A Distributed Customer Management System for the Insurance Industry and Its Implementation Method

Abstract

This invention discloses a distributed customer management system and its implementation method for the insurance industry, belonging to the field of information technology in the insurance industry. Addressing the problems of fragmented customer management information, services, data silos, and outdated architecture in traditional customer management systems, the system adopts a distributed microservice architecture, integrating multiple service modules such as lead generation, marketing activities, and customer resources, encompassing a three-layer architecture design of application, technology, and deployment. It supports multi-terminal access (A-end, C-end, E-end) and achieves front-end interaction through a unified interface. Leveraging data storage, data analysis, and intelligent tools, it achieves centralized management of customer information, automated service processes, and precise marketing, while also possessing functions such as compliance auditing and multi-channel communication. This system effectively improves customer management efficiency and customer satisfaction, enhances system scalability and maintainability, and provides insurance companies with an efficient and intelligent customer management solution, applicable to various customer management scenarios in the insurance industry.

Description

Technical Field

[0001] This invention relates to the field of information technology in the insurance industry, and in particular to a distributed customer management system based on the insurance industry and its implementation method. Background Technology

[0002] As an important component of the modern financial system, the customer management system has become a core technology for improving business efficiency in the insurance industry.

[0003] With the acceleration of digital transformation, traditional customer management methods are no longer sufficient to meet the urgent needs of the insurance industry for data integration, service collaboration, and intelligent decision-making. Specifically, existing technology systems typically employ a monolithic architecture, building a basic process from customer information management to marketing campaign execution through the collaborative operation of CRM systems, data platforms, and SMS / email platforms. Customer data management encompasses key aspects such as information collection, storage, and analysis; marketing services involve the entire process of campaign design, execution, and effectiveness evaluation; and multi-channel interaction relies on traditional communication methods such as telephone and email. However, existing customer management methods directly employ a distributed data storage architecture without establishing a unified data governance mechanism. This can lead to fragmented customer information, disjointed service processes, or delayed data analysis, thereby affecting the completeness of customer profiles, the accuracy of marketing strategies, and the real-time nature of service responses. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this invention is to propose a distributed customer management system based on the insurance industry and its implementation method.

[0006] Another objective of this invention is to propose a distributed customer management system and its implementation apparatus based on the insurance industry.

[0007] The third objective of this invention is to provide a computer device.

[0008] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0009] To achieve the above objectives, a first aspect of the present invention proposes a distributed customer management system and its implementation method based on the insurance industry, comprising: S1. Collect multi-source heterogeneous customer data through a distributed microservice architecture. The multi-source heterogeneous customer data includes basic information, purchase records, and service records. S2, Based on a unified interface, the multi-source heterogeneous customer data is standardized to form a unified customer view across systems; S3 utilizes an intelligent data governance engine to perform multi-dimensional tagging management of the unified customer view and builds dynamically updated customer profiles. S4. Real-time cross-service synchronization of customer profile data is achieved through message queues, and a distributed database cluster is used for unified storage and high-concurrency access support.

[0010] In one embodiment of the present invention, S1 includes: S11 collects behavioral data of potential customers in real time through website forms, social media, and advertising campaigns. S12 stores the collected customer data in the GaussDB distributed database cluster to form structured data storage.

[0011] In one embodiment of the present invention, S2 includes: S21 uses preset tag classification rules to classify customer data in multiple dimensions, including regional, age, and occupational attribute tags; S22 enables data interaction with the CRM system and peripheral systems of the data platform through the RESTful API interface specification.

[0012] In one embodiment of the present invention, the 3 includes: S31, Calculate the risk preference index based on customer purchase behavior data, using the following formula:

[0013] in, For customer risk preference index, For the first The weight of each behavioral feature, For the first Preference values ​​for each behavioral characteristic; S32 receives customer interaction data in real time through the Kafka message queue and triggers a dynamic update mechanism for customer profiles.

[0014] In one embodiment of the present invention, S4 includes: S41 uses the partitioning strategy of Kafka message queues to partition and transmit customer profile data according to geographical dimension. S42 ensures data consistency across nodes through GaussDB's distributed transaction mechanism.

[0015] To achieve the above objectives, a second aspect of the present invention provides a distributed customer management system and its implementation apparatus based on the insurance industry, comprising: The multi-source heterogeneous data acquisition module is used to collect multi-source heterogeneous customer data through a distributed microservice architecture. The multi-source heterogeneous customer data includes basic information, purchase records, and service records. The unified interface standardization processing module is used to standardize the multi-source heterogeneous customer data based on the unified interface to form a unified customer view across systems. The intelligent data governance engine module is used to perform multi-dimensional tagging management of the unified customer view and build dynamically updated customer profiles. The message queue real-time synchronization module is used to realize cross-service real-time synchronization of customer profile data through message queues, and uses a distributed database cluster for unified storage and high-concurrency access support.

[0016] This invention discloses a distributed customer management system and its implementation method and apparatus based on the insurance industry, which realizes the unified integration and efficient management of customer data in the insurance industry, solves the problems of information dispersion and data silos, and improves customer management efficiency and precision marketing capabilities.

[0017] To achieve the above objectives, a third aspect of this application provides a computer device, including a processor and a memory; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, for implementing a distributed customer management system and its implementation method based on the insurance industry as described in the first aspect embodiment.

[0018] To achieve the above objectives, the fourth aspect of this application proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a distributed customer management system and its implementation method based on the insurance industry as described in the first aspect embodiment.

[0019] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] Figure 1 This is a flowchart of a distributed customer management system based on the insurance industry and its implementation method according to an embodiment of the present invention; Figure 2 This is a customer management system architecture diagram according to an embodiment of the present invention; Figure 3 This is a distributed microservice architecture diagram according to an embodiment of the present invention; Figure 4 This is a system deployment architecture diagram according to an embodiment of the present invention; Figure 5This is a structural diagram of a distributed customer management system and its implementation device based on an embodiment of the present invention, which is applicable to the insurance industry. Figure 6 It is a computer device according to an embodiment of the present invention. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] The following description, with reference to the accompanying drawings, describes a distributed customer management system for the insurance industry, its implementation method, and apparatus, according to embodiments of the present invention.

[0024] Example 1 Figure 1 This is a flowchart illustrating a distributed customer management system for the insurance industry and its implementation method according to an embodiment of the present invention. Figure 1 As shown, it includes: S1. Collect multi-source heterogeneous customer data through a distributed microservice architecture. The multi-source heterogeneous customer data includes basic information, purchase records, and service records.

[0025] Furthermore, this step employs a Spring Cloud-based microservice architecture, combined with Kafka message queues and GaussDB databases, to achieve efficient and real-time collection and integration of customer data from different business systems, channels, and terminals. Specifically, the system interacts with external systems such as CRM systems, data / big data platforms, SMS platforms, email platforms, human resources management systems, and telecommunications outbound calling services through standardized API interfaces (such as RESTful APIs or gRPC), ensuring the compatibility and scalability of data collection.

[0026] Furthermore, the system supports multiple data acquisition methods, including but not limited to HTTP / HTTPS protocol transmission, FTP file upload, direct database connection (such as JDBC), and message queue subscription (such as Kafkatopic). The data acquisition frequency can be configured according to business needs, supporting real-time acquisition (such as event-driven) and scheduled acquisition (such as daily ETL tasks at midnight). Regarding data formats, the system is compatible with common data formats such as JSON, XML, and CSV, and ensures data structure consistency through a schema validation mechanism. In addition, the system supports data quality checks, such as field integrity checks, data type validation, and outlier filtering, ensuring the accuracy and usability of the collected data.

[0027] Furthermore, this step is widely applied to various stages of customer lifecycle management, such as new customer acquisition, existing customer maintenance, insurance product recommendation, and claims service optimization. For example, when a customer first inquires about or purchases an insurance product, the system can collect their basic information and purchasing behavior in real time and perform tagging through customer resource services, providing data support for subsequent personalized services. Regarding service records, the system records every interaction with the customer through customer contact service records, including phone calls, emails, and online chats, forming a complete customer service trajectory for easy analysis and optimization later.

[0028] Furthermore, this step, through a distributed architecture and standardized interfaces, enables unified access and management of multi-source heterogeneous data, solving the problems of data silos and information fragmentation in traditional systems. Simultaneously, a data quality control mechanism improves the accuracy and consistency of customer data, providing a high-quality data foundation for subsequent modules such as customer profiling, marketing campaign design, task allocation, and compliance auditing, thereby significantly improving the efficiency and accuracy of customer management.

[0029] Furthermore, S1 includes: S11 collects behavioral data of potential customers in real time through website forms, social media, and advertising campaigns.

[0030] Specifically, behavioral data of potential customers is collected in real time through website forms, social media, and advertising campaigns. This step is based on a multi-source data collection mechanism, combined with an event-driven architecture and real-time data processing technology, to achieve efficient capture and structured processing of customer behavior.

[0031] Furthermore, the system deploys various data collection interfaces at the front-end presentation layer, including but not limited to web form submission interfaces, social media API interfaces, and ad click tracking interfaces. In the website form scenario, the system uses HTML forms combined with a JavaScript event listener mechanism to collect real-time data on user actions such as filling in forms, submitting, dwell time, and click hotspots. In social media and advertising campaign scenarios, the system integrates third-party platform SDKs or APIs to collect data on user browsing, liking, commenting, clicking, and conversion behaviors. All collected data is transmitted via HTTP / HTTPS protocol to the NGINX reverse proxy server in the proxy layer, and then parsed and stored by the lead generation service in the service layer.

[0032] Furthermore, the system supports the collection of various behavioral data types, including user ID, access timestamp, access path, dwell time, click event type (such as button click, page jump), form field content, IP address, device type, and geographical location. During data processing, the system uses event timestamps (… ) and behavior type coding ( The data is structured to form a unified data model.

[0033] Furthermore, this step is widely applied in insurance companies' market expansion, product promotion, and customer conversion processes. For example, when launching a new insurance product, collecting user click behavior data through advertising campaigns and combining it with form submissions can quickly identify high-intent customer groups. In social media marketing, the system can track user interaction behavior in real time, providing data support for subsequent targeted marketing and customer segmentation.

[0034] Furthermore, this step enables real-time collection and structured processing of customer behavior data, providing high-quality data input for subsequent lead screening, customer profiling, and personalized marketing campaign design. Through the integration of multi-channel behavioral data, the system can gain a more comprehensive understanding of customer interests and needs, thereby improving customer conversion rates and marketing accuracy, and significantly enhancing the market responsiveness and customer management efficiency of the insurance business.

[0035] S12 stores the collected customer data in the GaussDB distributed database cluster to form structured data storage.

[0036] Specifically, the collected customer data is stored in a GaussDB distributed database cluster to form structured data storage. This step is based on the distributed architecture characteristics of GaussDB and combines the high concurrency, high consistency, and high availability requirements of customer data in the insurance industry to achieve unified management and efficient storage of customer information.

[0037] Furthermore, this step first acquires multi-dimensional data such as basic customer information, purchase records, service interactions, and tag data through the data acquisition module. After data acquisition, it undergoes format standardization processing through a unified data access layer, including field mapping, data cleaning, and type conversion, to ensure that the data conforms to GaussDB's storage specifications. Subsequently, the data is asynchronously transmitted through a Kafka message queue and written transactionally by GaussDB's distributed transaction engine, ensuring data consistency and reliability.

[0038] Furthermore, GaussDB employs a distributed consistency protocol to achieve cross-node data synchronization and supports ACID transaction characteristics. Its data storage combines columnar and row-based storage, supporting high-concurrency writes and efficient queries. In this system, the GaussDB cluster is typically configured with multiple replicas to improve disaster recovery capabilities, and reasonable partitioning strategies are set, such as partitioning by customer ID hash or by time range, to optimize query performance. In addition, the system supports automatic load balancing and failover to ensure stable service even under high-concurrency scenarios.

[0039] Furthermore, this step is widely applied in business processes such as customer information management, marketing campaign execution, task allocation, and compliance auditing. For example, in the process of customer lead collection, GaussDB's distributed write capabilities enable real-time entry and unified management of leads from multiple channels; in customer profile building, structured data provides a high-quality data foundation for subsequent machine learning model training; and in compliance auditing services, GaussDB's transaction logs and auditing functions effectively support the traceability and compliance verification of business operations.

[0040] Furthermore, this step, through GaussDB's distributed architecture and structured storage mechanism, achieves efficient integration and unified management of customer data, significantly improving the system's data processing capabilities and business response speed. Simultaneously, its high availability and strong consistency ensure the reliability of customer data in a multi-node environment, providing solid data support for subsequent business logic such as personalized marketing, precise recommendations, and task scheduling.

[0041] S2, based on a unified interface, standardize the multi-source heterogeneous customer data to form a unified customer view across systems.

[0042] Specifically, in some implementations, multi-source heterogeneous customer data is standardized based on a unified interface to form a unified customer view across systems. This step achieves the structuring, cleaning, mapping, and unified encoding of customer data from different business systems, channels, and terminals by building a unified data access layer and a data standardization engine, thereby providing a high-quality and consistent data foundation for subsequent customer profile building, marketing campaign execution, and task allocation.

[0043] Furthermore, this step first defines a standardized data model covering key fields such as basic customer information (e.g., name, gender, date of birth, contact information), purchase records (e.g., policy number, product type, purchase time, premium amount), and service records (e.g., consultation time, service type, processing status). During data access, the system receives raw data from external systems such as CRM systems, SMS platforms, email platforms, and HR management systems via RESTful APIs or Kafka message queues. Each interface adheres to a unified protocol specification, such as JSONSchema or AvroSchema, to ensure data format consistency.

[0044] Furthermore, the system employs an ETL (Extract, Transform, Load) process to clean, deduplicate, map fields, and convert data types from the raw data. For example, the customer gender field might be represented as "Male / Female," "M / F," or "1 / 0" in different systems; the system uses a mapping table to unify these into a standardized enumerated value. Time fields are uniformly converted to the ISO8601 standard format to support time-series analysis across time zones and systems.

[0045] Furthermore, the system supports configuring data cleaning rules and field mapping strategies, such as setting field missing rate thresholds and field consistency verification rules. Simultaneously, the system supports setting data synchronization frequency and data caching strategies to balance real-time performance and system performance.

[0046] Furthermore, this step is widely applied in business scenarios such as customer lifecycle management, precision marketing, and customer service optimization. For example, in the process of acquiring new customers, the system can uniformly access and standardize lead data from channels such as website forms, social media, and advertising platforms to form a complete view of customer leads for subsequent processing by lead generation services. In the maintenance of existing customers, the system can integrate customer behavior data from multiple service touchpoints (such as telephone, email, and APP) to build a unified customer profile, providing a basis for personalized recommendations and task allocation.

[0047] Furthermore, this step effectively addresses issues such as fragmented customer data, inconsistent formats, and inconsistencies in fields within traditional systems, significantly improving the availability and consistency of customer data. Through a unified interface and standardized processing mechanism, the system achieves cross-system data interoperability and sharing, laying a solid foundation for building a unified customer view, thereby enhancing the intelligence level of customer management and business response efficiency.

[0048] Furthermore, S2 includes: S21 uses preset tag classification rules to classify customer data in multiple dimensions, including regional, age, and occupational attribute tags.

[0049] Specifically, in some implementations, customer data is classified in multiple dimensions using preset tag classification rules, including regional, age, and occupational attribute tags. This step is based on the customer resource service module and combines the data processing and analysis capabilities provided by the data / big data platform. Through structured data modeling and tag system construction, efficient classification and tagging of customer information is achieved.

[0050] Furthermore, this step first extracts raw customer data from multiple data sources (such as CRM systems, marketing campaign records, customer contact logs, etc.) through an ETL (Extract, Transform, Load) process. Then, the system cleans and transforms the data according to preset tag classification rules, converting unstructured or semi-structured data into structured tag fields. For example, geographic tags can be divided by the customer's place of residence or registered address, supporting three-level classification: province, city, and district / county; age tags are calculated based on the date of birth, with ranges such as "18-25 years old" and "26-35 years old"; and occupation tags are mapped based on the customer's occupation code or description information, supporting classifications such as "financial professional," "education industry," and "freelancer."

[0051] Furthermore, tag classification rules can be configured in JSON format or dynamically loaded via a rule engine (such as Drools), supporting rule version management and canary releases. The system supports tag weight settings, such as... Indicates the first The importance coefficient of each tag in the customer profile, ranging from [value missing]. In addition, the tag update frequency can be set to real-time, daily, or weekly to adapt to the data timeliness requirements of different business scenarios.

[0052] Furthermore, this step is widely applied in business scenarios such as customer segmentation, precision marketing, product recommendation, and service strategy development. For example, in marketing campaign services, sales personnel can filter target groups such as "high-net-worth clients" or "small and medium-sized business owners" based on customers' occupational tags, thereby developing more targeted insurance product recommendation strategies. In the task management module, the system can assign customers to sales teams in corresponding regions based on geographic tags, improving service response efficiency.

[0053] Furthermore, this step, through the construction of a multi-dimensional tagging system, significantly improves the structuring and operability of customer data, providing a solid data foundation for subsequent customer profiling, personalized recommendations, and automated service processes. Simultaneously, the configurability and scalability of the tag classification rules enable the system to flexibly adapt to the customer management needs of different insurance companies and market environments, enhancing the system's intelligence and business adaptability.

[0054] S22 enables data interaction with the CRM system and peripheral systems of the data platform through the RESTful API interface specification.

[0055] Specifically, in this invention, data interaction with the CRM system and peripheral systems of the data platform is achieved through the RESTful API interface specification. This step is based on the HTTP / HTTPS protocol and adopts a unified interface design specification to realize data synchronization, service calls and business collaboration between systems, thereby improving the integration capability and response efficiency of the overall system.

[0056] Furthermore, RESTful API interfaces, through a resource-oriented design, abstract inter-system interactions into CRUD (Create, Read, Update, Delete) operations on resources. Each external system is defined as an independent resource service, uniquely identified by a URI. Interface requests use standard HTTP methods, and responses are uniformly formatted as JSON or XML, ensuring data compatibility and parsing efficiency across systems. In some implementations, the system also supports OAuth 2.0 or JWT authentication mechanisms to guarantee the security and access control of interface calls.

[0057] Furthermore, API requests typically include request headers, a request body, and query parameters. The request headers contain key parameters such as the AuthorizationToken, Content-Type (e.g., application / json), and X-Source identifier. The request body encapsulates specific business data, such as customer ID, activity ID, and task status, and its data structure must conform to the JSON Schema specification. In addition, the API response must include a status code (e.g., 200 for success, 401 for unauthorized, and 404 for resource not found), as well as the business data and error messages from the response body, ensuring that the caller can accurately parse the API's returned results.

[0058] Furthermore, this step is widely applied to business processes such as customer information synchronization, marketing campaign configuration, task allocation, and data collection and analysis. For example, in the customer management module, the system retrieves the latest customer information from the CRM system via a RESTful API and synchronizes it to the customer resource service for building customer profiles. During marketing campaign execution, the system pushes customer behavior data to the data platform via API for subsequent user segmentation and recommendation algorithm optimization. In addition, the SMS and email platforms also receive marketing content sent by the system via RESTful APIs, enabling automated message delivery.

[0059] Furthermore, this step, through standardized interface design, enables efficient, secure, and scalable data interaction between systems, reducing system coupling and improving the overall system's flexibility and maintainability. At the same time, the unified interface specifications facilitate subsequent monitoring, logging, and anomaly handling, ensuring system stability and business continuity.

[0060] S3 utilizes an intelligent data governance engine to perform multi-dimensional tagging management of the unified customer view, and builds dynamically updated customer profiles.

[0061] Specifically, in some implementations, an intelligent data governance engine is used to perform multi-dimensional tagging management of the unified customer view and build dynamically updated customer profiles. This step is based on a distributed microservice architecture, combined with customer resource services and data / big data platforms. Through processes such as data collection, cleaning, tag modeling, profile building and real-time updates, the structured processing and in-depth mining of customer information are achieved.

[0062] Furthermore, the intelligent data governance engine employs a hybrid tagging system based on rule engines and machine learning models to classify and tag customer data across multiple dimensions. Specifically, the system extracts raw data from multiple data sources, including CRM systems, marketing campaign records, customer contact logs, and purchase behavior data. It then performs data cleaning and standardization through an ETL (Extract, Transform, Load) process to ensure data quality meets the requirements for tag construction. The cleaned data is loaded into a data storage layer (such as GaussDB) and automatically tagged by the tagging engine according to preset tagging rules (such as customer age, occupation, purchase frequency, risk preference, etc.). In addition, the system introduces a clustering algorithm based on customer behavior sequences to dynamically group customers, thereby generating a more refined tagging system.

[0063] Furthermore, tag-based management involves several key metrics, including the number of tag dimensions (such as basic attributes, behavioral characteristics, and preference tags), tag update frequency (such as daily incremental updates and real-time event-triggered updates), tag coverage (such as the completeness of tag fields in customer profiles), and tag accuracy (derived through manual verification and model evaluation). In this system, the tag update frequency can be configured to once every 24 hours or triggered in real time based on customer behavior events. Tag dimensions typically exceed 50, covering multiple aspects such as customer lifecycle, product preferences, and risk assessment.

[0064] Furthermore, this step is widely applied in scenarios such as new customer acquisition, existing customer maintenance, personalized product recommendations, and marketing campaign optimization. For example, after collecting customer leads, the system can quickly identify high-value customers based on tags and assign them to suitable sales representatives; in marketing campaigns, the system can recommend matching insurance products based on customer profiles to improve conversion rates.

[0065] Furthermore, through multi-dimensional tagging management and dynamic customer profile construction, the system can significantly improve the efficiency of customer data utilization and the accuracy of marketing. On the one hand, the establishment of the tagging system makes customer information structured and quantifiable, facilitating subsequent automated processing and strategy formulation; on the other hand, the dynamic update mechanism ensures that customer profiles always reflect their latest behaviors and preferences, thereby improving customer experience and business responsiveness.

[0066] Furthermore, S3 includes: S31, Calculate the risk preference index based on customer purchase behavior data, using the following formula:

[0067] in, For customer risk preference index, For the first The weight of each behavioral feature, For the first Preference values ​​for each behavioral characteristic.

[0068] Specifically, in some implementations, a risk preference index is calculated based on customer purchase behavior data to build customer profiles and support the development of personalized marketing strategies. This step quantifies the risk characteristics of customers in their historical purchase behavior, extracts their preference for different types of insurance products, and thus forms a measurable risk preference index for subsequent precise recommendations and marketing campaign design.

[0069] Furthermore, firstly, the system extracts customers' historical purchase data from the data storage layer, including the type of product purchased, coverage amount, payment period, purchase frequency, and policy cancellation records. This data is cleaned and standardized through an ETL (Extract, Transform, Load) process to ensure data consistency and availability. Secondly, the system employs a weighted scoring mechanism, assigning different weights to each type of purchase behavior. For example, purchasing high-risk products (such as investment-linked insurance) has a higher weight, while purchasing low-risk products (such as pure protection insurance) has a lower weight. The weights can be set based on statistical analysis of historical data or adjusted through expert experience. Typically, the weight values ​​range from [0,1], and the sum of all weights is 1.

[0070] Furthermore, this step is typically deployed within the customer resource service's profile calculation submodule, working in conjunction with modules such as marketing campaign services and task management services. For example, in customer maintenance scenarios, the system recommends suitable insurance product combinations based on a risk preference index, improving customer satisfaction and product matching accuracy. In new customer conversion scenarios, this index can be used to screen potential customer groups and optimize the efficiency of marketing resource allocation.

[0071] Furthermore, this step improves the accuracy and operability of customer profiles by quantifying customer risk preferences, providing data support for subsequent personalized marketing and service strategies. At the same time, this method has good scalability, adapting to dynamic changes in different insurance product types and customer behavior patterns, enhancing the system's intelligence and business responsiveness.

[0072] S32 receives customer interaction data in real time through the Kafka message queue and triggers a dynamic update mechanism for customer profiles.

[0073] Specifically, in some implementations, the present invention receives customer interaction data in real time through a Kafka message queue and triggers a dynamic update mechanism for customer profiles. This step is based on an event-driven architecture and uses an asynchronous communication mechanism to ensure the stability and real-time performance of the system in high-concurrency, multi-source data access scenarios.

[0074] Furthermore, when customers interact with the system, the front-end presentation layer (such as H5 pages, management backend, and customer contact interfaces) encapsulates behavioral data (such as clicks, browsing, inquiries, purchases, complaints, etc.) into structured event messages and sends them to the customer contact service or marketing campaign service in the service layer via HTTP / HTTPS protocol. Upon receiving the event, the service layer transforms it into a message format conforming to the Kafka protocol and publishes it to the specified Topic through a Kafka producer. As a distributed stream processing platform, Kafka supports high-throughput, low-latency message transmission. Its message format is typically JSON, containing fields such as event type (event_type), customer identifier (customer_id), timestamp, and action parameters (action_params).

[0075] Furthermore, the throughput of the Kafka message queue can be configured to process thousands to tens of thousands of messages per second, with message latency controlled in the millisecond range (e.g., 99% of messages are consumed within 500ms). The frequency of customer profile updates can be set to real-time or batch updates according to business needs. Real-time updates are suitable for highly sensitive scenarios (such as customer complaints and purchasing behavior), while batch updates are suitable for low-frequency but high-value data (such as questionnaire feedback and historical behavior analysis).

[0076] Furthermore, this step is widely applied in scenarios such as customer lifecycle management, personalized recommendations, and marketing campaign response. For example, when a customer inquires about a health insurance product through online chat, the system can immediately update their "health concern" tag and trigger a marketing campaign to push relevant product information, thereby improving customer conversion rates.

[0077] Furthermore, this step enables real-time collection of customer data and dynamic updating of profiles, effectively solving the problems of data lag and static profiles in traditional systems, improving customer response speed and marketing accuracy, and providing a solid data foundation for subsequent personalized services and intelligent decision-making.

[0078] S4. Real-time cross-service synchronization of customer profile data is achieved through message queues, and a distributed database cluster is used for unified storage and high-concurrency access support.

[0079] Furthermore, this step utilizes mature message middleware technologies such as Kafka, RabbitMQ, or RocketMQ to build an asynchronous communication mechanism, enabling real-time synchronization of customer profile data across multiple service modules. Specifically, when customer information is updated in the customer management module (e.g., adding a purchase record, modifying contact information, updating tags), the system triggers a data change event through an event-driven mechanism, encapsulates the event into a structured message, and publishes it to a message queue. Service modules subscribing to this message queue (such as marketing campaign services, customer contact services, task management services, etc.) can consume and process this data in real time, thereby achieving cross-service data consistency and synchronization.

[0080] Furthermore, the message queue configuration must meet the requirements of high throughput and low latency. Message formats typically use JSON or Protobuf for serialization to ensure data structure compatibility and transmission efficiency in cross-service communication. Regarding the distributed database cluster, the system uses databases such as GaussDB that support distributed transactions and high-concurrency access, achieving horizontal scaling through database sharding and table partitioning strategies. For example, customer profile data can be hash-sharded by customer ID, with each shard deployed on an independent database node, supporting concurrent access capabilities of `QPS≥10000`, and improving system response speed and stability through read / write separation, load balancing, and other mechanisms.

[0081] Furthermore, this step is widely applied to business processes such as customer information updates, marketing campaign triggering, task allocation, and customer profile building. For example, when a customer submits a new policy application in the CRM system, the customer resource service updates their profile data and synchronizes the changes to the marketing campaign service via a message queue. The latter then dynamically adjusts its recommendation strategy based on the latest profile, thereby achieving precise marketing. In addition, in customer contact services, real-time synchronized customer profile data ensures that customer service personnel have access to the latest information when communicating with customers, improving service response quality.

[0082] Furthermore, this step effectively decouples various service modules through the asynchronous communication mechanism of message queues, improving the system's scalability and fault tolerance. Simultaneously, leveraging a distributed database cluster, it achieves unified storage and high-concurrency access support for customer profile data, ensuring data consistency and system stability. This technical solution demonstrates significant innovation and practicality in insurance industry customer management systems, providing a solid foundation for real-time sharing of customer information and intelligent decision-making.

[0083] Furthermore, S4 includes: S41 uses the partitioning strategy of Kafka message queues to partition and transmit customer profile data according to geographical dimensions.

[0084] Specifically, in some implementations, this invention employs the partitioning strategy of the Kafka message queue to partition and transmit customer profile data according to geographical dimensions, thereby achieving efficient data processing and load balancing. This step is technically based on Kafka's partitioning mechanism. By setting a reasonable partition key, customer profile data is categorized according to its geographical location and sent to the corresponding partition, ensuring that the data can be efficiently consumed and processed in a distributed environment.

[0085] Furthermore, Kafka's partitioning strategy relies on the partition key specified by the producer when sending messages. In this invention, the customer profile data includes a geographic field (such as the customer's province, city, or region code), which is used as the partition key. Kafka's partitioner determines which partition the message should be sent to based on the hash value of this key. For example, if the system is configured with 8 partitions, the partitioner takes the hash value of the geographic field modulo 8 to determine the partition number of the message.

[0086] Furthermore, the number of partitions in a Kafka topic Configuration is typically based on business scale and expected throughput. In this invention, it is recommended... The value should be between 8 and 16 to balance the number of partitions and system performance. Additionally, the region field... The coding should follow a unified geographic information standard, such as ISO 3166-2 coding or an internally defined regional coding system, to ensure the accuracy and consistency of data partitioning.

[0087] Furthermore, in practical applications, this step is primarily used for the real-time transmission and processing of customer profile data, especially in insurance business scenarios operating across multiple regions. For example, when a customer submits new behavioral data through a CRM system or marketing campaign service, the system will automatically extract their geographic information and send the customer profile data to the corresponding Kafka partition. Consumer services (such as customer resource services and marketing campaign services) then subscribe to and process data based on geographic partitions, thereby achieving personalized marketing and service responses at the geographic level.

[0088] Furthermore, this partitioning strategy not only improves the system's throughput but also enhances the parallelism and scalability of data processing. By centrally processing region-related customer profile data, the system can more efficiently execute tasks such as region-targeted marketing campaigns, customer segmentation, and risk assessment, thereby significantly improving the accuracy and responsiveness of customer management. This step plays a crucial role in the overall technical solution, providing high-quality, low-latency data input for subsequent customer profile analysis, personalized recommendations, and marketing automation.

[0089] S42 ensures data consistency across nodes through GaussDB's distributed transaction mechanism.

[0090] Specifically, in some implementations, GaussDB's distributed transaction mechanism ensures the consistency of data storage across nodes. GaussDB is a high-performance relational database system that supports distributed transaction processing. Its core mechanism is based on ACID transaction characteristics and distributed consistency protocols, such as two-phase commit or multi-version concurrency control, to ensure the atomicity and consistency of data operations in a multi-node environment.

[0091] Furthermore, when performing data operations involving multiple nodes, the system employs the distributed transaction interface provided by GaussDB to encapsulate multiple operations into a single logical transaction unit. During the execution of this transaction, the transaction coordinator first sends a Prepare request to all participating nodes. Each node then performs a pre-commit locally and returns a status indicating whether it meets the commit conditions. If all nodes return Ready, the coordinator sends a Commit command, and each node formally commits the transaction. If any node returns Abort, the coordinator sends a Rollback command to roll back the entire transaction, ensuring data consistency.

[0092] Furthermore, GaussDB's distributed transaction mechanism supports configuring parameters such as transaction timeout, retries, and consistency level. For example, the transaction timeout can be set to... The retry count can be set to seconds to avoid prolonged transaction blocking due to node failure; the retry count can be set to... This enhances the system's fault tolerance. Furthermore, the system supports isolation levels such as Read Committed and Repeatable Read to meet data consistency requirements in different business scenarios.

[0093] Furthermore, this mechanism is widely applied to key business processes such as customer information synchronization, marketing campaign status updates, and task allocation and execution. For example, in customer contact services, when sales personnel interact with customers and update customer status through multiple channels, the system uses GaussDB's distributed transaction mechanism to ensure that customer status information is updated synchronously on all nodes, avoiding business logic errors or a decline in customer experience due to data inconsistency.

[0094] Furthermore, this step leverages GaussDB's distributed transaction mechanism, enabling the system to achieve strongly consistent data operations across services and nodes in a multi-node, multi-service collaborative environment, thereby ensuring the integrity and reliability of customer business processes. Moreover, this mechanism also improves the system's transaction processing efficiency and fault tolerance, providing a solid data foundation for the high-concurrency, high-reliability business scenarios in the insurance industry.

[0095] The insurance customer data integration method based on distributed microservices in this invention realizes efficient integration and unified management of customer data, and improves the automation level of customer management and precision marketing capabilities.

[0096] Example 2 This invention proposes a customer management system for the insurance industry based on a distributed microservice architecture. By integrating multiple service functions, automating service processes, and intelligentizing data analysis, it significantly improves the efficiency and accuracy of customer management.

[0097] The customer management system described in this invention, such as Figure 2 As shown, the system includes multiple service modules such as lead generation, marketing material management, marketing campaigns, customer resources, customer contact, standard operating procedures (SOPs), and user benefits. These modules interact with the front-end presentation layer through a unified interface, providing users with a one-stop customer service experience. Simultaneously, the system supports multi-terminal access, including A-end (agent sales end), C-end (client end), and potentially E-end (enterprise employee end, adjustable according to specific business needs), meeting the needs of different user roles.

[0098] This system adopts a distributed microservice architecture, such as... Figure 3 As shown, it includes a protocol layer, proxy layer, service layer, middleware layer, data storage layer, and presentation layer. The protocol layer uses HTTP / HTTPS for data transmission; the proxy layer uses reverse proxy tools such as NGINX; the service layer uses microservice frameworks such as Spring Cloud for service governance; the middleware layer integrates message queues and caching components such as Kafka and Redis; the data storage layer uses high-performance databases such as GaussDB for data storage; and the presentation layer uses front-end frameworks such as Vue and Element-UI for page display.

[0099] System deployment architecture such as Figure 4 As shown, the system comprises two parts: an external network and an internal network, securely isolated by a gateway. The external network deploys an nginx reverse proxy server, a mobile H5 application, and a management backend sub-application; the internal network deploys core service modules such as the gateway, unified service management, activity service, and customer resource service. Additionally, the system is configured with infrastructure such as message queues, database clusters, and Redis clusters to ensure high availability and scalability.

[0100] In one embodiment of the present invention, the public front desk and the private front desk include: System Management: providing functions such as system configuration, user management, permission management, and log auditing to ensure the secure, stable, and efficient operation of the system. System administrators can comprehensively monitor and maintain the system through the system management module, and promptly identify and resolve problems. Customer Management: realizing centralized storage and unified management of customer information, including basic customer information, purchase records, service records, etc. Through the customer management module, sales personnel can easily query customer information, understand customer needs, and provide more personalized services to customers. Tag Management: providing functions such as tag creation, tag classification, and tag application, supporting refined classification and tag-based management of customer information. Sales personnel can tag customers according to their interests, purchasing behavior, and other information for more precise marketing and recommendations. Content Management: realizing centralized storage and management of marketing materials, promotional materials, and other content. The content management module supports operations such as uploading, reviewing, publishing, and deleting content to ensure the accuracy and timeliness of marketing materials. At the same time, the content management module also supports multi-dimensional classification and retrieval of marketing materials, making it convenient for sales personnel to quickly find the materials they need. Activity Management: Provides end-to-end management of marketing campaigns, including design, execution, feedback, and performance evaluation. The activity management module supports the creation of various marketing campaign templates, including coupons, discounts, and raffles, and allows for campaign configuration and publishing. Sales personnel can use the activity management module to view real-time participation and performance data, enabling timely adjustments to campaign strategies. Task Management: Enables the allocation, tracking, and evaluation of sales tasks.

[0101] Furthermore, the task management module supports the creation of various sales task templates, including new customer development and existing customer maintenance, and assigns tasks to relevant sales personnel. Sales personnel can view their task list and progress in the task management module to rationally allocate work time and improve work efficiency. Questionnaire management: Provides functions such as questionnaire design, publishing, collection, and analysis. Sales personnel can design various surveys through the questionnaire management module to understand customer satisfaction and opinions on products and services. The data collected from questionnaires can be automatically imported into the system for analysis and processing, providing strong support for improving products and services. Operational reports: Integrates system data and business data to generate various operational reports and analytical charts. The operational report module supports report generation and analysis by multiple dimensions such as time, channel, and customer type, helping sales personnel and management to comprehensively understand business operations, promptly identify problems, and formulate corresponding strategies.

[0102] In one embodiment of the present invention, the middleware service includes: Marketing Activity Service, an important component of the customer management system, responsible for designing and executing various marketing activities to promote sales, improve brand awareness, and enhance customer satisfaction. Activity Design: Supports the creation of various types of marketing activities, such as coupons, discount promotions, points redemption, and lucky draws. Activity Configuration: Provides activity parameter configuration functions, including activity time, participation conditions, and reward rules. Activity Publishing: Publishes the designed activities to the front-end display layer for user participation. Effectiveness Evaluation: Collects and analyzes activity data, evaluates activity effectiveness, and provides a basis for subsequent marketing decisions. Customer Resource Service is responsible for managing customer information, including basic customer information, purchase records, service records, etc., providing sales personnel with a comprehensive customer view. Customer Information Management: Enables centralized storage and unified management of customer information, supporting the addition, deletion, modification, and querying of customer information. Customer Classification and Tagging: Supports classification and tagging management based on customer characteristics, facilitating sales personnel to quickly find target customer groups. Customer Profiling: Constructs customer profiles through data analysis and mining to support personalized marketing. The SOP (Standard Operating Procedure) service provides standardized operating procedures to guide sales personnel in performing specific tasks or activities during client operations. Task Definition: Clearly states the purpose and scope of the SOP, defining the tasks to be completed. Steps and Methods: Lists each step in completing the task in detail, including required tools, equipment, materials, and specific operating methods. Training and Assessment: Provides SOP training materials to help sales personnel master the standard operating procedures and conducts assessments. User Benefits Service manages user benefits, including points, coupons, membership levels, etc., to improve user satisfaction and loyalty. Features: Benefits Issuance: Issues corresponding benefits based on user purchasing behavior, participation in activities, and other conditions.

[0103] Furthermore, the benefits management service supports functions such as querying, using, and redeeming benefits. Benefits analysis analyzes user benefit usage to provide a basis for subsequent benefit strategies. The lead generation service collects and analyzes potential customer leads, providing the sales team with precise business opportunities. Features include: Lead collection: collecting potential customer leads through various channels (such as website forms, social media, advertising campaigns, etc.); Lead filtering: filtering and categorizing leads according to preset rules and conditions; Lead allocation: allocating filtered leads to suitable sales personnel to improve sales efficiency. The marketing material management service manages and maintains various marketing materials, such as images, videos, and copy. Features include: Material upload and review: supporting sales personnel to upload marketing materials and reviewing them to ensure quality and compliance; Material classification and tagging: classifying and tagging materials for easy access by sales personnel; Material usage statistics: tracking material usage to provide a basis for subsequent material strategies. The customer contact service communicates and interacts with customers, providing high-quality customer service. Multi-channel communication: Supports communication with customers through various means such as telephone, email, and online chat.

[0104] Furthermore, the services include: Problem Handling: Timely handling of customer issues and complaints, providing satisfactory solutions. Customer Care: Regular contact with customers to understand their needs and provide personalized care and service. Compliance Audit Services: Conducting compliance audits of the client's operating systems to ensure business activities comply with relevant laws, regulations, and industry standards. Compliance Inspection: Conducting compliance inspections of the system's business processes, data processing, and user privacy protection. Risk Identification and Assessment: Identifying potential risk points, assessing the impact of risks, and proposing improvement measures. Audit Reports: Generating compliance audit reports to provide decision support for management. Marketing Support Tools Services: Providing a range of support tools to help sales personnel conduct marketing work more efficiently. Data Analysis Tools: Providing data analysis tools to help sales personnel analyze market trends, customer needs, and other information. Template Library: Providing marketing copy, email templates, and event templates to facilitate quick creation of marketing materials by sales personnel. Automation Tools: Providing automation tools such as bulk email and bulk SMS to improve marketing efficiency.

[0105] In one embodiment of the present invention, the peripheral system services include: services provided by a CRM system. CRM (Customer Relationship Management) is a software system that uses information technology to automate and standardize the management of customer relationships and customer data. Its main functions and services include: Customer Information Management: Centrally storing and managing customer contact information, communication records, purchase history, and other information, facilitating easy access and analysis by sales personnel. Sales Management: Tracking sales opportunities, managing sales teams, monitoring sales progress, and predicting sales performance, helping sales teams improve sales efficiency and performance. Marketing: Planning and executing marketing activities, managing potential customers, analyzing marketing effectiveness, and providing comprehensive support to the marketing team. Service Support: Tracking and resolving customer issues, managing customer service requests, and providing customer self-service, improving customer satisfaction and loyalty. Data Analysis: Analyzing customer and business data to generate detailed reports and insights, helping enterprises make data-driven decisions. Personalized Services: Through in-depth analysis of customer data, tailoring personalized marketing plans and services for each customer, improving customer satisfaction and loyalty. A data / big data platform refers to a comprehensive information technology platform centered on big data, integrating functions such as data storage, data management, data computing, data analysis, and data mining. Its services include: Data storage and management: providing the ability to store and manage massive amounts of data, supporting all stages of data collection, transmission, storage, and backup. Data processing and analysis: utilizing distributed computing technology to perform calculations and analysis on massive amounts of data, helping enterprises uncover potential patterns and value within the data. Data mining and machine learning: extracting valuable information and knowledge from data through data mining algorithms and machine learning models, providing support for enterprise decision-making.

[0106] In one embodiment of the present invention, data visualization: Complex data processing and analysis results are presented in visual forms such as graphics, images, and animations to help enterprises better understand and utilize data. Data services and application support: Services such as data query, data interface, and data customization are provided to enterprises to support business innovation and application development based on data. The SMS platform is a service platform that connects to the Internet through SMS channels directly provided by telecom operators, enabling batch and customized SMS sending. Its services include: SMS sending function: Supports single and group SMS sending, scheduled sending, and API interface integration, facilitating enterprises to send SMS quickly, efficiently, and stably. SMS template management: Provides functions for creating, editing, and approving SMS templates to help enterprises standardize SMS content and improve the efficiency and effectiveness of SMS sending. Sending report analysis: Provides functions such as SMS sending status query, arrival rate statistics, and visual chart analysis to help enterprises evaluate the effectiveness of SMS marketing and optimize SMS sending strategies. SMS reply and interaction: Supports SMS reply integration with the uplink interface to achieve two-way interaction with users and enhance customer experience.

[0107] Furthermore, a human resource management system (also known as a human resource information system or human resource management system) is a software solution that integrates information technology with human resource management practices. Its services include: Employee Information Management: Centrally storing and managing employees' personal information, work experience, educational background, emergency contact information, etc., facilitating employee information management for enterprises. Recruitment Management: Supporting the automation and digitalization of the enterprise recruitment process, including job requirement analysis, job posting, resume screening, interview scheduling, and final hiring decisions. Training and Development: Supporting enterprises in developing and implementing training plans, tracking employee learning progress and effectiveness, and promoting employee skills enhancement and career development. Performance Management: Supporting enterprises in setting employee performance goals, conducting regular performance evaluations, providing performance feedback, and developing individual development plans. Compensation and Benefits Management: Responsible for designing and implementing compensation structures, benefit plans, bonus distribution, tax processing, etc., ensuring the accuracy of compensation calculations and the rationality of benefit allocation. Compliance Supervision: Ensuring that enterprises comply with relevant labor laws and regulations, handling compliance reports, including work hour records, health and safety regulations, and equal employment opportunities.

[0108] Furthermore, outbound telecom calling services are systems that utilize modern communication technologies to automatically dial customer numbers and conduct voice interactions. The services offered include: Automatic dialing: Automatically dialing preset customer phone numbers without manual intervention, improving dialing efficiency. Voice interaction: Supporting voice navigation, voice recognition, and other functions, automatically handling simple customer inquiries and requests, such as checking balances and changing passwords. Data management: Real-time recording of various data during the call process, such as call duration, connection rate, and customer feedback, providing data support for subsequent marketing strategy optimization. Predictive outbound calling: Based on historical and real-time data, predicting customer connection rates and call demand over a future period, allowing for advance planning of agent work schedules. Personalized service: Classifying and managing customers through the collection and analysis of customer data to provide personalized services. Market research: Conducting market research through the outbound calling system, quickly collecting large amounts of customer data to support business decision-making.

[0109] The embodiments of this invention also have the following technical effects: An insurance industry customer management system based on a distributed microservice architecture achieves efficient integration and management of customer information. Technical effects: Improves the accuracy and consistency of customer information and reduces management costs. A customer management system supporting multi-terminal access meets the needs of different user roles. Technical effects: Enhances user experience and strengthens the system's flexibility and scalability. A customer management system integrating multiple service modules and functionalities automates service processes and enables intelligent data analysis. Technical effects: Improves service efficiency and enhances the accuracy and personalization of customer management. This invention, through advanced architectural design and technical means, solves the problems existing in traditional customer management methods, providing insurance companies with an efficient, accurate, and personalized customer management solution.

[0110] Example 3 To achieve the above embodiments, such as Figure 5 As shown, this embodiment also provides a distributed customer management system and its implementation device 10 based on the insurance industry, including: The multi-source heterogeneous data acquisition module 100 is used to collect multi-source heterogeneous customer data through a distributed microservice architecture. The multi-source heterogeneous customer data includes basic information, purchase records, and service records. The unified interface standardization processing module 200 is used to standardize the multi-source heterogeneous customer data based on the unified interface to form a unified customer view across systems. The intelligent data governance engine module 300 is used to perform multi-dimensional tagging management of the unified customer view using the intelligent data governance engine, and to build a dynamically updated customer profile. The message queue real-time synchronization module 400 is used to realize cross-service real-time synchronization of the customer profile data through the message queue, and uses a distributed database cluster for unified storage and high-concurrency access support.

[0111] Furthermore, the multi-source heterogeneous data acquisition module 100 is also used for: Collect behavioral data of potential customers in real time through website forms, social media, and advertising campaigns; The collected customer data is stored in the GaussDB distributed database cluster to form structured data storage.

[0112] Furthermore, the unified interface standardization processing module 200 is also used for: Customer data is classified in multiple dimensions using preset tag classification rules, including tags for region, age, and occupation. Data interaction with CRM systems and external systems of the data platform is achieved through the RESTful API interface specification.

[0113] This invention discloses a distributed customer management system and its implementation device based on the insurance industry, which realizes the unified integration and efficient management of customer data in the insurance industry, solves the problems of information dispersion and data silos, and improves customer management efficiency and precision marketing capabilities.

[0114] Example 4 To implement the methods of the above embodiments, the present invention also provides a computer device, such as... Figure 6 As shown, the computer device 600 includes a memory 601 and a processor 602; wherein, the processor 602 reads the executable program code stored in the memory 601 to run a program corresponding to the executable program code, so as to implement the various steps of the distributed customer management system and its implementation method based on the insurance industry described above.

[0115] Example 5 To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a distributed customer management system and its implementation method based on the insurance industry as described in the foregoing embodiments.

[0116] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0117] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A distributed customer management system based on the insurance industry and its implementation method, characterized in that, include: S1. Collect multi-source heterogeneous customer data through a distributed microservice architecture. The multi-source heterogeneous customer data includes basic information, purchase records, and service records. S2, Based on a unified interface, the multi-source heterogeneous customer data is standardized to form a unified customer view across systems; S3 utilizes an intelligent data governance engine to perform multi-dimensional tagging management of the unified customer view and builds dynamically updated customer profiles. S4. Real-time cross-service synchronization of customer profile data is achieved through message queues, and a distributed database cluster is used for unified storage and high-concurrency access support.

2. The method as described in claim 1, characterized in that, S1 includes: S11 collects behavioral data of potential customers in real time through website forms, social media, and advertising campaigns. S12 stores the collected customer data in the GaussDB distributed database cluster to form structured data storage.

3. The method as described in claim 1, characterized in that, S2 includes: S21 uses preset tag classification rules to classify customer data in multiple dimensions, including regional, age, and occupational attribute tags; S22 enables data interaction with the CRM system and peripheral systems of the data platform through the RESTful API interface specification.

4. The method as described in claim 1, characterized in that, The 3 includes: S31, Calculate the risk preference index based on customer purchase behavior data, using the following formula: in, For customer risk preference index, For the first The weight of each behavioral feature, For the first Preference values ​​for each behavioral characteristic; S32 receives customer interaction data in real time through the Kafka message queue and triggers a dynamic update mechanism for customer profiles.

5. The method as described in claim 1, characterized in that, The S4 includes: S41 uses the partitioning strategy of Kafka message queues to partition and transmit customer profile data according to geographical dimension. S42 ensures data consistency across nodes through GaussDB's distributed transaction mechanism.

6. A distributed customer management system based on the insurance industry and its implementation device, characterized in that, include: The multi-source heterogeneous data acquisition module is used to collect multi-source heterogeneous customer data through a distributed microservice architecture. The multi-source heterogeneous customer data includes basic information, purchase records, and service records. The unified interface standardization processing module is used to standardize the multi-source heterogeneous customer data based on the unified interface to form a unified customer view across systems. The intelligent data governance engine module is used to perform multi-dimensional tagging management of the unified customer view and build dynamically updated customer profiles. The message queue real-time synchronization module is used to realize cross-service real-time synchronization of customer profile data through message queues, and uses a distributed database cluster for unified storage and high-concurrency access support.

7. The apparatus as claimed in claim 6, characterized in that, The multi-source heterogeneous data acquisition module is also used for: Collect behavioral data of potential customers in real time through website forms, social media, and advertising campaigns; The collected customer data is stored in the GaussDB distributed database cluster to form structured data storage.

8. The apparatus as claimed in claim 6, characterized in that, The unified interface standardization processing module is also used for: Customer data is classified in multiple dimensions using preset tag classification rules, including tags for region, age, and occupation. Data interaction with CRM systems and external systems of the data platform is achieved through the RESTful API interface specification.

9. A computer device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement a distributed customer management system and its implementation method based on the insurance industry as described in any one of claims 1-5.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a distributed customer management system and its implementation method based on the insurance industry as described in any one of claims 1-5.

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