Big data-based insurance policy management and secondary development promotion method

Abstract

The invention discloses an insurance policy management and secondary development promotion method based on big data, and relates to the field of financial science and technology, and the method comprises an isolated policy resource library module which is used for constructing a unified and standardized database, and carrying out the centralized storage and management of isolated policy information and customer information. The accuracy and real-time performance of information are ensured by dynamically updating and integrating customer behavior data; and the solitary allocation algorithm module is connected with the solitary resource library module and is used for accurately allocating the solitary resources through an intelligent algorithm so as to ensure the fairness and efficiency of allocation. According to the invention, through integration of a plurality of modules and units, unified management, accurate distribution, dynamic tracking and intelligent analysis of the solitary insurance policy resources are realized through the solitary resource management system.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and in particular to a method for insurance policy management and secondary development promotion based on big data. Background Art

[0002] As the largest insurance company in the domestic market, China Life has accumulated a large number of orphan policies in the long-term service and salesperson turnover process, that is, policies whose original service salespersons have left but whose policy contracts are still valid. These orphan policies have contractual obligations that must be guaranteed, and their corresponding orphan customers are also valuable resources for business development.

[0003] Salesmen who mainly serve lonely resources have gradually formed a collection and development team as an important part of the individual insurance channel. In the long-term lonely service process, the collection and development team has used the advantages of lonely resources to summarize effective customer ice-breaking techniques such as three-way visits and service tracking, and has a high success rate in business process links such as platform invitations.

[0004] At present, the marketing team of the individual insurance channel is unable to pay attention to orphan resources. There is no way to allocate and take over the orphan policies left by the resigned salesmen in the stable marketing team. It is difficult to use the marketing team to expand the personal capabilities of the salesmen, and it is impossible to carry out secondary development of the orphan resources. Summary of the invention

[0005] The purpose of the present invention is to provide a method for insurance policy management and secondary development promotion based on big data to address the deficiencies in the background technology.

[0006] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for policy management and secondary development promotion based on big data, comprising:

[0007] The Lonely Resource Library module is used to build a unified and standardized database to centrally store and manage Lonely Insurance policy information and customer information. By dynamically updating and integrating customer behavior data, the accuracy and real-time nature of the information is ensured;

[0008] The solitary allocation algorithm module is connected to the solitary resource library module to accurately allocate solitary resources through intelligent algorithms to ensure fairness and efficiency of allocation. Through comprehensive capabilities and historical performance, the allocation strategy is dynamically adjusted to adapt to market changes;

[0009] The lonely resource module is connected with the lonely allocation algorithm module to focus on the integration, classification and management of lonely resources to ensure the efficient use of resources and the accuracy of allocation;

[0010] The business process connection module is connected with the isolated resource module to introduce isolated customer resources into the existing business process and to track and optimize the business data throughout the process, thereby improving the efficiency of the business process and customer satisfaction.

[0011] The data analysis module is connected with the business process connection module to conduct in-depth analysis of customer and business data through big data technology and machine learning algorithms to support business decision-making and optimize resource allocation;

[0012] The mining tool module is connected to the data analysis module. It will conduct in-depth mining and analysis of customer business data through big data processing and artificial intelligence technology to discover hidden patterns and trends and support business optimization.

[0013] The customer relationship management module is connected with the mining tool module to record and manage customer communication records, collect customer feedback, form a complete customer service file, and improve customer satisfaction and service quality;

[0014] The performance management module is connected to the customer relationship management module. By defining and managing the performance indicators of salesmen, it monitors the performance of salesmen in real time, conducts performance evaluation and provides incentives to improve the work enthusiasm and performance of salesmen.

[0015] The customer emotion module is connected to the customer relationship management module. It uses natural language processing technology to analyze the communication records between customers and sales staff, evaluate the customer's emotional state, and combine the results with the customer portrait to adjust the service strategy and improve customer satisfaction.

[0016] In a preferred embodiment, the lonely resource library module includes:

[0017] Data storage unit: provides storage of isolated insurance policies and customer information in a standard format;

[0018] Data aggregation unit: integrates single customers and their related policy information to form a comprehensive customer view;

[0019] Dynamic update unit: real-time update of customer information and policy status;

[0020] Behavioral data integration unit: Integrate customer behavior data from different channels to facilitate analysis and prediction of customer needs.

[0021] In a preferred embodiment, the lonely allocation algorithm module includes:

[0022] Algorithm model unit: establish basic allocation principles and algorithm logic;

[0023] Adaptive learning unit: uses adaptive learning algorithms to automatically adjust allocation strategies;

[0024] Multi-dimensional weight unit: Consider the comprehensive ability of the salesperson and establish a multi-dimensional weight model; dynamic programming unit: confirm the allocation of insurance policies and salespersons under restrictions.

[0025] In a preferred embodiment, the isolated resource module includes:

[0026] Resource integration unit: integrate and classify various types of isolated resources;

[0027] Resource allocation unit: accurately allocate resources according to the algorithm model;

[0028] Resource tracking unit: Track the usage of isolated resources throughout the process.

[0029] In a preferred embodiment, the business process connection module includes:

[0030] Process introduction unit: integrate isolated customer resources into existing business processes;

[0031] Process tracking unit: Track the entire process of business data;

[0032] Process optimization unit: automatically generates process optimization suggestions.

[0033] In a preferred embodiment, the data analysis module includes:

[0034] Data collection unit: collects and integrates customer and business data;

[0035] Data Mining Unit: Data mining and analysis using machine learning and predictive algorithms;

[0036] Data visualization unit: Visualize the analysis results to support business decisions.

[0037] In a preferred embodiment, the excavation tool module comprises:

[0038] Big Data Processing Unit: Processing and analyzing large-scale data sets;

[0039] Artificial Intelligence Unit: Use AI technology to conduct in-depth data analysis;

[0040] Rule Discovery Unit: Discover patterns and trends in customer behavior and business processes.

[0041] In a preferred embodiment, the customer relationship management module includes:

[0042] Communication Record Unit: Record and manage all customer communication records;

[0043] Feedback collection unit: establish a customer feedback system to collect customer comments and suggestions;

[0044] Customer file unit: forms a complete customer service file to facilitate subsequent follow-up.

[0045] In a preferred embodiment, the performance management module includes:

[0046] Performance Indicator Management Unit: Define and manage salesperson’s performance indicators;

[0047] Performance monitoring unit: real-time monitoring of salesperson’s work performance;

[0048] Performance Appraisal Unit: Conduct performance appraisals and provide incentives.

[0049] In a preferred embodiment, the customer emotion module includes:

[0050] Sentiment analysis unit: Analyze customer communication records and evaluate emotional status through NLP technology;

[0051] Emotional feedback unit: combines the results of emotional analysis with customer portraits to adjust service strategies;

[0052] Satisfaction tracking unit: track changes in customer satisfaction and adjust service plans in a timely manner.

[0053] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0054] The present invention realizes the unified management, precise allocation, dynamic tracking and intelligent analysis of lonely policy resources through the integration of multiple modules and units in the lonely resource management system. At the same time, the lonely resource library module ensures the standardized storage and dynamic update of lonely policy information and customer information, improves the accuracy and real-time nature of the data, and solves the problem of lonely resource data fragmentation. The lonely allocation algorithm module realizes the intelligent and precise allocation of lonely resources through adaptive learning and dynamic planning, takes into account the comprehensive ability and historical performance of the salesperson, improves the fairness and efficiency of resource allocation, ensures the efficient use and precise allocation of resources, avoids resource waste and repeated allocation, and then introduces lonely customer resources into the existing business process, and tracks and optimizes the business data throughout the process, which improves the business flow. The data analysis module uses big data technology and machine learning algorithms to conduct in-depth analysis of customer and business data, support business decision-making and optimal resource allocation, and improve the depth and accuracy of data analysis. Subsequently, big data processing and artificial intelligence technologies are used to conduct in-depth mining and analysis of customer business data to discover hidden patterns and trends and support business optimization. The customer relationship management module records and manages customer communication records, collects customer feedback, and forms a complete customer service file, thereby improving customer satisfaction and service quality. The performance management module defines and manages the performance indicators of salesmen, monitors their performance in real time, and conducts performance evaluation and incentives, thereby improving their work enthusiasm and performance, and can also enhance the secondary development of isolated resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0056] Figure 1 It is a system block diagram of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Example 1, please refer to Figure 1As shown, the method for policy management and secondary development promotion based on big data described in this embodiment includes:

[0059] The Lonely Resource Library module is used to build a unified and standardized database to centrally store and manage Lonely Insurance policy information and customer information. By dynamically updating and integrating customer behavior data, the accuracy and real-time nature of the information is ensured;

[0060] The solitary allocation algorithm module is connected to the solitary resource library module to accurately allocate solitary resources through intelligent algorithms to ensure fairness and efficiency of allocation. Through comprehensive capabilities and historical performance, the allocation strategy is dynamically adjusted to adapt to market changes;

[0061] The lonely resource module is connected with the lonely allocation algorithm module to focus on the integration, classification and management of lonely resources to ensure the efficient use of resources and the accuracy of allocation;

[0062] The business process connection module is connected with the isolated resource module to introduce isolated customer resources into the existing business process and to track and optimize the business data throughout the process, thereby improving the efficiency of the business process and customer satisfaction.

[0063] The data analysis module is connected with the business process connection module to conduct in-depth analysis of customer and business data through big data technology and machine learning algorithms to support business decision-making and optimize resource allocation;

[0064] The mining tool module is connected to the data analysis module. It will conduct in-depth mining and analysis of customer business data through big data processing and artificial intelligence technology to discover hidden patterns and trends and support business optimization.

[0065] The customer relationship management module is connected with the mining tool module to record and manage customer communication records, collect customer feedback, form a complete customer service file, and improve customer satisfaction and service quality;

[0066] The performance management module is connected to the customer relationship management module. By defining and managing the performance indicators of salesmen, it monitors the performance of salesmen in real time, conducts performance evaluation and provides incentives to improve the work enthusiasm and performance of salesmen.

[0067] The customer emotion module is connected to the customer relationship management module. It uses natural language processing technology to analyze the communication records between customers and sales staff, evaluate the customer's emotional state, and combine the results with the customer portrait to adjust the service strategy and improve customer satisfaction.

[0068] Further explanation: According to the common content of the greatest convention, the standard unified format of lonely policy information and customer information storage format is confirmed, and it can meet personalized needs. Then, according to the lonely allocation records manually operated by personal insurance personnel and the basic allocation principles sorted out, combined with the performance data of the salesperson, the allocation of lonely resources is analyzed, and an algorithm logic is formed to achieve automation and precision of lonely allocation. At the same time, for existing customer-based business processes such as the Wolf Warrior platform, lonely customers are introduced as a source; for lonely customer resources, in addition to the existing mature visit management, a process tracking of business data is established, and then advanced data analysis and mining tools, such as big data, artificial intelligence, etc., are used for lonely customers. For operations, in-depth analysis is conducted from aspects such as input-utility ratio and timeliness of business process actions to discover the regular trends behind them and provide more accurate support for business decisions. The original lonely information records are currently stored based on allocation records, which are the vertical process reflection of a single policy. They do not take into account the horizontal connections between policies and customers, and even the situation where multiple primary and secondary insurance policies with the same policy number are allocated to different salesmen. The new lonely system information records take lonely customers as the main consideration and aggregate customer information. For policy information related to specific policy allocation, the data itself determines the policy type. Only by using customer information as a foreign key can it be associated with specific business actions and guide the specific allocation of lonely policies.

[0069] In one embodiment, the isolated resource library module includes:

[0070] Data storage unit: provides storage of isolated insurance policies and customer information in a standard format;

[0071] Data aggregation unit: integrates single customers and their related policy information to form a comprehensive customer view;

[0072] Dynamic update unit: real-time update of customer information and policy status;

[0073] Behavioral data integration unit: Integrate customer behavior data from different channels to facilitate analysis and prediction of customer needs.

[0074] It is further explained that a unified standard format ensures that all orphan policy information and customer data can be recorded and stored in a consistent manner, which is convenient for subsequent processing and query. The standardized storage format not only improves the integrity and consistency of the data, but also facilitates data exchange and integration with other systems. It is then used to integrate orphan customers and their related policy information to form a comprehensive customer view. By aggregating multiple policy information and related data of the same customer, the overall situation of the customer can be more clearly presented, providing reliable data support for subsequent analysis and decision-making. For example, different policies, payment records, service records, etc. owned by a customer can all be displayed in a comprehensive view, and customer information and policy status are updated in real time. As customer information and policy status change, the unit can automatically update data to ensure that the information in the system is always up to date. This can avoid decision-making errors caused by information lag, and can timely reflect the latest needs of customers and changes in policy status. At the same time, by integrating these behavioral data, the system can more comprehensively understand the customer's behavior patterns and preferences, which helps to analyze and predict customer needs. For example, customers who frequently consult about a specific insurance type may have the intention to purchase the insurance type. By integrating these data, it can timely identify and adopt corresponding sales strategies, and then provide a comprehensive customer view and behavioral data analysis, which provides a solid foundation for the effective management and utilization of lonely resources. This module solves the problems of lonely resource data fragmentation and inefficient management, and significantly improves the utilization rate of lonely policy resources and the success rate of customer secondary development.

[0075] In one embodiment, the lonely allocation algorithm module includes:

[0076] Algorithm model unit: establish basic allocation principles and algorithm logic;

[0077] Adaptive learning unit: uses adaptive learning algorithms to automatically adjust allocation strategies;

[0078] Multi-dimensional weight unit: Consider the comprehensive ability of the salesperson and establish a multi-dimensional weight model; dynamic programming unit: confirm the allocation of insurance policies and salespersons under restrictions.

[0079] To further explain, the purpose of allocation is to serve and develop. In the specific business scenarios of different channels, there may be relatively different focuses. The principle of allocation is to obtain the highest expected harvest under the current purpose:

[0080] Confirm the scope and purpose of the allocation task: the lonely policies to be allocated will not be allocated all at once. There is already a reservoir for centralized management. The marketing channels are being built according to their own needs. The scope of each allocation is confirmed by the business channels. According to the lonely information storage format mentioned above, it is usually expanded to all lonely policies based on customers within the preliminary selection range. The purpose of allocation may be service-oriented, such as the lonely policies for the elderly with lifetime validity, and development-oriented, such as the lonely policies under the name of high-net-worth customers. For the confirmed allocation tasks, enter the key process.

[0081] Allocation algorithm process: The purpose is to assign a salesperson number to each policy number to be assigned. In the process, the policy number may be abstracted into a unique customer identifier after aggregation. For each policy number to be assigned, if a salesperson is not specifically designated, there may be one or more salespersons from a blood-related team as the basic range based on rigid business rules. Based on the above specific purpose, the selected salesperson is determined based on the information of the lone customer and the business data related to the salesperson.

[0082] Post-effect feedback: After the assignment, the business channel will have a specific summary and analysis of the results of the development of independent customers. The system algorithm will recalculate the results of the assigned tasks based on the results optimization. Currently, it mainly plays an auxiliary role and is provided as a summary reference for the business channel.

[0083] In one embodiment, the isolated resource module includes:

[0084] Resource integration unit: integrate and classify various types of isolated resources;

[0085] Resource allocation unit: accurately allocate resources according to the algorithm model;

[0086] Resource tracking unit: Track the usage of isolated resources throughout the process.

[0087] It is further explained that the resource integration unit establishes unified standards to classify and organize lonely resources to ensure that all resources can be effectively managed and used. The integrated resources will be classified according to different attributes, such as policy type, customer value, etc., to facilitate subsequent accurate allocation and use. Then, the lonely resources will be intelligently allocated using adaptive learning algorithms and multi-dimensional weight models. During the allocation process, the comprehensive ability, historical performance and customer needs of the salesperson are taken into consideration to ensure the fairness and efficiency of resource allocation. When problems arise, the entire process from allocation to use of each lonely resource is recorded, including the follow-up of the salesperson, customer feedback and resource utilization effects. Through tracking, it is possible to monitor the use of resources in real time and discover and solve possible problems.

[0088] In one embodiment, the business process connection module includes:

[0089] Process introduction unit: integrate isolated customer resources into existing business processes;

[0090] Process tracking unit: Track the entire process of business data;

[0091] Process optimization unit: automatically generates process optimization suggestions.

[0092] Further explanation: integration with the customer management system and business process platform ensures that lonely customer resources can be efficiently introduced into the existing business system. This not only makes full use of existing business processes and resources, but also enhances the management and service of lonely customers on the basis of existing processes, and then monitors and records all business activities related to lonely customers in real time. At the same time, customer visits, communication records, service feedback, etc. can be used. Through comprehensive tracking of these data, the execution status and effect of each link can be understood, ensuring that each process node can be effectively managed and optimized, identifying bottlenecks and inefficient links in the process, and proposing improvement plans. For example, by analyzing customer feedback and the execution records of salesmen, the process optimization unit can suggest optimizing the frequency of customer visits, adjusting communication strategies, or reallocating service resources, etc., to improve the efficiency of the overall business process and customer satisfaction.

[0093] In one embodiment, the data analysis module includes:

[0094] Data collection unit: collects and integrates customer and business data;

[0095] Data Mining Unit: Data mining and analysis using machine learning and predictive algorithms;

[0096] Data visualization unit: Visualize the analysis results to support business decisions.

[0097] Further explanation: obtain the basic information of customers, insurance policy information, behavioral data, etc. In addition, it also integrates the interaction records of customers in different business links, such as visit records, telephone communication records, email exchanges, etc. Through the comprehensive collection of data, a database covering all aspects of customers is formed, which provides a basis for subsequent data mining and analysis. Through the analysis of customer historical behavior data, the potential needs and behavior patterns of customers can be identified. By analyzing the customer's consumption records, insurance policy holdings and communication history, the customer's possible future needs can be predicted, helping the business team to formulate more accurate marketing strategies. Machine learning algorithms can continuously optimize the prediction model and improve the accuracy and effectiveness of the analysis. Then, through data preprocessing: cleaning and standardizing data, removing noise and outliers, feature selection: selecting key features according to business needs, such as customer age, insurance policy type, past purchase records, etc., model training: using historical data to train machine learning models, such as decision trees, random forests, neural networks, etc., model evaluation: evaluating model performance through cross-validation and test sets to ensure the accuracy and reliability of the model, predictive analysis: applying the trained model for prediction to generate prediction results of customer needs and behaviors.

[0098] In one embodiment, the excavation tool module comprises:

[0099] Big Data Processing Unit: Processing and analyzing large-scale data sets;

[0100] Artificial Intelligence Unit: Use AI technology to conduct in-depth data analysis;

[0101] Rule Discovery Unit: Discover patterns and trends in customer behavior and business processes.

[0102] Further explanation: using distributed computing and storage technologies, such as Hadoop and Spark, to efficiently process massive amounts of customer data and business data. Through steps such as data cleaning, data conversion and data loading, the raw data is converted into structured data for subsequent analysis and mining. When using it, data collection can be used: collecting raw data from multiple data sources (such as databases, log files, social media, etc.), data cleaning: removing noise, duplication and outliers in the data to ensure data quality, followed by data conversion: format conversion and standardization of the data, unifying the data format, and finally entering data loading: loading the processed data into a distributed storage system for efficient access and analysis, and modeling and predicting customer behavior and business data through machine learning algorithms and deep learning models. This unit is able to identify complex patterns and relationships, provide high-precision prediction results, and extract key features from raw data, such as customer attributes and behavior records. The machine learning model is trained using a training data set. Common algorithms include regression analysis, decision trees, random forests and neural networks. The performance of the model is evaluated using a validation data set, and model parameters are adjusted to improve prediction accuracy. The trained model is then applied to actual data to generate prediction results and analysis reports.

[0103] In one embodiment, the customer relationship management module includes:

[0104] Communication Record Unit: Record and manage all customer communication records;

[0105] Feedback collection unit: establish a customer feedback system to collect customer comments and suggestions;

[0106] Customer file unit: forms a complete customer service file to facilitate subsequent follow-up.

[0107] To further explain, through integration with the business system, the content of each communication with the customer is recorded in detail, including various forms such as telephone, email, and interviews. All communication records are organized in the form of a timeline, which is convenient for sales staff to review and follow up at any time. Through systematic communication record management, we can fully understand the customer's needs and historical interactions, and each communication with the customer will be automatically recorded in the system to reduce human omissions. It is classified and organized according to the communication type, time, subject, etc. for quick search. Sales staff can check the customer's historical communication records at any time to understand the customer's background and needs.

[0108] In one embodiment, the performance management module includes:

[0109] Performance Indicator Management Unit: Define and manage salesperson’s performance indicators;

[0110] Performance monitoring unit: real-time monitoring of salesperson’s work performance;

[0111] Performance Appraisal Unit: Conduct performance appraisals and provide incentives.

[0112] Further explanation: By formulating detailed performance appraisal standards and indicator systems, it is ensured that the performance of each salesperson can be accurately measured and evaluated. Performance indicators can include sales, customer satisfaction, number of completed tasks, etc. The unit also supports dynamic adjustment of performance indicators according to business needs to adapt to business goals at different stages, and then tracks the sales activities, customer service and task completion of the salesperson in real time. The unit provides a variety of monitoring methods, including automated data collection, real-time alarms and regular reports to ensure that management can understand the work status of each salesperson in a timely manner.

[0113] In one embodiment, the customer emotion module includes:

[0114] Sentiment analysis unit: Analyze customer communication records and evaluate emotional status through NLP technology;

[0115] Emotional feedback unit: combines the results of emotional analysis with customer portraits to adjust service strategies;

[0116] Satisfaction tracking unit: track changes in customer satisfaction and adjust service plans in a timely manner.

[0117] It is further explained that the customer's communication records are analyzed through NLP technology to evaluate the customer's emotional state. This unit is capable of processing various forms of communication records, including telephone recordings, transcribed texts, emails, online chat records, etc. By analyzing these text data, the customer's emotional tendencies are identified, and the customer's emotional state is comprehensively analyzed with their historical behavior and preference data to provide personalized service recommendations for the business team. For customers with low emotional scores, the system will recommend that the salesperson provide product services. The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in this application, which should be covered within the scope of protection of this application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for promoting insurance policy management and secondary development based on big data, characterized in that: include: The Lonely Resource Library module is used to build a unified and standardized database to centrally store and manage Lonely Insurance Policy information and customer information, and ensure the accuracy and timeliness of information by dynamically updating and integrating customer behavior data; The orphan allocation algorithm module is connected to the orphan resource library module to accurately allocate orphan resources through intelligent algorithms, ensuring the fairness and efficiency of allocation, and dynamically adjusting allocation strategies to adapt to market changes through comprehensive capabilities and historical performance; The lonely resource module is connected with the lonely allocation algorithm module to focus on the integration, classification and management of lonely resources to ensure the efficient use of resources and the accuracy of allocation; The business process connection module is connected with the isolated resource module to introduce isolated customer resources into the existing business process and to track and optimize the business data throughout the process, thereby improving the efficiency of the business process and customer satisfaction. The data analysis module is connected with the business process connection module to conduct in-depth analysis of customer and business data through big data technology and machine learning algorithms to support business decision-making and optimize resource allocation; The mining tool module is connected to the data analysis module. It will conduct in-depth mining and analysis of customer business data through big data processing and artificial intelligence technology to discover hidden patterns and trends and support business optimization. The customer relationship management module is connected with the mining tool module to record and manage customer communication records, collect customer feedback, form a complete customer service file, and improve customer satisfaction and service quality; The performance management module is connected to the customer relationship management module. By defining and managing the performance indicators of salesmen, it monitors the performance of salesmen in real time, conducts performance evaluation and provides incentives to improve the work enthusiasm and performance of salesmen. The customer emotion module is connected to the customer relationship management module. It uses natural language processing technology to analyze the communication records between customers and sales staff, evaluate the customer's emotional state, and combine the results with the customer portrait to adjust the service strategy and improve customer satisfaction.

2. The method for policy management and secondary development promotion based on big data according to claim 1, characterized in that: The lonely resource library module includes: Data storage unit: provides storage of isolated insurance policies and customer information in a standard format; Data aggregation unit: integrates single customers and their related policy information to form a comprehensive customer view; Dynamic update unit: real-time update of customer information and policy status; Behavioral data integration unit: Integrate customer behavior data from different channels to facilitate analysis and prediction of customer needs.

3. The method for policy management and secondary development promotion based on big data according to claim 1, characterized in that: The lonely allocation algorithm module includes: Algorithm model unit: establish basic allocation principles and algorithm logic; Adaptive learning unit: uses adaptive learning algorithms to automatically adjust allocation strategies; Multi-dimensional weight unit: Consider the comprehensive ability of the salesperson and establish a multi-dimensional weight model; dynamic programming unit: confirm the allocation of insurance policies and salespersons under restrictions.

4. The method for policy management and secondary development promotion based on big data according to claim 1, characterized in that: The lonely resource module includes: Resource integration unit: integrate and classify various types of isolated resources; Resource allocation unit: accurately allocate resources according to the algorithm model; Resource tracking unit: Track the usage of isolated resources throughout the process.

5. The method for policy management and secondary development promotion based on big data according to claim 1, characterized in that: The business process contact module includes: Process introduction unit: integrate isolated customer resources into existing business processes; Process tracking unit: Track the entire process of business data; Process optimization unit: automatically generates process optimization suggestions.

6. The method for policy management and secondary development promotion based on big data according to claim 1, characterized in that: The data analysis module includes: Data collection unit: collects and integrates customer and business data; Data Mining Unit: Data mining and analysis using machine learning and predictive algorithms; Data visualization unit: Visualize the analysis results to support business decisions.

7. The method for policy management and secondary development promotion based on big data according to claim 1, characterized in that: The excavation tool module comprises: Big Data Processing Unit: Processing and analyzing large-scale data sets; Artificial Intelligence Unit: Use AI technology to conduct in-depth data analysis; Rule Discovery Unit: Discover patterns and trends in customer behavior and business processes.

8. The method for policy management and secondary development promotion based on big data according to claim 1, characterized in that: The customer relationship management module includes: Communication Record Unit: Record and manage all customer communication records; Feedback collection unit: establish a customer feedback system to collect customer comments and suggestions; Customer file unit: forms a complete customer service file to facilitate subsequent follow-up.

9. The method for policy management and secondary development promotion based on big data according to claim 1, characterized in that: The performance management module includes: Performance Indicator Management Unit: Define and manage salesperson’s performance indicators; Performance monitoring unit: real-time monitoring of salesperson’s work performance; Performance Appraisal Unit: Conduct performance appraisals and provide incentives.

10. The method for policy management and secondary development promotion based on big data according to claim 1, characterized in that: The customer emotion module includes: Sentiment analysis unit: Analyze customer communication records and evaluate emotional status through NLP technology; Emotional feedback unit: combines the results of emotional analysis with customer portraits to adjust service strategies; Satisfaction tracking unit: track changes in customer satisfaction and adjust service plans in a timely manner.

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