An after-sales trigger management method and system based on multi-field analysis and fusion

Abstract

The present invention proposes an after-sales trigger management method and system based on multi-field analysis and fusion. Belonging to the technical field of after-sales service management, the method includes: collecting multi-dimensional data from different channels and systems, and integrating the collected multi-dimensional data; using a deep learning model to deeply analyze the multi-field data, extract key features and patterns, and combine domain knowledge to build a feature association network; based on the multi-dimensional data, a multi-dimensional situational model is constructed, and after-sales needs and service scenarios in different situations are simulated through simulation, and the situational model is dynamically adjusted in combination with historical data and real-time data. Through the comprehensive use of advanced technologies such as deep learning, reinforcement learning, and natural language processing, a high degree of automation and intelligence of after-sales trigger management is achieved, which significantly improves processing efficiency and accuracy and reduces the burden of manpower.

Description

Technical Field

[0001] The present invention proposes an after-sales trigger management method and system based on multi-field analysis and fusion, belonging to the technical field of after-sales service management. Background Art

[0002] With the rapid development of science and technology and increasingly fierce market competition, companies are paying more and more attention to after-sales service of their products. The traditional after-sales service management model often relies on manual processing, which has problems such as low efficiency, slow response speed, and difficulty in ensuring service quality. In order to meet the growing after-sales service needs of consumers and improve service quality and efficiency, an after-sales trigger management method based on multi-field analysis and fusion came into being.

[0003] At present, with the continuous development of technologies such as big data and artificial intelligence, enterprises have accumulated a large amount of multi-dimensional data from different channels and systems. These data contain rich customer information, product status, service records, etc., providing valuable data resources for the optimization of after-sales service. However, how to effectively integrate and utilize these data to realize the intelligence and automation of after-sales service has become an important problem that enterprises need to solve. Summary of the invention

[0004] The present invention provides a post-sales trigger management method and system based on multi-field analysis and fusion to solve the problems mentioned in the above background technology:

[0005] The present invention proposes an after-sales trigger management method based on multi-field analysis and fusion, the method comprising:

[0006] S1. Collect multi-dimensional data from different channels and systems, and integrate the collected multi-dimensional data;

[0007] S2. Use deep learning models to deeply analyze multi-field data, extract key features and patterns, and build a feature association network based on domain knowledge;

[0008] S3. Build a multi-dimensional situational model based on multi-dimensional data, simulate after-sales demand and service scenarios under different situations through simulation, and dynamically adjust the situational model by combining historical data and real-time data;

[0009] S4. Based on the results of deep analysis and situational simulation, an adaptive learning model is built to automatically adjust the after-sales trigger strategy and decision rules through reinforcement learning algorithms and based on historical data and real-time feedback;

[0010] S5. Through the intelligent execution system, the after-sales service tasks are automatically executed according to the decision instructions generated by the adaptive learning model, and the service execution process is monitored in real time through the Internet of Things and smart devices to collect real-time feedback data;

[0011] S6. Establish a feedback mechanism to collect feedback from different groups. Use machine learning algorithms to analyze and mine the feedback data to identify potential problems and areas for improvement. Based on the feedback results, continuously optimize the deep analysis model, situational simulation model, and adaptive learning model.

[0012] Furthermore, the S1 includes:

[0013] S11. Identify and classify data sources, and set a dedicated data interface for each data source, and receive multi-dimensional data in real time through the data interface;

[0014] S12, preprocessing the collected multi-dimensional data, wherein the preprocessing includes data cleaning, removing redundant, repeated or erroneous data, and converting the preprocessed data into a unified format and standard;

[0015] S13. Establish a data quality monitoring mechanism to regularly check data accuracy and completeness, and mark and process problematic data;

[0016] S14. Compress the preprocessed and standardized multi-dimensional data, and store the compressed results in a unified data warehouse or data lake through a multi-channel transmission protocol, and dynamically adjust the compression ratio based on the network bandwidth.

[0017] Furthermore, the S2 includes:

[0018] S21. Select key feature fields from the data warehouse or data lake for in-depth analysis based on business needs and data characteristics;

[0019] S22, performing preliminary processing on the selected fields, wherein the preliminary processing includes data scaling and encoding conversion, and enhancing the generalization ability of the model through a data enhancement algorithm;

[0020] S23, selecting a deep learning model, and using the data set to train the deep learning model;

[0021] S24. Combine domain knowledge and expert experience to analyze the potential connections and mutual influences between different fields, and build a feature association network based on the analysis results;

[0022] S25. Evaluate the deep learning model using multiple indicators, and optimize and adjust the deep learning model according to the evaluation results.

[0023] Furthermore, the S3 includes:

[0024] S31. Classify after-sales needs and service scenarios according to business scenarios, and define scenarios for each category;

[0025] S32. Based on multi-dimensional data, build different models for each scenario and use parameterization to represent the key factors in different scenarios;

[0026] S33. Through simulation, simulate after-sales demand and service scenarios under different scenarios, and record the simulation results;

[0027] S34. Integrate real-time data with historical data, and continuously update the situational model. Evaluate and optimize the situational model based on simulation results and actual business feedback.

[0028] Furthermore, the S34 includes:

[0029] Establish a data integration mechanism to seamlessly connect real-time data with historical data to form a complete data flow, clean the fused data, remove noise and outliers, and perform standardization;

[0030] Adopting incremental learning algorithms, the situational model can be updated based on newly added real-time data. The parameters of the situational model can be fine-tuned according to the characteristics of real-time data and changes in business scenarios.

[0031] Conduct detailed evaluation of simulation results and establish a feedback mechanism to collect user feedback and business data in actual business scenarios;

[0032] Compare and analyze simulation results with actual business feedback to identify problems and deficiencies in the model;

[0033] According to the results of comparative analysis, the structure of the situation model is optimized, and features are selected and enhanced based on domain knowledge and actual business scenarios;

[0034] And through ensemble learning methods, multiple situation models are combined;

[0035] Establish a regular evaluation mechanism to regularly evaluate and update the scenario model, use actual business feedback as input, and continuously iterate and optimize the scenario model through a feedback closed-loop mechanism to form a cycle of continuous optimization.

[0036] Furthermore, the S4 includes:

[0037] S41. Based on the results of deep analysis and situational simulation, an adaptive learning model is constructed, and initial decision rules and parameters are set. The after-sales trigger strategy and decision rules are automatically adjusted based on historical data and real-time feedback using reinforcement learning algorithms;

[0038] S42. Evaluate the effect of the adjusted decision and collect direct feedback from customers;

[0039] S43. Based on the evaluation results and feedback, the decision rules are optimized and iterated, and the adaptive learning mechanism is expanded.

[0040] Furthermore, the S5 includes:

[0041] S51. Automatically assign after-sales service tasks according to the decision instructions generated by the adaptive learning model, and optimize the decision instructions based on multiple factors;

[0042] S52, automatically execute assigned service tasks through the intelligent execution system and work collaboratively with relevant personnel;

[0043] S53. Use the Internet of Things and smart devices to monitor the service execution process in real time, handle abnormal situations during the execution process, and issue warnings and notifications;

[0044] S54. Collect real-time feedback data from customers during the service process, process and analyze it, regularly evaluate the quality of service execution, and make improvements and optimizations based on the evaluation results.

[0045] Furthermore, the S53 includes:

[0046] Connect IoT devices and smart devices involved in after-sales service, perform necessary configurations, integrate data from different devices and systems, and standardize data formats;

[0047] Monitor the data flow generated during service execution in real time and display the monitoring data in a visual form;

[0048] Based on historical data and business logic, define abnormal situations during service execution, set corresponding thresholds, analyze real-time data, and detect whether abnormal situations occur;

[0049] For predictable abnormal situations, as well as complex or unpredictable abnormal situations, the manual intervention process is triggered to notify relevant personnel for emergency processing;

[0050] Based on historical data and business needs, set warning rules, clarify the situations in which warnings need to be issued, and select notification methods based on the urgency of the warning and the preferences of the recipients;

[0051] Regularly evaluate the effectiveness of early warning notifications and make improvements based on the evaluation results. For complex situations that arise during service execution, introduce judgment logic to analyze and judge, and decide on response strategies;

[0052] Based on real-time data and historical experience, a decision support system is built to record all abnormal situations in detail, and statistical analysis of the recorded abnormal situations is performed regularly to find out the causes and patterns of the abnormal situations.

[0053] Furthermore, the S6 includes:

[0054] S61. Establish a feedback mechanism and collect feedback from different groups, pre-process the collected feedback data, and classify the pre-processed feedback data by type;

[0055] S62. Use natural language processing algorithms to perform sentiment analysis and topic extraction on text-based feedback data, and use machine learning algorithms to conduct in-depth analysis and mining of feedback data;

[0056] S63. Display the analysis results in a visual way, set key indicators and dashboards, and monitor the changes and trends of feedback data in real time;

[0057] S64. Generate optimization suggestions for the deep analysis model, the situation simulation model, and the adaptive learning model based on the feedback analysis results, and verify and test the optimization suggestions;

[0058] S65. Apply the verified optimization suggestions to the actual model, perform iterative updates, deploy the updated model to the production environment, and monitor its operation and effects;

[0059] S66. Document and build a knowledge base for each optimization and iteration process, and establish a mechanism for continuous optimization cycles.

[0060] The present invention proposes an after-sales trigger management system based on multi-field analysis and fusion, the system comprising:

[0061] Data collection module: collects multi-dimensional data from different channels and systems, and integrates the collected multi-dimensional data;

[0062] Network construction module: Use deep learning models to deeply analyze multi-field data, extract key features and patterns, and combine domain knowledge to build a feature association network;

[0063] Simulation module: Build a multi-dimensional situational model based on multi-dimensional data. Through simulation, simulate after-sales demand and service scenarios under different situations, and dynamically adjust the situational model by combining historical data and real-time data.

[0064] Model building module: Based on the results of deep analysis and situational simulation, an adaptive learning model is built to automatically adjust the after-sales trigger strategy and decision rules through reinforcement learning algorithms and based on historical data and real-time feedback;

[0065] Real-time feedback module: Through the intelligent execution system, the after-sales service tasks are automatically executed according to the decision instructions generated by the adaptive learning model, and the service execution process is monitored in real time through the Internet of Things and smart devices to collect real-time feedback data;

[0066] Optimization and adjustment module: Establish a feedback mechanism to collect feedback from different groups, analyze and mine the feedback data through machine learning algorithms, identify potential problems and improvement points, and continuously optimize the deep analysis model, situational simulation model and adaptive learning model based on the feedback results.

[0067] The beneficial effects of the present invention are as follows: through the comprehensive application of advanced technologies such as deep learning, reinforcement learning and natural language processing, a high degree of automation and intelligence of after-sales trigger management is achieved, which significantly improves processing efficiency and accuracy and reduces the manpower burden; combined with multi-dimensional data analysis of user historical behavior and preferences, it can provide more personalized and accurate after-sales service solutions for different users, enhancing user experience and satisfaction; through adaptive learning models and situational simulation, the system can continuously adjust strategies according to real-time data and feedback, dynamically adapt to market changes and user needs, and ensure the effectiveness and timeliness of service strategies; the integration of the Internet of Things and smart devices, coupled with a powerful data monitoring and early warning mechanism, can promptly discover and handle abnormal situations in the service execution process, reduce risks, and ensure service quality; the established closed-loop feedback mechanism and regular evaluation system ensure continuous iteration from user feedback to model optimization, promote continuous improvement of system performance and service processes, and form a virtuous cycle of continuous optimization; through accurate prediction and intelligent scheduling of after-sales demand, optimize resource allocation, avoid resource waste, improve overall operational efficiency, and reduce enterprise costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a step diagram of the method of the present invention;

[0069] Figure 2 This is a system module diagram of the present invention. DETAILED DESCRIPTION

[0070] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0071] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. The embodiments described are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0073] An embodiment of the present invention provides a post-sales trigger management method based on multi-field analysis and fusion, the method comprising:

[0074] S1. Collect multi-dimensional data from different channels and systems, and integrate the collected multi-dimensional data;

[0075] S2. Use deep learning models to deeply analyze multi-field data in multi-dimensional data, extract key features and patterns, and build a feature association network based on domain knowledge;

[0076] S3. Build a multi-dimensional situational model based on multi-dimensional data, simulate after-sales demand and service scenarios under different situations through simulation, and dynamically adjust the situational model by combining historical data and real-time data;

[0077] S4. Based on the results of deep analysis and situational simulation, an adaptive learning model is built to automatically adjust the after-sales trigger strategy and decision rules through reinforcement learning algorithms and based on historical data and real-time feedback;

[0078] S5. Through the intelligent execution system, the after-sales service tasks are automatically executed according to the decision instructions generated by the adaptive learning model, and the service execution process is monitored in real time through the Internet of Things and smart devices to collect real-time feedback data;

[0079] S6. Establish a feedback mechanism to collect feedback from different groups. Use machine learning algorithms to analyze and mine the feedback data to identify potential problems and areas for improvement. Based on the feedback results, continuously optimize the deep analysis model, situational simulation model, and adaptive learning model.

[0080] The working principle of the above technical solution is as follows: collect multi-dimensional data related to after-sales service from multiple sources (such as customer relationship management systems, sales systems, product usage logs, social media, etc.); pre-process the collected raw data to remove duplicate, erroneous or invalid data to ensure the accuracy and completeness of the data; integrate the cleaned data into a unified data warehouse or data lake for subsequent analysis and processing; use deep learning models (such as CNN and LSTM) to parse the integrated data at the field level to identify the key information and features in each field; based on the parsing results, extract the key features and patterns related to after-sales service, which may include product failure types, customer demand preferences, market trends, etc.; combine domain knowledge to build a feature association network to analyze the potential connections and mutual influences between different fields in order to better understand the complexity and dynamics of after-sales service; build multi-dimensional situational models based on the extracted key features and patterns, which describe after-sales needs and service scenarios in different situations; use simulation technology to simulate the situational models, simulate the after-sales service process, resource allocation, response speed, etc. in different situations to evaluate the effectiveness and efficiency of after-sales service; combine historical data and real-time data to analyze the situation Dynamically adjust the after-sales service model to adapt to the ever-changing actual situation and market demand; build an adaptive learning model based on the results of deep analysis and situation simulation, which can automatically learn and adapt to the complexity and uncertainty in after-sales service; use reinforcement learning algorithms to automatically adjust after-sales trigger strategies and decision rules based on historical data and real-time feedback to achieve the best after-sales service effect; make specific after-sales service decisions based on the output results of the adaptive learning model, such as service content, service time, service method, etc.; automatically execute after-sales service tasks according to the decision instructions generated by the adaptive learning model through intelligent execution systems (such as automation tools, robots, etc.); use the Internet of Things and smart devices to monitor the service execution process in real time, including personnel behavior, equipment status, service progress, etc.; collect real-time feedback data during the service execution process, such as customer feedback, service results, etc., for subsequent analysis and optimization; establish a feedback mechanism to collect feedback from different groups (such as customers, service teams, and other stakeholders); use machine learning algorithms to analyze and mine feedback data to find potential problems and improvement points; based on the feedback results, continuously optimize the deep analysis model, situation simulation model, and adaptive learning model to improve the quality and efficiency of after-sales service.

[0081] The effects of the above technical solutions are as follows: by collecting multi-dimensional data from different channels and systems, integrating and analyzing them, enterprises can obtain more comprehensive and accurate information. This makes after-sales service decisions more data-based, reduces subjective assumptions, and improves the scientificity and accuracy of decision-making; using deep learning models (such as CNN and LSTM) to deeply analyze multi-field data, it is possible to extract key features and patterns and predict possible after-sales needs and service scenarios. This enables enterprises to prepare in advance, respond quickly to customer needs, and improve the timeliness of services and customer satisfaction; by building a multi-dimensional scenario model and combining historical data with real-time data for simulation, enterprises can simulate after-sales service processes and effects in different scenarios. This enables enterprises to adjust service strategies according to different scenarios and better adapt to market changes and customer needs; based on the results of deep analysis and scenario simulation, an adaptive learning model is built, and after-sales trigger strategies and decision rules are automatically adjusted through reinforcement learning algorithms. This adaptive learning mechanism enables enterprises to continuously learn and improve, continuously optimize service processes and quality, and improve service efficiency and customer satisfaction; through the intelligent execution system, enterprises can automatically execute after-sales service tasks and monitor the service execution process in real time through the Internet of Things and smart devices. This reduces human intervention and errors, and improves the accuracy and efficiency of service execution. At the same time, the collection of real-time feedback data helps companies to identify problems and make improvements in a timely manner; establish a feedback mechanism, collect feedback from different groups, and analyze and mine the feedback data through machine learning algorithms. This helps companies identify potential problems and improvement points, and continuously optimize the deep analysis model, situation simulation model, and adaptive learning model based on the feedback results. This continuous feedback and optimization cycle enables companies to continuously improve and enhance the quality and efficiency of after-sales service.

[0082] In one embodiment of the present invention, the S1 includes:

[0083] S11. Identify and classify data sources, and set a dedicated data interface for each data source, and receive multi-dimensional data in real time through the data interface;

[0084] S12, preprocessing the collected multi-dimensional data, wherein the preprocessing includes data cleaning, removing redundant, repeated or erroneous data, and converting the preprocessed data into a unified format and standard;

[0085] S13. Establish a data quality monitoring mechanism to regularly check data accuracy and completeness, and mark and process problematic data;

[0086] S14. Compress the preprocessed and standardized multi-dimensional data, and store the compressed results in a unified data warehouse or data lake through a multi-channel transmission protocol, and dynamically adjust the compression ratio based on the network bandwidth.

[0087] The calculation formula of the compression ratio is: The calculation formula of the data compression ratio is:

[0088]

[0089] in, Indicates adjusting the ratio of bandwidth to original bit rate to adapt to different network conditions. as well as Adjust the impact of network delay and packet loss rate on compression ratio respectively; Is a coefficient that represents the complexity of the data. If the data is complex, a higher compression ratio is required to maintain quality. is a coefficient that indicates the user's requirement for data quality. If the quality requirement is high, the compression ratio needs to be reduced. Indicates the bit rate of the original data stream, Indicates the currently available network upload bandwidth. Indicates the current network delay. Indicates the current network packet loss rate. Represents very small positive numbers, used to prevent the denominator from being zero or negative.

[0090] The working principle of the above technical solution is: identify and determine all data sources related to after-sales service, including but not limited to CRM systems, ERP systems, e-commerce platforms, IoT devices, social media, etc. Classify these data sources and clarify the data types, frequencies and importance they each provide; set up dedicated data interfaces for each data source, which can be APIs, database connections or other forms. The main function of the data interface is to receive multi-dimensional data from the data source in real time. After the interface is set up, strict testing is required to ensure the accuracy and real-time nature of the data. The received raw data may contain redundant, repeated or erroneous information and needs to be cleaned. The cleaning process includes removing duplicates, correcting erroneous data, and processing missing values. Convert the cleaned data into a unified format and standard for subsequent analysis and processing. Involves operations such as encoding, decoding, and converting data types. The multi-dimensional data processed includes customer behavior data (such as purchase records, browsing behavior, etc.), product usage data (such as device status, usage time, etc.), and environmental data (such as temperature, humidity, etc.). Appropriately classify and label the data according to the data type and purpose. Establish a data quality monitoring mechanism to regularly check the accuracy and completeness of the data. The inspection can be performed automatically or manually based on preset rules or algorithms. The problem data found is marked and processed accordingly. The processing methods may include data correction, deletion or re-collection. The preprocessed and standardized multi-dimensional data is compressed to reduce storage space and transmission costs. The compression algorithm can be selected according to the data type and storage requirements. Use a multi-channel transmission protocol to transmit the compressed data to a unified data warehouse or data lake in sequence. Dynamically adjust the data compression ratio according to the actual network bandwidth. When the network bandwidth is high, the compression ratio can be appropriately reduced to increase the data transmission speed; conversely, the compression ratio can be increased to reduce the transmission time. The data transmitted to the data warehouse or data lake is stored and managed for a long time. The data warehouse or data lake should have efficient data retrieval and query capabilities for subsequent data analysis and processing.

[0091] The effects of the above technical solutions are: by identifying and classifying multiple data sources such as CRM systems, ERP systems, e-commerce platforms, IoT devices and social media, it ensures that after-sales service management can fully obtain multi-dimensional data related to after-sales; setting up a dedicated data interface for each data source, which can receive data in real time, ensure the timeliness and freshness of the data, and provide data support for rapid response to after-sales needs; ensuring the accuracy of data through rigorous testing, reducing decision-making errors and service inefficiencies caused by data quality issues; the data cleaning process can remove redundant, repeated or erroneous data, improve the quality and credibility of data, and provide an accurate data basis for subsequent analysis and processing; converting pre-processed data into a unified format and standard, which facilitates data exchange and sharing between different systems and improves the availability and analyzability of data; the combination of multi-dimensional data such as customer behavior data, product usage data and environmental data can provide a more comprehensive and in-depth analysis perspective for after-sales trigger management, helping companies to better understand Understand customer needs and market trends; build a data quality monitoring mechanism, conduct regular checks on data, ensure the accuracy and completeness of data, and promptly discover and deal with data problems; mark and process the discovered problematic data to avoid the negative impact of problematic data on decision-making and services, and improve the reliability and stability of services; through data quality monitoring, enterprises can continuously evaluate and improve the process of data collection, processing and analysis, and improve the overall data management level; compress the preprocessed and standardized data to reduce the storage space occupied and reduce storage costs; store the compressed data in a unified data warehouse or data lake in succession through a multi-channel transmission protocol, thereby improving the data transmission efficiency and reliability; dynamically adjust the compression ratio based on network bandwidth, and automatically adjust the data transmission rate and quality according to network conditions, ensuring the stability and efficiency of data transmission; store data in a unified data warehouse or data lake, which facilitates centralized management and analysis of data, and provides data support for the continuous improvement and optimization of after-sales services. The above formula can dynamically adjust the compression ratio according to different network conditions (such as bandwidth, delay, and packet loss rate) by adjusting parameters such as α, β, and γ, to ensure efficient transmission and reception of data. The F coefficient takes into account the complexity of the data. For complex data, a higher compression ratio is required to maintain quality, which helps to maintain the observability of the data under limited bandwidth. The X coefficient allows the compression ratio to be adjusted according to the user's requirements for data quality. If the user has high requirements for data quality, the compression ratio can be reduced to maintain higher data quality; conversely, if the user has low requirements for data quality, the compression ratio can be increased to save bandwidth. By adding ε to the denominator, the formula can remain valid even in extremely poor network conditions (such as extremely low bandwidth, high latency, and high packet loss rate), avoiding abnormal results caused by a zero or negative denominator.The parameters in the formula (α, β, γ, F, X) can be adjusted and optimized according to actual conditions, and are easy to implement and apply in actual systems. By dynamically adjusting the compression ratio, it can ensure that users can still receive relatively smooth data under poor network conditions, thereby improving user experience.

[0092] In one embodiment of the present invention, the S2 includes:

[0093] S21. Select key feature fields from the data warehouse or data lake for in-depth analysis based on business needs and data characteristics;

[0094] S22, performing preliminary processing on the selected fields, wherein the preliminary processing includes data scaling and encoding conversion, and enhancing the generalization ability of the model through a data enhancement algorithm;

[0095] S23, selecting a deep learning model, and using the data set to train the deep learning model;

[0096] S24. Combine domain knowledge and expert experience to analyze the potential connections and mutual influences between different fields, and build a feature association network based on the analysis results;

[0097] S25. Evaluate the deep learning model using multiple indicators, and optimize and adjust the deep learning model according to the evaluation results.

[0098] The working principle of the above technical solution is as follows: according to specific business needs and data characteristics, key feature fields are selected from the multi-dimensional data already stored in the data warehouse or data lake for in-depth analysis. These feature fields may cover multiple aspects such as user purchase frequency, product usage time, environmental humidity, etc., which are crucial for understanding business problems and building prediction models; after selecting key feature fields, these fields are subjected to preliminary data processing. This includes data scaling (such as normalization, standardization, etc.) to eliminate dimensional differences between different features; and encoding conversion to convert certain non-numeric data into numerical data for subsequent analysis and modeling. In addition, data enhancement techniques (such as adding noise, rotating images, etc., the specific method depends on the data type) are used to enhance the generalization ability of the model so that it can better adapt to different data distributions and changes. Next, according to the nature of the problem and the characteristics of the data, a suitable deep learning model is selected for training. These models may include convolutional neural networks (CNN) for image or time series data processing, long short-term memory networks (LSTM) for sequence data prediction, or Transformer models for natural language processing. The model is trained using the selected data set, and the parameters of the model are adjusted through the back propagation algorithm and the gradient descent method to minimize the prediction error. In the process of model training, the potential connections and mutual influences between different fields are analyzed by combining domain knowledge and expert experience. This analysis may be based on statistical methods, causal reasoning, or the knowledge base of the expert system. Based on these analysis results, a feature association network is constructed, which can describe the dependencies and mutual influences between different features, and help understand the business logic and potential laws behind the data. Finally, the trained deep learning model is evaluated by multiple indicators. These indicators may include cross-validation (such as K-fold cross-validation) to evaluate the stability and generalization ability of the model, ROC curves and AUC values ​​to evaluate the performance of the model in classification tasks, etc. According to the evaluation results, the model is optimized and adjusted, which may include adjusting the parameters of the model, changing the architecture of the model, adding or deleting certain feature fields, etc.

[0099] The effects of the above technical solutions are: selecting key feature fields according to business needs and data characteristics can ensure that the model focuses on the most relevant and influential data features, thereby improving the accuracy and efficiency of model predictions. Preliminary processing, including data scaling and encoding conversion, helps to eliminate dimensional differences between different features, making it easier for the model to learn and understand data. The application of data enhancement can increase the diversity and complexity of data, help the model better cope with complex situations in actual business, and improve the generalization ability of the model. Selecting suitable deep learning models (such as CNN, LSTM, Transformer, etc.) can provide the best solution for specific types of data and business problems. These deep learning models usually have better performance in processing large-scale, high-dimensional data than traditional machine learning algorithms. Combining domain knowledge and expert experience, analyzing the potential connections and mutual influences between different fields helps to reveal the business logic and potential laws behind the data. The feature association network constructed based on these analyses can further improve the prediction ability and interpretability of the model. Evaluating deep learning models through multiple indicators can comprehensively and objectively reflect the performance of the model. These evaluation indicators include cross-validation, ROC curves, and AUC values, which can evaluate the predictive ability, stability, and robustness of the model from different angles. Optimizing and adjusting the model based on the evaluation results can continuously improve the performance of the model and make it more in line with business needs. This iterative optimization process helps to build a more efficient and accurate prediction model. The entire technical solution not only focuses on the predictive performance of the model, but also focuses on revealing the business logic and potential laws behind the data through methods such as feature association networks. This helps companies understand business problems more deeply, discover potential opportunities and risks, and make more informed decisions.

[0100] In one embodiment of the present invention, the S24 includes:

[0101] Perform correlation analysis on selected feature fields based on statistical methods to identify feature pairs with significant correlation;

[0102] Based on domain knowledge and expert experience, business logic verification is performed on the initially analyzed feature pairs;

[0103] According to the characteristics of feature association and business requirements, a graph model is selected to build a feature association network. The features are used as nodes in the network, and the associations obtained by correlation analysis are used as edges in the network to build an initial feature association network.

[0104] According to business logic and domain knowledge, set parameters for nodes and edges in the network, identify different communities in the feature association network through community detection algorithms in graph theory, and analyze the potential relationship between communities and after-sales trigger management;

[0105] The key paths and nodes in the feature association network are analyzed, and the importance and influence of different feature nodes in after-sales trigger management are evaluated based on the network structure and parameters.

[0106] The working principle of the above technical solution is: use statistical methods (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.) to perform correlation analysis on the selected feature fields. These statistical methods can quantify the degree of correlation between features and identify feature pairs with significant correlation. Based on domain knowledge and expert experience, the business logic of the feature pairs analyzed initially is verified. This step is to ensure that the correlation analysis results obtained by the statistical method are meaningful in business and conform to the actual business logic. According to the characteristics of feature association and business needs, select a suitable graph model (such as undirected graph, directed graph, weighted graph, etc.) to build a feature association network. Take the features as nodes in the network and the associations obtained by the correlation analysis as edges in the network to build an initial feature association network. According to business logic and domain knowledge, set parameters for nodes and edges in the network. These parameters may include weights (indicating the strength of association) and directions (indicating the direction of influence). Apply community detection algorithms in graph theory (such as Louvain algorithm, label propagation algorithm, etc.) to identify different communities (i.e., feature groups) in the feature association network. Nodes within a community usually have a close association, while the associations between communities are relatively weak. Analyze the potential relationship between the identified communities and after-sales trigger management to understand the business significance that different communities may represent. Analyze the key paths and nodes in the feature association network. These paths and nodes may reveal the key influencing factors and potential risk points in after-sales trigger management. By evaluating the position, weight and connection of different feature nodes in the network with other nodes, their importance and influence in after-sales trigger management can be evaluated. The analysis results based on the network structure and parameters provide a basis for subsequent decision-making and service optimization. For example, targeted improvement measures can be formulated for key nodes or paths to improve the efficiency and effectiveness of after-sales service.

[0107] The effect of the above technical solution is: by using statistical methods (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.) to perform correlation analysis on selected feature fields, feature pairs with significant correlation can be accurately identified. This method overcomes the subjective and one-sided problems of human judgment in traditional methods and improves the accuracy and reliability of feature association analysis. After the feature pairs are preliminarily analyzed, the business logic is verified in combination with domain knowledge and expert experience to ensure the rationality and practicality of the correlation analysis results. This helps to eliminate statistical accidental associations and improve the pertinence and effectiveness of feature association analysis. According to the characteristics of feature association and business needs, an appropriate graph model is selected to construct a feature association network, and complex feature relationships are displayed in an intuitive graphical manner. This enables managers and decision makers to understand the association relationship between features more quickly and provide support for subsequent decision-making. Different communities (i.e., feature groups) in the feature association network are identified through the community detection algorithm in graph theory, and the potential relationship between the community and after-sales trigger management is analyzed. This helps to discover feature sets with similar characteristics and their potential connection with after-sales trigger management, providing a basis for formulating targeted after-sales management strategies. Analyzing the key paths and nodes in the feature association network can reveal the key influencing factors and potential risk points in after-sales trigger management. This analysis not only helps to understand the root cause and nature of the problem, but also provides guidance for formulating effective countermeasures and risk management strategies. Based on the network structure and parameters, the importance and influence of different feature nodes in after-sales trigger management are evaluated to provide a basis for subsequent decision-making and service optimization. This helps companies grasp the key points of after-sales management more accurately, formulate more accurate and effective management strategies, and improve the satisfaction and efficiency of after-sales services.

[0108] In one embodiment of the present invention, the S25 includes:

[0109] Before evaluation, select the evaluation indicators and divide the data set into training set, validation set and test set;

[0110] Use K-fold cross validation (such as K=5 or 10) to train and evaluate the model; use different data subsets for training and testing during the iteration process; and record the evaluation results of each cross validation;

[0111] Compare the model's evaluation results with the benchmark model, analyze the model's performance strengths and weaknesses in various indicators, conduct in-depth analysis of samples with model prediction errors, and identify the causes and types of errors;

[0112] According to the model performance analysis and evaluation results, the hyperparameters of the deep learning model are adjusted, and the feature selection is optimized based on the analysis results of the feature association network and domain knowledge.

[0113] Consider using ensemble learning methods to combine multiple deep learning models, iteratively train the models according to optimization strategies, and continuously adjust and optimize the model parameters and structures;

[0114] Use independent test sets to verify the optimized model, establish a feedback loop mechanism, continuously collect and analyze feedback data from actual applications, and continuously improve and optimize the model.

[0115] The working principle of the above technical solution is as follows: according to business needs and data characteristics, select appropriate evaluation indicators, such as cross-validation accuracy, ROC curve, AUC value, F1 score, recall rate, precision rate, etc., to comprehensively evaluate the performance of the model; divide the original data set into training set, validation set and test set. The training set is used for model training, the validation set is used for model selection and hyperparameter adjustment, and the test set is used to evaluate the performance of the final model; use the K-fold cross-validation method to train and evaluate the model. Divide the data set into K subsets, select K-1 subsets as training sets each time, and the remaining subset as validation set. Iterate K times, using different subsets as validation sets each time. Record the evaluation results of each cross-validation, including the specific values ​​and trend changes of each indicator, for subsequent analysis and optimization. Compare the evaluation results of the model with the benchmark model to analyze the advantages and disadvantages of the model in terms of various indicators. This helps to understand the advantages and problems of the model and provide direction for subsequent tuning; conduct in-depth analysis of samples with model prediction errors to identify the causes and types of errors, such as data noise, improper feature selection, and insufficient model complexity. This helps to develop effective optimization strategies for specific problems. Based on the results of model performance analysis and evaluation, adjust the hyperparameters of the deep learning model, such as learning rate, batch size, number of iterations, etc. This helps to optimize the performance and generalization ability of the model. Based on the analysis results of the feature association network and domain knowledge, optimize the feature selection, remove redundant or irrelevant features, and improve the efficiency and accuracy of the model. Consider using ensemble learning methods such as bagging and boosting to combine multiple deep learning models. By integrating the prediction results of multiple models, the overall prediction performance and stability can be improved. Iteratively train the model according to the optimization strategy, and continuously adjust and optimize the parameters and structure of the model. This can be achieved through automated parameter adjustment tools or manual adjustments. Use independent test sets to verify the optimized model to ensure that the performance of the model in actual applications meets the expected requirements. Establish a feedback loop mechanism to continuously collect and analyze feedback data from actual applications. Continuously improve and optimize the model based on feedback data to adapt to business changes and data changes.

[0116] The effect of the above technical solution is: by selecting a variety of evaluation indicators such as cross-validation accuracy, ROC curve, AUC value, F1 score, recall rate, precision rate, etc., the performance of the deep learning model can be comprehensively and accurately measured to ensure that the model can meet business needs in multiple dimensions. Dividing the data set into training set, validation set and test set, and using K-fold cross validation for model training and evaluation can make full use of data resources and improve the reliability and stability of model evaluation. By iteratively using different data subsets for training and testing, the risk of overfitting and underfitting can also be effectively reduced. Comparing the evaluation results of the model with the benchmark model can clearly show the advantages and disadvantages of the model. By conducting in-depth analysis of samples with model prediction errors, the causes and types of errors can be identified, providing a clear direction for subsequent model optimization. According to the model performance analysis and evaluation results, the hyperparameters of the deep learning model are accurately adjusted, which can significantly improve the performance and generalization ability of the model. At the same time, optimizing feature selection based on the analysis results of the feature association network and domain knowledge can remove redundant or irrelevant features and improve the efficiency and accuracy of the model. Consider using ensemble learning methods, such as bagging and boosting, to combine multiple deep learning models, which can fully utilize the advantages of different models and improve the overall prediction performance and stability. This is very effective for processing complex data and coping with various challenges. Using an independent test set to verify the optimized model can ensure that the performance of the model in actual applications meets the expected requirements. At the same time, establishing a feedback loop mechanism to continuously collect and analyze feedback data in actual applications can continuously improve and optimize the model to adapt to business changes and data changes. Through the above technical solutions, it can be ensured that the deep learning model performs well in prediction and classification tasks, and provide accurate and reliable data support for business decisions. This helps companies better grasp market trends and formulate effective strategies and plans.

[0117] In one embodiment of the present invention, S3 includes:

[0118] S31. Classify after-sales needs and service scenarios according to business scenarios, and define scenarios for each category;

[0119] S32. Based on multi-dimensional data, build different models for each scenario and use parameterization to represent the key factors in different scenarios;

[0120] S33. Through simulation, simulate after-sales demand and service scenarios under different scenarios, and record the simulation results;

[0121] S34. Integrate real-time data with historical data, and continuously update the situational model. Evaluate and optimize the situational model based on simulation results and actual business feedback.

[0122] The working principle of the above technical solution is as follows: according to the actual needs of after-sales service, after-sales needs and service scenarios are classified, such as product quality problems, improper use, etc. This classification helps to understand and handle different after-sales problems more accurately. Define specific scenarios for each classified after-sales problem, including detailed descriptions of the problem, possible causes, user behavior, etc. This helps to build a clear and operational after-sales processing framework; collect multi-dimensional data related to after-sales problems, such as user feedback, product performance data, maintenance records, etc. These data are the basis for building situational models; based on the collected data, different models are built for each situation. These models can be statistical models, machine learning models, or other types of models to describe the key factors in the situation and their interactions; use parameterized methods to represent the key factors in different situations. These parameters can be adjusted and optimized based on actual data to more accurately reflect the actual situation; use the constructed situational model to simulate and simulate after-sales needs and service scenarios in different situations. This helps to predict possible problems and challenges before the real situation occurs; record the simulation results, including various indicators, trends, anomalies, etc. These records are an important basis for subsequent analysis and optimization; real-time collected data is integrated with historical data to obtain more comprehensive information. This helps to more accurately evaluate the performance of the scenario model; based on simulation results and actual business feedback, the scenario model is evaluated and optimized. This may include adjusting the model's parameters, improving the model's structure, or introducing new model elements. Through continuous iteration and optimization, the scenario model can better adapt to changes in actual business needs.

[0123] The effects of the above technical solutions are: by clearly classifying after-sales needs and service scenarios, such as product quality issues, improper use, etc., enterprises can identify problem types more quickly and accurately, and take corresponding treatment measures; defining scenarios for each category helps enterprises to deeply understand the background and causes of the problem, and then formulate more accurate solutions and preventive measures; using multi-dimensional data to build a model for each scenario, it can comprehensively consider various factors (such as user behavior, product performance, environmental factors, etc.) to make the model more comprehensive and accurate; using parameterization to represent key factors in different scenarios, it is convenient to flexibly adjust and optimize the model to adapt to business changes and data updates; through simulation, enterprises can predict possible after-sales needs and service scenarios in different scenarios, and identify potential risks in advance. , to provide support for decision-making; recording simulation results will help companies accumulate after-sales service experience, form a knowledge base, and provide reference and reference for future after-sales service; integrating real-time data with historical data can ensure that the situational model always keeps pace with the actual business situation, and improve the company's response speed and accuracy to after-sales problems; evaluating and optimizing the situational model based on simulation results and actual business feedback can continuously improve the model, improve its prediction accuracy and stability, and achieve continuous improvement of after-sales service; through precise classification, comprehensive model construction, effective simulation and continuous model optimization, companies can solve after-sales problems more quickly and accurately, and improve customer satisfaction and loyalty; by reducing unnecessary waste of resources and reducing the incidence of after-sales problems, companies can reduce operating costs and improve economic benefits.

[0124] In one embodiment of the present invention, the S32 includes:

[0125] Clarify the boundary conditions of after-sales needs and service scenarios, and set a unique identifier for each scenario;

[0126] Designing an overall framework of the situation model, the overall framework includes an input layer, a processing layer, and an output layer; the input layer receives multi-dimensional data, the processing layer processes and calculates the data, and the output layer generates a prediction or decision for the situation;

[0127] Integrate information from different data sources and pre-process raw data;

[0128] Based on domain knowledge and expert experience, identify the key factors that affect after-sales demand and service scenarios, and design parameter representation methods for each key factor;

[0129] According to the characteristics of the situation and data features, choose machine learning or deep learning models to build situational models. Use the integrated multi-dimensional data and parameterized key factors as input to train the model; ensure that the model can accurately capture the relationship between key factors and after-sales needs and service scenarios.

[0130] Use the validation set to perform preliminary validation on the trained model, evaluate the model's prediction or decision-making capabilities in known situations, and adjust and optimize the model based on the validation results.

[0131] Models for different situations are combined to form a unified situational model library, and a model management mechanism is established to maintain and update the situational model library.

[0132] The working principle of the above technical solution is as follows: clarify the boundary conditions of after-sales demand and service scenarios, such as clearly distinguishing between product quality problems and improper use. Then, set a unique identifier for each scenario for subsequent management and identification. Design the overall framework of the scenario model, which usually includes an input layer, a processing layer, and an output layer. The input layer is responsible for receiving multi-dimensional data from different data sources, the processing layer processes and calculates these data, and finally the output layer generates predictions or decisions for the scenario. Integrate information from different data sources, such as user feedback, product logs, sales data, etc. Perform necessary preprocessing on the original data, such as data cleaning, conversion, and standardization to ensure data quality and consistency. Based on domain knowledge and expert experience, identify the key factors that affect after-sales demand and service scenarios. These key factors may include user behavior, product attributes, environmental factors, etc. Design appropriate parameter representation methods for each key factor, such as continuous variables, categorical variables, or combined variables, to ensure that the parameters can accurately reflect the changes and impacts of key factors. According to the characteristics of the scenario and the characteristics of the data, select appropriate machine learning or deep learning models to build the scenario model. These models may include decision trees, random forests, neural networks, etc. The model is trained using the integrated multi-dimensional data and parameterized key factors as input. During the training process, the model learns the relationship between key factors and after-sales needs and service scenarios, and tries to improve the accuracy of predictions or decisions. The trained model is preliminarily verified using the validation set to evaluate the model's prediction or decision-making capabilities in known scenarios. Based on the validation results, the model is adjusted and optimized, such as adjusting parameters and changing the model structure, to improve the model's accuracy and generalization capabilities. Models for different scenarios are combined to form a unified scenario model library. A model management mechanism is established to maintain and update the scenario model library. This includes regularly evaluating the performance of the model, updating data sources and parameter representation methods, etc., to ensure that the model can always accurately reflect the actual business situation. The constructed scenario model is applied to actual business, and real-time data is collected and analyzed. Based on the actual application results and feedback data, the scenario model is continuously improved and optimized to form a closed-loop feedback loop mechanism. This helps the model to continuously adapt to business changes and data updates, and improve the accuracy and reliability of predictions or decisions.

[0133] The effects of the above technical solutions are as follows: by clarifying the boundary conditions of after-sales needs and service scenarios, consistency and accuracy in handling after-sales issues are ensured. Each scenario has a unique identifier to facilitate subsequent management and query; a scenario model framework consisting of an input layer, a processing layer, and an output layer is designed to ensure standardization and systematization of data processing and prediction; information from different data sources, such as user feedback, product logs, sales data, etc., is integrated to provide rich data support for the model, so that the model can have a more comprehensive understanding of the background and causes of after-sales problems; raw data is preprocessed to improve data quality and accuracy, providing a better foundation for subsequent model training; key factors are identified based on domain knowledge and expert experience to ensure the effectiveness and accuracy of key factors; appropriate parameter representation methods are designed for each key factor, such as continuous variables, categorical variables, or combined variables, so that the model can more accurately capture the changes and impacts of key factors; appropriate machine learning or deep learning models are selected according to the characteristics of the scenario and data characteristics to ensure the applicability and accuracy of the model. ; Use the integrated multi-dimensional data and parameterized key factors as input to train the model, thereby improving the model's prediction or decision-making capabilities; Use the validation set to conduct preliminary verification of the trained model, evaluate the model's prediction or decision-making capabilities in known scenarios, and ensure the reliability and stability of the model; Adjust and optimize the model based on the verification results, such as adjusting parameters, changing the model structure, etc., to improve the model's accuracy and generalization capabilities; Combine models for different scenarios to form a unified scenario model library, which facilitates the management and use of the model; Establish a model management mechanism to maintain and update the scenario model library, ensuring the timeliness and effectiveness of the model; Through the prediction and decision-making of the scenario model, enterprises can respond to after-sales needs more quickly and accurately, and improve after-sales service efficiency; Based on the prediction results of the model, enterprises can allocate resources more reasonably, reduce unnecessary waste, and improve resource utilization efficiency.

[0134] In one embodiment of the present invention, the S34 includes:

[0135] Establish a data integration mechanism to seamlessly connect real-time data with historical data to form a complete data flow, clean the fused data, remove noise and outliers, and perform standardization;

[0136] Adopting incremental learning algorithms, the situational model can be updated based on newly added real-time data. The parameters of the situational model can be fine-tuned according to the characteristics of real-time data and changes in business scenarios.

[0137] Conduct detailed evaluation of simulation results and establish a feedback mechanism to collect user feedback and business data in actual business scenarios;

[0138] Compare and analyze simulation results with actual business feedback to identify problems and deficiencies in the model;

[0139] According to the results of comparative analysis, the structure of the situation model is optimized, and features are selected and enhanced based on domain knowledge and actual business scenarios;

[0140] And through ensemble learning methods, multiple situation models are combined;

[0141] Establish a regular evaluation mechanism to regularly evaluate and update the scenario model, use actual business feedback as input, and continuously iterate and optimize the scenario model through a feedback closed-loop mechanism to form a cycle of continuous optimization.

[0142] The working principle of the above technical solution is as follows: the data integration mechanism ensures that real-time data and historical data can be seamlessly connected to form a complete data flow. Real-time data reflects the current business status and user behavior, while historical data provides past experience and trends; the fused data is cleaned to remove noise and outliers to ensure the accuracy and reliability of the data. At the same time, the data is standardized to eliminate the dimensional differences between data of different dimensions, providing a consistent data basis for subsequent model updates and evaluations; an incremental learning algorithm is used to enable the situation model to self-update based on the newly added real-time data. This algorithm can gradually learn new data without affecting the existing knowledge of the model, so that the model keeps in sync with the business scenario; fine-tune the parameters of the situation model according to the characteristics of real-time data and changes in business scenarios. This fine-tuning can be automatic or based on the judgment and suggestions of domain experts or business personnel. By fine-tuning parameters, the model can better adapt to new business environments and user needs; the results of simulations are evaluated in detail, including accuracy, stability and reliability. This evaluation can quantify the performance of the model in a simulated environment and provide guidance for subsequent model optimization; a feedback mechanism is established to collect user feedback and business data in actual business scenarios. These feedback data can directly reflect the effects and problems of the model in actual application, and provide a direct basis for model optimization; according to the results of comparative analysis, the structure of the situational model is optimized. This may include increasing or decreasing the level of the model, changing the connection mode of the model, etc. By optimizing the model structure, the expressiveness and prediction accuracy of the model can be improved; based on domain knowledge and actual business scenarios, features are selected and enhanced. Selecting appropriate features can improve the prediction ability of the model, while enhancing features can further mine useful information in the data and improve the robustness of the model; multiple situational models are combined through ensemble learning methods such as bagging and boosting. This combination can improve the stability and generalization ability of the model and reduce the deviations and errors that may exist in a single model; establish a regular evaluation mechanism to regularly evaluate and update the situational model. This evaluation can be based on multiple indicators such as simulation results and actual business feedback to comprehensively evaluate the performance and application effect of the model; through the feedback closed-loop mechanism, the situational model is continuously iterated and optimized. This iterative optimization can be based on new data, new business scenarios or new requirements to ensure that the model always keeps pace with and adapts to the business environment.

[0143] The effects of the above technical solutions are as follows: the seamless connection between real-time data and historical data ensures the integrity and continuity of the data flow, providing a solid data foundation for subsequent model training and updating; through data cleaning and standardization processing, noise and outliers are effectively removed, the quality and reliability of data are improved, thereby enhancing the accuracy and stability of model training; the incremental learning algorithm is used to enable the situational model to update itself based on the newly added real-time data, so as to quickly respond to changes in business scenarios; according to the characteristics of real-time data and changes in business scenarios, the model parameters are fine-tuned to make the model more in line with actual business needs, improving the accuracy and effectiveness of predictions and decisions; a detailed evaluation of the results generated by the simulation, including accuracy, stability and reliability, provides comprehensive guidance for model optimization; a feedback mechanism is established to collect user feedback and business data in actual business scenarios to provide guidance for model iteration. It provides a direct basis for the optimization and optimization; according to the results of comparative analysis, the structure of the situational model is optimized, such as increasing or decreasing the level of the model, changing the connection method of the model, etc., which improves the expressiveness and prediction accuracy of the model; based on domain knowledge and actual business scenarios, features are selected and enhanced to further mine useful information in the data and improve the robustness and generalization ability of the model; through integrated learning methods such as Bagging and Boosting, multiple situational models are combined to improve the stability and prediction accuracy of the model and reduce the deviations and errors that may exist in a single model; a regular evaluation mechanism is established to regularly evaluate and update the situational model to ensure that the model is always synchronized and adaptable to the business environment; through the feedback closed-loop mechanism, the situational model is continuously iterated and optimized to form a continuous optimization cycle to ensure the continuous improvement of model performance and business results.

[0144] In one embodiment of the present invention, the S4 includes:

[0145] S41. Based on the results of deep analysis and situational simulation, an adaptive learning model is constructed, and initial decision rules and parameters are set. The after-sales trigger strategy and decision rules are automatically adjusted based on historical data and real-time feedback using reinforcement learning algorithms;

[0146] S42. Evaluate the effect of the adjusted decision and collect direct feedback from customers;

[0147] S43. Based on the evaluation results and feedback, the decision rules are optimized and iterated, and the adaptive learning mechanism is expanded.

[0148] The working principle of the above technical solution is as follows: Based on the results of the in-depth analysis of after-sales needs and service scenarios and the situational simulation in the early stage, an adaptive learning model is constructed. This model aims to automatically adjust the triggering strategy and decision rules of after-sales service according to the specific situation; set the initial decision rules and parameters for the adaptive learning model. These rules and parameters are based on historical data and domain knowledge, providing preliminary behavioral guidance for the model; through the reinforcement learning algorithm, the model can automatically adjust the triggering strategy and decision rules of after-sales service based on historical data and real-time feedback. Reinforcement learning allows the model to learn which strategies are more effective based on feedback (such as customer satisfaction) after trying different actions (i.e. different after-sales service strategies); evaluate the decision effect after the model is automatically adjusted. The evaluation criteria include key business indicators such as customer satisfaction and service response time. Customer satisfaction reflects the customer's satisfaction with after-sales service, while service response time measures the timeliness and efficiency of the service; in addition to the evaluation indicators, customer feedback is also collected directly. Customer direct feedback provides intuitive information about the quality of after-sales service, which helps to more accurately understand the effect and potential problems of the model; based on the evaluation results and customer feedback, the decision rules of the adaptive learning model are optimized and iterated. This can be achieved by adjusting the parameters of the model, improving the learning algorithm, or introducing new features; extending the optimized adaptive learning mechanism to more fields and scenarios, such as product recommendations, market forecasts, etc. This expansion can leverage the model's successful experience in the after-sales service field to provide intelligent decision support for other fields and achieve broader value.

[0149] The effects of the above technical solutions are as follows: by building an adaptive learning model, the after-sales service system can quickly adjust the trigger strategy and decision rules according to real-time data and feedback, so as to flexibly respond to changes in various business scenarios; by using the reinforcement learning algorithm, the model can autonomously learn and optimize decisions, which improves the intelligence level of after-sales service and reduces the need for manual intervention; the initial decision rules and parameter settings are based on in-depth analysis and situational simulation, which can better meet the personalized needs of different customer groups and improve customer satisfaction; through the evaluation of customer satisfaction and service response time, the effect of after-sales service can be accurately understood, providing strong support for subsequent optimization; directly collecting customer feedback By using the evaluation results and feedback, we can understand customers’ needs and expectations more directly and further improve service quality. Based on the evaluation results and feedback, we can continuously improve after-sales service to ensure continuous improvement in service quality and customer satisfaction. According to the evaluation results and feedback, we can optimize and iterate the decision-making rules to continuously improve the accuracy and efficiency of after-sales service and ensure that the service quality always remains at the leading position. By extending the adaptive learning mechanism to more fields and scenarios, such as product recommendation and market forecast, we can further broaden the service scope and improve the added value and competitiveness of services. Through continuous expansion and optimization, the after-sales service system can better adapt to changes in the market and customers and maintain the ability to continuously innovate.

[0150] In one embodiment of the present invention, the S41 includes:

[0151] Collect the output results of deep analysis and scenario simulation, extract key features from the results, and analyze the correlation between key features and after-sales needs and service effects;

[0152] Based on business logic and domain knowledge, design the initial decision rule framework, clarify the triggering conditions and execution actions of the rules, and set initial values ​​for the parameters in the decision rules based on historical data and expert experience;

[0153] According to the complexity of the business scenario and the characteristics of the data, select the reinforcement learning algorithm, build a reinforcement learning environment corresponding to the after-sales scenario, and use the situational simulation results to simulate the environment;

[0154] Train reinforcement learning agents in a simulation environment to generate optimized post-sales trigger strategies and decision rules through trial and error and feedback adjustment;

[0155] Collect real-time feedback from customers on after-sales services, merge the real-time feedback with historical data, and update the training data set of the reinforcement learning agent;

[0156] Regularly evaluate the triggering strategies and decision rules generated by the reinforcement learning agent to measure their performance under historical data and real-time feedback;

[0157] Based on the evaluation results, the parameters of the triggering strategy and decision rules are automatically adjusted using reinforcement learning algorithms.

[0158] The working principle of the above technical solution is: collect the output results of deep analysis and scenario simulation, which contain key information such as after-sales demand prediction and service scenario simulation results under different scenarios. Then, extract key features from these results, and analyze the correlation between these features and after-sales demand and service effect. This step is to ensure that the model built later can accurately capture the key factors affecting after-sales service. Based on business logic and domain knowledge, design the initial decision rule framework. This framework clarifies the trigger conditions and execution actions of the rules, providing clear guidance for after-sales service. At the same time, according to historical data and expert experience, set initial values ​​for the parameters in the decision rules, such as trigger thresholds, weights, etc. These initial values ​​will serve as the basis for subsequent learning. According to the complexity of the business scenario and the characteristics of the data, select appropriate reinforcement learning algorithms, such as Q-learning, PolicyGradient, etc. Then, build a reinforcement learning environment corresponding to the after-sales scenario. This environment simulates the real after-sales service scenario, including customer requests, service responses, feedback and other links. Use the scenario simulation results to simulate the environment and provide training data for the reinforcement learning agent. Train the reinforcement learning agent in the simulation environment. The agent adjusts its behavior strategy by continuous trial and error and receiving feedback to maximize long-term benefits. In this process, the agent generates optimized after-sales trigger strategies and decision rules. These strategies and rules can be automatically adjusted according to real-time data and feedback to adapt to different business scenarios. Collect real-time feedback from customers on after-sales service, including key indicators such as satisfaction and response time. These feedback data can reflect the actual effect of after-sales service and customer needs. Integrate real-time feedback with historical data to update the training data set of the reinforcement learning agent. In this way, the agent can optimize its decision-making ability based on richer data. Regularly evaluate the trigger strategies and decision rules generated by the reinforcement learning agent. The evaluation process mainly measures the performance of these strategies and rules under historical data and real-time feedback. Through the evaluation results, we can understand the pros and cons of the current strategies and rules and the directions that need to be improved. Based on the evaluation results, the parameters of the trigger strategies and decision rules are automatically adjusted using the reinforcement learning algorithm. This process is an iterative process. By constantly trying new parameter combinations and evaluating their effects, the optimal parameter settings are finally found. This automatic adjustment mechanism ensures that after-sales service is always in the best state.

[0159] The effect of the above technical solution is: by collecting the output results of deep analysis and scenario simulation and extracting key features, we can deeply understand the relationship between after-sales demand and service effect. This helps to formulate more accurate and business-oriented decision rules; collect customer feedback in real time and integrate it with historical data to ensure the timeliness and richness of the training data set. This data-driven approach makes after-sales service decisions more in line with customer needs and market changes; the use of reinforcement learning algorithms such as Q-learning and PolicyGradient enables the after-sales service system to automatically learn and optimize trigger strategies and decision rules based on historical data and real-time feedback. This intelligent and adaptive optimization capability greatly improves the efficiency and quality of after-sales service; through continuous trial and error and feedback adjustment, the reinforcement learning agent can generate optimized after-sales trigger strategies and decision rules. These rules and strategies can be continuously iterated and optimized in practice to adapt to complex and changing business scenarios; the initial decision rule framework designed based on business logic and domain knowledge provides clear guidance for after-sales service. At the same time, the initial parameter values ​​set based on historical data and expert experience also provide a basis for subsequent automatic adjustments; the trigger strategies and decision rules generated by the reinforcement learning agent are regularly evaluated, and the parameters are automatically adjusted based on the evaluation results. This regular evaluation and parameter adjustment can ensure that the after-sales service system is always in the best state and improve decision-making efficiency and effectiveness; by collecting customer feedback in real time, the after-sales service system can promptly understand customer needs and expectations and respond quickly. This ability to respond quickly helps to improve customer satisfaction and loyalty; based on the results of deep analysis and situational simulation and the application of reinforcement learning algorithms, the after-sales service system can provide customers with more personalized and accurate services. This personalized service can better meet customer needs and expectations and improve customer satisfaction.

[0160] In one embodiment of the present invention, S5 includes:

[0161] S51. Automatically assign after-sales service tasks according to the decision instructions generated by the adaptive learning model, and optimize the decision instructions based on multiple factors;

[0162] S52, automatically execute assigned service tasks through the intelligent execution system and work collaboratively with relevant personnel;

[0163] S53. Use the Internet of Things and smart devices to monitor the service execution process in real time, handle abnormal situations during the execution process, and issue warnings and notifications;

[0164] S54. Collect real-time feedback data from customers during the service process, process and analyze it, regularly evaluate the quality of service execution, and make improvements and optimizations based on the evaluation results.

[0165] The working principle of the above technical solution is as follows: according to the decision instructions output by the adaptive learning model, the system starts to assign after-sales service tasks; when assigning tasks, the system considers multiple factors for optimization, including the priority of the task (such as urgency, importance, etc.) and the skill matching of the executor. Such optimization ensures that the task can be assigned to the most suitable person, improving service efficiency and customer satisfaction; the system automatically executes the assigned service tasks through the intelligent execution system. This can include automatically sending notifications, allocating work resources, etc.; during the execution process, the system collaborates with relevant personnel (such as customer service, maintenance personnel, etc.). This may involve information sharing, task updates, progress feedback, etc., to ensure that all parties can cooperate efficiently and respond quickly to customer needs; using IoT technology and smart devices, the system can monitor the service execution process in real time. This includes tracking the location, work status, equipment status, etc. of the service personnel; during the monitoring process, once an abnormal situation (such as equipment failure, service delay, etc.) is found, the system will immediately handle it, including sending early warning notifications, adjusting task allocation, and providing solutions. This real-time processing capability helps to respond to problems quickly and reduce service interruption time. During the service process, the system collects real-time feedback data from customers, including satisfaction evaluation, problem description, suggestions, etc. The collected feedback data is processed and analyzed to evaluate the quality of service execution. This includes calculating indicators such as customer satisfaction and problem resolution rate. Based on the evaluation results, the system identifies problems and deficiencies in service execution and takes corresponding improvement measures to optimize it. This may involve adjusting decision rules, optimizing task allocation, improving personnel skills, etc.

[0166] The effects of the above technical solutions are as follows: through the decision instructions generated by the adaptive learning model, after-sales service tasks can be automatically assigned, which greatly reduces the time and cost of manual intervention and improves overall work efficiency; based on multiple factors (such as task priority and personnel skills), the decision instructions are optimized to ensure that tasks are assigned to the most appropriate personnel, improving resource utilization and service quality; through the intelligent execution system, service tasks can be automatically executed, reducing human errors and delays, and improving service response speed and accuracy; collaborative work with relevant personnel (such as customer service, maintenance personnel, etc.) ensures timely sharing and updating of information, improves team collaboration efficiency and service quality; uses the Internet of Things and smart devices to monitor the service execution process in real time, Managers can understand the service status in real time and make timely decisions and adjustments; quickly handle abnormal situations during the execution process, reduce service interruption time, and improve customer satisfaction and loyalty; through early warning and notification functions, timely remind relevant personnel to deal with potential problems, reduce risks and improve service quality; collect real-time feedback data from customers during the service process, pay attention to customer experience and needs, and make services more in line with customer expectations; regularly evaluate the quality of service execution by processing and analyzing feedback data, and make improvements and optimizations based on the evaluation results to ensure continuous improvement in service quality; service quality evaluation based on customer feedback data provides data support for management, facilitating the formulation of more effective service strategies and plans.

[0167] In one embodiment of the present invention, the S53 includes:

[0168] Connect IoT devices and smart devices involved in after-sales service, perform necessary configurations, integrate data from different devices and systems, and standardize data formats;

[0169] Monitor the data flow generated during service execution in real time and display the monitoring data in a visual form;

[0170] Based on historical data and business logic, define abnormal situations during service execution, set corresponding thresholds, analyze real-time data, and detect whether abnormal situations occur;

[0171] For predictable abnormal situations, as well as complex or unpredictable abnormal situations, the manual intervention process is triggered to notify relevant personnel for emergency processing;

[0172] Among them, the predictable abnormal situations include:

[0173] Equipment performance degradation: After running for a long time, some equipment may suffer performance degradation due to overheating, excessive resource usage, etc. Such abnormal conditions can be predicted by the equipment monitoring system. Once the performance indicators reach the preset threshold, the system can automatically restart the equipment to restore the normal operation of the equipment.

[0174] Network connection interruption: In network services, network connection may be interrupted for various reasons, such as unstable signal, equipment failure, etc. When the system detects that the network connection is interrupted, it can automatically switch to the backup network or reconnect the network to ensure the continuity of service.

[0175] Insufficient resources: During the service execution process, you may encounter insufficient resources, such as insufficient memory, insufficient storage space, etc. The system can monitor the usage of these resources and automatically release unnecessary resources or trigger the resource application process when the preset threshold is reached.

[0176] Complex or unpredictable abnormal situations include

[0177] Hardware failure: Hardware failure is usually difficult to predict, such as motherboard damage, hard disk failure, etc. Such abnormal situations require professional technicians to diagnose and repair. The system can detect signs of hardware failure through the device monitoring system, such as abnormal device status, error information, etc., and trigger the manual intervention process to notify relevant personnel for emergency processing.

[0178] Software conflicts: During the service execution process, you may encounter software conflicts, such as incompatibility between different versions of software, software installation errors, etc. Such abnormal situations may cause the service to not operate normally or to exhibit abnormal behavior. The system can detect signs of software conflicts through log analysis, error reporting, etc., and trigger the manual intervention process, where professional technicians will analyze and repair the problem.

[0179] External factors: External factors such as natural disasters and man-made damage may cause unpredictable abnormal situations during service execution. The system needs to be able to deal with such abnormal situations, such as ensuring service continuity and availability through backup recovery and emergency switching. At the same time, the system also needs to be able to promptly notify relevant personnel for emergency processing to reduce losses and impacts.

[0180] Based on historical data and business needs, set warning rules, clarify the situations in which warnings need to be issued, and select notification methods based on the urgency of the warning and the preferences of the recipients;

[0181] Regularly evaluate the effectiveness of early warning notifications and make improvements based on the evaluation results. For complex situations that arise during service execution, introduce judgment logic to analyze and judge, and decide on response strategies;

[0182] Based on real-time data and historical experience, a decision support system is built to record all abnormal situations in detail, and statistical analysis of the recorded abnormal situations is performed regularly to find out the causes and patterns of the abnormal situations.

[0183] The working principle of the above technical solution is as follows: connect the IoT devices and smart devices involved in after-sales service, and ensure that they can transmit data in real time through necessary configuration. Integrate data from different devices and systems, and manage and analyze data from different sources on a unified platform through data standardization. Monitor the data streams generated during service execution in real time, including service progress, personnel behavior, equipment status, etc. Display the monitoring data in a visual form, such as a real-time dashboard, service flow chart, etc., so that relevant personnel can quickly understand the service status and make timely decisions. Define abnormal situations during service execution based on historical data and business logic, and set corresponding thresholds. Analyze real-time data to detect whether abnormal situations occur. For predictable abnormal situations, design automatic processing strategies, such as automatically restarting the device, sending reminder notifications, etc. For complex or unpredictable abnormal situations, trigger the manual intervention process and notify relevant personnel for emergency processing. Set warning rules based on historical data and business needs to clarify the situations in which warnings need to be issued. According to the urgency of the warning and the preferences of the recipient, choose the appropriate notification method for notification, such as SMS, email, APP push, etc. Regularly evaluate the effectiveness of early warning notifications, including indicators such as reception rate and response time. Improve the early warning notification process based on the evaluation results to improve the effectiveness and timeliness of early warnings. For complex situations that arise during service execution, introduce judgment logic to analyze and judge, and decide on response strategies. Build a decision support system based on real-time data and historical experience. Record all abnormal situations in detail, including abnormality type, occurrence time, and handling process. Regularly conduct statistical analysis of recorded abnormal situations to find out the causes and patterns of abnormalities and provide data support for future service optimization.

[0184] The effects of the above technical solutions are as follows: real-time data transmission of IoT devices and smart devices ensures that the after-sales service team can immediately obtain the latest information during the service execution process; by real-time monitoring of the data flow during the service execution process, problems and bottlenecks in the service can be discovered in a timely manner, providing the possibility for rapid response and processing; for predictable abnormal situations, automatic processing strategies such as automatic restart of equipment, sending reminder notifications, etc. can greatly reduce the burden of manual intervention and improve processing efficiency; for complex or unpredictable abnormal situations, the introduction of judgment logic for analysis and judgment helps to quickly locate problems and make reasonable response strategies; early warning rules set based on historical data and business needs can accurately predict potential problems and issue early warnings to avoid service interruptions or failures; select appropriate notification methods according to the recipient's preferences, such as SMS, email, APP push, etc., to ensure that the warning information can be conveyed to relevant parties in a timely and accurate manner. personnel; regularly evaluate the effectiveness of early warning notifications, including reception rate, response time, etc., which helps to discover deficiencies in the early warning mechanism and make corresponding improvements; build a decision support system to record and analyze all abnormal situations in detail, provide data support for management, and facilitate the formulation of more effective service strategies and plans; display monitoring data in a visual form, such as real-time dashboards, service flow charts, etc., so that relevant personnel can intuitively and quickly understand the service status and execution status; the use of visualization tools helps different departments to communicate and collaborate better and jointly solve problems encountered in the service execution process; record and analyze all abnormal situations in detail, which helps to discover the causes and patterns of abnormalities and provide data support for future service optimization; through the early warning and exception handling mechanism, potential risks can be discovered and handled in a timely manner, the impact of service failures on customers can be reduced, and customer satisfaction and loyalty can be improved.

[0185] In one embodiment of the present invention, S6 includes:

[0186] S61. Establish a feedback mechanism and collect feedback from different groups, pre-process the collected feedback data, and classify the pre-processed feedback data by type;

[0187] S62. Use natural language processing algorithms to perform sentiment analysis and topic extraction on text-based feedback data, and use machine learning algorithms to conduct in-depth analysis and mining of feedback data;

[0188] S63. Display the analysis results in a visual way, set key indicators and dashboards, and monitor the changes and trends of feedback data in real time;

[0189] S64. Generate optimization suggestions for the deep analysis model, the situation simulation model, and the adaptive learning model based on the feedback analysis results, and verify and test the optimization suggestions;

[0190] S65. Apply the verified optimization suggestions to the actual model, perform iterative updates, deploy the updated model to the production environment, and monitor its operation and effects;

[0191] S66. Document and build a knowledge base for each optimization and iteration process, and establish a mechanism for continuous optimization cycles.

[0192] The working principle of the above technical solution is as follows: collect customer feedback through multiple channels such as online surveys, customer service calls, social media, feedback within mobile applications, and email subscriptions; pre-process the collected feedback data, including removing duplicate, invalid or irrelevant information, and then classify the pre-processed feedback data by type (such as product quality issues, service experience, etc.) for subsequent analysis; use natural language processing algorithms to perform sentiment analysis on text-type feedback data to understand the customer's satisfaction with the product or service; extract key topics from the feedback data through natural language processing algorithms to discover the main concerns of customer feedback; combine machine learning algorithms to conduct in-depth analysis and mining of feedback data to discover potential problems, trends or opportunities; display the analysis results in a visual way, such as using tools such as charts and dashboards to facilitate Enable relevant personnel to intuitively understand the changes and trends of feedback data; set key indicators and dashboards to monitor the changes and trends of feedback data in real time to ensure timely discovery and handling of problems; generate optimization suggestions for deep analysis models, scenario simulation models, and adaptive learning models based on feedback analysis results; verify and test the generated optimization suggestions to ensure the effectiveness and feasibility of the optimization suggestions; apply the verified optimization suggestions to the actual model for iterative updates; deploy the updated model to the production environment, and monitor its operation and effects to ensure that the model can continue to provide customers with high-quality services; document and knowledge base each optimization and iteration process for subsequent reference and reference; establish a mechanism for continuous optimization cycles to ensure that the company can continuously learn and improve from customer feedback and improve the quality of products and services.

[0193] The effects of the above technical solutions are: through multiple channels such as online surveys, customer service calls, social media, feedback within mobile applications, and email subscription feedback, it is ensured that customer feedback from different groups can be fully collected; pre-processing and classification of the collected feedback data helps to more accurately understand the intention and focus of customer feedback, and provide a basis for subsequent analysis; using natural language processing algorithms to perform sentiment analysis and topic extraction on text-type feedback data can deeply understand customers' satisfaction with products or services and their main concerns; through in-depth analysis and mining of feedback data through machine learning algorithms, potential problems, trends or opportunities can be discovered to support corporate decision-making; the analysis results are displayed in a visual way, so that relevant personnel can intuitively and quickly understand the changes and trends in feedback data; setting key indicators and dashboards to monitor the changes and trends of feedback data in real time helps companies to issue timely The company can identify and solve problems and improve service quality; generate optimization suggestions for deep analysis models, situational simulation models and adaptive learning models based on feedback analysis results, which helps to improve the accuracy and efficiency of the models; through in-depth analysis of customer feedback data, enterprises can more accurately understand market and customer needs, and provide strong decision-making support for product development, market strategy formulation, etc.; apply verified optimization suggestions to actual models and iteratively update them, which helps the models to continuously adapt to market changes and changes in customer needs; establish a mechanism for continuous optimization cycles to ensure that enterprises can continuously learn and improve from customer feedback and improve the quality of products and services; document and document each optimization and iteration process, which helps enterprises accumulate valuable experience and knowledge and lay a solid foundation for future development; by establishing a knowledge base, these experiences and knowledge can be better managed and utilized, and the learning and innovation capabilities of the entire organization can be enhanced.

[0194] One embodiment of the present invention is a post-sales trigger management system based on multi-field analysis and fusion, the system comprising:

[0195] Data collection module: collects multi-dimensional data from different channels and systems, and integrates the collected multi-dimensional data;

[0196] Network construction module: Use deep learning models to deeply analyze multi-field data in multi-dimensional data, extract key features and patterns, and combine domain knowledge to build a feature association network;

[0197] Simulation module: Build a multi-dimensional situational model based on multi-dimensional data. Through simulation, simulate after-sales demand and service scenarios under different situations, and dynamically adjust the situational model by combining historical data and real-time data.

[0198] Model building module: Based on the results of deep analysis and situational simulation, an adaptive learning model is built to automatically adjust the after-sales trigger strategy and decision rules through reinforcement learning algorithms and based on historical data and real-time feedback;

[0199] Real-time feedback module: Through the intelligent execution system, the after-sales service tasks are automatically executed according to the decision instructions generated by the adaptive learning model, and the service execution process is monitored in real time through the Internet of Things and smart devices to collect real-time feedback data;

[0200] Optimization and adjustment module: Establish a feedback mechanism to collect feedback from different groups, analyze and mine the feedback data through machine learning algorithms, identify potential problems and improvement points, and continuously optimize the deep analysis model, situational simulation model and adaptive learning model based on the feedback results.

[0201] The working principle of the above technical solution is as follows: collect multi-dimensional data related to after-sales service from multiple sources (such as customer relationship management systems, sales systems, product usage logs, social media, etc.); pre-process the collected raw data to remove duplicate, erroneous or invalid data to ensure the accuracy and completeness of the data; integrate the cleaned data into a unified data warehouse or data lake for subsequent analysis and processing; use deep learning models (such as CNN and LSTM) to parse the integrated data at the field level to identify the key information and features in each field; based on the parsing results, extract the key features and patterns related to after-sales service, which may include product failure types, customer demand preferences, market trends, etc.; combine domain knowledge to build a feature association network to analyze the potential connections and mutual influences between different fields in order to better understand the complexity and dynamics of after-sales service; build multi-dimensional situational models based on the extracted key features and patterns, which describe after-sales needs and service scenarios in different situations; use simulation technology to simulate the situational models, simulate the after-sales service process, resource allocation, response speed, etc. in different situations to evaluate the effectiveness and efficiency of after-sales service; combine historical data and real-time data to analyze the situation Dynamically adjust the after-sales service model to adapt to the ever-changing actual situation and market demand; build an adaptive learning model based on the results of deep analysis and situation simulation, which can automatically learn and adapt to the complexity and uncertainty in after-sales service; use reinforcement learning algorithms to automatically adjust after-sales trigger strategies and decision rules based on historical data and real-time feedback to achieve the best after-sales service effect; make specific after-sales service decisions based on the output results of the adaptive learning model, such as service content, service time, service method, etc.; automatically execute after-sales service tasks according to the decision instructions generated by the adaptive learning model through intelligent execution systems (such as automation tools, robots, etc.); use the Internet of Things and smart devices to monitor the service execution process in real time, including personnel behavior, equipment status, service progress, etc.; collect real-time feedback data during the service execution process, such as customer feedback, service results, etc., for subsequent analysis and optimization; establish a feedback mechanism to collect feedback from different groups (such as customers, service teams, and other stakeholders); use machine learning algorithms to analyze and mine feedback data to find potential problems and improvement points; based on the feedback results, continuously optimize the deep analysis model, situation simulation model, and adaptive learning model to improve the quality and efficiency of after-sales service.

[0202] The effects of the above technical solutions are as follows: by collecting multi-dimensional data from different channels and systems, integrating and analyzing them, enterprises can obtain more comprehensive and accurate information. This makes after-sales service decisions more data-based, reduces subjective assumptions, and improves the scientificity and accuracy of decision-making; using deep learning models (such as CNN and LSTM) to deeply analyze multi-field data, it is possible to extract key features and patterns and predict possible after-sales needs and service scenarios. This enables enterprises to prepare in advance, respond quickly to customer needs, and improve the timeliness of services and customer satisfaction; by building a multi-dimensional scenario model and combining historical data with real-time data for simulation, enterprises can simulate after-sales service processes and effects in different scenarios. This enables enterprises to adjust service strategies according to different scenarios and better adapt to market changes and customer needs; based on the results of deep analysis and scenario simulation, an adaptive learning model is built, and after-sales trigger strategies and decision rules are automatically adjusted through reinforcement learning algorithms. This adaptive learning mechanism enables enterprises to continuously learn and improve, continuously optimize service processes and quality, and improve service efficiency and customer satisfaction; through the intelligent execution system, enterprises can automatically execute after-sales service tasks and monitor the service execution process in real time through the Internet of Things and smart devices. This reduces human intervention and errors, and improves the accuracy and efficiency of service execution. At the same time, the collection of real-time feedback data helps companies to identify problems and make improvements in a timely manner; establish a feedback mechanism, collect feedback from different groups, and analyze and mine the feedback data through machine learning algorithms. This helps companies identify potential problems and improvement points, and continuously optimize the deep analysis model, situation simulation model, and adaptive learning model based on the feedback results. This continuous feedback and optimization cycle enables companies to continuously improve and enhance the quality and efficiency of after-sales service.

[0203] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A post-sales trigger management method based on multi-field analysis and fusion, characterized in that: The method comprises: S1. Collect multi-dimensional data from different channels and systems and integrate the collected multi-dimensional data; the multi-dimensional data includes customer information, product status and service records; S2. Use deep learning models to deeply analyze multi-field data, extract key features and patterns, and build a feature association network based on domain knowledge; S3. Build a multi-dimensional situational model based on multi-dimensional data, simulate after-sales demand and service scenarios under different situations through simulation, and dynamically adjust the situational model by combining historical data and real-time data; S4. Based on the results of deep analysis and situational simulation, an adaptive learning model is built to automatically adjust the after-sales trigger strategy and decision rules through reinforcement learning algorithms and based on historical data and real-time feedback; S5. Through the intelligent execution system, the after-sales service tasks are automatically executed according to the decision instructions generated by the adaptive learning model, and the service execution process is monitored in real time through the Internet of Things and smart devices to collect real-time feedback data; S6. Establish a feedback mechanism to collect feedback from different groups. Use machine learning algorithms to analyze and mine the feedback data to identify potential problems and areas for improvement. Based on the feedback results, continuously optimize the deep analysis model, situational simulation model, and adaptive learning model.

2. The after-sales trigger management method based on multi-field analysis and fusion according to claim 1 is characterized in that: Said S1 comprises: S11. Identify and classify data sources, and set a dedicated data interface for each data source, and receive multi-dimensional data in real time through the data interface; S12, preprocessing the collected multi-dimensional data, wherein the preprocessing includes data cleaning, removing redundant, repeated or erroneous data, and converting the preprocessed data into a unified format and standard; S13. Establish a data quality monitoring mechanism to regularly check data accuracy and completeness, and mark and process problematic data; S14. Compress the preprocessed and standardized multi-dimensional data, and store the compressed results in a unified data warehouse or data lake through a multi-channel transmission protocol, and dynamically adjust the compression ratio based on the network bandwidth.

3. According to the after-sales trigger management method based on multi-field analysis and fusion according to claim 1, it is characterized in that: The S2 comprises: S21. Select key feature fields from the data warehouse or data lake for in-depth analysis based on business needs and data characteristics; S22, performing preliminary processing on the selected fields, wherein the preliminary processing includes data scaling and encoding conversion, and enhancing the generalization ability of the model through a data enhancement algorithm; S23, selecting a deep learning model, and using the data set to train the deep learning model; S24. Combine domain knowledge and expert experience to analyze the potential connections and mutual influences between different fields, and build a feature association network based on the analysis results; S25. Evaluate the deep learning model using multiple indicators, and optimize and adjust the deep learning model according to the evaluation results.

4. According to claim 1, the after-sales trigger management method based on multi-field analysis and fusion is characterized in that: The S3 includes: S31. Classify after-sales needs and service scenarios according to business scenarios, and define scenarios for each category; S32. Based on multi-dimensional data, build different models for each scenario and use parameterization to represent the key factors in different scenarios; S33. Through simulation, simulate after-sales demand and service scenarios under different scenarios, and record the simulation results; S34. Integrate real-time data with historical data, and continuously update the situational model. Evaluate and optimize the situational model based on simulation results and actual business feedback.

5. The after-sales trigger management method based on multi-field analysis and fusion according to claim 4 is characterized in that: The S34 comprises: Establish a data integration mechanism to seamlessly connect real-time data with historical data to form a complete data flow, clean the fused data, remove noise and outliers, and perform standardization; Adopting incremental learning algorithms, the situational model can be updated based on newly added real-time data. The parameters of the situational model can be fine-tuned according to the characteristics of real-time data and changes in business scenarios. Conduct detailed evaluation of simulation results and establish a feedback mechanism to collect user feedback and business data in actual business scenarios; Compare and analyze simulation results with actual business feedback to identify problems and deficiencies in the model; According to the results of comparative analysis, the structure of the situation model is optimized, and features are selected and enhanced based on domain knowledge and actual business scenarios; And through ensemble learning methods, multiple situation models are combined; Establish a regular evaluation mechanism to regularly evaluate and update the scenario model, use actual business feedback as input, and continuously iterate and optimize the scenario model through a feedback closed-loop mechanism to form a cycle of continuous optimization.

6. According to claim 1, the after-sales trigger management method based on multi-field analysis and fusion is characterized in that: The S4 comprises: S41. Based on the results of deep analysis and situational simulation, an adaptive learning model is constructed, and initial decision rules and parameters are set. The after-sales trigger strategy and decision rules are automatically adjusted based on historical data and real-time feedback using reinforcement learning algorithms; S42. Evaluate the effect of the adjusted decision and collect direct feedback from customers; S43. Based on the evaluation results and feedback, the decision rules are optimized and iterated, and the adaptive learning mechanism is expanded.

7. The after-sales trigger management method based on multi-field analysis and fusion according to claim 1 is characterized in that: The S5 comprises: S51. Automatically assign after-sales service tasks according to the decision instructions generated by the adaptive learning model, and optimize the decision instructions based on multiple factors; S52, automatically execute assigned service tasks through the intelligent execution system and work collaboratively with relevant personnel; S53. Use the Internet of Things and smart devices to monitor the service execution process in real time, handle abnormal situations during the execution process, and issue warnings and notifications; S54. Collect real-time feedback data from customers during the service process, process and analyze it, regularly evaluate the quality of service execution, and make improvements and optimizations based on the evaluation results.

8. The after-sales trigger management method based on multi-field analysis and fusion according to claim 7 is characterized in that: The S53 comprises: Connect and configure IoT devices and smart devices involved in after-sales service, integrate data from different devices and systems, and standardize data formats; Monitor the data flow generated during service execution in real time and display the monitoring data in a visual form; Based on historical data and business logic, define abnormal situations during service execution, set corresponding thresholds, analyze real-time data, and detect whether abnormal situations occur; For predictable abnormal situations, for complex or unpredictable abnormal situations, trigger the manual intervention process and notify relevant personnel for emergency processing; Based on historical data and business needs, set warning rules, clarify the situations in which warnings need to be issued, and select notification methods based on the urgency of the warning and the preferences of the recipients; Regularly evaluate the effectiveness of early warning notifications and make improvements based on the evaluation results. For complex situations that arise during service execution, introduce judgment logic to analyze and judge, and decide on response strategies; Based on real-time data and historical experience, a decision support system is built to record all abnormal situations in detail, and statistical analysis of the recorded abnormal situations is performed regularly to find out the causes and patterns of the abnormal situations.

9. The after-sales trigger management method based on multi-field analysis and fusion according to claim 1 is characterized in that: The S6 comprises: S61. Establish a feedback mechanism and collect feedback from different groups, pre-process the collected feedback data, and classify the pre-processed feedback data by type; S62. Use natural language processing algorithms to perform sentiment analysis and topic extraction on text-based feedback data, and use machine learning algorithms to conduct in-depth analysis and mining of feedback data; S63. Display the analysis results in a visual way, set key indicators and dashboards, and monitor the changes and trends of feedback data in real time; S64. Generate optimization suggestions for the deep analysis model, the situation simulation model, and the adaptive learning model based on the feedback analysis results, and verify and test the optimization suggestions; S65. Apply the verified optimization suggestions to the actual model, perform iterative updates, deploy the updated model to the production environment, and monitor its operation and effects; S66. Document and build a knowledge base for each optimization and iteration process, and establish a mechanism for continuous optimization cycles.

10. An after-sales trigger management system based on multi-field analysis and fusion, characterized in that: The system comprises: Data collection module: collects multi-dimensional data from different channels and systems and integrates the collected multi-dimensional data; the multi-dimensional data includes customer information, product status and service records; Network construction module: Use deep learning models to deeply analyze multi-field data, extract key features and patterns, and combine domain knowledge to build a feature association network; Simulation module: Build a multi-dimensional situational model based on multi-dimensional data. Through simulation, simulate after-sales demand and service scenarios under different situations, and dynamically adjust the situational model by combining historical data and real-time data. Model building module: Based on the results of deep analysis and situational simulation, an adaptive learning model is built to automatically adjust the after-sales trigger strategy and decision rules through reinforcement learning algorithms and based on historical data and real-time feedback; Real-time feedback module: Through the intelligent execution system, the after-sales service tasks are automatically executed according to the decision instructions generated by the adaptive learning model, and the service execution process is monitored in real time through the Internet of Things and smart devices to collect real-time feedback data; Optimization and adjustment module: Establish a feedback mechanism to collect feedback from different groups, analyze and mine the feedback data through machine learning algorithms, identify potential problems and improvement points, and continuously optimize the deep analysis model, situational simulation model and adaptive learning model based on the feedback results.

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