A customer management method and system based on a cloud server

Abstract

The present invention relates to the technical field of customer management, and particularly to a customer management method and system based on a cloud server. The method includes the following steps: obtaining customer information data by using a cloud server; introducing an intelligent interaction plug-in based on the customer information data to obtain intelligent Q&A plug-in usage data; screening customer Q&A content from the customer information data through the intelligent Q&A plug-in usage data to obtain customer Q&A content collection data; performing data preprocessing on the customer Q&A content collection data to generate standard customer Q&A content data; performing semantic analysis on the standard customer Q&A content data to generate customer Q&A content semantic analysis data; and performing user behavior modeling on the customer information data according to the customer Q&A content semantic analysis data to generate a virtual digital twin model. The present invention improves the dynamic and precision of customer management through a cloud server, intelligent customer behavior modeling, and dynamic interaction strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of customer management, and particularly to a customer management method and system based on a cloud server. Background Art

[0002] Initially, customer management systems mainly relied on local servers for data storage and processing, and mostly used stand-alone software. This approach had drawbacks such as data silos, poor scalability, and high maintenance costs. As the scale of enterprises expanded and the amount of data grew, the traditional approach became increasingly difficult to meet the requirements of enterprises for the efficiency and real-time nature of customer management. The rise of cloud computing technology provided a new solution for customer management. By migrating the computing and data storage of the customer management system to the cloud, cloud servers enable enterprises to avoid investing a large amount of hardware resources and management costs. The operation and maintenance of the system are the responsibility of professional cloud service providers. This transformation not only significantly reduces the IT costs of enterprises but also improves the scalability, flexibility, and real-time data synchronization ability of the system. However, in current traditional customer management, customers are usually processed in a "batch" manner, and the management of customers is relatively rigid, which in turn leads to more dynamic customer management and lower accuracy. Summary of the Invention

[0003] Based on this, it is necessary to provide a customer management method and system based on a cloud server to solve at least one of the above technical problems.

[0004] To achieve the above object, a customer management method based on a cloud server, the method includes the following steps:

[0005] Step S1: Use a cloud server to obtain customer information data; introduce an intelligent interaction plugin based on the customer information data to obtain intelligent Q&A plugin usage data; screen the customer information data through the intelligent Q&A plugin usage data to obtain customer Q&A content collection data; perform data preprocessing on the customer Q&A content collection data to generate standard customer Q&A content data;

[0006] Step S2: Perform semantic analysis on the standard customer Q&A content data to generate customer Q&A content semantic analysis data; perform user behavior modeling on the customer information data according to the customer Q&A content semantic analysis data to generate a virtual digital twin model, where the virtual digital twin model includes a surface digital model and a hidden layer digital model;

[0007] Step S3: Perform customer behavior tracking on the hidden layer digital model to obtain customer behavior tracking data; perform customer behavior prediction on the hidden layer digital model based on the customer behavior tracking data to generate customer behavior prediction data; use the customer behavior prediction data to adjust the dynamic interaction strategy of the surface digital model to generate an intelligent customer interaction strategy;

[0008] Step S4: Collect data on customer Q&A content through an intelligent customer interaction strategy for customer sentiment feedback, generating customer sentiment feedback data; construct a portrait based on the customer sentiment feedback data to obtain a virtual customer portrait, and store the virtual customer portrait in the cloud to perform customer management operations.

[0009] The present invention obtains customer information data through a cloud server, and based on this data, introduces an intelligent Q&A plugin for screening and preprocessing to generate standard customer Q&A content data. This step ensures the high quality and structuring of customer information data, enabling subsequent processing to be carried out accurately; through data screening and preprocessing, noise is removed, improving the usability and accuracy of the data. Semantic parsing is performed on the standard customer Q&A content data, and based on the parsing results, user behavior modeling of customer information is carried out to generate a virtual digital twin model. Customer behavior is tracked for the hidden layer digital model, and the surface layer digital model is adjusted using behavior prediction data to generate an intelligent customer interaction strategy. The real-time tracking and prediction of customer behavior enable the interaction strategy to be dynamically adjusted based on the real-time needs and behaviors of customers, improving the accuracy and personalization of customer interaction and enhancing the customer experience. Customer sentiment feedback is carried out based on the intelligent customer interaction strategy to generate customer sentiment feedback data, and a virtual customer portrait is constructed and stored in the cloud. Through sentiment feedback, the system can better capture customer sentiment changes, timely adjust the interaction strategy, and optimize the customer experience. The generation and cloud storage of the virtual customer portrait ensure the continuous update and accessibility of customer data, facilitating long-term customer relationship management. Therefore, the present invention improves the dynamics and accuracy of customer management through a cloud server, intelligent customer behavior modeling, and dynamic interaction strategies.

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

[0011] Step S11: Use a cloud server to obtain customer information data;

[0012] Step S12: Conduct a compatibility analysis of customer information ports based on the customer information data to generate customer information port compatibility data; introduce an intelligent interaction plugin based on the customer information port compatibility data to obtain intelligent Q&A plugin usage data;

[0013] Step S13: Screen the customer Q&A content of the customer information data through the intelligent Q&A plugin usage data to obtain customer Q&A content collection data;

[0014] Step S14: Perform data preprocessing on the customer Q&A content collection data to generate standard customer information data, where the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization.

[0015] Through the cloud server, customer information data can be centrally obtained, enabling efficient data collection and storage, ensuring the unity and reliability of data sources, and providing sufficient data support for subsequent analysis. At the same time, the high-performance computing power of the cloud server can accelerate data processing and improve efficiency. By performing compatibility analysis on customer information ports, it ensures the smooth interaction of data among different devices, platforms, or systems, enhancing the adaptability and scalability of the system. Introducing intelligent interaction plugins can optimize the customer Q&A interaction experience, improve the efficiency and accuracy of information collection, and reduce the cost of manual participation. Using intelligent Q&A plugins to screen Q&A content for customer information data can accurately extract the core needs and key information of customers, avoiding interference from redundant data. Collecting data through Q&A content lays a foundation for in-depth analysis of customer behavior and needs, enhancing the pertinence and practicality of data. Cleaning, denoising, filling missing values, and standardizing the collected data can effectively improve the quality and consistency of data, avoiding the impact of messy or missing data on the accuracy of subsequent analysis results. This process ensures the high-quality output of standard customer information data and provides reliable basic data support for subsequent steps. Through data collection by the cloud server, optimization of interaction by intelligent plugins, fine screening of Q&A content, and high-quality data preprocessing, the efficient acquisition, cleaning, and standardization of customer information data are achieved, laying a solid foundation for subsequent intelligent analysis and decision-making. The entire process improves the efficiency, accuracy, and adaptability of data processing, significantly optimizing the customer information management and service experience.

[0016] Preferably, the introduction of intelligent interaction plugins based on customer information port-compatible data includes:

[0017] Performing demand feature analysis on customer information data based on customer information port-compatible data to obtain customer demand feature data; according to the customer demand feature data, screening matching types of intelligent interaction plugins and conducting plugin adaptability evaluation to obtain intelligent plugin adaptation data;

[0018] According to the intelligent plugin adaptation data, loading the intelligent Q&A plugin configuration parameters corresponding to the intelligent plugin to generate plugin initialization parameter data; according to the plugin initialization parameter data, loading the intelligent Q&A plugin functional module for customer information data to generate intelligent plugin interaction binding data;

[0019] Integrating customer information and plugin interaction records according to the intelligent plugin interaction binding data to generate intelligent Q&A plugin usage data.

[0020] Through the analysis of the demand characteristics of customer information data, the true needs and preferences of customers are accurately mined, enabling the system to provide targeted solutions, improve customer satisfaction, and at the same time provide a clear basis for plug-in screening to avoid waste of resources. Matching the appropriate type of intelligent interaction plug-in according to the customer demand characteristic data and conducting an adaptability assessment can ensure a high degree of compatibility between the selected plug-in and the customer information system, avoid function loss or performance degradation caused by plug-in incompatibility, and at the same time improve the stability and interaction efficiency of the system operation. Through the initialization parameter configuration of the intelligent plug-in, it is ensured that the plug-in can run correctly and respond quickly to customer needs when loaded, improve the startup efficiency of the intelligent Q&A plug-in, shorten the user waiting time, and optimize the user experience. The function module loading and interaction binding can accurately integrate the plug-in functions into the customer information data, realize the seamless connection between the intelligent Q&A plug-in and customer needs, and improve the intelligent and personalized service level of the interaction process. By integrating customer information and plug-in interaction records, the key data in the interaction process can be comprehensively recorded, providing valuable data support for subsequent optimization of plug-in performance, improvement of service strategies, and analysis of customer behavior. The above steps achieve the precise screening, adaptation, and loading of the intelligent interaction plug-in, thereby effectively improving the compatibility and operation efficiency between the plug-in and the customer system. Through the introduction of the intelligent Q&A plug-in and the integration of interaction records, the efficient response to customer needs and the comprehensive optimization of the service experience are realized, and at the same time, a scientific basis is provided for the dynamic adjustment and continuous improvement of the system.

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

[0022] Step S21: Retrieve customer Q&A keywords from the standard customer Q&A content data to obtain customer Q&A keywords; based on the customer Q&A keywords, perform user Q&A semantic parsing on the standard customer Q&A content data to generate customer Q&A content semantic parsing data;

[0023] Step S22: Mine the customer behavior characteristic patterns of the customer information data according to the customer Q&A content semantic parsing data to generate customer behavior pattern characteristic data, where the customer behavior pattern characteristic data includes interaction behavior pattern data, browsing behavior pattern data, and time and environment behavior pattern data;

[0024] Step S23: Use the customer behavior pattern characteristic data to model the customer explicit behavior of the customer information data to generate surface digital model data;

[0025] Step S24: Through the surface digital model data and the customer behavior pattern characteristic data, conduct in-depth speculation modeling on the customer implicit behavior of the customer information data to generate hidden layer digital model data; perform multi-layer information fusion on the surface digital model data and the hidden layer digital model data to generate a virtual digital twin model.

[0026] The present invention can quickly extract the core content concerned by customers through keyword retrieval, improving the data processing efficiency; semantic parsing can deeply understand the potential intentions and context relationships expressed by customers, providing semantic-precise data support for subsequent behavior analysis and pattern mining. By mining the behavior feature patterns of semantic parsing data, multi-dimensional characteristics such as customers' interactions, browsing, and time and environmental behaviors can be revealed, forming a comprehensive customer behavior portrait, which helps to accurately identify customers' needs and preferences and supports more targeted service optimization. Based on the customer behavior pattern feature data, explicit behavior modeling can clearly present the direct behavior characteristics of customers, such as clicks and access paths, providing an intuitive reference basis for real-time interaction and personalized services, while improving the response speed and efficiency of the system. By inferring the implicit behavior characteristics of customers, such as potential needs and preference tendencies, from explicit behavior data and pattern feature data, the deficiencies of explicit behavior modeling can be complemented, achieving a deep understanding of customer behavior and providing a scientific basis for precision marketing and personalized recommendations. By fusing multi-layer information of the surface layer and hidden layer models, a virtual digital twin model can be generated, which can comprehensively simulate customer behavior characteristics and change rules, provide a dynamic customer portrait, support multi-scenario business optimization, trend prediction, and intelligent decision-making, and improve customer experience and enterprise competitiveness. By accurately extracting customer intentions through keyword retrieval and semantic parsing, comprehensively presenting explicit and implicit behavior characteristics by using behavior feature pattern mining and modeling, and generating a digital twin model through multi-layer information fusion, a precise, dynamic, and comprehensive customer data analysis system is constructed. This system can enhance data insight and provide strong support for the optimization of intelligent interaction systems, precision recommendation, and behavior prediction.

[0027] Preferably, step S23 includes the following steps:

[0028] Step S231: Use the interaction behavior pattern data in the customer behavior pattern feature data to collect the customer's direct operation records from the customer information data, and analyze the sequence of customer operations based on the extracted customer direct operation records to construct a user operation path sequence; extract the user operation frequency, operation duration, and key operation points from the customer information data according to the user operation path sequence, and mark them as direct operation feature data;

[0029] Step S232: Use the browsing behavior pattern data in the customer behavior pattern feature data to capture the target elements clicked by the customer from the customer information data to obtain customer click events; perform click heat zone statistics on the customer click events to obtain user click heat zone data; analyze the click preferences of the customer click events based on the user click heat zone data, and mark the analysis results as click behavior feature data;

[0030] Step S233: using the time and environment behavior pattern data in the customer behavior pattern feature data to monitor the explicit demand expression input by the user in the customer information data, and generate user input demand data; performing demand type analysis on the user input demand data, and marking the analysis result as explicit demand feature data;

[0031] Step S234: performing digital index and parameter conversion on the direct operation feature data, the click behavior feature data and the explicit demand feature data, so as to generate surface digital model data.

[0032] By constructing a sequence of user operation paths, the present invention can clearly present the logic and process of the customer's operation behavior, help identify key interaction nodes, and optimize the user interface design and functional layout; extracting the operation frequency, operation duration, and key operation points can accurately describe the customer's direct operation behavior, and provide data support for the in-depth analysis of the operation behavior and system improvement. Capturing customer click events and performing click hot zone statistics can help identify the high-frequency interaction areas and unpopular areas of the user interface, providing an intuitive basis for interface optimization; by analyzing the click preference characteristics, it is possible to gain insight into the customer's interest in different functional modules or content, and support personalized recommendations and precise content push. Explicit demand expression monitoring can capture the direct needs of customer input in real time to ensure that user needs are responded to quickly; by analyzing the explicit demand type, it is possible to identify the customer's specific goals or expectations, and support the system to dynamically adjust the functional modules to better meet customer needs. Digitally transforming direct operation features, click behavior features, and explicit demand features can standardize and structure customer behavior data and generate an accurate surface digital model; the model can provide an intuitive overview of customer behavior and provide a solid foundation for subsequent deep modeling and data application. By comprehensively capturing and analyzing customer operation paths, click preferences, and explicit demands, we have built a systematic digital model of customer behavior surfaces. This model can clearly reflect the direct behavioral characteristics of customers, provide an important reference for user experience optimization, interface design improvement, and personalized services, and significantly improve the interaction efficiency of intelligent systems and customer satisfaction.

[0033] Preferably, performing deep inference modeling of customer implicit behavior on customer information data through surface digital model data and customer behavior pattern feature data includes:

[0034] Perform cross-feature extraction on customer behavior pattern feature data through surface digital model data to obtain customer joint behavior feature data; construct a Bayesian network structure based on customer joint behavior feature data to generate Bayesian network topology data;

[0035] Partition the customer information data into data sets to generate a model training set and a model test set; use the model training set to calculate the conditional probability distribution of the Bayesian network topology data to obtain conditional probability distribution data; speculate on the probability of customers' implicit needs for the conditional probability distribution data to generate demand probability distribution data;

[0036] Decompose the potential intentions of the customer information data according to the demand probability distribution data to generate potential intention feature data; calculate the intention scores for the potential intention feature data to obtain intention score data, where the formula for calculating the intention scores is as follows:

[0037] ;

[0038] In the formula, represents the intention score, represents the weight of the th demand, represents the demand probability, represents the weight of the feature function, represents the customer feature function value, represents the customer feature set;

[0039] Verify the prediction accuracy of the demand probability distribution data through the model test set, thereby generating hidden layer digital model data.

[0040] Through the cross - feature extraction of the surface digital model and the customer behavior pattern feature data, the present invention can integrate multi - dimensional behavior features, explore the potential correlation of customer behaviors, and form a more comprehensive joint behavior feature description. By constructing a Bayesian network topology structure, it can show the causal relationship between features in the form of a probability model, providing a scientific and reliable modeling basis for inferring customers' implicit behaviors. The Bayesian network inference model based on conditional probability calculation can infer the probability distribution of unknown features through known features, thereby accurately capturing the probability of customers' implicit needs. The demand probability distribution data can provide a quantitative basis for mining potential demands, helping the system identify customers' future behavior tendencies. The decomposition of potential intentions can concretize implicit demands into a feature set, making each demand interpretable and facilitating subsequent precision marketing or product recommendation. By comprehensively evaluating the weights of different demands and customer features through the intention scoring formula, quantitative intention scoring data can be generated, providing a clear priority basis for personalized services and product optimization. The feature weights and function flexibility in the intention scoring formula can adapt to different scenario requirements and achieve efficient decision - making support. The division of the model training and test sets can ensure the universality and reliability of the model, avoid overfitting, and improve the generalization ability of the model. The generation of the hidden - layer digital model comprehensively integrates explicit and implicit behaviors, constructs a multi - dimensional digital portrait of customers, and provides a solid foundation for deeper data analysis and prediction. Unifying the modeling of explicit and implicit behaviors and covering different dimensions of customer behaviors provide a more complete data perspective for intelligent systems. Through Bayesian network inference and intention scoring calculation, the accuracy of inferring implicit demands is significantly improved, providing accurate data support for intelligent interaction and personalized recommendation.

[0041] Preferably, step S3 includes the following steps:

[0042] Step S31: Conduct customer behavior tracking on the hidden - layer digital model to obtain customer behavior tracking data; calculate the emotional trend based on the customer behavior tracking data to obtain customer emotional trend data;

[0043] Step S32: Predict the customer behavior pattern on the hidden - layer digital model based on the customer emotional trend data to obtain customer behavior prediction data;

[0044] Step S33: Use the customer behavior prediction data to evaluate the interaction strategy of the surface digital model, generate interaction strategy evaluation data; formulate a dynamic adjustment plan based on the interaction strategy evaluation data, and optimize the interaction design in the surface digital model based on the dynamic adjustment plan to obtain a dynamic interaction strategy plan;

[0045] Step S34: Deploy and adjust the interaction design in real - time according to the dynamic interaction strategy plan to generate an intelligent customer interaction strategy.

[0046] The present invention tracks customer behavior through a hidden layer digital model, records the real-time behavior data of customers, and forms customer behavior tracking data. By using the customer behavior tracking data and combining sentiment analysis technology, the sentiment trend of customers in different interaction scenarios is calculated to generate customer sentiment trend data. Through behavior tracking and sentiment analysis, the state and demand changes of customers can be understood in real time. The sentiment trend data provides an important basis for further predicting customer behavior and optimizing interaction design. By using the customer sentiment trend data, the behavior patterns in the hidden layer digital model are dynamically predicted to generate customer behavior prediction data. The prediction results include the operation path, preferences, and potential needs of customers. The prediction of behavior patterns helps the system anticipate the next operation or needs of customers, so as to formulate response strategies in advance. By predicting customer preferences, personalized recommendations and intelligent interactions are realized. Based on the customer behavior prediction data, the interaction strategies in the surface layer digital model are evaluated to form interaction strategy evaluation data. According to the evaluation results, a dynamic adjustment plan is formulated to optimize the interaction design in the model and generate a dynamic interaction strategy plan. The evaluation of interaction strategies can locate the deficiencies in the interaction design and achieve continuous optimization through dynamic adjustment. The dynamic interaction strategy plan enables the system to adapt to the real-time changes of customer behavior and improve the interaction experience. According to the dynamic interaction strategy plan, the interaction design is deployed and adjusted in real time. Through continuous optimization of the design, an intelligent customer interaction strategy is generated to meet the personalized needs of customers. The interaction strategy is data-driven, can respond to customer needs in real time, and provide efficient services. The ability of real-time deployment ensures that the system can operate efficiently in complex and changing scenarios.

[0047] Preferably, formulating a dynamic adjustment plan according to the interaction strategy evaluation data and optimizing the interaction design in the surface layer digital model based on the dynamic adjustment plan includes:

[0048] Conduct customer behavior feedback analysis on the interaction strategy evaluation data to obtain customer behavior feedback data; according to the user behavior feedback data, classify and identify the high-frequency needs, low-frequency needs, and abnormal needs in user interaction to obtain interaction requirement classification data;

[0049] Adjust the interaction interface layout and interaction logic according to the interaction requirement classification data for the recognition results of high-frequency needs in user interaction to obtain high-frequency interaction optimization design data; improve the response by adding guiding information or optimizing the operation path according to the interaction requirement classification data for the recognition results of low-frequency needs in user interaction to obtain low-frequency interaction optimization design data;

[0050] Introduce an exception handling mechanism for the recognition results of abnormal requirements in user interaction based on the classified data of interaction requirements, so as to obtain abnormal interaction processing data, where the introduction of the exception handling mechanism includes a multi-round Q&A process or manual intervention; construct a dynamic adjustment plan based on the high-frequency interaction optimization design data, low-frequency interaction optimization design data, and abnormal interaction processing data, and generate dynamic optimized interaction design data;

[0051] Optimize the interaction design in the surface digital model by using the dynamic optimized interaction design data, so as to obtain a dynamic interaction strategy plan.

[0052] The present invention analyzes the customer behavior feedback of the interaction strategy evaluation data, mines the potential problems and improvement points in user interaction, and generates customer behavior feedback data. According to the customer behavior feedback data, three types of requirements in user interaction are classified and identified: functions and operation paths frequently used by users. Functions that are occasionally used by users but have a low usage rate due to inconvenient design. Requirements that are unexpected or exceed the conventional interaction logic in user behavior. The classification of interaction requirements provides a targeted basis for subsequent optimization design. The comprehensive coverage of high-frequency, low-frequency, and abnormal requirements ensures that no important scenarios are missed in the optimization design. Using the classified data of interaction requirements, optimize the high-frequency requirements: adjust the interface layout of high-frequency operations, reduce the user click path, and improve the operation convenience. Improve the operation logic of high-frequency functions to make it more intuitive and efficient. The optimized interface and logic greatly improve the efficiency of users in high-frequency operations. The smooth high-frequency operation experience improves the user's satisfaction with the system. According to the classified data of interaction requirements, optimize the low-frequency requirements: add tooltips, operation guides, etc. to guide users to use low-frequency functions more efficiently. Simplify the operation steps of low-frequency requirements and improve their usage convenience. Through guidance and optimization, low-frequency functions are more easily discovered and used by users. The simplified design of low-frequency requirements reduces the user's learning cost.

[0053] Preferably, step S4 includes the following steps:

[0054] Step S41: Generate customer emotion feedback data by performing customer emotion feedback on the data collected from customer Q&A content through an intelligent customer interaction strategy;

[0055] Step S42: Construct a portrait based on the customer emotion feedback data to obtain a virtual customer portrait; perform portrait layering on the virtual customer portrait to generate virtual customer portrait layer data; label the virtual customer portrait according to the virtual customer portrait layer data, and store the labeled virtual customer portrait in the cloud to perform customer management operations.

[0056] By performing sentiment feedback analysis on customer Q&A content, the present invention can more accurately capture the emotional tendencies of customers, thereby improving the quality of interaction with customers and enhancing the customer experience. A virtual customer portrait is constructed based on customer sentiment feedback data to help comprehensively understand customer needs and preferences. This portrait not only reflects the basic information of customers but also includes their emotional reactions and behavior patterns. By stratifying and tagging the virtual customer portrait, more personalized services and marketing strategies can be provided for customer groups at different levels and with different tags, thereby improving customer satisfaction and loyalty. The tagged virtual customer portrait is stored in the cloud for easy access and analysis, enabling more efficient customer management and decision support, especially with significant advantages when dealing with a large amount of customer data.

[0057] In this specification, a customer management system based on a cloud server is provided for implementing the above-mentioned customer management method based on a cloud server. The customer management system based on a cloud server includes:

[0058] An interactive plug-in introduction module for obtaining customer information data using a cloud server; introducing an intelligent interactive plug-in based on the customer information data to obtain intelligent Q&A plug-in usage data; screening customer Q&A content from the customer information data through the intelligent Q&A plug-in usage data to obtain customer Q&A content collection data; and performing data preprocessing on the customer Q&A content collection data to generate standard customer Q&A content data.

[0059] A behavior modeling module for performing semantic parsing on the standard customer Q&A content data to generate customer Q&A content semantic parsing data; performing user behavior modeling on the customer information data according to the customer Q&A content semantic parsing data to generate a virtual digital twin model, where the virtual digital twin model includes a surface digital model and a hidden digital model.

[0060] A dynamic interaction module for tracking customer behavior on the hidden digital model to obtain customer behavior tracking data; predicting customer behavior on the hidden digital model based on the customer behavior tracking data to generate customer behavior prediction data; and adjusting the dynamic interaction strategy of the surface digital model using the customer behavior prediction data to generate an intelligent customer interaction strategy.

[0061] A sentiment feedback module for providing customer sentiment feedback on the customer Q&A content collection data through the intelligent customer interaction strategy to generate customer sentiment feedback data; constructing a portrait based on the customer sentiment feedback data to obtain a virtual customer portrait, and storing the virtual customer portrait in the cloud to perform customer management operations.

[0062] The beneficial effects of the present invention are as follows: By combining a cloud server and an intelligent interaction plugin, customer information data can be quickly and accurately obtained, and effective customer Q&A content screening can be carried out through an intelligent Q&A plugin. This module can significantly improve the processing speed and quality of customer information, and reduce the workload of manual screening. By generating standard customer Q&A content data through data preprocessing, the consistency and availability of the data are ensured, laying a good foundation for subsequent modeling and analysis. Through semantic parsing and behavior modeling, in-depth analysis of customers can be carried out to generate a virtual digital twin model. This model not only includes surface digital information but also can capture the hidden behavior characteristics of customers, providing a more comprehensive customer portrait to help enterprises better understand the needs and behavior patterns of customers. Through the construction of the virtual digital twin model, enterprises can achieve more accurate customer behavior prediction and customized service strategies. By tracking the customer behavior of the hidden layer digital model, the behavior dynamics of customers can be grasped in real time, thereby achieving accurate prediction of customer behavior. Enterprises can adjust the interaction strategy based on these prediction data to achieve personalized and dynamic customer communication and service. By dynamically adjusting the interaction strategy, the customer service process will become more flexible and efficient, improving customer satisfaction and reducing ineffective interactions. Through the generation and analysis of customer emotional feedback, the emotional state of customers can be understood in real time, providing emotional services to customers, which helps to enhance customer loyalty and timely handle potential negative emotions, reducing customer churn. By constructing and updating the virtual customer portrait through emotional feedback data, enterprises can ensure the timeliness and dynamics of customer information, better follow up on customer needs and optimize service strategies. Therefore, the present invention improves the dynamic and accuracy of customer management through a cloud server, intelligent customer behavior modeling, and dynamic interaction strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the step flow of a customer management method based on a cloud server;

[0064] Figure 2 is Figure 1 a schematic diagram of the detailed implementation step flow of step S2 in

[0065] Figure 3 is Figure 1 a schematic diagram of the detailed implementation step flow of step S3 in

[0066] The realization, functional characteristics, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0068] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0069] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0070] To achieve the above object, please refer to Figures 1 to 3 , a customer management method based on a cloud server, the method comprising the following steps:

[0071] Step S1: Obtain customer information data using a cloud server; introduce an intelligent interaction plugin based on the customer information data to obtain intelligent Q&A plugin usage data; screen the customer information data through the intelligent Q&A plugin usage data to obtain customer Q&A content collection data; perform data preprocessing on the customer Q&A content collection data to generate standard customer Q&A content data;

[0072] Step S2: Perform semantic parsing on the standard customer Q&A content data to generate customer Q&A content semantic parsing data; perform user behavior modeling on the customer information data according to the customer Q&A content semantic parsing data to generate a virtual digital twin model, where the virtual digital twin model includes a surface digital model and a hidden layer digital model;

[0073] Step S3: Track the customer behavior of the hidden-layer digital model to obtain customer behavior tracking data; predict the customer behavior based on the customer behavior tracking data to generate customer behavior prediction data; use the customer behavior prediction data to adjust the dynamic interaction strategy of the surface-layer digital model to generate an intelligent customer interaction strategy;

[0074] Step S4: Provide customer emotion feedback on the customer question-and-answer content collection data through the intelligent customer interaction strategy to generate customer emotion feedback data; construct a portrait based on the customer emotion feedback data to obtain a virtual customer portrait, and store the virtual customer portrait in the cloud to perform customer management operations.

[0075] In the present invention, customer information data is obtained through a cloud server, and an intelligent Q&A plugin is introduced based on these data for screening and preprocessing to generate standard customer question-and-answer content data. This step ensures the high quality and structuring of the customer information data, enabling subsequent processing to be carried out accurately; through data screening and preprocessing, noise is removed, improving the usability and accuracy of the data. Semantic parsing is performed on the standard customer question-and-answer content data, and user behavior modeling of the customer information is carried out based on the parsing results to generate a virtual digital twin model. The customer behavior of the hidden-layer digital model is tracked, and the surface-layer digital model is adjusted using the behavior prediction data to generate an intelligent customer interaction strategy. The real-time tracking and prediction of customer behavior enable the interaction strategy to be dynamically adjusted based on the real-time needs and behaviors of the customers, improving the accuracy and personalization of customer interaction and enhancing the customer experience. Customer emotion feedback is provided based on the intelligent customer interaction strategy to generate customer emotion feedback data, and a virtual customer portrait is constructed and stored in the cloud. Through emotion feedback, the system can better capture changes in customer emotions, timely adjust the interaction strategy, and optimize the customer experience. The generation and cloud storage of the virtual customer portrait ensure the continuous update and accessibility of customer data, facilitating long-term customer relationship management. Therefore, the present invention improves the dynamics and accuracy of customer management through a cloud server, intelligent customer behavior modeling, and dynamic interaction strategies.

[0076] In the embodiment of the present invention, refer to Figure 1 As shown, it is a schematic diagram of the step flow of a customer management method based on a cloud server according to the present invention. In this example, the customer management method based on a cloud server includes the following steps:

[0077] Step S1: Use a cloud server to obtain customer information data; introduce an intelligent interaction plugin based on the customer information data to obtain intelligent Q&A plugin usage data; screen the customer information data through the intelligent Q&A plugin usage data to obtain customer question-and-answer content collection data; perform data preprocessing on the customer question-and-answer content collection data to generate standard customer question-and-answer content data;

[0078] In the embodiments of the present invention, a customer information management system is built by using a cloud server platform (such as AWS, Azure, etc.). By docking with the enterprise's CRM (Customer Relationship Management) system, online customer service system or other data sources, basic customer information (such as name, contact information, purchase records, etc.) and interaction data (such as customer service records, customer feedback, etc.) are obtained. Through API interfaces or database synchronization, these customer information data are uploaded to the cloud server for centralized storage and management. An intelligent question-and-answer plugin (such as a chatbot based on natural language processing or an FAQ automatic response system) is integrated into the customer information management system. According to the customer's input (such as through online customer service, voice assistant or web form, etc.), the intelligent question-and-answer plugin automatically answers the customer's questions and records the usage data of the plugin (including the customer's question content, the plugin's answer content, the customer interaction duration, etc.). The usage data of the intelligent question-and-answer plugin is analyzed to screen out the specific questions raised by the customers and the relevant question-and-answer content. Through data cleaning, deduplication and classification, effective customer question-and-answer content is screened out to ensure the quality and accuracy of the question-and-answer content. Natural language processing techniques (such as keyword extraction, topic modeling, etc.) are used to screen the question-and-answer content to remove irrelevant or duplicate information. Standard preprocessing is performed on the screened customer question-and-answer content. The specific operations include: removing redundant punctuation, stop words, and unifying the format (such as capitalization, tense, etc.). A word segmentation tool is used to segment the text and mark the part-of-speech, preparing for subsequent semantic parsing and behavior modeling. According to business requirements, the question-and-answer data is uniformly standardized, for example, unifying the date format, number format, etc. The cleaned customer question-and-answer content data is saved to a cloud database or data warehouse to ensure the accessibility and security of the data. Quality inspection is performed on the generated standard customer question-and-answer content data to ensure the accuracy and integrity of the data. Through manual review and automated verification means, incorrect or abnormal data is corrected.

[0079] Step S2: Perform semantic parsing on the standard customer question-and-answer content data to generate customer question-and-answer content semantic parsing data; perform user behavior modeling on the customer information data according to the customer question-and-answer content semantic parsing data to generate a virtual digital twin model, where the virtual digital twin model includes a surface digital model and a hidden digital model;

[0080] In the embodiments of the present invention, semantic analysis is performed on the standard customer Q&A content data by using NLP technology. The specific steps include: First, tokenize the customer's Q&A content and label the part of speech of each word (such as verb, noun, adjective, etc.) to ensure the accuracy of semantic parsing. Identify the key entities in the customer's Q&A (such as customer name, product name, service type, etc.), and provide a basis for subsequent analysis through entity extraction. Analyze the sentiment tendency in the customer's Q&A (such as positive, negative or neutral sentiment) to understand the customer's emotional needs and experiences. Construct a syntactic tree of the Q&A content through syntactic analysis to capture the sentence structure and ensure the accurate understanding of the customer's intention during the semantic parsing process. Based on the above steps, generate a vector representation of each customer Q&A content (such as using technologies like Word2Vec, BERT, etc. for word vector or sentence vector embedding), and construct semantic parsing data for the customer Q&A content in combination with semantic tags. Combine the semantic parsing data of the customer's Q&A content and the customer's historical interaction records (such as purchase history, browsing history, service requests, etc.) to model the customer's behavior. Analyze the customer's interest points (such as a certain type of product, service type, common questions, etc.) based on the semantic information of the Q&A content, and combine these interest points with the customer's historical behavior to form a customer behavior portrait. Use machine learning algorithms (such as decision trees, support vector machines, deep learning, etc.) to predict the customer's needs, and identify the customer's future needs or behavior changes based on the semantic parsing data. Establish a surface digital model of the customer according to the customer's basic information, Q&A content, behavior data, etc. This model represents the basic attributes of the customer (such as identity information, purchase preferences, sentiment tendency, etc.), and has a high update frequency to reflect the customer's immediate needs. The hidden layer model generates the customer's deep behavior pattern by comprehensively considering the customer's deep behavior patterns (such as potential needs, long-term preferences, etc.). This model is based on a large amount of behavior data, prediction algorithms, and historical trend analysis, and reflects the customer's long-term development trend and potential changes. The combination of the surface layer and the hidden layer models together constitutes a virtual digital twin model. The surface layer model provides immediate and superficial customer information, while the hidden layer model reveals the customer's long-term development trend and potential needs. The two cooperate with each other to ensure that the customer management system can perform comprehensive and accurate digital management of customers. Regularly update and optimize the surface layer and hidden layer digital models according to the new customer Q&A content, behavior data, and real-time interaction feedback to reflect the changes in customer behavior. Adjust the parameters in the virtual digital twin model through further customer interactions (such as follow-up questions, purchase behaviors, or satisfaction feedback) to ensure the continuous effectiveness of the model.

[0081] Step S3: Conduct customer behavior tracking on the hidden layer digital model to obtain customer behavior tracking data; based on the customer behavior tracking data, conduct customer behavior prediction on the hidden layer digital model to generate customer behavior prediction data; use the customer behavior prediction data to adjust the dynamic interaction strategy of the surface layer digital model to generate an intelligent customer interaction strategy;

[0082] In the embodiments of the present invention, behavioral data of customers is collected through multiple channels, such as website browsing records, APP usage, social media interactions, purchase records, customer support interactions, etc. These data can be collected in real time through methods such as log analysis, sensors, and API integration. Technologies such as time series analysis and sequence labeling (such as HMM or LSTM networks) are used to track the behaviors of customers in real time. These algorithms can capture the behavioral patterns of customers at different time periods, helping to identify changes in customer interests, fluctuations in demands, and potential actions. Key features (such as click-through rate, dwell time, behavior frequency, etc.) are extracted from the customer behavior tracking data to provide data support for subsequent behavior prediction and model optimization. Machine learning or deep learning algorithms (such as random forest, XGBoost, neural networks, reinforcement learning, etc.) are used to train and optimize the hidden layer digital model to predict the future behaviors of customers. Predict the short-term behavioral changes of customers, such as the likelihood of recent purchases and attention to product updates. Based on the historical behaviors of customers and their changing trends, predict long-term demand changes, potential departure risks, or changes in loyalty of customers. Use the multiple behavioral data of customers (such as purchases, browsing, comments, interactions, etc.) as inputs and adopt multi-task learning methods to simultaneously perform multiple prediction tasks (such as purchase prediction, sentiment prediction, demand prediction, etc.) to generate specific customer behavior prediction data, including customers' future demands, behavior conversion probabilities, loyalty, etc. According to the customer behavior prediction data, dynamically adjust the customer interaction strategy in the surface layer digital model. For example: Based on the predicted customer purchase behavior, adjust product or service recommendations to better meet the current needs of customers. According to the purchase prediction of customers, adjust price or discount strategies to enhance customers' purchase intentions. Combine real-time data and customer behavior prediction to dynamically adjust the parameters of the surface layer digital model. For example, adjust service content or recommendation strategies according to the real-time status of customers, so that customer interactions can be more accurate and personalized. Based on the adjusted surface layer digital model, design intelligent customer interaction strategies. The content of the strategies includes: Push customized content, such as promotional information, product recommendations, coupons, etc., through different channels based on customers' interests and demands. Provide personalized customer service support for customers according to their historical behaviors and future predictions, such as solutions to specific problems or exclusive customer manager services. Implement the generated intelligent customer interaction strategies into customer interaction platforms (such as customer service systems, email marketing platforms, social media, etc.). Further optimize the interaction strategies through feedback on the implementation results (such as customer participation rate, purchase conversion rate, customer satisfaction, etc.). Through self-learning mechanisms such as deep learning or reinforcement learning, continuously automatically adjust the interaction strategies according to customers' behavioral feedback, so as to continuously optimize the customer interaction experience and maximize customer satisfaction and business value.

[0083] Step S4: Collect data on customer Q&A content through intelligent customer interaction strategies for customer sentiment feedback, generating customer sentiment feedback data; construct a portrait based on the customer sentiment feedback data to obtain a virtual customer portrait, and store the virtual customer portrait in the cloud to perform customer management operations.

[0084] In an embodiment of the present invention, the data collected from the customer's Q&A content is processed by using advanced sentiment analysis technology. The sentiment analysis algorithm can analyze the customer's emotional tendency through text sentiment classification (such as positive, negative, and neutral). Based on NLP (natural language processing) technology, each customer's Q&A content is mapped to emotional feedback through semantic understanding, sentiment tag generation, and other means. Identifying positive feedback in customer emotions, such as satisfaction, gratitude, excitement, etc., indicates that the customer highly recognizes the product or service. Identifying negative emotional feedback from customers, such as disappointment, frustration, anger, etc., indicates that the customer is dissatisfied and needs further processing or service optimization. In addition to sentiment classification, quantitative analysis can also be performed based on sentiment intensity. For example, the strength of emotion is represented by sentiment scores to further improve the refinement of sentiment analysis. The emotional feedback information (such as sentiment category, sentiment intensity, sentiment keywords, etc.) extracted from the customer's Q&A content is integrated into customer emotional feedback data. This data may include multiple dimensions: an emotional label (such as "positive", "negative", "neutral") is provided for each customer's Q&A content. An emotional intensity score is generated for each customer feedback to characterize the depth of customer emotion. Generate a data model about customer emotion changes by analyzing the trend of customer emotion feedback (such as the time series of customer emotion changes). De-noise and standardize the emotion feedback data to remove irrelevant data and ensure the accuracy and consistency of the feedback data. Extract the customer's behavior pattern and emotion characteristics based on the customer's emotion feedback data and their behavior data (such as purchase history, interaction records, etc.). For example, analyze the customer's emotion fluctuations in different situations and identify the customer's emotional tendencies at different stages. Identify whether the customer's emotion is positive (such as loyal customers) or negative (such as lost customers). Evaluate the fluctuation of customer emotions to determine whether the customer's emotions are stable and whether there is potential dissatisfaction. Based on the above feature data, build a detailed virtual customer portrait, including but not limited to the following: such as the customer's basic identity information (age, gender, region, etc.), customer's interests, preferences, purchasing behavior, etc., including the customer's emotion type (such as common emotional state) and emotion fluctuation trend. According to the customer's emotional stability and behavior pattern, evaluate their loyalty level, and predict the customer's potential needs and future behavior through the combination of emotion feedback and behavior data. Use data visualization tools to display the various dimensions of the customer portrait, so that managers can quickly understand the overall picture of the customer. The constructed virtual customer profile data is stored in a cloud database through cloud services (such as AWS, Google Cloud, Azure, etc.). Use an efficient database management system (such as NoSQL or relational database) to ensure efficient data storage and fast query. To ensure the privacy and security of customer data, the stored data should be protected by encryption technology to prevent unauthorized access. Customer profiles should be updated regularly to ensure that they reflect the customer's latest behavior, emotions, and needs at any time.Ensure the timeliness and accuracy of customer profile data through an automated data synchronization and update mechanism. Based on the virtual customer profile stored in the cloud, perform specific customer management tasks, such as: conduct precision marketing based on the customer profile, and push customized promotion, product recommendation and other information. Optimize the customer service process according to customer emotions and behavior patterns, such as personalized customer support services, customized interactive experiences, etc. Identify potential churn customers through emotional feedback data and provide corresponding retention measures.

[0085] Preferably, step S1 includes the following steps:

[0086] Step S11: Obtain customer information data by using a cloud server;

[0087] Step S12: Conduct a compatibility analysis of the customer information port based on the customer information data to generate customer information port compatibility data; introduce an intelligent interaction plug-in based on the customer information port compatibility data to obtain intelligent Q&A plug-in usage data;

[0088] Step S13: Screen the customer Q&A content of the customer information data through the intelligent Q&A plug-in usage data to obtain customer Q&A content collection data;

[0089] Step S14: Perform data preprocessing on the customer Q&A content collection data to generate standard customer information data, where the data preprocessing includes data cleaning, data denoising, missing value filling, and data standardization.

[0090] In the embodiments of the present invention, through a cloud server, using distributed computing and data storage technologies, customer information from various channels (such as websites, CRM systems, social platforms, etc.) is obtained. The cloud server can process a large amount of data and ensure the real-time update and storage of the data. Analyze the diversity of customer information data and the compatibility of different sources to ensure that the information can be uniformly formatted for further processing. This analysis can use data matching algorithms to judge the compatibility of data between different ports and generate compatible data. Based on the compatible data, a suitable intelligent question-and-answer plugin (such as an AI plugin based on natural language processing) is selected to deeply analyze and interact with the customer information. The intelligent question-and-answer plugin can process customer questions in real time and provide corresponding feedback. By using the data through the plugin, the plugin performance and customer feedback can be analyzed. Collect the questions raised by customers and their feedback through the intelligent question-and-answer plugin, and screen out the effective question-and-answer content. The screening process includes semantic analysis of the question-and-answer content, filtering out irrelevant or duplicate questions, and extracting the data of the real needs of customers. Technologies such as text classification, keyword extraction, and sentiment analysis can be adopted. Remove duplicate data, correct the format, and process outliers for the collected data to ensure the accuracy of the data. Use denoising technologies (such as filters, clustering, etc.) to eliminate the noise in the question-and-answer content and ensure the extraction of effective information. Adopt appropriate filling methods (such as mean filling, median filling, KNN filling, etc.) to process the missing items in the data to ensure the integrity of the data. Uniformly standardize all the data (such as Z-score standardization, Min-Max scaling, etc.) to ensure that the data can maintain consistency when used in different systems, facilitating subsequent analysis and processing.

[0091] Preferably, the introduction of the intelligent interaction plugin based on the compatible data of the customer information port includes:

[0092] Analyze the demand characteristics of the customer information data based on the compatible data of the customer information port to obtain customer demand characteristic data; according to the customer demand characteristic data, screen the matching type of intelligent interaction plugin and conduct an evaluation of the plugin adaptability to obtain intelligent plugin adaptation data;

[0093] According to the intelligent plugin adaptation data, load the configuration parameters of the intelligent question-and-answer plugin corresponding to the intelligent plugin to generate plugin initialization parameter data; according to the plugin initialization parameter data, load the function module of the intelligent question-and-answer plugin for the customer information data to generate intelligent plugin interaction binding data;

[0094] Integrate the customer information and the plugin interaction records according to the intelligent plugin interaction binding data to generate intelligent question-and-answer plugin usage data.

[0095] In the embodiments of the present invention, by using machine learning and data mining technologies to analyze customer information data, potential patterns of customer needs are identified. For example, based on customers' historical behaviors, purchase records, interest preferences, etc., methods such as clustering algorithms and association rule mining are used to extract specific demand characteristics from the data. Data preprocessing technologies (such as standardization and normalization) are used to process customer information, and classification algorithms (such as decision trees and SVM) or deep learning models (such as neural networks) are combined to analyze customer demand characteristics and generate customer demand characteristic data. According to the customer demand characteristic data, the most suitable type of intelligent interaction plug-in is selected. Recommendation system algorithms (such as collaborative filtering and content recommendation) can be used to recommend the most suitable plug-in type. The selected plug-in type is evaluated for suitability to analyze whether it can be effectively integrated with the existing customer information data system and platform. The evaluation criteria can include performance, compatibility, response time, etc. The evaluation process can be carried out by means of automated testing, regression analysis, etc. The suitability of the plug-in is evaluated using a suitability model and an indicator scoring system to generate intelligent plug-in suitability data. According to the suitability data, the configuration information corresponding to the intelligent interaction plug-in (such as plug-in version, function module, etc.) is loaded. The configuration parameters include language settings, interface configuration, database connection settings, etc. The loaded configuration parameters are sorted into standardized initialization parameter data for subsequent plug-in initialization. Configuration management tools (such as Ansible and Chef) are used to automate the configuration of the plug-in's initialization parameters to ensure that all parameters meet the requirements of the intelligent interaction plug-in. Based on the initialization parameter data, the specific function modules of the intelligent question-and-answer plug-in (such as natural language processing module, speech recognition module, sentiment analysis module, etc.) are loaded. During the loading process, it is necessary to ensure that the plug-in module can be seamlessly connected to the existing customer information system and can accurately process customer inputs. Binding data is generated for each function module and customer information data, which includes the customer's historical interaction data, customer demand characteristic data, etc. The binding data connects the customer information with the function modules of the plug-in to ensure that the plug-in can provide personalized services for customers. Through API integration technologies (such as RESTful API and GraphQL), the function modules are connected to the customer data system to generate interactive binding data. The interaction records between the customer and the plug-in are integrated into an analyzable format, and these records include the questions raised by the customer, the answers of the plug-in, the feedback of the plug-in, etc. These interaction records are integrated through a log analysis tool (such as ELK Stack and Splunk). Based on the integrated interaction records, usage data of the intelligent question-and-answer plug-in is generated, including the response time, accuracy rate, customer satisfaction, etc. of the plug-in. These data help to evaluate the performance of the plug-in and provide a basis for future plug-in optimization. Data analysis tools (such as Apache Spark and Pandas) are used to analyze the interaction data to generate usage data of the intelligent question-and-answer plug-in to ensure data integrity and traceability.

[0096] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0097] Step S21: Retrieve customer Q&A keywords from the standard customer Q&A content data to obtain customer Q&A keywords; perform user Q&A semantic parsing on the standard customer Q&A content data based on the customer Q&A keywords to generate customer Q&A content semantic parsing data;

[0098] Step S22: Mine customer behavior feature patterns from the customer information data according to the customer Q&A content semantic parsing data to generate customer behavior pattern feature data, where the customer behavior pattern feature data includes interaction behavior pattern data, browsing behavior pattern data, and time and environment behavior pattern data;

[0099] Step S23: Use the customer behavior pattern feature data to perform customer explicit behavior modeling on the customer information data to generate surface digital model data;

[0100] Step S24: Perform in-depth speculation modeling of customer implicit behavior on the customer information data through the surface digital model data and the customer behavior pattern feature data to generate hidden layer digital model data; perform multi-layer information fusion on the surface digital model data and the hidden layer digital model data to generate a virtual digital twin model.

[0101] In the embodiments of the present invention, keyword extraction of customer Q&A content data is performed by using natural language processing (NLP) technology. The TF-IDF (term frequency-inverse document frequency) algorithm or a deep learning model (such as BERT, GPT) can be used for keyword extraction. The goal of keyword retrieval is to find the words that best represent the core of the question in each Q&A, so as to assist subsequent semantic parsing and behavior analysis. Open-source NLP toolkits (such as spaCy, NLTK) are used for preprocessing and extracting keywords. More accurate keywords can be obtained through TF-IDF calculation or a context-based Embedding model (such as BERT). Based on the extracted keywords, semantic analysis is performed to understand the deep meaning of the Q&A content. Semantic parsing can be performed through pre-trained language models (such as BERT, T5) for context understanding of the text. By parsing the Q&A content, the customer's needs, intentions, and emotional attitudes are identified, thereby generating semantic parsing data for the customer Q&A content. Semantic analysis tools (such as SpaCy's NER (named entity recognition), OpenAI GPT series, etc.) are used to perform semantic parsing on the standard Q&A data. The customer's emotional attitude and needs are further judged through an emotion analysis model (such as VADER, BERT emotion classification). Mining algorithms (such as association rule mining, clustering analysis) are used to mine the customer's behavior patterns from the semantic parsing data. According to the customer's Q&A content, the customer's interaction behaviors (such as Q&A frequency, occurrence frequency of keywords, etc.) are analyzed. The customer's behavior data is subjected to clustering analysis to identify different behavior patterns, and the customer's behaviors are labeled to generate behavior pattern feature data. Clustering algorithms (such as K-means, DBSCAN) or deep learning-based clustering methods (such as autoencoders) can be used to mine the customer's behavior patterns. Association rule mining techniques (such as Apriori) are used to analyze the relationships between customer behaviors. The customer's behavior pattern feature data can be generated according to the following types: such as the customer's question frequency, question type, question time, etc. Such as the customer's historical records of browsing product pages, browsing duration, browsing frequency, etc. Such as the customer's behavior changes at different times, on different devices, or in different environments. A data processing framework (such as Apache Spark, Pandas) is used to extract features from the behavior data to generate a multi-dimensional behavior feature dataset. Methods such as time series analysis and environmental analysis are used to extract time- and environment-related behavior features. Based on the customer's explicit behaviors (such as browsing records, click data, purchase history, etc.), an explicit behavior model is constructed. Traditional machine learning models (such as decision trees, random forests, support vector machines, etc.) or deep learning models (such as LSTM, CNN) are used for modeling to generate surface digital model data, which mainly focuses on directly observable behavior data. A regression model or a classification model is used to model the customer's explicit behaviors to predict the customer's future behaviors or preferences. Deep learning methods (such as DNN) are used to perform deeper modeling on the behavior features.Based on the explicit behavior model data, infer the implicit behaviors of customers (such as unexpressed needs, potential interests, etc.). In this step, technologies such as autoencoders and generative adversarial networks (GANs) in deep learning can be used to infer implicit features, generate digital model data for the hidden layer, and capture the potential behaviors or implicit needs of customers. Use an autoencoder or variational autoencoder (VAE) to learn the potential representation of customers' implicit behaviors from the surface digital model. GAN (generative adversarial network) can be used to generate data approximating real customer behaviors. Integrate the surface digital model data and the hidden layer digital model data to form a complete virtual digital twin model of the customer. This model not only represents the explicit behaviors of customers but also can infer their implicit needs. Adopt technologies such as multi-layer perceptron (MLP) and ensemble learning to fuse the two models and achieve deep integration of information. Use ensemble learning methods (such as random forest, XGBoost) to perform weighted fusion on the results of the surface and hidden layer models. Combine cross-modal fusion technologies in deep learning to integrate explicit and implicit data and generate a virtual digital twin model of the customer.

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

[0103] Step S231: Use the interaction behavior pattern data in the customer behavior pattern feature data to collect customer direct operation records for the customer information data, and analyze the sequence of customer operations based on the extracted customer direct operation records to construct a user operation path sequence; extract the user operation frequency, operation duration, and key operation points from the customer information data according to the user operation path sequence, and mark them as direct operation feature data;

[0104] Step S232: Use the browsing behavior pattern data in the customer behavior pattern feature data to capture the target elements clicked by the customer for the customer information data to obtain customer click events; perform click heat zone statistics on the customer click events to obtain user click heat zone data; analyze the click preferences of the customer click events based on the user click heat zone data, and mark the analysis results as click behavior feature data;

[0105] Step S233: Use the time and environment behavior pattern data in the customer behavior pattern feature data to monitor the explicit demand expressions input by the user for the customer information data to generate user input demand data; analyze the demand types of the user input demand data, and mark the analysis results as explicit demand feature data;

[0106] Step S234: Perform digital index and parameter conversion on the direct operation feature data, click behavior feature data, and explicit demand feature data to generate surface digital model data.

[0107] In the embodiments of the present invention, each operation data of customers is captured by using a log recording system on the Web or the application side, and behaviors such as clicks, scrolls, inputs, submissions, etc. of customers on the platform are recorded. These operations are classified to ensure that the operation sequence and timestamp of each customer can be traced, and an operation record set is constructed. The front-end JavaScript SDK or event tracking tools for mobile applications (such as Google Analytics, Mixpanel) are used to record the operations in detail. The data includes information such as operation type, operation object, timestamp, etc. The chronological analysis of the operation records of customers is carried out to construct the user operation path sequence. Sequence mining techniques (such as FP-Growth, Apriori) are used to analyze the common operation paths and conversion paths of customers. The frequency, duration, and key operation points (such as click to purchase, add to cart, etc.) of the operations are extracted through path analysis and marked as direct operation feature data. The operations are sorted based on the timestamp, and then sequence pattern mining algorithms (such as sequence pattern mining, Markov chain) are used to analyze the common operation sequences. Key operation points are extracted by using time series analysis algorithms, such as the behaviors before and after a user completes a certain operation. According to the user operation path, the frequency of each operation, the average duration of the operation, and key nodes (such as the access frequency of the checkout page, the click frequency of specific products) are counted. Each operation is marked as direct operation feature data for subsequent modeling. The data processing tool (such as Pandas) is used to count the frequency of customer operation records. The duration analysis of the operations is combined with a behavior analysis framework (such as RFM analysis) and marked as feature data. The click events of users on the page are captured through front-end JavaScript code or mobile SDK. The elements (such as buttons, links, pictures, etc.) clicked by customers and the corresponding timestamps are recorded. The captured data includes click position, ID of the clicked object, URL, etc. The front-end event listener (such as addEventListener) is used to capture all user click operations and upload the relevant data to the backend database. For mobile applications, a dedicated SDK, such as Firebase Analytics, can be used to capture click events. The heat zone statistics of the click events of customers are carried out, and heat map analysis tools (such as Hotjar, Crazy Egg) are used to generate the click heat zone map of users. The frequency of clicks on which page areas (such as buttons, images, links, etc.) is counted. The click frequency data of each heat zone is extracted, and the attention degree of users to specific areas is counted. The click heat zone map is automatically generated by using the heat map analysis tool, indicating the areas with intensive user clicks. Cluster analysis is carried out on the heat zone data to determine the hot spots. Based on the heat zone data, the click preferences of customers are analyzed to identify which page elements or products are most favored by customers. Combining the heat data of the click position, the product categories, price ranges, layout preferences, etc. preferred by customers are analyzed. These preferences are marked as click behavior feature data and used as the input for subsequent analysis.Use data visualization tools (such as Tableau, PowerBI) to display click heatmaps and preference distributions. Conduct cluster analysis on click behaviors, and classify customers' click preferences using methods such as K-means or DBSCAN. Monitor the explicit need expressions of customers during system interaction (such as search box input, question asking, etc.), and record their input content, frequency, and context. Identify customers' explicit needs (such as product specifications, service requests, etc.) by extracting keywords from the input content. Use text analysis tools (such as NLTK, spaCy) to extract keywords from customers' input content. Conduct sentiment analysis on the input natural language to determine whether customers have expressed needs. Classify the demand types according to customers' input needs, and identify the demand types of customers (such as product requirements, service consultations, technical support, etc.). Mark the demand type results as explicit demand feature data. Use classification models (such as logistic regression, SVM) to automatically classify customers' needs. Combine NLP technology to train the demand classification model on a large number of user input data sets to improve the classification accuracy. Numerically process the collected behavioral feature data (such as operation frequency, click heatmaps, demand types, etc.), and convert text and categorical data into numerical indicators (such as using one-hot encoding, standardization, normalization, etc.). The converted data includes operation frequency, duration, number of hot zone clicks, demand classification labels, etc. Use data preprocessing libraries (such as Pandas, Scikit-learn) to standardize and normalize the feature data to ensure data consistency and comparability. Perform one-hot encoding on discrete demand types to convert text categories into numbers. Integrate all digitized feature data into a unified surface digitized model to form a customer behavioral feature model. The surface digitized model data includes multi-dimensional features such as operation behaviors, click behaviors, and explicit needs, constituting a comprehensive digital description of customer behaviors. Combine all the transformed feature data to form a structured data set suitable for subsequent machine learning model training or data analysis. Use table processing tools (such as Excel, Pandas) and databases (such as SQL, NoSQL) to store and manage this surface digitized model data.

[0108] Preferably, the in-depth speculation modeling of customers' implicit behaviors for customer information data through surface digitized model data and customer behavior pattern feature data includes:

[0109] Extract cross features from customer behavior pattern feature data through surface digitized model data to obtain customer combined behavior feature data; construct a Bayesian network structure based on the customer combined behavior feature data to generate Bayesian network topology data;

[0110] Partition the customer information data into data sets to generate a model training set and a model test set; use the model training set to calculate the conditional probability distribution of the Bayesian network topology data to obtain conditional probability distribution data; speculate on the probability of customers' implicit needs for the conditional probability distribution data to generate demand probability distribution data;

[0111] Decompose the potential intentions of the customer information data according to the demand probability distribution data to generate potential intention feature data; calculate the intention scores for the potential intention feature data to obtain intention score data, where the formula for calculating the intention scores is as follows:

[0112] ;

[0113] In the formula, represents the intention score, represents the weight of the th demand, represents the demand probability, represents the weight of the feature function, represents the customer feature function value, represents the customer feature set;

[0114] Verify the prediction accuracy of the demand probability distribution data through the model test set, thereby generating hidden layer digital model data.

[0115] In the embodiments of the present invention, by utilizing multi-dimensional features in the surface digital model data and customer behavior pattern feature data, cross-feature extraction is performed. A cross-feature refers to the combination of different features to generate new features, which helps to reveal hidden correlation relationships. For example, a new feature "demand and operation frequency" can be generated by crossing the two features "operation frequency" and "explicit demand type" to form customer combined behavior feature data. Use feature engineering methods (such as feature crossing, merge operation in Pandas) to combine multiple features into new composite features. For categorical variables, label encoding or one-hot encoding can be used for conversion. Use the customer combined behavior feature data to construct a Bayesian network. A Bayesian network is a directed acyclic graph (DAG), where nodes represent variables and edges represent the dependence relationships between variables. The purpose of constructing a Bayesian network is to infer the conditional dependence structure between various features. Calculate the mutual relationships between features to construct the network topology. For example, methods such as information gain, mutual information, and chi-square test are used to evaluate the dependence relationships between variables. The pgmpy library or pomegranate library in Python can be used to construct a Bayesian network, and these tools support automatic learning of the network structure and parameter estimation. Learn the conditional probability through Maximum Likelihood Estimation (MLE) or Bayesian Parameter Estimation. Divide the customer information data into a training set and a test set, usually using a ratio of 80 / 20 or 70 / 30 for the division. The training set is used for model training, and the test set is used for evaluating the model performance to ensure that there is no data leakage between the training data and the test data. Use the train_test_split (from sklearn.model_selection) function to randomly divide the data to ensure that the feature distributions of the training set and the test set are consistent. Perform parameter learning on the Bayesian network through the training set data, and calculate the conditional probability distribution (CPD) of each node (feature). The CPD describes the conditional probability of each feature given its parent nodes. Use Bayesian network learning algorithms, such as the EM (Expectation-Maximization) algorithm or maximum likelihood estimation, to estimate the conditional probability of each node. Use methods in libraries such as pgmpy or pomegranate to estimate the conditional probability in the Bayesian network. The specific method is fit(), which automatically learns the conditional probability distribution in the data. Use methods in libraries such as pgmpy or pomegranate to estimate the conditional probability in the Bayesian network. The specific method is fit(), which automatically learns the conditional probability distribution in the data. Use Bayesian network inference methods (such as VariableElimination in pgmpy) to perform probability speculation on the demand, thereby generating demand probability distribution data. Based on the demand probability distribution data, perform the decomposition of potential intentions.The potential intention is the deep motivation behind customer behavior and can be inferred by analyzing the changes in demand probability. The dimensionality of the demand data is reduced through clustering algorithms or principal component analysis (PCA) to extract potential intention features. The dimensionality of the demand probability distribution data is reduced using clustering algorithms (such as K-means, DBSCAN) or PCA to extract potential intention feature data. The intention score of the customer is calculated using the given formula. The formula for calculating the intention score is: ; where represents the intention score, represents the weight of the th demand, represents the demand probability, represents the weight of the feature function, represents the customer feature function value, represents the customer feature set; in the dataset, for each customer, the intention score is calculated according to the formula using a Python script. The weight and probability values of the customer are input into the formula, and the score is combined with the customer features. The prediction accuracy of the model is verified using the test set data, and the error between the predicted value and the actual value of the demand probability distribution is calculated. Common evaluation metrics include accuracy, precision, recall, F1 score, etc. The performance of the model in predicting latent demands is evaluated based on these metrics. The prediction results are evaluated using the evaluation functions (such as accuracy_score, mean_squared_error) in sklearn.metrics. Cross-validation is used to ensure the generalization ability of the model.

[0116] As an example of the present invention, as shown in Figure 3 , in this example, step S3 includes:

[0117] Step S31: Track the customer behavior of the hidden layer digital model to obtain customer behavior tracking data; calculate the emotional trend based on the customer behavior tracking data to obtain customer emotional trend data;

[0118] Step S32: Predict the customer behavior pattern of the hidden layer digital model based on the customer emotional trend data to obtain customer behavior prediction data;

[0119] Step S33: Use the customer behavior prediction data to evaluate the interaction strategy of the surface layer digital model, generate interaction strategy evaluation data; formulate a dynamic adjustment plan based on the interaction strategy evaluation data, and optimize the interaction design in the surface layer digital model based on the dynamic adjustment plan to obtain a dynamic interaction strategy plan;

[0120] Step S34: Deploy and adjust the interaction design in real time according to the dynamic interaction strategy plan to generate an intelligent customer interaction strategy.

[0121] In the embodiments of the present invention, by tracking customer behavior in the hidden layer digital model, behavioral data of customers in multiple interaction stages is collected, including operation logs, click events, session history, purchase behavior, etc. Customer behavioral data is tracked in terms of user access paths, interaction times, and interaction content, etc., to construct behavioral trajectory data. An event tracking tool (such as Google Analytics, Mixpanel, or a custom behavioral analysis system) is used to collect and store user behavioral data. The behavioral data is transmitted to the analysis platform through JavaScript, API calls, or SDK integration. Data storage can be managed using a log database (such as Elasticsearch) or a time series database (such as InfluxDB). The emotional trend of the customer behavior tracking data is calculated through an emotional analysis algorithm. For example, based on the customer's text feedback, social media dynamics, or interaction records, the emotional tendency of the customer (such as positive, negative, or neutral) is identified. The emotional trend of the customer is dynamically tracked, the changing trend of the emotion is analyzed, and modeling is performed according to time series to generate customer emotional trend data. Natural language processing (NLP) techniques and emotional analysis algorithms (such as VADER, TextBlob, BERT model) are used to perform emotional classification on the text data. For non-text data, the changing trend of the emotion can be inferred by combining the changes in behavioral data (such as changes in clicks and purchase behavior). Machine learning methods (such as LSTM, RNN) can be used to predict the trend of time series emotional data. Based on the customer emotional trend data, combined with historical behavioral data, the future behavior patterns of customers are predicted. A prediction model is used to identify the relationship between emotional changes and behavioral changes, and the future behavior of customers (such as continued purchase, churn, demand changes, etc.) is predicted according to these patterns. The model can predict the future behavior of customers through time series analysis methods, such as customer behavior conversion rate, preference changes, etc. Time series analysis methods (such as ARIMA, LSTM network) or regression models based on customer segmentation (such as Logistic regression) are used to predict the future behavior of customers. For large datasets, ensemble learning algorithms such as XGBoost can be used to improve the prediction accuracy. The existing interaction strategies in the surface digital model are evaluated using the customer behavior prediction data. By simulating the behavioral changes of customers in different scenarios, the effectiveness and potential problems of the interaction design are analyzed. Methods such as A / B testing are used to evaluate the effects of different interaction strategies, such as comparing the response degrees of different user interfaces, message push, or recommendation strategies. Multivariate regression analysis or reinforcement learning algorithms are used for interaction strategy evaluation. Reverse testing can be combined with simulated data to predict the performance of customers under different interaction strategies. Multidimensional data needs to be considered in the evaluation, covering customer emotional data, historical behavior, environmental data, etc. According to the interaction strategy evaluation data, the deficiencies in the current interaction design are dynamically adjusted, such as adjusting the UI / UX design, information prompt strategy, push content, or interaction process.The dynamic adjustment plan should be formulated based on customer behavior prediction data to make the interaction design more personalized and effective. Use optimization algorithms (such as genetic algorithms, particle swarm optimization) to dynamically adjust the interaction strategy. Through real-time data feedback, establish an adaptive interaction design system to automatically adjust the display order of interaction elements, content presentation methods, etc., to improve user satisfaction and engagement. Deploy the optimized interaction design plan to the customer interaction platform in real time, including Web, mobile applications, chatbots, customer service systems, etc. Dynamically adjust the interaction design to adapt to the personalized needs and emotional changes of customers, and update the interaction elements (such as recommendations, tips, notifications, etc.) in the system in real time. Use CI / CD (Continuous Integration / Continuous Deployment) tools (such as Jenkins, GitLab CI) to achieve real-time deployment and adjustment of the interaction design. Integrate a real-time data stream processing platform (such as Apache Kafka, Apache Flink) to achieve real-time streaming processing and feedback of customer interaction data.

[0122] Preferably, the dynamic adjustment plan is formulated according to the interaction strategy evaluation data, and the interaction design in the surface digital model is optimized based on the dynamic adjustment plan, including:

[0123] Conduct customer behavior feedback analysis on the interaction strategy evaluation data to obtain customer behavior feedback data; according to the user behavior feedback data, classify and identify high-frequency needs, low-frequency needs, and abnormal needs in user interaction to obtain interaction need classification data;

[0124] Adjust the interaction interface layout and interaction logic according to the interaction need classification data for the recognition results of high-frequency needs in user interaction to obtain high-frequency interaction optimization design data; improve the response of adding guiding information or optimizing the operation path according to the interaction need classification data for the recognition results of low-frequency needs in user interaction to obtain low-frequency interaction optimization design data;

[0125] Introduce an exception handling mechanism according to the interaction need classification data for the recognition results of abnormal needs in user interaction to obtain abnormal interaction handling data, where the introduction of the exception handling mechanism includes a multi-round question-and-answer process or manual intervention; construct a dynamic adjustment plan based on the high-frequency interaction optimization design data, low-frequency interaction optimization design data, and abnormal interaction handling data to generate dynamically optimized interaction design data;

[0126] Use the dynamically optimized interaction design data to optimize the interaction design in the surface digital model to obtain a dynamic interaction strategy plan.

[0127] In the embodiments of the present invention, by analyzing in detail the behavioral data generated by customers during use, mainly including behavioral data such as customer clicks, operation duration, page jumps, feedback and ratings. By statistically analyzing and mining these data, the feedback of customers on the interaction strategy is identified to help evaluate the effectiveness of the current interaction strategy. Customer behavior feedback data can be extracted from customer behavior analysis tools, such as heat map analysis tools (e.g., Hotjar), user behavior analysis platforms (e.g., Mixpanel), etc. Use data analysis tools (such as Pandas, NumPy in Python) and visualization tools (such as Tableau, PowerBI) to statistically analyze the customer's operation records, stay duration, click stream, etc. to generate behavior feedback data. By counting the common operations of customers, frequently accessed function modules, and recurring operation processes, the high-frequency needs of users are identified. For example, customers frequently view the detailed information of a certain product or frequently use a specific function. Identify the operations or needs that do not occur frequently, but have an important impact on the customer experience when they occur, such as service requests for special needs or complex operations. Identify some unconventional operation patterns or behaviors, such as operation steps that do not meet expectations, abnormal jumps, or page loading failures. Use data mining algorithms (such as clustering analysis, K-means algorithm, decision tree) to analyze the user behavior data and classify the user behavior data according to the demand type. Use natural language processing (NLP) technology to analyze the text feedback to identify potential abnormal needs. According to the high-frequency needs of customers, adjust the interface layout to ensure that the functions and content commonly used by customers are more prominent and accessible. For example, adjust the size, color, or position of important buttons, or place the function modules frequently accessed by users in prominent positions. Simplify and optimize the interaction process so that customers can reach their goals more quickly. Improve the response efficiency of high-frequency needs by reducing unnecessary steps or adding shortcuts. Use UX / UI design tools (such as Sketch, Figma) to design and optimize the interface layout and interaction process. Combine customer behavior data to conduct A / B testing to verify the effectiveness of the optimization plan. For low-frequency needs, detailed guidance information can be provided to customers, such as help documents, frequently asked questions (FAQ), or guiding prompts, which can help users quickly understand how to operate and reduce the uncertainty in operations. For low-frequency needs, optimize the operation path, such as simplifying the operation steps of some functions or adding shortcut operation entrances to improve the operation convenience of low-frequency needs. Design a dynamic prompt system or pop-up window guidance to display the guidance information in the form of prompt boxes, floating help buttons, etc. Combine the user's behavior path and use user behavior analysis tools (such as Google Analytics) to conduct path optimization analysis. For abnormal needs, such as the situation where users repeatedly ask questions or cannot complete tasks, introduce multi-round question-and-answer or conversational interfaces to gradually guide users to find solutions.For example, when a user cannot complete registration in the conventional way, the system can help the user complete registration through a series of questions. For problems that the system cannot solve, a manual customer service intervention mechanism is provided to solve problems for customers through chatbots or manual customer service, ensuring that the customer experience is not affected. Use a dialogue system (such as Dialogflow, Microsoft Bot Framework) to implement a multi-round Q&A process, and combine chatbot algorithms to handle abnormal requests from users. For abnormal requirements that cannot be processed automatically, guide users to the manual customer service channel. Integrate the optimized design for high-frequency requirements, the optimized design for low-frequency requirements, and the handling solutions for abnormal requirements to build a dynamic interaction adjustment plan. This plan should be customized according to different user needs, behaviors, and emotional trends and support real-time adjustment. During the process of building the plan, give priority to the user experience, ensure that personalization and flexibility are reflected in the design plan, so that the interaction design can adapt to different situations. Use agile development methods (such as Scrum) for the rapid development and testing of the dynamic adjustment plan to ensure that the design plan can respond to changes in user behavior in real time. Combine real-time feedback and data stream processing platforms (such as Apache Kafka, AWS Kinesis) for dynamic optimization and adjustment. Based on the data of dynamically optimized interaction design, optimize the interaction design in the surface digital model, including updating the interface layout, interaction process, and functional modules to better meet customer needs. Regularly re-evaluate and adjust the interaction strategy according to user feedback, behavior data, and emotional analysis results to ensure the continuous optimization of the interaction strategy. Integrate the data of dynamically optimized interaction design into the surface digital model, and continuously improve the interaction strategy through A / B testing, user feedback, and data stream analysis. Achieve cross-platform synchronous updates to ensure that the user experience on mobile devices, PC devices, or other devices can be uniformly optimized.

[0128] Preferably, step S4 includes the following steps:

[0129] Step S41: Use an intelligent customer interaction strategy to collect data on customer Q&A content for customer emotional feedback and generate customer emotional feedback data;

[0130] Step S42: Based on the customer emotional feedback data, construct a portrait to obtain a virtual customer portrait; layer the virtual customer portrait to generate virtual customer portrait hierarchy data; label the virtual customer portrait according to the virtual customer portrait hierarchy data, and store the labeled virtual customer portrait in the cloud to perform customer management operations.

[0131] In the embodiments of the present invention, in an intelligent customer interaction system, the customer's question-and-answer content is analyzed through natural language processing (NLP) technology to evaluate the customer's sentiment. Sentiment analysis models (such as VADER, BERT) are used to classify the sentiment of the customer's language during the interaction, and to judge their sentiment tendency (such as positive, negative or neutral) and sentiment intensity. The voice, text feedback of the customer during the interaction with the system, as well as the keywords, emojis, etc. used during the question-and-answer process are collected, their sentiment tendency is analyzed, and sentiment feedback data is generated. An emotion analysis API (such as Google Cloud Natural Language API, IBM Watson NLU) is used to perform emotion analysis on the customer's question-and-answer content. A text analysis library in Python (such as TextBlob, NLTK) is used to perform sentiment scoring on the collected customer question-and-answer, classify it as positive, negative or neutral, and generate sentiment feedback data, such as the customer's sentiment score, sentiment type (for example, joy, anger, confusion, etc.). Based on the customer's sentiment feedback data, combined with information such as their past interaction behaviors, consumption habits, and preference settings, a virtual customer portrait is constructed. The virtual customer portrait not only includes the customer's basic information, but also should cover multi-dimensional information such as sentiment trends, purchase behaviors, and feedback patterns. Through data mining and analysis, the customer's behavioral characteristics are identified, such as the frequently visited pages, preferred services, common problems, etc., to comprehensively construct a personalized virtual customer portrait. Combining customer behavior data (such as clickstream, operation path, historical purchase records, etc.) with sentiment data, machine learning algorithms (such as clustering analysis, K-means algorithm) are used to construct a virtual customer portrait. The results of sentiment analysis are combined with customer behavior data, and libraries such as Scikit-learn and TensorFlow in Python are used for analysis to generate portrait data containing features such as sentiment, preference, and purchase intention. According to dimensions such as the customer's activity, value, and needs, the virtual customer portraits are stratified. For example, customers can be divided into high-value customers, potential customers, ordinary customers, and low-value customers, and different strategy adjustments are made according to these levels. The customer portraits are stratified using the behavioral patterns of the customer portraits (such as purchase frequency, browsing behavior, sentiment feedback) to ensure that the customer portraits at each level are more accurate and facilitate the implementation of personalized service strategies. The hierarchical clustering method (such as K-means clustering, DBSCAN, etc.) is used to divide the customer portraits into levels. Based on features such as the customer's sentiment feedback data, interaction frequency, and purchase history, a hierarchical model is created and a corresponding label is assigned to each level. The Sklearn library in Python is used for clustering analysis, or a deep learning model (such as Autoencoder) is used to stratify the customers. According to the hierarchical data, behavioral characteristics, sentiment feedback, etc. of the virtual customer portraits, labels are added to each virtual customer portrait.For example, the tags include "highly active", "potential customer", "loyal customer", "price sensitive", etc., for targeted management. Each tag should be dynamically adjusted according to the customer's behavior and emotional feedback. For example, if the customer's emotional feedback becomes negative, the tag can be automatically updated to "needs attention". Use a tag generation system (such as ElasticSearch, Apache Solr) to automatically add tags to each customer profile. Appropriate tags can be assigned to customers based on the customer's behavior data, sentiment analysis data, and information in the hierarchical model, using a rule engine or machine learning algorithm. Store the tagged customer profiles in a cloud data warehouse, such as AWS, Google Cloud Storage, or Azure, to ensure that customer data can be accessed and updated in real time. Synchronize all tagged customer profile data to the cloud for cross-platform access and update. Use an API interface (such as a RESTful API) to ensure data flow between different systems. A cloud data management platform (such as AWS S3, Google BigQuery) can be used to store and analyze customer profile data, and integrate with a customer relationship management (CRM) system to implement personalized customer management strategies.

[0132] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0133] The above description is only a specific implementation manner of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather conform to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A customer management method based on a cloud server, characterized in that: The following steps are involved: Step S1: Obtain customer information data using a cloud server; Introduce intelligent interactive plug-ins based on customer information data to obtain usage data of intelligent question-and-answer plug-ins; The intelligent question and answer plug-in uses data to filter customer information data for customer question and answer content, thereby obtaining customer question and answer content collection data; Pre-process the collected data of customer Q&A content to generate standard customer Q&A content data; Step S2 includes: Perform customer question and answer keyword retrieval on the standard customer question and answer content data to obtain customer question and answer keywords; perform customer question and answer semantic analysis on the standard customer question and answer content data based on the customer question and answer keywords to generate customer question and answer content semantic analysis data; Mining customer behavior feature patterns of customer information data based on semantic analysis data of customer question and answer content to generate customer behavior pattern feature data, wherein the customer behavior pattern feature data includes interaction behavior pattern data, browsing behavior pattern data, and time and environment behavior pattern data; The interactive behavior pattern data in the customer behavior pattern feature data is used to collect customer direct operation records from the customer information data, and the customer operation sequence is analyzed based on the extracted customer direct operation records to construct a customer operation path sequence; the customer operation frequency, operation duration, and key operation points are extracted from the customer information data according to the customer operation path sequence, and marked as direct operation feature data; Utilize the browsing behavior pattern data in the customer behavior pattern feature data to capture the target elements clicked by the customer in the customer information data to obtain the customer click event; perform click hot zone statistics on the customer click event to obtain the customer click hot zone data; analyze the click preference of the customer click event based on the customer click hot zone data, and mark the analysis result as the click behavior feature data; Using the time and environment behavior pattern data in the customer behavior pattern feature data, the customer information data is monitored for explicit demand expressions input by the customer, and customer input demand data is generated; the customer input demand data is analyzed for demand types, and the analysis results are marked as explicit demand feature data; Perform digital index and parameter conversion on direct operation feature data, click behavior feature data and explicit demand feature data, so as to generate a surface digital model; Through the surface digital model, cross-feature extraction is performed on the customer behavior pattern feature data to obtain customer joint behavior feature data; based on the customer joint behavior feature data, a Bayesian network structure is constructed to generate Bayesian network topology data; Divide the customer information data into data sets to generate model training sets and model test sets; use the model training sets to calculate the conditional probability distribution of the Bayesian network topology data to obtain conditional probability distribution data; perform customer implicit demand probability inference on the conditional probability distribution data to generate demand probability distribution data; The customer information data is decomposed into potential intentions according to the demand probability distribution data to generate potential intention feature data; the intention score is calculated for the potential intention feature data to obtain the intention score data, wherein the formula for the intention score calculation is as follows: ； In the formula, Denoted as the intent score, Expressed as The weight of a requirement, Expressed as the demand probability, Expressed as the weight of the feature function, Expressed as customer characteristic function value, Represented as a set of customer characteristics; The prediction accuracy of the demand probability distribution data is verified through the model test set to generate a hidden digital model; the surface digital model and the hidden digital model are multi-layered to fuse information to generate a virtual digital twin model; Step S3 includes: Track customer behavior on the hidden digital model to obtain customer behavior tracking data; calculate the sentiment trend based on the customer behavior tracking data to obtain customer sentiment trend data; Predict customer behavior patterns using the latent digital model based on customer sentiment trend data, thereby obtaining customer behavior prediction data; Use customer behavior prediction data to evaluate the interaction strategy of the surface digital model and generate interaction strategy evaluation data; formulate a dynamic adjustment plan based on the interaction strategy evaluation data, and optimize the interaction design in the surface digital model based on the dynamic adjustment plan, so as to obtain a dynamic interaction strategy plan; Deploy and adjust interaction design in real time according to dynamic interaction strategy solutions to generate intelligent customer interaction strategies; Step S4: Collect customer sentiment feedback from customer Q&A content through intelligent customer interaction strategies to generate customer sentiment feedback data; construct a profile based on the customer sentiment feedback data to obtain a virtual customer profile, and store the virtual customer profile in the cloud to perform customer management tasks.

2. The cloud server-based customer management method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtain customer information data using a cloud server; Step S12: Perform customer information port compatibility analysis based on the customer information data to generate customer information port compatibility data; introduce the intelligent interactive plug-in based on the customer information port compatibility data to obtain intelligent question and answer plug-in usage data; Step S13: Using the intelligent question and answer plug-in to use data, the customer information data is screened for customer question and answer content to obtain customer question and answer content collection data; Step S14: preprocess the data collected from customer question and answer content to generate standard customer information data, wherein the data preprocessing includes data cleaning, data denoising, missing value filling and data standardization.

3. The cloud server-based customer management method according to claim 2, characterized in that: The introduction of intelligent interactive plug-ins based on customer information port compatible data includes: Perform demand feature analysis on customer information data based on customer information port compatibility data, thereby obtaining customer demand feature data; screen matching intelligent interactive plug-in types based on customer demand feature data, and perform plug-in adaptability evaluation, thereby obtaining intelligent plug-in adaptation data; According to the smart plug-in adaptation data, the smart question and answer plug-in configuration parameters corresponding to the smart plug-in are loaded to generate plug-in initialization parameter data; according to the plug-in initialization parameter data, the smart question and answer plug-in function module is loaded on the customer information data to generate smart plug-in interaction binding data; According to the smart plug-in interaction binding data, customer information and plug-in interaction records are integrated to generate smart question and answer plug-in usage data.

4. The method for managing customers based on a cloud server according to claim 1, characterized in that: Formulate dynamic adjustment plans based on interaction strategy evaluation data, and optimize the interaction design in the surface digital model based on the dynamic adjustment plans, including: Conduct customer behavior feedback analysis on interaction strategy evaluation data to obtain customer behavior feedback data; classify and identify high-frequency demands, low-frequency demands, and abnormal demands in customer interactions based on user behavior feedback data to obtain interaction demand classification data; The interactive interface layout and interactive logic are adjusted based on the high-frequency demand identification results in customer interaction through interactive demand classification data, so as to obtain high-frequency interaction optimization design data; the low-frequency demand identification results in customer interaction are improved by adding guidance information or optimizing the operation path, so as to obtain low-frequency interaction optimization design data; According to the interaction demand classification data, an exception handling mechanism is introduced into the abnormal demand identification results in customer interaction, so as to obtain abnormal interaction processing data, wherein the introduction of the exception handling mechanism includes a multi-round question-and-answer process or manual intervention; a dynamic adjustment plan is constructed based on high-frequency interaction optimization design data, low-frequency interaction optimization design data and abnormal interaction processing data to generate dynamic optimization interaction design data; The interactive design in the surface digital model is optimized using dynamic optimization interactive design data to obtain a dynamic interactive strategy solution.

5. The method for managing customers based on a cloud server according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing customer emotional feedback on the customer question and answer content collection data through an intelligent customer interaction strategy to generate customer emotional feedback data; Step S42: construct a portrait based on the customer sentiment feedback data to obtain a virtual customer portrait; stratify the virtual customer portrait to generate virtual customer portrait hierarchical data; label the virtual customer portrait according to the virtual customer portrait hierarchical data, and store the labeled virtual customer portrait in the cloud to perform customer management operations.

6. A customer management system based on a cloud server, characterized in that: Used to execute the cloud server-based customer management method according to claim 1, the cloud server-based customer management system comprises: The interactive plug-in introduction module is used to obtain customer information data using the cloud server; introduce intelligent interactive plug-ins based on the customer information data to obtain intelligent question and answer plug-in usage data; filter customer question and answer content on the customer information data through the intelligent question and answer plug-in usage data to obtain customer question and answer content collection data; perform data preprocessing on the customer question and answer content collection data to generate standard customer question and answer content data; The behavior modeling module is used to perform semantic analysis on standard customer Q&A content data to generate customer Q&A content semantic analysis data; perform customer behavior modeling on customer information data based on the customer Q&A content semantic analysis data to generate a virtual digital twin model, wherein the virtual digital twin model includes a surface digital model and a hidden digital model; The dynamic interaction module is used to track customer behavior on the hidden digital model to obtain customer behavior tracking data; predict customer behavior on the hidden digital model based on the customer behavior tracking data to generate customer behavior prediction data; use the customer behavior prediction data to adjust the dynamic interaction strategy of the surface digital model to generate intelligent customer interaction strategy; The emotional feedback module is used to collect customer emotional feedback on customer question and answer content through intelligent customer interaction strategies to generate customer emotional feedback data; construct a portrait based on the customer emotional feedback data to obtain a virtual customer portrait, and store the virtual customer portrait in the cloud to perform customer management tasks.

Images