An intelligent product after-sales management method and system

Abstract

The present invention relates to the technical field of product management, and particularly to an intelligent product after-sales management method and system. The method includes the following steps: obtaining a record set of intelligent product after-sales problems, where the record set of intelligent product after-sales problems includes at least one or more intelligent product after-sales problem records; quantifying the customer demand degree of the intelligent product after-sales problem records to generate data on the urgency degree of intelligent product after-sales demand; comparing the data on the urgency degree of intelligent product after-sales demand with a preset standard demand urgency threshold to generate priority response demand data and delayed response demand data; performing a fault processing correlation analysis on the record set of intelligent product after-sales problems through the priority response demand data and the delayed response demand data to generate an automated solution for product fault processing. The present invention improves the efficiency and timeliness of after-sales management through demand quantification, fault correlation analysis, feedback diagnosis, and trend evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of product management, and particularly to an intelligent product after-sales management method and system. Background Art

[0002] With the rapid development of modern technology, intelligent products have been widely used in various industries. From early single-function electronic devices to today's multi-functional intelligent devices, the complexity of products and the diversity of user needs have increased significantly. Traditional after-sales management methods, such as manual repair requests, telephone support, and paper records, are gradually unable to meet users' expectations for efficient services due to low efficiency, slow response, and error-prone. To optimize the after-sales management process, enterprises have begun to introduce information technology, such as customer relationship management systems (CRM) and enterprise resource planning systems (ERP). These systems have achieved partial automation of processes through digital storage and management of customer information. However, this method still mainly focuses on static data processing and cannot dynamically respond to the large amount of real-time data requirements of intelligent products. In recent years, with the maturity of the Internet of Things (IoT), artificial intelligence (AI), and big data analysis technologies, intelligent product after-sales management has entered a new stage. However, currently, traditional after-sales processing mainly relies on manual analysis, which is prone to missed diagnoses or misjudgments. At the same time, in the existing after-sales process, failure handling often lacks effective feedback and diagnosis mechanisms, resulting in low efficiency of repeated repairs, and thus low efficiency and timeliness of after-sales management. Summary of the Invention

[0003] Based on this, it is necessary to provide an intelligent product after-sales management method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, an intelligent product after-sales management method, the method includes the following steps:

[0005] Step S1: Obtain an intelligent product after-sales problem record set, where the intelligent product after-sales problem record set includes at least one or more intelligent product after-sales problem records; quantify the customer demand for the intelligent product after-sales problem records to generate intelligent product after-sales demand urgency data;

[0006] Step S2: Compare the intelligent product after-sales demand urgency data with a preset standard demand urgency threshold to generate priority response demand data and delayed response demand data; perform fault handling correlation analysis on the intelligent product after-sales problem record set through the priority response demand data and the delayed response demand data to generate an automated solution for product fault handling; collect the first remote processing feedback result for the automated solution for product fault handling to obtain the first after-sales failure handling result;

[0007] Step S3: Based on the after-sales failure handling results, conduct a functional damage assessment on the intelligent product to generate product functional damage assessment data; based on the product functional damage assessment data, conduct an intervention fault diagnosis on the after-sales failure handling results to generate intervention fault diagnosis data; collect the second remote processing feedback results through the intervention fault diagnosis data to obtain the second after-sales failure handling result; conduct after-sales repair scheduling on the second after-sales failure result to generate after-sales repair service scheduling data;

[0008] Step S4: Conduct a phased after-sales trend analysis on the first after-sales failure handling result, the second after-sales failure handling result, and the after-sales repair service scheduling data to generate phased after-sales trend data; visualize the after-sales quality trend data to perform the optimization operation of intelligent product after-sales management.

[0009] The present invention helps to comprehensively master the types and urgency of after-sales problems, realize the priority division of problems, ensure the reasonable allocation of resources, and improve the after-sales response efficiency by obtaining the after-sales problem record set of intelligent products and quantifying the customer demand degree. By generating priority response and delayed response demand data through intelligent analysis and conducting fault handling correlation analysis, the key points of the problem can be accurately identified and an automated solution can be quickly formulated, reducing human intervention and improving the success rate and efficiency of the initial remote processing. Based on the after-sales failure results, conduct a functional damage assessment and intervention fault diagnosis to further refine the problem location, optimize the solution through multiple rounds of remote processing feedback, and reasonably schedule maintenance resources to improve the maintenance success rate and the efficiency of solving repeated problems. By comprehensively analyzing multiple processing results and scheduling data, generating phased after-sales trend data and visualizing it, it helps to discover the potential laws and trends of after-sales problems, provides data support and decision-making basis for after-sales management optimization, and improves service quality and customer satisfaction. Each step from the acquisition, analysis, diagnosis to trend assessment of after-sales problem records forms a full-process closed-loop management, significantly improving the efficiency, accuracy and service quality of after-sales processing, and contributing to the continuous optimization of intelligent product after-sales management. Therefore, the present invention improves the efficiency and timeliness of after-sales management through demand quantification, fault correlation analysis, feedback diagnosis and trend assessment.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain the after-sales problem record set of intelligent products, where the after-sales problem record set of intelligent products contains at least one or more after-sales problem records of intelligent products;

[0012] Step S12: Classify the after-sales problem record set of intelligent products to generate text type records and voice type records; perform voice recognition on the after-sales problem records of intelligent products according to the voice type records to generate after-sales demand voice recognition data;

[0013] Step S13: Analyze the user demand speech rate of the after-sales demand speech recognition data to generate user after-sales demand speech rate data; perform semantic recognition on the intelligent product after-sales problem records through text type records to generate after-sales demand semantic recognition data.

[0014] Step S14: Extract modal particles based on the after-sales demand semantic recognition data to obtain after-sales demand modal particle data; quantify the customer demand degree of the intelligent product after-sales problem records through the after-sales demand modal particle data and the user after-sales demand speech rate data to generate the emergency degree data of the intelligent product after-sales demand.

[0015] The present invention distinguishes text and speech type records, processes different forms of data separately (such as speech recognition and text semantic recognition), makes full use of the information in the after-sales problem records, and improves the comprehensiveness and accuracy of data processing. Analyzing the user demand speech rate of the voice record can identify the urgency of the user's expression; combined with the extraction of modal particles in the text, it can further infer the user's emotional state and the urgency of the demand, providing key references for after-sales service. Through the combined analysis of the speech rate and modal particles, the user demand is quantified to generate the emergency degree data of the after-sales demand, which can provide a scientific basis for subsequent priority processing and improve the efficiency of after-sales service. Based on the generated emergency degree data, the after-sales service team can quickly respond to high-priority demands, optimize resource allocation, achieve more accurate and personalized services, and improve customer satisfaction. Systematically recording and analyzing after-sales problems helps enterprises discover potential product defects or service bottlenecks, providing data support for product improvement and service process optimization.

[0016] Preferably, quantifying the customer demand degree of the intelligent product after-sales problem records through the after-sales demand modal particle data and the user after-sales demand speech rate data includes:

[0017] Analyze the tone characteristics of the after-sales demand modal particle data to obtain customer tone characteristic data; extract the speech rate change characteristics of the user after-sales demand speech rate data to obtain user speech rate characteristic data.

[0018] Perform emotional fluctuation analysis based on the user speech rate characteristic data and the customer tone characteristic data to obtain customer emotional fluctuation data; perform preliminary quantification of the after-sales problem priority through the customer emotional fluctuation data to obtain after-sales problem priority quantification data.

[0019] Collect chronological after-sales records for the intelligent product after-sales problem records to obtain chronological after-sales records; perform comprehensive demand emergency degree analysis on the after-sales problem priority quantification data based on the chronological after-sales records to obtain the emergency degree data of the intelligent product after-sales demand.

[0020] The present invention extracts emotional information (such as impatience, happiness, anxiety, etc.) in the customer's tone through modal particle data, providing in-depth information for evaluating the customer's mood. By extracting the characteristics of the user's speech rate change, the sense of urgency or the smoothness of the speech rate in the user's expression is captured, further improving the judgment of the urgency of the user's needs. Based on the analysis of the emotional fluctuations of the user's speech rate characteristics and tone characteristics data, the change range and trend of the user's emotions can be quantified, providing a scientific basis for the quantification of the priority of after-sales problems. The emotional fluctuation data provides more dimensions of information for after-sales service personnel, helping to more accurately judge the urgency of the user's needs. Using the customer's emotional fluctuation data, the after-sales problems are quickly prioritized, providing a basis for efficient processing. Through the collection of time-series after-sales records and combined with the quantified priority data, the urgency of the problems is comprehensively analyzed, avoiding the deviation caused by a single-dimensional evaluation, and improving the accuracy of the judgment of the urgency of the needs. Through intelligent priority evaluation, the after-sales team can quickly respond to high-urgency problems, optimize resource allocation, shorten the problem-solving time, and thus improve customer satisfaction. Dynamically updating and adjusting the priority can ensure the flexibility and effectiveness of the after-sales service process.

[0021] Preferably, step S2 includes the following steps:

[0022] Step S21: Compare the data of the urgency degree of the after-sales needs of the intelligent product with the preset standard urgency degree threshold of the needs. When the data of the urgency degree of the after-sales needs of the intelligent product is greater than or equal to the preset standard urgency degree threshold of the needs, then give a priority response to the corresponding after-sales problem record of the intelligent product, and generate priority response demand data;

[0023] Step S22: When the data of the urgency degree of the after-sales needs of the intelligent product is less than the preset standard urgency degree threshold of the needs, then give a delayed response to the corresponding after-sales problem record of the intelligent product, and generate delayed response demand data; Use the priority response demand data and the delayed response demand data to predict the product failure for the after-sales problem record set of the intelligent product, and generate product failure prediction data;

[0024] Step S23: Construct a product knowledge graph; Import the product failure prediction data into the product knowledge graph for fault handling correlation analysis, and generate an automated solution for product fault handling;

[0025] Step S24: Collect the first remote processing feedback data for the automated solution for product fault handling. When no corresponding remote processing feedback data is collected, then repeat the data collection until the remote processing feedback data is received; Divide the feedback results of the remote processing feedback data, and exclude the first after-sales successful handling result to obtain the first after-sales failure handling result.

[0026] The present invention distinguishes high-priority problems and low-priority problems by comparing the urgency of after-sales requirements with a standard threshold. High-priority problems are promptly responded to (generating priority response requirement data) to ensure the rapid handling of critical problems and improve customer satisfaction. Low-priority problems are responded to with a delay (generating delayed response requirement data) to reasonably allocate resources and reduce operating costs. Analyzing the priority response requirement data and the delayed response requirement data can predict potential product failure problems, enabling proactive measures to be taken in advance to prevent the problems from further expanding. It enhances the foresight and preventiveness of after-sales service, reducing customer complaints and potential risks. By constructing a product knowledge graph, the failure prediction data is associated with existing knowledge to quickly locate the cause of the failure and shorten the problem diagnosis time. Based on the association analysis of the knowledge graph, an automated failure handling solution is generated, reducing manual intervention and improving the handling efficiency and accuracy. By collecting and classifying the first remote handling feedback data (successful handling and failed handling), a closed-loop feedback mechanism is formed. Further analyzing and optimizing the failed handling results ensures a gradual increase in the success rate of subsequent handling. When feedback data is not collected, the system repeats the collection to ensure the integrity and reliability of the data, providing high-quality data support for subsequent decision-making.

[0027] Preferably, product failure prediction for the intelligent product after-sales problem record set using the priority response requirement data and the delayed response requirement data includes:

[0028] Performing time series analysis of after-sales problems based on the priority response requirement data and the delayed response requirement data to obtain after-sales problem time series data; extracting the demand frequency distribution of after-sales problems from the after-sales problem time series data to obtain after-sales problem demand frequency distribution data;

[0029] Using the after-sales problem demand frequency distribution data to classify the demand characteristics of the priority response requirement data and the delayed response requirement data to obtain priority demand characteristic data and delayed demand characteristic data; performing feature clustering analysis on the problem record set for the priority demand characteristic data and the delayed demand characteristic data to obtain problem record feature clustering data;

[0030] Performing potential failure mode matching through the problem record feature clustering data to obtain potential product failure mode data; calculating the failure probability prediction for the intelligent product after-sales problem record set based on the potential product failure mode data to obtain product failure prediction data.

[0031] The present invention can accurately capture the time regularity and frequency characteristics of after-sales problems through time series analysis and demand frequency distribution extraction, thereby improving the prediction accuracy of the trend of after-sales problems. The priority response demand and delayed response demand are classified separately, and the demand characteristics are classified in combination with the frequency distribution data, so that the problem classification results are more targeted, providing a high-quality data basis for subsequent problem clustering and fault analysis. Through problem record feature clustering analysis and potential fault mode matching, the potential rules in the problem records can be effectively identified, and hidden fault modes can be mined, thereby achieving in-depth analysis of complex after-sales problems. Fault probability prediction calculation based on potential fault mode data can provide quantitative risk assessment results for the after-sales team, help formulate countermeasures in advance, and reduce the uncontrollability of fault occurrence. Providing demand feature data with clear priority and potential fault prediction data allows the after-sales team to respond quickly according to the priority demand characteristics, while reasonably allocating resources to solve delayed demands and improve the efficiency of after-sales problem solving. By analyzing potential fault modes and predicting fault probabilities, feedback data can be provided for product design and manufacturing links, so that common fault points can be improved to improve product reliability and user experience.

[0032] Preferably, step S23 includes the following steps:

[0033] Step S231: Obtain a product basic information data set and a historical intelligent product after-sales problem record set; extract common failure modes and corresponding solutions from the historical intelligent product after-sales problem record set to generate a historical failure solution data set; use graph structure construction technology to construct an initial product knowledge graph for the product basic information data set and the historical failure solution data set to generate an initial product knowledge graph;

[0034] Step S232: semantic enhancement processing is performed on the initial product knowledge graph to generate a product knowledge graph; the core elements in the product fault prediction data are analyzed, including potential fault types, occurrence probabilities, impact ranges, and time ranges, to generate a fault prediction factor data set; the fault prediction factor data set is matched with nodes and edges in the product knowledge graph, and the association relationship between the fault prediction data and existing knowledge is established through similarity analysis to generate a product dynamic knowledge graph;

[0035] Step S233: Perform path analysis on the product dynamic knowledge graph to extract functional modules, components and historical processing methods related to potential faults, thereby building a fault processing association model; use the fault processing association model to perform fault processing association mining on product fault prediction data to generate an initial product fault processing automation solution;

[0036] Step S234: Perform step-by-step and logic verification on the initial product fault handling automation solution, thereby generating a product fault handling automation solution.

[0037] The present invention integrates the product basic information data set and the historical fault solution data set, and uses the graph structure construction technology to construct the initial product knowledge graph, which provides comprehensive and systematic knowledge support for product problem analysis and solutions. Semantic enhancement processing makes the knowledge graph more accurate in expressing product information and fault relationships, and provides high-quality semantic association data for subsequent analysis. The fault prediction element data set analyzes the core elements of potential faults (such as type, probability, impact range and time range), and establishes the association relationship between fault prediction data and knowledge graph through similarity analysis, thereby generating a dynamic knowledge graph and realizing the knowledge interpretation of the prediction results. The real-time update of the dynamic knowledge graph enhances the timeliness and accuracy of knowledge and adapts to the changes in product status and fault mode. Through the path analysis of the dynamic knowledge graph, the functional modules, components and historical processing methods related to potential faults are extracted, and the fault processing association model is constructed, which provides a data-driven logical basis for the analysis and solution of complex faults. The fault processing association model is used for association mining to generate an initial automated solution, and through the step-by-step and logical verification of the solution, an efficient and accurate automated solution is finally output, which greatly reduces the need for manual intervention. By integrating fault prediction data with dynamic knowledge graphs, the weights of associated nodes in the semantically enhanced knowledge graph are updated, forming a dynamic optimization closed-loop mechanism for the knowledge graph, which continuously enhances the fault handling capability.

[0038] Preferably, step S3 comprises the following steps:

[0039] Step S31: performing a functional damage assessment on the intelligent product based on the after-sales failure processing result, and generating product functional damage assessment data;

[0040] Step S32: performing interventional fault diagnosis on the after-sales failure processing result according to the product function damage assessment data to generate interventional fault diagnosis data; performing a second remote processing feedback data collection based on the interventional fault diagnosis data, and when the corresponding remote processing feedback data is not collected, repeating the data collection until the remote processing feedback data is received;

[0041] Step S33: dividing the feedback results of the remote processing feedback data, eliminating the second after-sales successful processing result, and obtaining the second after-sales failed processing result; performing after-sales maintenance scheduling on the second after-sales failure result, thereby generating after-sales maintenance service scheduling data.

[0042] By conducting a damage assessment of product functions based on the results of after-sales failure handling, the present invention can accurately identify and quantify the functional problems of intelligent products, providing clear basic data for subsequent fault diagnosis and repair. The function damage assessment provides a systematic perspective for further fault analysis, reducing misjudgments and omissions of product problems. Through the analysis of function damage assessment data, it helps to accurately diagnose the root cause of problems, thereby reducing unnecessary fault troubleshooting and optimizing the repair process. The collection of remote processing feedback data provides a basis for dynamically adjusting after-sales strategies, ensuring that each fault diagnosis can be optimized based on the latest data. The repeated data collection mechanism ensures the integrity and accuracy of information regardless of whether the feedback is timely. The division of feedback results can accurately distinguish successful and failed after-sales handling, avoiding interference from incorrect data in after-sales decision-making. By excluding successful handling results and only retaining failed results, subsequent processing can focus more on actual existing problems. The generation of after-sales repair service scheduling can automatically schedule repair resources based on the second after-sales failure handling result, making the repair process more efficient and in line with the priority and severity of the actual product faults.

[0043] Preferably, the intervention fault diagnosis of the after-sales failure handling result according to the product function damage assessment data includes:

[0044] Marking the damaged modules of the intelligent product according to the product function damage assessment data to obtain the intelligent product damaged module marking data; performing module fault intervention analysis on the after-sales failure handling result based on the intelligent product damaged module marking data to generate intervention analysis result data;

[0045] Using the product knowledge graph to expand the fault chain of the intervention analysis result data to generate fault diagnosis expansion data; performing fault mode priority sorting on the fault diagnosis expansion data to identify the optimal processing path and generate fault diagnosis path data; performing simulation verification on the fault diagnosis path data to generate optimized fault diagnosis data;

[0046] Integrating the optimized fault diagnosis data into the product knowledge graph for fault expansion node mapping, thereby generating intervention fault diagnosis data.

[0047] Through the analysis of the intelligent product function damage assessment data, the present invention accurately identifies and marks the specific damaged modules in the product, which provides a clear direction and focus for subsequent fault diagnosis and reduces the blind investigation of problems. The accurate marking of damaged modules helps to improve the efficiency of fault diagnosis and avoid wasting unnecessary time on irrelevant modules. By precisely intervening and analyzing the after-sales failure handling results based on the damaged module marking data, the generated intervention analysis result data can quickly locate the faulty module and further confirm the cause of the problem through detailed analysis, ensuring the pertinence and effectiveness of fault handling. By expanding the fault chain of the intervention analysis results through the product knowledge graph, the potential associations between faulty modules can be revealed, thereby identifying the scope of influence and the source of the fault. This process provides a comprehensive perspective for fault diagnosis and reduces the neglect of other modules caused by single-module faults. The expansion of the fault chain helps to discover hidden problems, improve the depth and breadth of fault diagnosis, and avoid missed or misdiagnosed cases. The fault mode priority ranking and the identification of the optimal handling path can rank different fault modes according to the severity and occurrence probability of the faults, helping the after-sales team to prioritize the handling of high-risk and high-priority faults and ensuring that critical faults can be resolved as early as possible. After identifying the optimal handling path, it can provide a clear and direct fault-solving route for the repair team, reduce redundant steps, and improve the repair efficiency.

[0048] Preferably, step S4 includes the following steps:

[0049] Step S41: Conduct a phased after-sales trend analysis on the first after-sales failure handling result, the second after-sales failure handling result, and the after-sales repair service scheduling data to generate phased after-sales trend data; conduct an after-sales quality assessment on the phased after-sales quality trend data to generate intelligent product after-sales quality assessment data;

[0050] Step S42: Visualize the intelligent product after-sales quality assessment data to generate an intelligent product after-sales quality assessment report for performing intelligent product after-sales management optimization operations.

[0051] Through the comprehensive analysis of the first after-sales failure handling result, the second after-sales failure handling result, and the after-sales maintenance service scheduling data, the generated phased after-sales trend data can comprehensively display the trend changes and distribution rules of after-sales problems. The after-sales quality assessment conducts in-depth analysis based on the above trend data to generate intelligent product after-sales quality assessment data, accurately revealing the core issues affecting after-sales service quality and providing data support for optimization. By visualizing the intelligent product after-sales quality assessment data to generate an assessment report, the after-sales service performance is presented in an intuitive manner, including key indicators such as trend charts, failure rate distributions, service response times, and success rates. The visual presentation of data is easy to understand, not only providing the management with the ability to quickly insight into problems but also facilitating communication and coordination among different departments. The intelligent product after-sales quality assessment report provides a clear guiding direction for the optimization of after-sales management, supporting the formulation of improvement strategies, such as strengthening the reliability testing of key modules, optimizing service processes, or enhancing resource allocation. The phased trend analysis helps the management predict potential problems, improve the response speed to after-sales needs, deploy resources in advance, and avoid the concentrated outbreak of problems.

[0052] In this specification, an intelligent product after-sales management system is provided for implementing the above-mentioned intelligent product after-sales management method. The intelligent product after-sales management system includes:

[0053] An urgency analysis module for obtaining a record set of intelligent product after-sales problems, where the record set of intelligent product after-sales problems contains at least one or more intelligent product after-sales problem records; quantifying the customer demand degree of the intelligent product after-sales problem records to generate intelligent product after-sales demand urgency data;

[0054] An automated processing module for comparing the intelligent product after-sales demand urgency data with a preset standard demand urgency threshold to generate priority response demand data and delayed response demand data; conducting failure handling correlation analysis on the record set of intelligent product after-sales problems through the priority response demand data and the delayed response demand data to generate an automated solution for product failure handling; collecting the first after-sales failure handling result for the first remote processing feedback result of the automated solution for product failure handling;

[0055] An intervention processing module for evaluating the function damage of the intelligent product based on the after-sales failure handling result to generate product function damage evaluation data; conducting intervention fault diagnosis on the after-sales failure handling result according to the product function damage evaluation data to generate intervention fault diagnosis data; collecting the second after-sales failure handling result for the second remote processing feedback result through the intervention fault diagnosis data; performing after-sales maintenance scheduling on the second after-sales failure result to generate after-sales maintenance service scheduling data;

[0056] The after-sales quality assessment module is used to conduct phased after-sales trend analysis on the first after-sales failure handling result, the second after-sales failure handling result, and after-sales repair service scheduling data to generate phased after-sales trend data; and conduct visualization of after-sales quality assessment on the phased after-sales quality trend data to perform intelligent product after-sales management optimization operations.

[0057] The beneficial effects of the present invention are as follows. The urgency analysis module can clearly distinguish the urgency levels of different problems by quantifying customer requirements for after-sales problem records, so as to assign reasonable priorities to each problem. This mechanism ensures that critical problems can be processed within the shortest time, avoiding customer dissatisfaction caused by delayed processing. By comparing with the standard demand urgency threshold, it can ensure that important problems are given priority responses, reduce the response time for lower-priority problems, and thus improve service efficiency. The automated processing module can reduce manual intervention, lower the risk of human errors, and improve the accuracy of fault handling by automatically generating automated solutions for product fault handling and providing the first remote processing feedback. The automated solutions can improve the consistency and efficiency of processing, shorten the processing cycle, ensure that problems can be effectively solved during the first remote processing, and reduce the need for subsequent manual intervention. The intervention processing module can accurately locate the cause of the fault through function damage assessment based on the first after-sales failure handling result and further intervention fault diagnosis. This process can provide more accurate repair solutions by combining historical data and fault chain expansion, reduce unnecessary rework, and improve the overall repair efficiency. Through multiple rounds of remote processing feedback collection, the system can track the progress of fault repair in real time to ensure that the repair solutions are verified and improved in multiple stages. After analyzing the after-sales trend data, the after-sales quality assessment module can generate a detailed after-sales quality assessment report, and help enterprise management clearly understand the problems by visualizing the after-sales service quality, and further optimize the after-sales service process. By analyzing the phased after-sales quality trend data, it can quickly identify service bottlenecks or areas with quality decline, make timely strategic adjustments for the enterprise, and enhance customer trust and satisfaction. Therefore, the present invention improves the efficiency and timeliness of after-sales management through demand quantification, fault correlation analysis, feedback diagnosis, and trend assessment. Description of the Drawings

[0058] Figure 1 It is a schematic diagram of the step flow of an intelligent product after-sales management method;

[0059] Figure 2 is Figure 1 a detailed implementation step flow diagram of step S2 in

[0060] Figure 3 is Figure 1 a detailed implementation step flow diagram of step S3 in

[0061] The implementation, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0062] The technical method of the present invention patent will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0063] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0064] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed related items.

[0065] To achieve the above object, please refer to Figures 1 to 3 , an intelligent product after-sales management method, the method includes the following steps:

[0066] Step S1: Obtain a record set of intelligent product after-sales problems, where the record set of intelligent product after-sales problems contains at least one or more intelligent product after-sales problem records; quantify the customer demand degree of the intelligent product after-sales problem records to generate intelligent product after-sales demand urgency data;

[0067] Step S2: Compare the emergency level data of the after-sales requirements of the intelligent product with the preset standard requirement emergency threshold to generate priority response requirement data and delayed response requirement data; perform a fault processing correlation analysis on the intelligent product after-sales problem record set through the priority response requirement data and the delayed response requirement data to generate an automated product fault processing solution; collect the first remote processing feedback result for the automated product fault processing solution to obtain the first after-sales failure processing result;

[0068] Step S3: Based on the after-sales failure processing result, conduct a functional damage assessment on the intelligent product to generate product functional damage assessment data; perform an intervention fault diagnosis on the after-sales failure processing result according to the product functional damage assessment data to generate intervention fault diagnosis data; collect the second remote processing feedback result through the intervention fault diagnosis data to obtain the second after-sales failure processing result; perform after-sales repair scheduling on the second after-sales failure result to generate after-sales repair service scheduling data;

[0069] Step S4: Conduct a phased after-sales trend analysis on the first after-sales failure processing result, the second after-sales failure processing result, and the after-sales repair service scheduling data to generate phased after-sales trend data; visualize the after-sales quality trend data to perform the optimization operation of the intelligent product after-sales management.

[0070] The present invention helps to comprehensively grasp the types and emergency levels of after-sales problems, realize the priority division of problems, ensure the reasonable allocation of resources, and improve the after-sales response efficiency by obtaining the intelligent product after-sales problem record set and quantifying the customer demand degree. By generating priority response and delayed response requirement data through intelligent analysis and performing a fault processing correlation analysis, the key points of the problem can be accurately identified and an automated solution can be quickly formulated, reducing human intervention and improving the success rate and efficiency of the initial remote processing. Based on the after-sales failure result, a functional damage assessment and an intervention fault diagnosis are carried out to further refine the problem location, optimize the solution through multiple rounds of remote processing feedback, and reasonably schedule the repair resources, improving the repair success rate and the efficiency of solving repeated problems. By comprehensively analyzing multiple processing results and scheduling data, generating phased after-sales trend data and visualizing it, it helps to discover the potential laws and trends of after-sales problems, provides data support and decision-making basis for after-sales management optimization, and improves the service quality and customer satisfaction. Each step forms a full-process closed-loop management from the acquisition, analysis, diagnosis of after-sales problems to trend assessment, significantly improving the efficiency, accuracy, and service quality of after-sales processing, and contributing to the continuous optimization of the intelligent product after-sales management. Therefore, the present invention improves the efficiency and timeliness of after-sales management through demand quantification, fault correlation analysis, feedback diagnosis, and trend assessment.

[0071] In the embodiment of the present invention, refer to Figure 1As shown, it is a schematic diagram of the step process of an intelligent product after-sales management method of the present invention. In this example, the intelligent product after-sales management method includes the following steps:

[0072] Step S1: Obtain a record set of intelligent product after-sales problems, where the record set of intelligent product after-sales problems contains at least one or more intelligent product after-sales problem records; quantify the customer demand for the intelligent product after-sales problem records to generate data on the urgency of intelligent product after-sales demand;

[0073] In the embodiment of the present invention, through the enterprise after-sales service system and customer feedback platforms (such as online customer service, emails, call records, etc.), collect after-sales problem records related to intelligent products. Screen out data directly related to product failures, usage problems, repair requests, etc. to ensure the integrity and accuracy of the after-sales problem record set. Perform standardization processing on the collected records, including duplicate removal, format unification, and semantic cleaning, to generate a structured after-sales problem record set. Use natural language processing (NLP) technology to analyze the text content of the after-sales problem records, extract keyword fields reflecting the urgency of customer needs (such as "immediately", "seriously", "unusable", etc.). Calculate the occurrence frequency and context semantic relationship of the keywords to generate a preliminary customer demand measurement index. Define multiple evaluation dimensions affecting the urgency of demand, such as: Problem type weight: product function deficiency, performance problem, appearance problem, etc. Customer level weight: VIP customer, ordinary customer, etc. Problem impact scope: single device problem or batch device problem. Score each record based on these dimensions to form a preliminary demand quantification model. Use a weighted model to calculate the comprehensive score: ; where is the urgency index, is the speech rate data, is the frequency of occurrence of modal particles, and are the weight coefficients (adjusted according to actual applications) respectively. Generate data on the urgency of intelligent product after-sales demand based on the calculation results, and classify the after-sales problem records according to the comprehensive score (such as "high priority", "medium priority", "low priority"). Bind the urgency data to the original problem records and store them as a dataset of the urgency of intelligent product after-sales demand for subsequent processing and decision support.

[0074] Step S2: Compare the data of the urgency level of the after-sales requirements of the intelligent product with a preset standard requirement urgency threshold to generate priority response requirement data and delayed response requirement data; perform a fault handling correlation analysis on the intelligent product after-sales problem record set through the priority response requirement data and the delayed response requirement data to generate an automated solution for product fault handling; collect the first remote processing feedback result for the automated solution for product fault handling to obtain the first after-sales failure handling result;

[0075] In the embodiment of the present invention, by defining the criteria for urgency classification, for example: high-priority threshold: records exceeding 90 points; medium-priority threshold: between 60 and 90 points; low-priority threshold: records below 60 points. Compare the data of the urgency level of the after-sales requirements item by item with the threshold: If the urgency level score ≥ high-priority threshold, mark it as "priority response requirement data"; otherwise, mark it as "delayed response requirement data". Store the classification results in two groups of data sets in a hierarchical manner: The priority response requirement data set contains all problem records that require quick response and immediate handling, and the delayed response requirement data set contains problem records that can be processed later for subsequent batch analysis and optimization. Establish a rule base between the product fault type and the handling solution, and the rule base includes: fault causes (hardware, software, operation problems, etc.); handling steps (remote debugging, component replacement, on-site maintenance, etc.). Use data mining methods (such as association rule analysis, decision tree, etc.), combined with the characteristic data of the after-sales problem records, to identify the association patterns between problems and historical solutions. Based on the priority response requirement data and the delayed response requirement data, generate an automated solution for product fault handling according to the following logic: First, match the historical solution in the rule base; if there is no direct match, use a machine learning model (such as random forest, XGBoost) to predict the optimal solution; generate an automated solution for product fault handling, including fault classification, cause analysis, and recommended handling process. Based on the generated automated solution for product fault handling, perform the following remote processing operations: Push software patches or configuration adjustment instructions to the customer device; remotely debug the faulty module and record the handling log; request the customer to provide more diagnostic information (such as uploading device logs, taking pictures of problems, etc.). Collect the feedback data after remote processing, mainly including: recording the time when the problem is successfully solved, the steps of the solution execution, and recording the reasons for failure (such as the instruction not being successfully executed, the problem exceeding the remote capabilities, etc.). Store the failure handling result as the "first after-sales failure handling result" for subsequent further analysis.

[0076] Step S3: Based on the after-sales failure processing result, a functional damage assessment is performed on the intelligent product to generate product functional damage assessment data; based on the product functional damage assessment data, an interventional fault diagnosis is performed on the after-sales failure processing result to generate interventional fault diagnosis data; a second remote processing feedback result is collected through the interventional fault diagnosis data to obtain a second after-sales failure processing result; after-sales maintenance scheduling is performed on the second after-sales failure result, thereby generating after-sales maintenance service scheduling data;

[0077] In the embodiment of the present invention, after-sales failure processing result data is collected, including product model, fault description, fault occurrence time, processing measures and results, etc. The functional module list and corresponding performance indicators of the intelligent product are extracted. Based on the fault description and processing results, the affected functional modules are identified and the degree of damage is marked. The degree of functional damage of each damaged module (e.g., mild damage, moderate damage, complete failure) is evaluated using historical data and machine learning models (such as classification models or regression models). The evaluation results are integrated to generate product functional damage evaluation data, including the damage level and damage cause of each module. According to the functional damage evaluation data, the specific damaged module is located, the relevant fault information is extracted, and the damaged module marking data is generated. The module fault intervention analysis is performed on the marked data, and detailed fault causes and associated modules are output. The damaged module is matched with the fault cause by using the product knowledge graph to generate fault chain extension data. The fault modes are sorted according to the probability of occurrence and the scope of influence, and the optimal processing path is selected. The optimal fault diagnosis path is simulated and verified to test the effectiveness and reliability of the path. The diagnostic path is optimized and the final intervention fault diagnosis data is generated. Establish a data collection process to monitor remote processing results, including maintenance operation records, user feedback, and performance test data. If not enough remote processing feedback data is collected, repeat the collection process until complete data is obtained. Classify the collected data, eliminate successful processing results, and filter out the second failed processing results. Conduct in-depth analysis of the second failed processing results to identify the types and areas of faults that need to be handled first. Prioritize faults according to the urgency and scope of impact of the faults, and use scheduling optimization algorithms (such as linear programming, genetic algorithms, etc.) to allocate resources to maintenance tasks, determine maintenance time and execution team. Output after-sales maintenance service scheduling data containing scheduling plans, including maintenance team allocation, task priority, estimated completion time, etc.

[0078] Step S4: Perform a phased after-sales trend analysis on the first after-sales failure processing result, the second after-sales failure processing result, and the after-sales repair service scheduling data to generate phased after-sales trend data; visualize the after-sales quality assessment of the phased after-sales quality trend data to perform intelligent product after-sales management optimization operations.

[0079] In the embodiments of the present invention, the first after-sales failure handling result, the second after-sales failure handling result, and the after-sales repair service scheduling data are integrated into a phased after-sales data set. The frequency and cause distribution of failed repairs are statistically analyzed by time period (such as weekly, monthly, quarterly) to generate time trend data. Based on the failure classification (such as hardware, software, interface problems), the frequency and proportion of each type of failure are statistically analyzed to generate failure type trend data. The distribution of repair failures is statistically analyzed by region or market partition to generate regional trend data. Methods such as linear regression and moving average are used to predict the future trend of repair failures. For example, ARIMA or Prophet models are used to analyze long-term trends and periodic fluctuations. The analysis results are integrated to generate phased after-sales trend data, including time trend, failure type trend, and regional trend. Based on the phased after-sales trend data, various evaluation indicators are calculated to form intelligent product after-sales quality evaluation data, where various evaluation indicators include: the proportion of successful repairs, the average time from failure report to repair completion, the after-sales service quality measured by feedback scores, and the recurrence proportion of the same type of problems after repair. Visualization tools such as Power BI and Tableau are used to visualize the after-sales quality trend data for after-sales quality evaluation, and the visualization charts are integrated into the intelligent product after-sales quality evaluation report.

[0080] Preferably, step S1 includes the following steps:

[0081] Step S11: Obtain the intelligent product after-sales problem record set, where the intelligent product after-sales problem record set contains at least one or more intelligent product after-sales problem records;

[0082] Step S12: Classify the record types of the intelligent product after-sales problem record set to generate text type records and voice type records; perform voice recognition on the intelligent product after-sales problem records according to the voice type records to generate after-sales demand voice recognition data;

[0083] Step S13: Analyze the user demand speech rate of the after-sales demand voice recognition data to generate user after-sales demand speech rate data; perform semantic recognition on the intelligent product after-sales problem records through the text type records to generate after-sales demand semantic recognition data;

[0084] Step S14: Extract modal particles based on the after-sales demand semantic recognition data to obtain after-sales demand modal particle data; quantify the customer demand of the intelligent product after-sales problem records through the after-sales demand modal particle data and the user after-sales demand speech rate data to generate intelligent product after-sales demand urgency data.

[0085] In the embodiments of the present invention, historical after-sales service records are extracted from the enterprise after-sales system, including text records and voice call records feedback by customers. Ensure that the data covers a variety of intelligent product categories and problem types, such as equipment failures, functional abnormalities, user guidance, etc. Construct a record set of after-sales problems of intelligent products. The record set should include the timestamp, product model, problem description, and basic customer information of each record. Automatically classify the data in the record set of after-sales problems of intelligent products into text-type records and voice-type records according to the content format. Use format detection algorithms, such as techniques based on file extensions (such as.txt or.mp3), file sizes, data header parsing, etc., to complete rapid classification. For voice-type records, use a speech recognition algorithm (such as an ASR model based on deep learning) to extract the text content in the voice and generate after-sales demand speech recognition data. During the recognition process, handle the problems of multilingual support and background noise interference in the voice to ensure the accuracy of text transcription. Use the speech recognition results to extract the time length of the voice segment and the number of words in the transcribed text, calculate the speech rate index of each record, and generate user after-sales demand speech rate data. Perform clustering analysis on the speech rate data to distinguish normal speech rate, faster speech rate, and hasty speech rate to reflect the urgency of the customer. For text-type records, use natural language processing (NLP) techniques for semantic analysis to identify the key problem descriptions and demand intentions in the customer feedback and generate after-sales demand semantic recognition data. Extract specific keywords, sentiment tendencies (such as positive, negative, neutral), and problem categories (such as hardware failures, software problems, operation consultations, etc.). Perform further analysis of modal particles on the after-sales demand semantic recognition data to extract words reflecting the customer's tone (such as "urgent", "immediately", "as soon as possible", etc.) and generate after-sales demand modal particle data. Use a predefined modal particle dictionary and context-dependent analysis methods to accurately extract modal particle-related information. Combine the speech rate data with the modal particle data and calculate the urgency of the customer's demand through a weighted algorithm: ; where is the urgency index, is the speech rate data, is the frequency of occurrence of modal particles, and are weight coefficients (adjusted according to actual applications). Generate after-sales demand urgency data for intelligent products according to the calculation results and classify the data by urgency level (such as normal, relatively urgent, urgent) to guide the prioritization of after-sales service.

[0086] Preferably, the quantification of customer demand for the record set of after-sales problems of intelligent products through after-sales demand modal particle data and user after-sales demand speech rate data includes:

[0087] Analyze the tone features of the post - sales demand modal particle data to obtain customer tone feature data; extract the speech rate change features from the user's post - sales demand speech rate data to obtain the user's speech rate feature data;

[0088] Conduct emotional fluctuation analysis based on the user's speech rate feature data and customer tone feature data to obtain customer emotional fluctuation data; conduct a preliminary quantification of the post - sales problem priority through the customer emotional fluctuation data to obtain the post - sales problem priority quantification data;

[0089] Collect sequential after - sales records from the intelligent product after - sales problem records to obtain sequential after - sales records; conduct a comprehensive demand urgency analysis on the post - sales problem priority quantification data based on the sequential after - sales records to obtain the intelligent product after - sales demand urgency data.

[0090] In the embodiment of the present invention, by using the post - sales demand modal particle data, the frequency and distribution of the particles are statistically analyzed. A modal particle feature vector is constructed. For example, the feature weights are extracted through TF - IDF (Term Frequency - Inverse Document Frequency). Using an emotion analysis model (such as a BERT - based emotion classification model), the modal particles are combined with the context to analyze the customer's tone emotion tendency (such as anxious, angry, satisfied, etc.), and customer tone feature data is generated, including emotion category, emotion intensity, and tone type. By calculating the speech rate change rate (such as the speech rate difference between sentences or paragraphs), the speech rate change trend and fluctuation amplitude are extracted. The speech rate change features are classified into three categories: "stable", "slight fluctuation", and "severe fluctuation" to generate user speech rate feature data, reflecting the anxiety or calm state in the customer's expression. Combining the user speech rate feature data and customer tone feature data, machine learning models such as decision trees and random forests are used to analyze the user's emotional fluctuation pattern. According to the feature model, the customer emotional fluctuation intensity score (such as 0 - 100, the higher the score, the greater the fluctuation) is output to generate customer emotional fluctuation data, including fluctuation type (such as stable, fluctuating), fluctuation intensity, and trend. Combining the emotional fluctuation data with the post - sales demand classification data, a preliminary quantification is carried out according to the customer problem urgency rule: ; where, is the priority quantification score, is the emotional fluctuation intensity, is the problem category weight, and As an adjustment parameter, the preliminary priority score of each after-sales problem is output to generate quantified after-sales problem priority data. Based on the multiple after-sales records of customers, the problem records are sorted in chronological order to form chronological after-sales records, marking the occurrence time and progress of the problems. Using time series analysis techniques, the changing trends of customer needs in the records are extracted to predict subsequent emergencies. Based on the chronological after-sales records, the preliminary quantified priority data is combined with the time series information to analyze whether there is a need to improve the priority, generate data on the urgency of intelligent product after-sales needs, and classify them according to the urgency level (such as normal, relatively urgent, urgent).

[0091] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0092] Step S21: Compare the data on the urgency of intelligent product after-sales needs with a preset standard urgency threshold for needs. When the data on the urgency of intelligent product after-sales needs is greater than or equal to the preset standard urgency threshold for needs, then give a priority response to the corresponding intelligent product after-sales problem record to generate priority response requirement data;

[0093] Step S22: When the data on the urgency of intelligent product after-sales needs is less than the preset standard urgency threshold for needs, then give a delayed response to the corresponding intelligent product after-sales problem record to generate delayed response requirement data; Use the priority response requirement data and the delayed response requirement data to predict product failures for the set of intelligent product after-sales problem records to generate product failure prediction data;

[0094] Step S23: Construct a product knowledge graph; Import the product failure prediction data into the product knowledge graph for correlation analysis of fault handling to generate an automated solution for product fault handling;

[0095] Step S24: Collect the first remote processing feedback data for the automated solution for product fault handling. When no corresponding remote processing feedback data is collected, then repeat the data collection until the remote processing feedback data is received; Divide the feedback results of the remote processing feedback data, and eliminate the first after-sales successful handling results to obtain the first after-sales failed handling results.

[0096] In the embodiments of the present invention, by obtaining the data of the urgency degree of after-sales requirements of intelligent products and a preset standard requirement urgency threshold, comparing the data of the urgency degree of after-sales requirements with the standard requirement urgency threshold. When the urgency degree data is greater than or equal to the threshold, the corresponding after-sales problem record is marked as a priority response requirement, and priority response requirement data is generated for the rapid processing of after-sales problems. When the urgency degree data is less than the threshold, the corresponding after-sales problem record is marked as a delayed response requirement. Summarize the priority response requirement data and the delayed response requirement data, analyze all after-sales problem records, apply a fault prediction algorithm (such as time series analysis or a machine learning model) to generate product fault prediction data. Summarize the product life cycle data, including information on links such as design, production, sales, use, and after-sales. Define the relationship nodes between product characteristics, common fault types, fault causes, and handling methods. Use a graph database (such as Neo4j) to establish a dynamic knowledge graph. Import the product fault prediction data into the knowledge graph to analyze the correlation between faults and handling methods. Apply a rule reasoning algorithm (such as the OWL framework based on logical reasoning) to automatically generate an automated solution for product fault handling. Perform a remote processing operation on the automated solution for product fault handling, and monitor the feedback data in real time through an API interface. If no feedback data is received, set up a retry mechanism to collect data cyclically until the remote processing feedback data is received. Analyze the received feedback data: if the processing is successful, record it as "the first after-sales successful processing result". If the processing fails, after excluding the successful records, organize the remaining data as "the first after-sales failed processing result".

[0097] Preferably, product fault prediction for the after-sales problem record set of intelligent products through the priority response requirement data and the delayed response requirement data includes:

[0098] Perform time series analysis of after-sales problems based on the priority response requirement data and the delayed response requirement data to obtain after-sales problem time series data; extract the demand frequency distribution of after-sales problems from the after-sales problem time series data to obtain after-sales problem demand frequency distribution data;

[0099] Use the after-sales problem demand frequency distribution data to classify the demand characteristics of the priority response requirement data and the delayed response requirement data to obtain priority demand characteristic data and delayed demand characteristic data; perform feature clustering analysis on the priority demand characteristic data and the delayed demand characteristic data in the problem record set to obtain problem record feature clustering data;

[0100] Perform potential fault mode matching through the problem record feature clustering data to obtain potential product fault mode data; calculate the fault probability prediction for the after-sales problem record set of intelligent products based on the potential product fault mode data to obtain product fault prediction data.

[0101] In the embodiments of the present invention, time series modeling is performed on the priority response demand data and the delayed response demand data based on the time dimension. Select a suitable time series analysis method (such as ARIMA model, LSTM model, etc.) to generate after-sales problem time series data. Analyze the temporal trend and periodic changes of after-sales problems, and identify the peak and trough time periods of demand fluctuations. Count the occurrence frequency of demands in the after-sales problem time series, and extract the frequency distribution characteristics according to time windows (such as days, weeks, or months). Use frequency distribution curves or histograms for data visualization to clarify the distribution characteristics of priority demands and delayed demands. Use classification algorithms (such as KNN, decision tree, or SVM) to classify the characteristics of priority response demands and delayed response demands. Extract the key characteristics of demands (such as problem type, occurrence area, urgency, processing time, etc.) to generate priority demand feature data and delayed demand feature data. Perform clustering analysis on problem records based on the feature data, using K-means, DBSCAN, or hierarchical clustering algorithms to discover the feature similarity of problem records. The clustering dimensions include problem occurrence frequency, device model, geographical distribution, etc., to generate problem record feature clustering data. Construct a potential failure mode database (including historical failure modes and problem records). Based on the clustering results and the failure mode database, use pattern matching algorithms (such as nearest neighbor algorithm or semantic similarity matching) to identify potential failure modes related to the problem record clustering data. Use Bayesian probability models or machine learning models (such as random forest, XGBoost) to perform correlation analysis and failure probability calculation on potential product failure modes and problem record sets. Output the probability values of each potential failure mode to identify high-risk potential failures.

[0102] Preferably, step S23 includes the following steps:

[0103] Step S231: Obtain the product basic information data set and the historical intelligent product after-sales problem record set; extract the common failure modes and corresponding solutions in the historical intelligent product after-sales problem record set to generate a historical failure solution data set; use graph structure construction technology to construct an initial product knowledge graph for the product basic information data set and the historical failure solution data set, and generate an initial product knowledge graph;

[0104] Step S232: Perform semantic enhancement processing on the initial product knowledge graph to generate a product knowledge graph; analyze the core elements in the product failure prediction data, including potential failure types, occurrence probabilities, influence ranges, and time ranges, to generate a failure prediction element data set; match the failure prediction element data set with the nodes and edges in the product knowledge graph, and establish the association relationship between the failure prediction data and the existing knowledge through similarity analysis to generate a product dynamic knowledge graph;

[0105] Step S233: Conduct path analysis on the product dynamic knowledge graph, extract the functional modules, components, and historical processing methods related to potential faults, so as to construct a fault handling association model; use the fault handling association model to perform fault handling association mining on the product fault prediction data to generate an initial automated product fault handling solution;

[0106] Step S234: Perform step-by-step processing and logical verification on the initial automated product fault handling solution to generate an automated product fault handling solution.

[0107] In the embodiment of the present invention, by collecting basic information related to the product, such as model, configuration, production batch, key technical parameters, etc. Obtain the intelligent fault problems and their solutions recorded in the past product after-sales service to form a historical fault solution dataset. Analyze the fault modes in the historical after-sales problem records, and extract the corresponding solutions to generate a historical fault solution dataset. Adopt graph structure construction technology, by integrating the product basic information dataset and the historical fault solution dataset, associate various attributes, fault modes, solutions, etc. of the product as nodes and edges of the graph to construct an initial product knowledge graph. Perform semantic enhancement on the initial product knowledge graph, by adding more association information, context understanding, reasoning rules, etc., to make the knowledge graph more in-depth and accurate, and finally generate a product knowledge graph. Analyze the product fault prediction data, extract key factors such as potential fault types, occurrence probabilities, influence ranges, and time ranges, etc. to generate a fault prediction factor dataset. Match each factor in the fault prediction factor dataset with the nodes (such as functional modules, components, historical processing methods, etc.) and edges (such as association relationships) in the product knowledge graph, and establish an association relationship between the fault prediction data and the existing knowledge through similarity analysis to generate a product dynamic knowledge graph. Conduct path analysis on the product dynamic knowledge graph, and extract the functional modules, components, and their historical fault handling methods related to potential faults. These paths will help to deeply understand the root cause of the fault and the solution. By analyzing the association relationship between the fault mode and the solution, construct a fault handling association model to guide the logic of fault handling. Use the constructed fault handling association model to mine the fault prediction data to generate an initial automated product fault handling solution. Perform step-by-step processing on the initial automated product fault handling solution to clarify the operation requirements and processing methods of each step. Verify the automated solution to ensure that each step can be effectively executed in actual application and the processing logic is correct, and finally generate a complete and effective automated product fault handling solution.

[0108] As an example of the present invention, refer to Figure 3 shown, in this example, step S3 includes:

[0109] Step S31: Based on the after-sales failure handling result, conduct a functional damage assessment on the intelligent product to generate product functional damage assessment data;

[0110] Step S32: According to the product functional damage assessment data, conduct an intervention fault diagnosis on the after-sales failure handling result to generate intervention fault diagnosis data; collect the second remote processing feedback data through the intervention fault diagnosis data. When no corresponding remote processing feedback data is collected, repeat the data collection until the remote processing feedback data is received;

[0111] Step S33: Classify the remote processing feedback data, eliminate the second after-sales successful handling result to obtain the second after-sales failure handling result; conduct after-sales repair scheduling on the second after-sales failure result, thereby generating after-sales repair service scheduling data.

[0112] In the embodiment of the present invention, by collecting and analyzing past after-sales failure handling records, including the types of failures, repaired or replaced components, handling time and costs, etc. Based on the after-sales failure handling result data, a functional damage assessment model is constructed. This model comprehensively considers factors such as product usage conditions, failure frequency, component damage degree, repair history, etc., to evaluate the functional damage degree of the product. The functional damage situation of the product is output through the evaluation model to generate product functional damage assessment data. The assessment data will help identify the fault modules that need key attention and subsequent processing steps. According to the product functional damage assessment data, conduct an intervention fault diagnosis on each problematic product. This diagnosis is based on the damaged functional module and historical fault information to analyze the root cause of the fault and generate intervention fault diagnosis data. According to the intervention fault diagnosis data, the remote technical support team will perform a second remote processing operation. After the processing, collect the remote feedback data, which includes whether the fault handling is successful, the effectiveness of the handling method, etc. If sufficient remote feedback data is not obtained in the first collection, the data collection process needs to be repeated. The system will continuously monitor until valid remote processing feedback data is received. Classify the collected remote processing feedback data, eliminate the "second after-sales successful handling result" among them, and retain the "second after-sales failure handling result". This step will help identify the products for which the problems have not been solved. For the second after-sales failure handling result, conduct the scheduling arrangement of after-sales repair services according to the failed fault type, influence range, and the functional damage assessment of the product. This scheduling arrangement will allocate the relevant products to the appropriate repair stations or technicians, and determine the repair priority, schedule, etc. Based on the scheduling plan, generate after-sales repair service scheduling data to ensure that each fault handling link can be carried out on time and effectively, so as to improve the efficiency and quality of after-sales service.

[0113] Preferably, conducting an intervention fault diagnosis on the after-sales failure handling result according to the product functional damage assessment data includes:

[0114] Mark the damaged modules of the intelligent product according to the product function damage evaluation data to obtain the intelligent product damaged module marking data; conduct module fault intervention analysis on the after-sales failure handling result based on the intelligent product damaged module marking data to generate intervention analysis result data;

[0115] Use the product knowledge graph to expand the fault chain of the intervention analysis result data to generate fault diagnosis expansion data; perform priority sorting on the fault modes of the fault diagnosis expansion data to identify the optimal processing path and generate fault diagnosis path data; conduct simulation verification on the fault diagnosis path data to generate optimized fault diagnosis data;

[0116] Integrate the optimized fault diagnosis data into the product knowledge graph for fault expansion node mapping, thereby generating intervention fault diagnosis data.

[0117] In the embodiments of the present invention, the damage conditions of each module are extracted from the product function damage assessment data. The assessment data includes information such as the number of fault occurrences, failure probability, and influence range (such as whether it affects the functions of other modules) of each module. Machine learning models such as decision trees and random forests are used to process the assessment data to mark whether each module is damaged and assign a priority to each damaged module (for example, according to the core nature, damage severity, and occurrence frequency of the module). The marked data of each module is integrated into "intelligent product damage module marked data", which includes the fault status and priority of each module (for example: Module 1, damaged, priority 1). By analyzing historical fault data (such as fault types, repair methods, etc.), fault mode analysis is performed on the damaged modules. For example, if Module 1 often has power supply faults, its root causes (such as power management chip faults, voltage instability, etc.) can be analyzed. Preset treatment plans are proposed for each fault mode, the efficiency and cost of different treatment plans are analyzed, and preliminary intervention analysis results are generated for each fault mode (for example, the repair method for the power supply fault of Module 1 is to replace the power management chip). Intervention analysis result data is output, recording the diagnosis and recommended treatment plans for each fault mode. According to the knowledge graph of the product, the fault chain is extended using the nodes in the graph (module types, fault causes, historical repair methods, etc.). First, the fault modes in the intervention analysis result data are matched with the relevant nodes in the graph (for example, the power supply fault is associated with the "battery module" or "power chip" nodes). The extended fault chain will contain multi-level information. For example, the fault of a certain module is not only "power management chip damage", but also related to "battery voltage instability" or "software configuration problems", generating a diagnostic extension data containing all potential fault sources and repair paths (for example, power supply fault chain: battery damage -> power management chip fault -> power supply module failure). A priority sorting algorithm based on the severity and influence range of the fault mode is adopted. For example, if the fault of the power management chip can cause the entire device to paralyze, while only insufficient battery power only affects the device's battery life, the power management chip fault can be listed as a high priority. Based on the priority sorting result, fault diagnosis path data is generated. The path includes multiple steps, such as first checking the power supply module, then checking the battery, and finally viewing the system settings. Each step is sorted according to importance to ensure that high-priority faults are processed first. Simulation tools such as fault tree analysis and Monte Carlo simulation are used to verify the fault diagnosis path. By simulating the impact of different treatment paths on the device fault recovery, the success rate and time cost of each treatment plan are evaluated. For example, simulate the repair paths for insufficient battery power and power management chip faults to observe which path can restore the device to normal operation faster. The fault diagnosis path is optimized according to the simulation results. If the simulation shows that some paths are more efficient than others, the diagnostic strategy can be adjusted to generate a final optimized version of the fault diagnosis data.Integrate the optimized fault diagnosis data into the existing product knowledge graph so that when a similar fault occurs again, the system can quickly reference historical data and give the best repair suggestions. For example, when a power management chip fails, the system can refer to historical repair records and quickly recommend replacing the chip or checking the battery module. Create mappings for each new fault expansion node in the product knowledge graph. For example, after adding a diagnostic path for "power management chip failure", this fault path will be marked as a new fault expansion node in the knowledge graph, forming new relationships with other related nodes (such as the battery, power supply module, etc.). Through node mapping, ensure the expansion and upgrade of the product knowledge graph. Finally, integrate the optimized diagnostic data into the product knowledge graph to form the final "intervention fault diagnosis data", which will be used for future fault diagnosis, intelligent processing path recommendation, and fault mode recognition.

[0118] Preferably, step S4 includes the following steps:

[0119] Step S41: Conduct a phased after-sales trend analysis on the first after-sales failure handling result, the second after-sales failure handling result, and the after-sales repair service scheduling data to generate phased after-sales trend data; conduct an after-sales quality assessment on the phased after-sales quality trend data to generate intelligent product after-sales quality assessment data;

[0120] Step S42: Visualize the intelligent product after-sales quality assessment data to generate an intelligent product after-sales quality assessment report for performing intelligent product after-sales management optimization operations.

[0121] In the embodiments of the present invention, the first after-sales failure handling result, the second after-sales failure handling result, and the after-sales repair service scheduling data are collected and integrated. The data collection comes from multiple channels, such as customer feedback, service center records, repair history, etc. These data are divided according to the time dimension (for example, by month, by quarter, etc.), and the after-sales failure handling results and repair service scheduling within each time period will be summarized. Data mining and analysis methods (such as moving average method, time series analysis, trend line analysis, etc.) are used to analyze the data of each time period. The goal of the analysis is to identify trends such as common after-sales failure types, frequency changes, repair response times, etc. in a certain stage. For example: within a certain quarter, the battery failure rate increases, the user feedback processing speed is slower, or a certain module (such as the display screen) has frequent failures. Based on the analysis results, phased after-sales trend data is generated. The data will show the fluctuation trends of after-sales problems, such as the frequency change of failure types, the time periods with increased customer complaints, the after-sales service response time, etc. According to the after-sales trend data, a series of after-sales quality assessment indicators are set. These indicators can include: the frequency of failures within a certain time, the average time from receiving a failure report to completion of repair, the customer satisfaction score for after-sales handling, usually obtained through customer questionnaires or return visits, and the proportion of handling customer complaints. Using the phased after-sales trend data, the values of each assessment indicator are calculated. Weights can be set (for example, the failure rate accounts for 40%, the repair efficiency accounts for 30%, and the user satisfaction accounts for 30%), and the final after-sales quality score is calculated through weighted average. All indicators are summarized and calculated, and finally after-sales quality assessment data is generated. For example, the after-sales quality score within a certain period is 85 points, indicating that the after-sales quality in this stage is good. Based on the after-sales quality assessment data, appropriate visualization methods are selected. For example: line charts are used to display after-sales trend data, such as the change trend of the failure rate over time. Bar charts are used to display the occurrence frequencies of different types of failures, or the distribution of after-sales service response times. Pie charts are used to display the proportion of various types of failures in the total failures. Radar charts are used to display the comprehensive performance of multiple assessment indicators, highlighting the advantages and disadvantages of different dimensions. Data labels are added to the visualization charts to indicate the specific values and time points of each data point (for example, the battery failure rate in a certain month is 15%). At the same time, annotations can be added to point out the reasons for abnormal fluctuations (for example, the increase in display screen failures in a certain time period is specifically due to the quality problems of a new model batch). Visualization tools (such as Power BI, Tableau, Matplotlib and Seaborn in Python, etc.) are used to automatically generate reports and export them in PDF or Excel format for further analysis and sharing.

[0122] In this specification, an intelligent product after-sales management system is provided for implementing the above-mentioned intelligent product after-sales management method. The intelligent product after-sales management system includes:

[0123] An urgency analysis module, which is used to obtain a record set of after-sales problems of intelligent products, where the record set of after-sales problems of intelligent products contains at least one or more after-sales problem records of intelligent products; quantify the customer demand degree of the after-sales problem records of intelligent products to generate data on the urgency degree of after-sales demand of intelligent products;

[0124] An automated processing module, which is used to compare the data on the urgency degree of after-sales demand of intelligent products with a preset standard demand urgency threshold to generate data on priority response demands and data on delayed response demands; conduct a correlation analysis of fault handling on the record set of after-sales problems of intelligent products through the data on priority response demands and data on delayed response demands to generate an automated solution for product fault handling; collect the first remote processing feedback result for the automated solution for product fault handling to obtain the first after-sales failure handling result;

[0125] An intervention processing module, which is used to evaluate the function damage of intelligent products based on the after-sales failure handling result to generate product function damage evaluation data; conduct an intervention fault diagnosis on the after-sales failure handling result according to the product function damage evaluation data to generate intervention fault diagnosis data; collect the second remote processing feedback result through the intervention fault diagnosis data to obtain the second after-sales failure handling result; schedule after-sales maintenance for the second after-sales failure result, thereby generating after-sales maintenance service scheduling data;

[0126] An after-sales quality evaluation module, which is used to conduct a phased after-sales trend analysis on the first after-sales failure handling result, the second after-sales failure handling result, and the after-sales maintenance service scheduling data to generate phased after-sales trend data; visualize the after-sales quality evaluation of the phased after-sales quality trend data to perform an optimization operation for the after-sales management of intelligent products.

[0127] The beneficial effects of the present invention are as follows: The urgency analysis module can clearly distinguish the urgency levels of different problems by quantifying customer requirements for after-sales problem records, thereby assigning reasonable priorities to each problem. This mechanism ensures that critical problems can be processed within the shortest time, avoiding customer dissatisfaction caused by delayed processing. By comparing with the standard requirement urgency threshold, it can ensure that important problems are given priority responses, reduce the response time for lower-priority problems, and thus improve service efficiency. The automated processing module can reduce manual intervention, lower the risk of human errors, and improve the accuracy of fault handling by automatically generating automated solutions for product fault handling and providing the first remote processing feedback. The automated solutions can improve the consistency and efficiency of processing, shorten the processing cycle, ensure that problems can be effectively solved during the first remote processing, and reduce the need for subsequent manual intervention. The intervention processing module can accurately locate the cause of the fault through function damage assessment based on the first after-sales failure handling result and further intervention fault diagnosis. This process can provide more accurate repair solutions by combining historical data and fault chain extension, reduce unnecessary rework, and improve the overall repair efficiency. Through multiple rounds of remote processing feedback collection, the system can track the progress of fault repair in real time, ensuring that the repair solutions are verified and improved in multiple stages. The after-sales quality assessment module can generate a detailed after-sales quality assessment report by analyzing after-sales trend data. By visually displaying the after-sales service quality, it helps enterprise management clearly understand the problems and further optimize the after-sales service process. By analyzing the phased after-sales quality trend data, it can quickly identify areas with service bottlenecks or quality decline, make timely strategic adjustments for the enterprise, and enhance customer trust and satisfaction. Therefore, the present invention improves the efficiency and timeliness of after-sales management through requirement quantification, fault correlation analysis, feedback diagnosis, and trend assessment.

[0128] Therefore, in any aspect, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0129] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent product after-sales management method, characterized in that: The following steps are involved: Step S1: obtaining a set of after-sales problem records of intelligent products, wherein the set of after-sales problem records of intelligent products includes at least one or more after-sales problem records of intelligent products; quantifying customer demand for after-sales problem records of intelligent products, and generating urgency data of after-sales demand of intelligent products; Step S2: Compare the intelligent product after-sales demand urgency data with the preset standard demand urgency threshold to generate priority response demand data and delayed response demand data; Through the priority response demand data and delayed response demand data, the fault handling correlation analysis of the intelligent product after-sales problem record set is carried out to generate an automated solution for product fault handling; Collect the first remote processing feedback results of the product failure handling automation solution and obtain the first after-sales failure handling result; Step S2 includes the following steps: Step S21: Compare the intelligent product after-sales demand urgency data with the preset standard demand urgency threshold. When the intelligent product after-sales demand urgency data is greater than or equal to the preset standard demand urgency threshold, give priority response to the corresponding intelligent product after-sales problem record and generate priority response demand data; Step S22: when the intelligent product after-sales demand urgency data is less than the preset standard demand urgency threshold, a delayed response is performed on the corresponding intelligent product after-sales problem record to generate delayed response demand data; product failure prediction is performed on the intelligent product after-sales problem record set by using the priority response demand data and the delayed response demand data to generate product failure prediction data; Step S23: construct a product knowledge graph; import the product failure prediction data into the product knowledge graph to perform failure handling association analysis and generate an automated solution for product failure handling; Step S23 includes the following steps: Step S231: Obtain a product basic information data set and a historical intelligent product after-sales problem record set; extract common failure modes and corresponding solutions from the historical intelligent product after-sales problem record set to generate a historical failure solution data set; use graph structure construction technology to construct an initial product knowledge graph for the product basic information data set and the historical failure solution data set to generate an initial product knowledge graph; Step S232: semantic enhancement processing is performed on the initial product knowledge graph to generate a product knowledge graph; the core elements in the product fault prediction data are analyzed, including potential fault types, occurrence probabilities, impact ranges, and time ranges, to generate a fault prediction factor data set; the fault prediction factor data set is matched with nodes and edges in the product knowledge graph, and the association relationship between the fault prediction data and existing knowledge is established through similarity analysis to generate a product dynamic knowledge graph; Step S233: Perform path analysis on the product dynamic knowledge graph to extract functional modules, components and historical processing methods related to potential faults, thereby building a fault processing association model; use the fault processing association model to perform fault processing association mining on product fault prediction data to generate an initial product fault processing automation solution; Step S234: performing step-by-step and logic verification on the initial product fault handling automation solution, thereby generating a product fault handling automation solution; Step S24: performing the first remote processing feedback data collection for the product fault handling automation solution, and when the corresponding remote processing feedback data is not collected, repeating the data collection until the remote processing feedback data is received; dividing the feedback results of the remote processing feedback data, eliminating the first after-sales successful processing result, and obtaining the first after-sales failed processing result; Step S3: Based on the after-sales failure processing result, a functional damage assessment is performed on the intelligent product to generate product functional damage assessment data; based on the product functional damage assessment data, an interventional fault diagnosis is performed on the after-sales failure processing result to generate interventional fault diagnosis data; a second remote processing feedback result is collected through the interventional fault diagnosis data to obtain a second after-sales failure processing result; after-sales maintenance scheduling is performed on the second after-sales failure result, thereby generating after-sales maintenance service scheduling data; Step S4: Perform a phased after-sales trend analysis on the first after-sales failure processing result, the second after-sales failure processing result, and the after-sales repair service scheduling data to generate phased after-sales trend data; visualize the after-sales quality assessment of the phased after-sales quality trend data to perform intelligent product after-sales management optimization operations.

2. The intelligent product after-sales management method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining a smart product after-sales problem record set, wherein the smart product after-sales problem record set includes at least one or more smart product after-sales problem records; Step S12: classifying the intelligent product after-sales problem record set into record types to generate text type records and voice type records; performing voice recognition on the intelligent product after-sales problem record according to the voice type record to generate after-sales demand voice recognition data; Step S13: Perform user demand speech speed analysis on the after-sales demand voice recognition data to generate user after-sales demand speech speed data; perform semantic recognition on the intelligent product after-sales problem records through text type records to generate after-sales demand semantic recognition data; Step S14: extract modal particles based on the after-sales demand semantic recognition data to obtain after-sales demand modal particle data; quantify customer demand for intelligent product after-sales problem records through the after-sales demand modal particle data and user after-sales demand speech speed data to generate intelligent product after-sales demand urgency data.

3. The intelligent product after-sales management method according to claim 2, characterized in that: The customer demand quantification of intelligent product after-sales problem records through after-sales demand modal particle data and user after-sales demand speech speed data includes: Perform tone feature analysis on the tone particle data of after-sales demand, so as to obtain customer tone feature data; perform speech rate change feature extraction on the speech rate data of user after-sales demand, so as to obtain user speech rate feature data; Emotional fluctuation analysis is performed based on the user speech speed feature data and the customer tone feature data, thereby obtaining customer emotional fluctuation data; after-sales problem priorities are preliminarily quantified based on customer emotional fluctuation data, thereby obtaining quantitative data on after-sales problem priorities; The after-sales problem records of intelligent products are collected in time series to obtain time series after-sales records; based on the time series after-sales records, a comprehensive demand urgency analysis is performed on the quantitative data of after-sales problem priorities to obtain the after-sales demand urgency data of intelligent products.

4. The intelligent product after-sales management method according to claim 1, characterized in that: The product failure prediction of intelligent product after-sales problem record set by using priority response demand data and delayed response demand data includes: Perform after-sales problem time series analysis based on priority response demand data and delayed response demand data, thereby obtaining after-sales problem time series data; perform demand frequency distribution extraction on the after-sales problem time series data, thereby obtaining after-sales problem demand frequency distribution data; The priority response demand data and the delayed response demand data are classified by the frequency distribution data of after-sales problem demand, so as to obtain the priority demand characteristic data and the delayed demand characteristic data; the priority demand characteristic data and the delayed demand characteristic data are clustered by the problem record concentration feature analysis, so as to obtain the problem record feature clustering data; Potential failure mode matching is performed through problem record feature clustering data to obtain potential product failure mode data; failure probability prediction calculation is performed on the intelligent product after-sales problem record set based on the potential product failure mode data to obtain product failure prediction data.

5. The intelligent product after-sales management method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing a functional damage assessment on the intelligent product based on the after-sales failure processing result, and generating product functional damage assessment data; Step S32: performing interventional fault diagnosis on the after-sales failure processing result according to the product function damage assessment data to generate interventional fault diagnosis data; performing a second remote processing feedback data collection based on the interventional fault diagnosis data, and when the corresponding remote processing feedback data is not collected, repeating the data collection until the remote processing feedback data is received; Step S33: dividing the feedback results of the remote processing feedback data, eliminating the second after-sales successful processing result, and obtaining the second after-sales failed processing result; performing after-sales maintenance scheduling on the second after-sales failure result, thereby generating after-sales maintenance service scheduling data.

6. The intelligent product after-sales management method according to claim 5, characterized in that: Interventional fault diagnosis of after-sales failure processing results based on product function damage assessment data includes: Mark the damaged modules of intelligent products according to the product function damage assessment data to obtain the damaged module marking data of intelligent products; perform module failure intervention analysis on the after-sales failure processing results based on the damaged module marking data of intelligent products to generate intervention analysis result data; Use product knowledge graph to expand the fault chain of intervention analysis result data to generate extended fault diagnosis data; prioritize fault modes of extended fault diagnosis data, identify optimal processing paths, and generate fault diagnosis path data; perform simulation verification on fault diagnosis path data to generate optimized fault diagnosis data; The optimized fault diagnosis data is integrated into the product knowledge graph for fault extension node mapping, thereby generating intervention fault diagnosis data.

7. The intelligent product after-sales management method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing a phased after-sales trend analysis on the first after-sales failure processing result, the second after-sales failure processing result, and the after-sales repair service scheduling data to generate phased after-sales trend data; performing a after-sales quality evaluation on the phased after-sales quality trend data to generate intelligent product after-sales quality evaluation data; Step S42: Visualize the intelligent product after-sales quality assessment data to generate an intelligent product after-sales quality assessment report to perform intelligent product after-sales management optimization operations.

8. An intelligent product after-sales management system, characterized in that: Used to execute the intelligent product after-sales management method according to claim 1, the intelligent product after-sales management system comprises: The urgency analysis module is used to obtain a set of intelligent product after-sales problem records, wherein the set of intelligent product after-sales problem records contains at least one or more intelligent product after-sales problem records; quantify customer demand for the intelligent product after-sales problem records, and generate intelligent product after-sales demand urgency data; The automated processing module is used to compare the urgency data of after-sales demand of intelligent products with the preset standard demand urgency threshold, and generate priority response demand data and delayed response demand data; perform fault processing association analysis on the after-sales problem record set of intelligent products through the priority response demand data and delayed response demand data, and generate a product fault processing automated solution; perform the first remote processing feedback result collection on the product fault processing automated solution, and obtain the first after-sales failure processing result; The intervention processing module is used to evaluate the functional damage of the intelligent product based on the after-sales failure processing result and generate product functional damage evaluation data; perform intervention fault diagnosis on the after-sales failure processing result according to the product functional damage evaluation data and generate intervention fault diagnosis data; collect the second remote processing feedback result through the intervention fault diagnosis data to obtain the second after-sales failure processing result; perform after-sales maintenance scheduling on the second after-sales failure result, thereby generating after-sales maintenance service scheduling data; The after-sales quality evaluation module is used to perform a phased after-sales trend analysis on the first after-sales failure processing result, the second after-sales failure processing result and the after-sales repair service scheduling data to generate phased after-sales trend data; and to visualize the after-sales quality evaluation of the phased after-sales quality trend data to perform intelligent product after-sales management optimization operations.

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