sales systems of authorized automobile dealerships nationwide. The system integrates real-world repair cases from multiple vehicle models and sources. Repair technicians can quickly retrieve relevant cases based on multiple dimensions such as vehicle model, fault code, region, and time, obtaining systematic diagnostic approaches, solutions, and practical methods, thereby significantly improving their individual repair skills and efficiency in handling complex faults. Leveraging a nationwide case-sharing mechanism, it effectively helps repair personnel reduce their reliance on traditional instant messaging methods, transforming scattered and easily lost expert experience into structured knowledge assets that are recordable, searchable, and reusable. The system supports flexible and autonomous learning and querying, facilitating repair personnel to access the necessary technical support anytime, anywhere.

[0044] Furthermore, through continuous accumulation and intelligent analysis of case data, the system provides automobile manufacturers with real-time and accurate first-line maintenance insights, supporting their scientific decision-making in areas such as warranty strategy optimization, accurate spare parts prediction, and improvement of technical training systems. This significantly enhances the responsiveness and operational efficiency of the after-sales management system. The system has established a strict information security management and access control mechanism to fully protect data privacy and trade secrets, achieving a secure, reliable, and efficient collaborative ecosystem between automobile manufacturers and dealers. Attached Figure Description

[0045] Figure 1 This is a structural diagram of the intelligent after-sales empowerment system for automobiles of this invention;

[0046] Figure 2 This is a flowchart of the after-sales intelligent empowerment method of the present invention;

[0047] Figure 3 This is a push instance model diagram. Detailed Implementation

[0048] To facilitate understanding of the present invention, a more comprehensive description of the present invention will be given below with reference to the accompanying drawings, which illustrate several embodiments of the present invention. However, the present invention can be implemented in different forms and is not limited to the embodiments described in the text. Rather, these embodiments are provided to make the disclosure of the present invention more thorough and complete.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly associated with those skilled in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments and is not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0050] Firstly, such as Figure 1 As shown, this embodiment of the invention provides an intelligent after-sales empowerment system for automobiles, including:

[0051] The case collection module is used to receive resolved fault cases submitted by dealer repair technicians and to input the cases in a structured manner. The structured information input includes at least vehicle information, fault codes, symptom descriptions, diagnostic procedures, solutions, and related spare parts.

[0052] The case review module is used to review the validity and standardization of cases, select representative or valuable cases, and generate review results.

[0053] The knowledge sharing module is used to store cases in a case database and make them available for retrieval and access by authorized users;

[0054] The intelligent recommendation module is used to calculate recommendation weights based on user profiles, case quality indicators, and multi-dimensional interactive data, and to personalize the sorting and distribution of cases in the case database.

[0055] The mutual help and communication module is used to receive unresolved problems submitted by users and to support other users in replying and discussing them;

[0056] The backend data analysis module is used to analyze the case database and user interaction data, extract fault patterns and knowledge elements, and generate structured technical intelligence; and

[0057] The business empowerment module is used to distribute technical information to at least one of the following business systems: technical support, warranty management, spare parts planning, and training management.

[0058] Specifically, this invention relates to the field of automotive aftermarket repair technology, and in particular to a fault case management and technical exchange system for use by automotive brand dealership aftermarket repair personnel. The system enables functions such as submitting repair cases, querying them, providing technical consultation and answering questions. It also helps automotive brands to keep abreast of front-line repair dynamics and provides data support for business improvement and technical support.

[0059] This invention aims to effectively solve several core problems in the current automotive repair technology field, including: the difficulty for automotive manufacturers to accurately and comprehensively grasp the dynamics of front-line repairs and the actual skill level of technicians, resulting in the inability to provide timely and accurate specialized technical support; limited sources of case data and doubts about their authenticity; lack of efficient collaboration and knowledge-sharing mechanisms among repair technicians in various dealerships; slow technical growth of repair personnel; low workshop repair efficiency; and the urgent need to improve customer satisfaction.

[0060] In this embodiment of the invention, an after-sales repair efficiency improvement system and method based on case sharing and knowledge mining are provided. This system and method not only realize the sharing of repair experience at the grassroots level, but also build a two-way empowerment channel between automobile manufacturers and dealers. Specifically, it includes the following steps:

[0061] Structured case submission: Repair technicians from each authorized dealer will organize and upload fully resolved fault cases to the platform according to the standardized format set by the platform (including vehicle model, fault code, symptoms, diagnostic process, solution and spare parts involved).

[0062] Case Review and Publication: After initial screening by the system, submitted cases can be reviewed by the after-sales technical expert team of the automobile manufacturer. Cases that are typical, innovative or of high value will be evaluated and published to the platform's shared library.

[0063] Intelligent Search and Learning: Platform users can quickly search for cases using various methods such as vehicle model, fault code, and keywords. Viewers can like and comment on the practicality and logic of the cases, fostering an interactive learning environment.

[0064] Problem editing and mutual assistance: Users can edit and submit unresolved problems and request help. Other technicians or experts on the platform can provide answers, forming a technical assistance community.

[0065] In this embodiment of the invention, the case collection module assigns an initial recommendation weight to newly uploaded cases to ensure their fair exposure in the recommendation mechanism.

[0066] In this embodiment of the invention, the intelligent recommendation module considers the following factors when calculating the recommendation weight:

[0067] (1) User profile factors, including user identity role, preferred car models and preferred systems;

[0068] (2) Case quality factors, including review level, technical difficulty, and completeness;

[0069] (3) Interaction popularity factors, including the number of likes, the number of favorites, the number of comments, and the number of views;

[0070] (4) Time decay factor, used to make the case popularity decrease over time;

[0071] (5) Negative feedback factor, used to reduce the recommendation weight based on downvote or report behavior.

[0072] In this embodiment of the invention, the user profile includes three types of attributes: user identity role, vehicle type of expertise, and system type of expertise. The identity role includes at least technical expert, electromechanical technician, sheet metal technician, paint technician, service advisor, warranty officer, and workshop manager.

[0073] In this embodiment of the invention, the intelligent recommendation module is equipped with a dynamic decay unit for popularity value, which is used to perform exponential decay calculation on the popularity value of a case over time to obtain the popularity value H of the case on day n after uploading. t H t Determined by the following formula:

[0074] H t =H0×(1-r) t ×F neg

[0075] Where H0 is the initial popularity value when the case is uploaded, r is the daily decay rate, with a value of 0.015, indicating that the case popularity value decreases by 1.5% per day over time, and t is the number of days after upload; F neg The adjustment coefficient for negative feedback is F; when there is no negative feedback... neg=1, when a case is downvoted or reported, the value is 0 according to the rules. <F neg <1, if recommended freeze is triggered, then F neg =0.

[0076] The system introduces a daily 1.5% time decay factor r into the popularity value and freezes or reduces the recommendation weight of the corresponding case when negative feedback (downvotes or reports) is received. The initial popularity value H0 is determined by the following formula:

[0077] H0=(A×1)+(B×3)+(C×2)+(D×0.1)

[0078] Among them, A is the number of likes, reflecting mild approval behavior; B is the number of favorites, reflecting deep approval behavior, with the highest weight; C is the number of comments, reflecting the level of interaction and discussion; and D is the number of views, reflecting the exposure of the content.

[0079] If negative feedback such as "dislikes" or "reports" is received but the recommendation is not frozen, the system will adjust the rating to F based on the intensity of the feedback. neg =1-q, where q represents the percentage decrease in popularity (e.g., q = 0.8 means an 80% decrease in popularity); if the recommendation freeze mechanism is triggered, then F neg =0.

[0080] This algorithm ensures continuous exposure for high-quality content while providing a fair competitive opportunity for newly published cases, preventing abnormal accumulation of content popularity, and maintaining the objectivity and dynamism of recommendation ranking. It ensures that the dynamic updates of case popularity are consistent with genuine user reviews, achieving long-term fairness in content ranking and the sustainable development of the platform's knowledge ecosystem.

[0081] In this embodiment of the invention, the case review module classifies and labels cases according to their technical difficulty, innovativeness of solutions, customer value, educational value, record completeness, and format standardization.

[0082] In this embodiment of the invention, the business empowerment module includes:

[0083] The technical support submodule is used to build standardized diagnostic processes based on knowledge units;

[0084] The warranty management submodule is used to identify abnormal claim patterns and adjust the review strategy.

[0085] The spare parts planning submodule is used to predict spare parts demand based on the frequency of failures and the correlation between components.

[0086] The training management submodule is used to generate a training course library based on typical cases and push personalized learning content.

[0087] The technical support submodule is used to build standardized diagnostic processes based on knowledge units. The backend data analysis module performs cluster analysis and causal relationship identification on various fault cases, extracting knowledge elements related to symptoms, causes, detection steps, and repair measures to form knowledge units. This submodule then structurally combines these knowledge units according to fault type, vehicle platform, and system function, automatically generating diagnostic process templates. When dealership repair personnel input vehicle fault symptoms or fault codes, the system can quickly invoke the matching diagnostic process, guiding them to perform troubleshooting and repair operations according to standard procedures, thereby achieving standardization and knowledge-based diagnostic processes. Furthermore, the system can dynamically optimize the diagnostic process based on case popularity and resolution success rate, continuously improving process effectiveness.

[0088] The warranty management submodule is used to identify abnormal claim patterns and adjust review strategies. The system constructs an abnormal claim identification model by comparing historical claim data, repair hours, parts replacement frequency, and case popularity from multiple dimensions. When a significantly high claim frequency is detected for a specific dealer, vehicle model, or part, the system automatically issues an alert and triggers strategy adjustments. Strategies include, but are not limited to, raising the claim review threshold, adjusting the authorization level, or initiating a manual review process. This submodule can effectively identify fraudulent claims and duplicate repair requests, improving the accuracy and efficiency of warranty reviews and reducing after-sales operating costs.

[0089] The spare parts planning submodule predicts spare parts demand based on failure frequency and component correlation. The system uses repair cases and warranty records uploaded by dealers in various regions to statistically analyze failure frequencies and component failure modes through a component-failure correlation matrix. It also establishes a spare parts demand prediction model by incorporating parameters such as seasonality, usage intensity, and vehicle operating environment. The prediction results are synchronized to the OEM and supply chain systems to guide spare parts inventory distribution, procurement planning, and logistics scheduling. Through dynamic prediction and tiered early warning mechanisms, the system can reduce spare parts shortages and inventory backlogs, and improve spare parts supply response speed.

[0090] The training management submodule generates a training course library based on typical cases and pushes personalized learning content. The backend analysis module filters representative cases, extracts typical fault characteristics, handling procedures, and key decision-making nodes, and automatically generates standardized teaching scripts and video courseware. The system intelligently matches and pushes relevant courses based on the maintenance personnel's personal profile (including job level, area of ​​expertise, historical error types, and learning records). Furthermore, the system can synchronize the latest high-profile cases, solutions to complex problems, and warranty policy updates to the training platform, forming a dynamic knowledge update mechanism. Through this approach, the training process shifts from experience-dependent to data-driven, realizing an intelligent training system that enables continuous updates and on-demand learning.

[0091] This design not only enhances the OEM's ability to intelligently manage the dealer service network, but also makes data exchange and knowledge sharing possible across all after-sales processes, significantly improving the system's intelligence and business agility.

[0092] In this embodiment of the invention, the background data analysis module can identify high-frequency failure modes based on historical case data and push the results to the quality management department of the automobile manufacturer.

[0093] The back-end data analysis module uses pattern recognition and cluster analysis to extract the correlation between fault codes, symptoms and components, forming standardized knowledge units.

[0094] The automotive manufacturer's expert team systematically reviews and deeply processes the frontline repair cases collected by the case collection module. First, they verify the validity and authenticity of the cases, eliminating invalid information, and then categorize and label them based on criteria such as technical value and universality. Subsequently, by summarizing root causes and identifying common problem patterns, they transform unstructured, fragmented descriptions into standardized, structured data such as fault codes, symptoms, and solutions, completing the process of refining raw "cases" into high-value "intelligence."

[0095] Secondly, such as Figure 2 As shown, this embodiment of the invention provides an intelligent after-sales empowerment method for automobiles based on the above-mentioned intelligent after-sales empowerment system, including the following steps:

[0096] S1. Receive fault cases uploaded by dealer repair personnel and input them in a structured format;

[0097] Recommended content is generated based on user profiles and interaction data;

[0098] Receive questions submitted by users and organize experts or maintenance personnel to answer them;

[0099] S2. Review, classify, and label the cases;

[0100] Cluster analysis and knowledge extraction were performed on case studies and interactive data.

[0101] S3. Distribute the extraction results to the relevant business systems of automobile manufacturers to achieve business collaboration and empowerment.

[0102] In step S1 above, the case collection module receives resolved fault cases submitted by dealer repair technicians and performs structured input on the received resolved fault cases. The structured information entered includes vehicle information, fault codes, symptom descriptions, diagnostic procedures, solutions, and related spare parts.

[0103] In step S1 above, the system's platform homepage (i.e., the system's portal interface) provides a unified user interaction entry point, including a general search bar and three functional modules: "Share Cases," "Ask a Question," and "Answer a Question." The general search bar is linked to the backend intelligent retrieval module, enabling multi-dimensional searches based on vehicle model, fault code, and keywords. The "Share Cases" function is linked to the case management module, enabling structured submission, review, and publication of repair cases. Users submit resolved repair cases through this entry point, and the system automatically calls the case structured entry submodule to standardize and upload the fault symptoms, diagnostic approach, solutions, and spare parts information to the database. The "Ask a Question" and "Answer a Question" functions are linked to the mutual assistance and communication module, generating standardized question entries and pushing them to the expert Q&A area, as well as enabling online responses and interactive communication regarding fault issues. Through this design, a clear calling relationship is formed between the user interaction entry point and the backend logic modules, achieving integrated management of case sharing, mutual assistance, and knowledge accumulation.

[0104] Users can fill in information such as vehicle model, fault code, symptom description, and attempted measures through the "Ask a Question" function. The system automatically generates structured question entries and pushes them to the platform's help area for other repair technicians or experts to browse and answer. Users can directly submit answers, comments, or additional information in the "Answer" interface. The system incorporates interaction data into the case popularity calculation model in real time and dynamically adjusts the content ranking based on interaction weight.

[0105] In step S1 above, when viewing shared cases, after the user clicks the share button, the system not only displays the total number of shareable cases, but also dynamically presents content based on a personalized recommendation mechanism, rather than simply listing all cases. The core input factors of the case recommendation system can be summarized as follows: the system dynamically generates a personalized recommendation stream by integrating user profiles (such as job title, preferred car models and systems), case quality (such as official certification tags), multi-dimensional interaction popularity (likes / favorites / comments), timeliness value (new car models or frequently occurring faults), and the relationships between publishers followed by the user, ultimately achieving the intelligent distribution goal of "precisely matching high-quality content with users with high needs".

[0106] User profiles include multiple dimensions, such as identity and role (technical expert, electromechanical technician, sheet metal technician, paint technician, service advisor, warranty officer, workshop manager, etc.), types of vehicles they are good at, and systems they are good at (such as body and interior / exterior trim, in-vehicle electrical systems, chassis, powertrain, etc.).

[0107] The quality of the case studies is assessed by the car manufacturer or back-end technical maintenance personnel to be either an excellent or average case study. The evaluation criteria include the following aspects:

[0108] 1. Technical difficulty and innovation:

[0109] Difficult and complicated problems: Successfully resolved uncommon, sporadic, and complex faults, avoiding misdiagnosis and repeated repairs;

[0110] Solution innovation: It provides new ideas, methods, or tips beyond textbooks or repair manuals, effectively improving repair efficiency;

[0111] 2. Customer and Business Value:

[0112] Customer satisfaction: Successfully resolved major issues that led to strong customer complaints or grievances, thereby improving customer satisfaction;

[0113] Profitability Improvement: The solutions in the case studies can significantly reduce maintenance time, lower spare parts costs, or avoid unnecessary assembly replacements, creating direct economic benefits for dealers;

[0114] 3. Teaching and promotion value:

[0115] By analogy: its diagnostic approach and methods are applicable to a batch of vehicles of the same platform and model, and have broad reference value;

[0116] Skills Enhancement: The case studies are clearly structured, which greatly helps technicians, especially novice technicians, to understand and learn the system principles and diagnostic logic;

[0117] 4. Content quality and standardization:

[0118] Complete record: The fault phenomenon, diagnosis process, measurement data (current, voltage, waveform), final solution, and spare parts information are recorded in detail, with pictures and text, logical closed loop, and no key information is missing;

[0119] Formatting guidelines: Strictly adhere to the standard template; ensure clear images, accurate annotations, and fluent language.

[0120] 5. Official nature and timeliness:

[0121] Important Notice: Includes interpretations and application examples of the latest technical announcements, recall information, or software upgrades released by automobile manufacturers;

[0122] Early warning value: First or early fault reports involving new vehicle series or new technologies have important early warning and guidance value for dealers across the network.

[0123] The multi-dimensional interaction popularity system assigns different weights to different interactive behaviors, reflecting varying degrees of user engagement (favorites > comments > likes > clicks). The intelligent recommendation module calculates the popularity score using the following model:

[0124] The intelligent recommendation module includes a dynamic decay unit for popularity values, which performs exponential decay calculations on the popularity values ​​of cases over time to obtain the popularity value H of a case on day n after upload.t H t Determined by the following formula:

[0125] H t =H0×(1-r) t ×F neg

[0126] Where H0 is the initial popularity value when the case is uploaded, r is the daily decay rate, with a value of 0.015, indicating that the case popularity value decreases by 1.5% per day over time, and t is the number of days after upload; F neg The adjustment coefficient for negative feedback is F; when there is no negative feedback... neg =1, when a case is downvoted or reported, the value is 0 according to the rules. <F neg <1, if recommended freeze is triggered, then F neg =0.

[0127] The system introduces a daily 1.5% time decay factor r into the popularity value and freezes or reduces the recommendation weight of the corresponding case when negative feedback (downvotes or reports) is received. The initial popularity value H0 is determined by the following formula:

[0128] H0=(A×1)+(B×3)+(C×2)+(D×0.1)

[0129] Among them, A is the number of likes, reflecting mild approval behavior; B is the number of favorites, reflecting deep approval behavior, with the highest weight; C is the number of comments, reflecting the level of interaction and discussion; and D is the number of views, reflecting the exposure of the content.

[0130] If negative feedback such as "dislikes" or "reports" is received but the recommendation is not frozen, the system will adjust the rating to F based on the intensity of the feedback. neg =1-q, where q represents the percentage decrease in popularity (e.g., q = 0.8 means an 80% decrease in popularity); if the recommendation freeze mechanism is triggered, then F neg =0.

[0131] In this embodiment of the invention, to ensure fairness and content quality, the system also introduces the following rules:

[0132] Time decay factor: The popularity value of a case decreases by 1.5% every day over time, ensuring the freshness of the content library, allowing new high-quality cases to emerge, and preventing old cases from occupying the list for a long time;

[0133] Negative feedback regulation: Introduce "downvote" or "report" functions. Cases that receive negative feedback will have their popularity value significantly reduced or their recommendations temporarily frozen. This will quickly identify and suppress cases with false, low-quality, or misleading content, thus maintaining the quality of the community.

[0134] Newcomer protection mechanism: All newly published cases will receive a certain amount of initial traffic recommendations, providing a fair starting opportunity for new cases and helping the algorithm quickly discover potential content.

[0135] When viewing troubleshooting inquiries, the platform uses a clear and direct single-column list layout, ensuring users can efficiently browse questions one by one in the order they were posted. This design facilitates timely responses from those answering questions and helps those asking questions find answers quickly. Key interface elements are highlighted, and the posting time of each question is prominently displayed (e.g., 2 hours ago, yesterday at 2:30 PM), serving as the core visual information for reverse chronological order.

[0136] When publishing case studies, users need to clearly fill in items such as fault symptoms, diagnostic approach, and solutions to form a standardized repair report; when posting questions, users need to provide key information such as vehicle model, fault code, and attempted operations to ensure a complete problem description. This structured input method aims to improve content quality and facilitate accurate retrieval and knowledge accumulation.

[0137] In step S2 above, the case review module receives and stores the collected front-line repair cases. An expert team assembled by the automobile manufacturer uses this module to conduct systematic review and in-depth processing of the cases: First, the case review module verifies the validity and authenticity of each case, eliminating invalid information. Then, the expert team uses the module to classify and label the approved cases according to the module's preset classification standards such as technical value and universality. Subsequently, the expert team continues to use the information processing function of the case review module to further refine the classified and labeled cases. The background data analysis module is used to perform statistical analysis and knowledge mining on the case database and user interaction data, including summarizing the root causes of failures, identifying common problem patterns, and transforming the scattered unstructured descriptions in the cases into standardized structured data such as fault codes, symptoms, and solutions. Finally, this module completes the transformation process from raw cases to high-value intelligence.

[0138] Meanwhile, the case review module can select representative or valuable cases and generate corresponding review results, which serve as the basis for the expert team to carry out graded labeling and information extraction, thus achieving synergistic integration between the module's functions and the expert team's professional capabilities.

[0139] In embodiments of this invention, the case review module receives, stores, and manages frontline repair cases. An expert team assembled by the automobile manufacturer utilizes this module for systematic review and in-depth processing, including verifying case validity, grading and labeling, and structuring information. The backend data analysis module performs statistical analysis and knowledge mining on the case database and user interaction data, identifying fault patterns and common problems, extracting key knowledge elements, and generating structured technical intelligence. This structured technical intelligence can be used in the business empowerment module, supporting functional modules such as technical support, warranty management, spare parts planning, and training management, achieving a closed-loop transformation from raw cases to actionable knowledge. The backend data analysis module and the case review module work collaboratively, with the case review module ensuring the authenticity and structured quality of the case data, and the data analysis module responsible for further mining and refining valuable information.

[0140] In step S3, during the business empowerment and value distribution phase, high-value intelligence, after in-depth refinement and standardization, is accurately pushed to core business departments such as technical support, warranty management, and spare parts planning through the data platform and business system, effectively driving refined operations and decision-making.

[0141] Specifically, the technical support team can quickly build a knowledge base of typical faults and standardized diagnostic processes, significantly improving the repair efficiency and first-time repair rate of the nationwide service network; the warranty management team can accurately identify abnormal claim patterns and potential fraudulent activities, dynamically adjust review strategies and policy rules, effectively control warranty costs and reduce resource waste; the spare parts planning team can predict medium- and long-term high-frequency demands by analyzing fault frequency and component correlation, achieving precise optimization of inventory structure and improved turnover efficiency. The service operations team can identify potential high-risk customers through dynamic analysis of historical repair data and vehicle status, such as: repeated occurrence of the same fault code, failure of vulnerable parts within a specific mileage range, etc. The system can trigger proactive service tasks, allowing the customer service team to invite customers to the station in advance and push personalized inspection solutions or maintenance packages, transforming "repair after the fault occurs" into "intervention before the problem occurs," improving customer satisfaction and zero-service absorption rate. The training management team, based on common problems, technical difficulties and the latest fault characteristics extracted from case studies, builds a modular and hierarchical technician training course library. The online learning platform can push customized learning content and assessments based on technician levels and weaknesses. Simultaneously, it correlates and analyzes real-vehicle repair results with training effectiveness, continuously optimizing course design and forming a closed-loop learning path of "case study – training reinforcement – ​​capability verification – repair improvement." This mechanism accelerates the growth of technicians' capabilities, connecting a value loop from market feedback to analysis and insight, and then to business improvement. The quality team generates quality reports from typical cases involving component failures, design defects, or insufficient processes in after-sales fault intelligence and pushes them to the R&D, procurement, and supplier management teams. A cross-departmental rapid response mechanism is established to promote component specification optimization, production process improvement, or design changes, shortening the quality problem improvement cycle and reducing fault recurrence and warranty costs from the source.

[0142] This ultimately forms a closed loop of "market feedback → analysis and insight → business improvement," transforming firsthand knowledge into the company's core competitiveness.

[0143] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution of the present invention, or the direct application of the inventive concept and technical solution of the present invention to other occasions without modification, are all within the protection scope of the present invention.

Claims

1. An intelligent empowerment system for automotive after-sales service, characterized in that: include: The case collection module is used to receive resolved fault cases submitted by dealer repair technicians and to input the cases in a structured manner. The structured information input includes at least vehicle model information, fault codes, symptom descriptions, diagnostic procedures, solutions, and related spare parts. The case review module is used to review the validity and standardization of the cases, select representative or valuable cases, and generate review results. The knowledge sharing module is used to store cases in a case database and make them available for retrieval and access by authorized users; The intelligent recommendation module is used to calculate recommendation weights based on user profiles, case quality indicators, and multi-dimensional interactive data, and to personalize the sorting and distribution of cases in the case database. The mutual help and communication module is used to receive unresolved problems submitted by users and to support other users in replying and discussing them; The background data analysis module is used to analyze the case database and user interaction data, extract fault patterns and knowledge elements, and generate structured technical intelligence. as well as The business empowerment module is used to distribute the technical information to at least one of the following business systems: technical support, warranty management, spare parts planning, and training management.

2. The intelligent after-sales empowerment system for automobiles according to claim 1, characterized in that, When calculating recommendation weights, the intelligent recommendation module considers the following factors comprehensively: (1) User profile factors, including user identity role, preferred car models and preferred systems; (2) Case quality factors, including review level, technical difficulty, and completeness; (3) Interaction popularity factors, including the number of likes, the number of favorites, the number of comments, and the number of views; (4) Time decay factor, used to make the case popularity decrease over time; (5) Negative feedback factor, used to reduce the recommendation weight based on downvote or report behavior.

3. The intelligent after-sales empowerment system for automobiles according to claim 2, characterized in that, The intelligent recommendation module includes a dynamic decay unit for popularity values, used to perform exponential decay calculations on the popularity values ​​of cases over time, where the popularity value H... t Determined by the following formula: H t =H0×(1-r) t ×F neg Where H0 is the initial popularity value when the case is uploaded, r is the daily decay rate with a value of 0.015, and t is the number of days after upload; F neg The adjustment coefficient for negative feedback is F; when there is no negative feedback... neg =1, when a case is downvoted or reported, the value is 0 according to the rules. <F neg <1, if recommended freeze is triggered, then F neg =0.

4. The intelligent after-sales empowerment system for automobiles according to claim 3, characterized in that, The initial heat value H0 is determined by the following formula: H0=(A×1)+(B×3)+(C×2)+(D×0.1) Where A represents the number of likes, B represents the number of favorites, C represents the number of comments, and D represents the number of views.

5. The automotive after-sales intelligent empowerment system according to any one of claims 1 to 3, characterized in that, The case review module classifies and labels cases based on their technical difficulty, innovativeness of solutions, customer value, educational value, completeness of records, and format compliance.

6. The intelligent after-sales empowerment system for automobiles according to any one of claims 1 to 3, characterized in that, The business empowerment module includes: The technical support submodule is used to build a standardized diagnostic process based on the knowledge units; The warranty management submodule is used to identify abnormal claim patterns and adjust the review strategy. The spare parts planning submodule is used to predict spare parts demand based on the frequency of failures and the correlation between components. The training management submodule is used to generate a training course library based on typical cases and push personalized learning content.

7. The automotive after-sales intelligent empowerment system according to any one of claims 1 to 3, characterized in that, The background data analysis module can identify high-frequency failure modes based on historical case data and push the results to the quality management department of the automobile manufacturer.

8. The automotive after-sales intelligent empowerment system according to any one of claims 1 to 3, characterized in that, The background data analysis module extracts the correlation between fault codes, symptoms and components through pattern recognition and cluster analysis, forming standardized knowledge units.

9. A method for intelligent empowerment of automotive after-sales service based on the automotive after-sales intelligent empowerment system according to any one of claims 1 to 8, characterized in that, Includes the following steps: S1. Receive fault cases uploaded by dealer repair personnel and input them in a structured format; Recommended content is generated based on user profiles and interaction data; Receive questions submitted by users and organize experts or maintenance personnel to answer them; S2. Review, classify, and label the cases; Cluster analysis and knowledge extraction were performed on case studies and interactive data. S3. Distribute the extraction results to the relevant business systems of automobile manufacturers to achieve business collaboration and empowerment.

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