Method and device for unitized construction of life insurance core business system

Abstract

The invention discloses a unitized construction method for a life insurance core business system, and relates to the technical field of computers, and the method comprises the steps: dividing the life insurance core business system into independent system units such as a product unit, a contract unit, a preservation unit, a claim settlement unit, a group insurance core unit, an insurance policy management unit and a universal center, and each unit comprises an exclusive database, an operation environment and a code library; a standardized interface protocol containing a unified data format, HTTPS transmission and request response flow is defined, and it is guaranteed that interaction is efficient and accurate; each unit is realized by a micro-service architecture, and faults are isolated by means of service registration discovery, load balancing and a fusing mechanism; the system integrates DevOps and AIOPS systems, realizes continuous delivery through automation, predicts faults through machine learning, and visually manages micro-services through a unified operation and maintenance monitoring platform. According to the invention, the expansibility and flexibility of the life insurance core service system can be improved, the maintenance cost is reduced, and the complex service processing capability is enhanced.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method for modular construction of a core life insurance business system. Background Technology

[0002] The life insurance core business system, as a core support platform for the digital transformation of the insurance industry, is widely used in key business processes such as contract management, policy maintenance services, and claims processing. In related technologies, traditional systems often adopt a monolithic, tightly coupled architecture, constructing a complete business process from policy application to claims through deep integration of a centralized database with business logic. Specifically, this system covers core functional modules such as product configuration, underwriting decisions, policy generation, and customer service, with the contract center, policy maintenance center, and claims center forming the three pillars of business processing. With the rapid development of demands for diversified insurance products (such as group insurance and individual insurance), increasingly complex business rules (such as dynamic underwriting strategies), and diversified channels (such as mobile terminals and bank-insurance integration), existing systems can no longer support innovative business scenarios such as real-time underwriting and intelligent claims processing, necessitating architectural innovation to resolve the contradiction between system flexibility and stability.

[0003] However, existing life insurance core systems often employ a monolithic architecture without implementing unitized isolation of business modules. This can lead to technical bottlenecks such as limited system scalability and soaring maintenance costs. Specifically, the strong coupling between the product center and the contract center in traditional architectures necessitates modifications to core code for new insurance product development, taking an average of 3-6 months. Adjustments to underwriting rules can trigger a chain reaction in contract and policy maintenance modules, leading to business interruption risks. According to Gartner, the annual maintenance cost of such systems can reach 15%-20% of revenue, and fault isolation is poor; a single module failure can paralyze the entire system (e.g., a leading insurance company experienced a 72-hour system outage in 2021 due to an underwriting module failure). Furthermore, the success rate of cross-module transaction processing is below 95%, impacting customer experience and hindering innovation breakthroughs in real-time and personalized aspects of the insurance business. Summary of the Invention

[0004] The main objective of this invention is to provide a method for modularizing the construction of a core life insurance business system.

[0005] Another objective of this invention is to provide a modular construction device for a core life insurance business system.

[0006] The third objective of this invention is to provide an electronic device.

[0007] The fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0008] To achieve the above objectives, a first aspect of the present invention proposes a method for modular construction of a core life insurance business system, comprising: S1 divides the core life insurance business system into multiple independent system units. Each system unit contains an independent database, operating environment, and code library. The system units include a product center, contract center, policy maintenance center, claims center, group insurance core, policy management center, and general center. S2, Based on the system unit, a standardized interface communication protocol is defined. The protocol includes a unified data format, HTTPS transmission method, and request and response processing flow to ensure the efficiency and accuracy of data interaction and communication between system units. S3 uses a microservice architecture to implement each system unit. Through service registration and discovery mechanisms, load balancing technology, and service governance and circuit breaking mechanisms, each system unit runs as an independent microservice and dynamically isolates faults. S4 integrates DevOps and AIOPS intelligent operation and maintenance systems, achieves continuous delivery through automated deployment, testing and integration technologies, uses machine learning algorithms to analyze system operation data in real time and predict potential failures, and establishes a unified operation and maintenance monitoring platform to perform visual management of each microservice.

[0009] Optionally, the life insurance core business system is divided into multiple independent system units, each containing an independent database, operating environment, and code library. These system units include a product center, contract center, policy maintenance center, claims center, group insurance core, policy management center, and general center, and further include: S11. Based on business needs, the product center is divided into a product definition module, a pricing strategy module, and a configuration management module. The product definition module is used to define the basic attributes of insurance products, the pricing strategy module generates dynamic pricing schemes based on market research data and cost models, and the configuration management module supports the rapid adjustment of product rules through parameterized configuration. S12 divides the contract center into an insurance rule verification module and an underwriting decision module. The insurance rule verification module verifies the insurance information in real time through a preset set of compliance verification rules, while the underwriting decision module generates underwriting recommendations based on a risk assessment model and customer credit scores.

[0010] Optionally, the standardized interface communication protocol defined based on the system unit, the protocol including a unified data format, HTTPS transmission method, and request and response processing flow, to ensure the efficiency and accuracy of data interaction and communication between system units, further includes: S21 uses JSON format as the data interaction format between the contract center and the underwriting center. The JSON data structure includes fields for policyholder basic information, risk assessment parameters, and underwriting results. S22, when defining the request-response structure, the response data includes a business status code field, an operation log identifier field, and a data verification result field. The business status code field adopts a three-digit encoding rule, and the operation log identifier field is associated with the log management system of the general center.

[0011] Optionally, the method of implementing each system unit using a microservice architecture, and enabling each system unit to run as an independent microservice and dynamically isolate faults through service registration and discovery mechanisms, load balancing technology, and service governance and circuit breaker mechanisms, further includes: S31, the service registration and discovery mechanism adopts the Nacos registry center. Each microservice automatically registers its service name, IP address and port number when it starts up, and maintains the service availability status through heartbeat detection. S32, the load balancing technology uses a weighted round-robin algorithm to dynamically adjust the request allocation weights based on the real-time load metrics of the microservices (including CPU utilization, memory usage, and request response time).

[0012] Optionally, the integrated DevOps and AIOPS intelligent operation and maintenance system achieves continuous delivery through automated deployment, testing, and integration technologies, utilizes machine learning algorithms to perform real-time analysis of system operation data and predict potential faults, and establishes a unified operation and maintenance monitoring platform for visualized management of each microservice, further including: S41 uses Docker containerization technology for automated deployment and implements canary releases of microservices through Kubernetes cluster management. The canary release process includes: building images, creating canary release groups, and gradually replacing old version service instances. S42, the machine learning algorithm uses a Long Short-Term Memory (LSTM) network to perform time-series analysis on the system's operational data, through the formula... Predict potential failures, among which For activation function, , This is the weight matrix. This is a bias term.

[0013] Optionally, the method further includes: S5, the steps to build a data service center include: S51 uses data warehouse and data lake technologies to classify and store business data generated by various system units. The data warehouse is used for OLAP analysis of structured data, and the data lake is used for raw storage of unstructured data. S52, establish a data cleaning and transformation mechanism, use ETL tools to standardize the raw data, ensure that the data quality meets the requirements, and achieve full-link traceability of business data through data lineage tracing.

[0014] To achieve the above objectives, a second aspect of the present invention provides a modular construction apparatus for a core life insurance business system, comprising: The system unit division module is used to divide the core life insurance business system into multiple independent system units. Each system unit contains an independent database, operating environment, and code library. The system units include product center, contract center, policy maintenance center, claims center, group insurance core, policy management center, and general center. The standardized interface communication protocol definition module is used to define a standardized interface communication protocol based on the system unit. The protocol includes a unified data format, HTTPS transmission method, and request and response processing flow to ensure the efficiency and accuracy of data interaction and communication between system units. The microservice architecture implementation module is used to implement each system unit using a microservice architecture. Through service registration and discovery mechanisms, load balancing technology, and service governance and circuit breaker mechanisms, each system unit runs as an independent microservice and dynamically isolates faults. The DevOps and AIOPS integration module is used to integrate the DevOps and AIOPS intelligent operation and maintenance system. It achieves continuous delivery through automated deployment, testing and integration technologies, and uses long short-term memory networks to perform time-series analysis on system operation data and predict potential failures.

[0015] To achieve the above objectives, a third aspect of this application provides an electronic device, including a processor and a memory; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, for implementing a modular construction method for a life insurance core business system as described in the first aspect embodiment.

[0016] 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 modular construction method for a life insurance core business system as described in the first aspect embodiment.

[0017] The embodiments of the present invention have the following beneficial effects: through system unitization and microservice architecture design, the life insurance core business system achieves high scalability, low maintenance cost and rapid response to business changes, significantly improving the efficiency of complex business processing and system stability. Attached Figure Description

[0018] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating a modular construction method for a core life insurance business system provided in an embodiment of the present invention; Figure 2 A flowchart illustrating the technical solution of a life insurance core business system based on system unitization and its implementation method provided in this embodiment of the invention.

[0019] Figure 3 This is a structural diagram of a modular construction device for a core life insurance business system provided in an embodiment of the present invention. Detailed Implementation

[0020] 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.

[0021] 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.

[0022] The following description, with reference to the accompanying drawings, describes a method and apparatus for modular construction of a core life insurance business system according to an embodiment of the present invention.

[0023] Example 1 This embodiment provides a method for modular construction of a core life insurance business system. Figure 1 This is a flowchart illustrating a method for modularizing a core life insurance business system according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps: Step S1: Divide the life insurance core business system into multiple independent system units. Each system unit contains an independent database, operating environment, and code library. The system units include a product center, contract center, policy maintenance center, claims center, group insurance core, policy management center, and general center.

[0024] Specifically, in this embodiment of the invention, dividing the core life insurance business system into multiple independent system units is a key step in achieving a highly modular and unitized architecture. Each system unit (such as the product center, contract center, policy maintenance center, claims center, group insurance core, policy management center, and general center) has its own independent database, operating environment, and code repository, thereby achieving functional decoupling, resource isolation, and independent deployment. This step is based on the microservice architecture design principle and adopts a service-oriented decomposition strategy to divide the originally highly coupled business logic according to functional boundaries, ensuring that each unit has a single responsibility and high cohesion.

[0025] It is clear that the division of system units must adhere to certain granularity control standards. For example, each unit should have an independent database instance, supporting transaction isolation and data consistency guarantees, typically employing distributed transaction mechanisms (such as TCC, SAGA) or an eventual consistency model to meet the reliability requirements of cross-unit data interaction. Regarding the runtime environment, each unit is deployed in an independent container or virtual machine, supporting independent resource scheduling and elastic scaling; resources such as CPU, memory, and network bandwidth can be dynamically adjusted according to business load. As for the codebase, each unit uses an independent version control system (such as Git), supporting an independent CI / CD pipeline to ensure the automation and efficiency of development, testing, and deployment processes.

[0026] In this embodiment of the invention, this step applies to the full lifecycle management of life insurance business. For example, the product center is responsible for the design and configuration of insurance products, supporting flexible definition of product parameters (such as insurance period, premium rate, and coverage); the contract center handles the underwriting and insurance processes, supporting key business logic such as underwriting rule verification and underwriting decisions; and the claims center is responsible for case acceptance, settlement, and payment, supporting multi-dimensional claims strategy configuration. Through modular design, each business module can be developed, tested, and deployed independently, significantly improving the system's response speed and maintainability.

[0027] The technical advantage of this step lies in its ability to effectively improve the system's scalability, flexibility, and stability through independent deployment and resource isolation of system units. When faced with changes in business requirements or the launch of new features, only the relevant units need to be updated, without affecting the entire system, thereby reducing maintenance costs and improving system iteration efficiency. Furthermore, the unitized architecture enhances the system's fault tolerance; when one unit fails, it does not affect the normal operation of other units, ensuring business continuity.

[0028] Furthermore, S1 includes: S11, based on business needs, divides the product center into a product definition module, a pricing strategy module, and a configuration management module. The product definition module is used to define the basic attributes of insurance products, the pricing strategy module generates dynamic pricing schemes based on market research data and cost models, and the configuration management module supports rapid adjustment of product rules through parameterized configuration.

[0029] Specifically, in one embodiment of the present invention, the division of the product center is one of the key steps in realizing the modular architecture of the system. Its core lies in decoupling the design, pricing, and configuration functions of insurance products into three independent modules: a product definition module, a pricing strategy module, and a configuration management module, thereby achieving high cohesion and low coupling of functions. The product definition module is used to define the basic attributes of insurance products, including product type (such as term life insurance, whole life insurance, endowment insurance, etc.), insurance period, coverage, liability clauses, and underwriting conditions. These attributes are stored through a structured data model, typically managed using relational databases (such as MySQL, Oracle) or NoSQL databases (such as MongoDB), supporting multi-version control and product lifecycle management. In specific implementation, the product definition module models the product structure through a Product Metadata Model, ensuring the standardization and scalability of product information.

[0030] The pricing strategy module generates dynamic pricing schemes based on market research data and cost models. This module integrates external market data interfaces (such as APIs or ETL tools) to obtain industry pricing benchmarks, historical sales data, and customer behavior analysis, and combines this with internal cost models (such as actuarial models and cost structure models) to calculate premiums. In some implementations, the pricing strategy module uses machine learning algorithms (such as linear regression, decision trees, and random forests) to model pricing factors, including customer age. ,gender Health status Sum Insured Insurance period The pricing formula can be expressed as follows: ,in This module provides pricing functions that support dynamic adjustments and strategy version management. It also supports multi-strategy parallel testing (A / B testing) and strategy rollback mechanisms to ensure the flexibility and controllability of the pricing scheme.

[0031] The configuration management module supports rapid adjustments to product rules through parameterized configuration. This module uses a configuration center (such as Nacos or Apollo) to dynamically load and hot-update product rules, supporting rule version control, canary releases, and rollback functionality. In its implementation, the configuration management module abstracts product rules into key-value pairs or structured configuration files (such as YAML or JSON), and provides a unified configuration interface. ,in Indicates product identification. This module indicates the configuration version, enabling rules to take effect in real time and be flexibly adjusted. In practical applications, it is widely used to support rapid product iteration, multi-channel differentiated configurations, and immediate response to regulatory policies, significantly improving system flexibility and responsiveness. This step plays a crucial role in the overall system architecture. Through modular design, it achieves standardization and automation of product lifecycle management, reduces system coupling, improves development efficiency and business responsiveness, and lays a solid foundation for subsequent microservice governance and intelligent operation and maintenance.

[0032] S12 divides the contract center into an insurance rule verification module and an underwriting decision module. The insurance rule verification module verifies the insurance information in real time through a preset set of compliance verification rules, while the underwriting decision module generates underwriting recommendations based on a risk assessment model and customer credit scores.

[0033] Specifically, in the implementation of the contract center, the system is divided into two key modules: the "Insurance Rule Verification Module" and the "Underwriting Decision Module," which respectively handle the compliance verification of insurance information and risk assessment decision-making. In some implementations, the Insurance Rule Verification Module loads a pre-defined set of compliance verification rules to perform real-time verification of the information submitted by the policyholder, ensuring that it complies with regulatory requirements and the company's internal business specifications. This rule set is typically deployed in the form of a rule engine, supporting flexible configuration and dynamic updates. For example, it can use open-source rule engine frameworks such as Drools or Easy Rules to achieve hot loading and immediate effect of rules. Verification rules may include policyholder age restrictions, completeness of health declarations, compliance of insured amount and income ratio, etc. Each rule can be configured with priority, triggering conditions, and verification logic, such as... , wait.

[0034] Furthermore, the underwriting decision module generates underwriting recommendations based on a risk assessment model and a customer credit scoring system. The risk assessment model can employ machine learning algorithms (such as random forests, XGBoost, or deep neural networks) to model multi-dimensional features of the insured, including historical data, health information, and occupational risk levels, and outputs a risk score. Credit scoring obtains a customer's credit rating by connecting with third-party credit reporting platforms (such as Sesame Credit and the People's Bank of China's credit reporting system). This is combined with internal customer behavior data for weighted calculation. In practical applications, this module can be configured with threshold strategies, such as... and When this happens, the system automatically recommends "standard coverage"; if... or If so, a manual review process will be triggered.

[0035] This step plays a crucial role in the system. By decoupling compliance verification from risk assessment, it improves the efficiency and accuracy of contract processing, reduces the proportion of manual intervention, and enhances the system's configurability and scalability. It is applicable to the underwriting process of various types of insurance products, such as life insurance, health insurance, and auto insurance.

[0036] Step S2: Define a standardized interface communication protocol based on the system unit. The protocol includes a unified data format, HTTPS transmission method, and request and response processing flow to ensure the efficiency and accuracy of data interaction and communication between system units.

[0037] In this embodiment of the invention, the step of "defining a standardized interface communication protocol based on the system units, the protocol including a unified data format, HTTPS transmission method, and request and response processing flow to ensure the efficiency and accuracy of data interaction and communication between system units" is a key step in realizing a modular and unitized system architecture. This step, by establishing unified communication standards, ensures that each system unit can exchange data efficiently, securely, and accurately under a microservice architecture, thereby improving the overall system's collaborative capabilities and business response speed.

[0038] Specifically, the standardized interface communication protocol is designed based on a RESTful API architecture, using a unified JSON or XML data format for data encapsulation. JSON, due to its clear structure and high parsing efficiency, is the primary data exchange format in this invention. Each interface request includes standard HTTP methods (such as GET, POST, PUT, DELETE), and the request header defines authentication information (such as OAuth2.0 Token), content type (Content-Type: application / json), request timestamp, and request source identifier (source_id) to enhance the security and traceability of the interface. The request body encapsulates business data. For example, when the contract center sends insurance information to the underwriting center, the request body includes fields such as the policyholder's identity information, coverage amount, and insurance period. Its structure must conform to ISO20022 or industry-standard insurance data exchange standards.

[0039] This invention employs the HTTPS protocol for data transmission, ensuring data encryption and authentication during communication. HTTPS is based on TLS 1.3 and above, supporting mutual authentication (mTLS) to establish a secure channel between the server and client, preventing data from being tampered with or stolen during transmission. Furthermore, the system supports a multi-layered authentication mechanism based on IP whitelists, API keys, and JWT (JSON Web Token) to meet the security needs of different business scenarios.

[0040] Regarding the request and response processing flow, the system defines a standard request and response structure, including HTTP status codes, response headers, and a response body. The response body contains business data and error information. The error information follows the RFC 7807 standard, returning the error type, description, and suggested handling method in a structured manner. For example, when the contract center requests a risk assessment from the underwriting center, if the underwriting center returns a status code 400 (Bad Request), the response body will include specific error fields and the reason for the error, facilitating the caller to quickly locate and correct the problem.

[0041] It is clear that the interface response time should be controlled within 200ms to meet the real-time requirements of life insurance business; the interface availability should reach 99.99% or higher to ensure the stability of the system under high concurrency scenarios; and the data transmission encryption strength should not be lower than AES-256 to meet the strict standards of data security in the financial industry.

[0042] This step is widely used in practice for data interaction between various system units, such as product configuration synchronization between the product center and the contract center, policy status updates between the policy maintenance center and the policy management center, and risk information sharing between the claims center and the underwriting center. Through standardized interfaces, the system can achieve seamless integration across units and channels, improving the automation level and processing efficiency of business processes.

[0043] This step significantly improves the system's interoperability and communication efficiency, reduces the complexity of interface development and maintenance, reduces business errors caused by interface inconsistencies, and enhances the system's security and compliance, providing a solid data communication foundation for subsequent DevOps and AIOPS intelligent operations and maintenance.

[0044] Furthermore, S2 includes: S21 uses JSON format as the data interaction format between the contract center and the underwriting center. The JSON data structure includes fields for basic information of the policyholder, risk assessment parameters, and underwriting results.

[0045] Specifically, in this embodiment of the invention, the data interaction between the contract center and the underwriting center is encapsulated and transmitted using JSON format. This step is a key link in achieving efficient communication between modules in the system's modular architecture. After receiving the insurance application information submitted by the policyholder, the contract center first performs structured processing on the application data and encapsulates it according to a predefined JSON Schema format. This JSON structure contains three main fields: the policyholder's basic information field, the risk assessment parameter field, and the underwriting result field.

[0046] The policyholder's basic information field describes core information such as the policyholder's identity, age, occupation, and health status. Examples include fields like applicant.name, applicant.age, and applicant.occupation. The data type and format must conform to ISO / IEC 20022 or industry-standard insurance data exchange standards. The risk assessment parameter field transmits the risk assessment input data required by the underwriting center, such as health questionnaire results, financial status, and past medical history. Examples include risk.healthQuestionnaire and risk.insuranceHistory. The content must meet the input requirements of the underwriting model to ensure the accuracy of the assessment algorithm.

[0047] The underwriting result field is used to return the assessment conclusion of the underwriting center, including whether the policy is approved, whether an additional premium is required, and the reason for rejection, such as underwriting.decision, underwriting.reason, and underwriting.premiumAdjustment. In the specific implementation, the structure of this field must match the subsequent processing logic of the contract center to ensure the automation and consistency of the underwriting process.

[0048] In this embodiment of the invention, the transmission of JSON data must comply with the HTTPS protocol to ensure data security during transmission. Meanwhile, to improve system response efficiency, the size of the JSON data should be controlled within a reasonable range; it is recommended that a single request not exceed 5MB and the response time should be less than 500ms to meet the real-time requirements of life insurance business.

[0049] This step is widely used in practice for data exchange between the contract and underwriting modules in the insurance application process. For example, after a customer submits an insurance application through PICC E-Pass, the contract center encapsulates the data in JSON format and calls the underwriting center's service interface via a RESTful API to complete risk assessment and underwriting decisions. After processing, the underwriting center returns the results in JSON format to the contract center for subsequent policy generation or rejection notification operations.

[0050] The technical benefits of this step are that, through a standardized JSON data structure, efficient, secure, and scalable data interaction between the contract center and the underwriting center is achieved, reducing the complexity of system integration, improving the automation level and response speed of business processing, and providing a solid data communication foundation for the unitization and microservices of the entire life insurance core business system.

[0051] S22, when defining the request-response structure, the response data includes a business status code field, an operation log identifier field, and a data verification result field. The business status code field adopts a three-digit encoding rule, and the operation log identifier field is associated with the log management system of the general center.

[0052] Specifically, in the design of the system interface protocol, defining the request-response structure, including a business status code field, an operation log identifier field, and a data verification result field in the response data, is a key technical step to achieve efficient communication between system units and traceability of business processes. This step, through the design of structured response data, ensures that the system can provide clear feedback information when processing business requests, facilitating unified processing and analysis by front-end applications, operation and maintenance systems, and log platforms.

[0053] Specifically, the business status code field uses a three-digit encoding rule to identify the business result status of the request processing. For example, 200 indicates successful business processing, 400 indicates incorrect input parameters, and 500 indicates an internal system error. This encoding rule conforms to the general design principles of HTTP status codes and can be extended according to business needs, such as 601 indicating underwriting rejection and 602 indicating data validation failure, thereby enabling refined management of various abnormal situations in life insurance business.

[0054] The operation log identifier field is used to associate with the log management system in the general center. Its value is typically a globally unique identifier (UUID) to uniquely identify a request operation. This field is generated by the gateway or front-end service when a request enters the system and is transmitted and recorded throughout the entire business process to ensure the integrity of the log chain. In the log system, this identifier can serve as a key index for log aggregation and tracing, supporting unified querying and analysis of logs from multiple system units, improving system observability and troubleshooting efficiency.

[0055] The data validation result field is used to provide feedback on the validity of the requested data. It is typically encapsulated in SON format and includes structured content such as field names, validation rules, and validation results. For example, if the `phone` field fails the mobile number format validation, it returns `{"field":"phone", "rule":"mobile_format", "valid":false}`. This field supports the combined application of various validation rules, such as required field validation, format validation, and business logic validation, to ensure the accuracy and compliance of the input data.

[0056] This step plays a crucial role in the overall system architecture, especially in a microservice environment. A standardized response structure effectively reduces the complexity of inter-service communication, improving system maintainability and scalability. Furthermore, by integrating DevOps and AIOPS technologies, response data can be automatically collected, analyzed, and used for alerts, enabling intelligent operations and maintenance and business process optimization.

[0057] Step S3: Implement each system unit using a microservice architecture. Through service registration and discovery mechanisms, load balancing technology, and service governance and circuit breaker mechanisms, each system unit runs as an independent microservice and dynamically isolates faults.

[0058] Specifically, adopting a microservice architecture to implement each system unit is one of the key technical steps in building a highly modular and unitized life insurance core business system. In some implementations, each system unit (such as the product center, contract center, policy maintenance center, claims center, etc.) is encapsulated as an independent microservice, with its own database, runtime environment, and code repository, thereby achieving functional decoupling, resource isolation, and flexible deployment.

[0059] In this embodiment of the invention, the microservice architecture achieves dynamic service discovery and invocation through a service registration and discovery mechanism (such as a registry center based on ZooKeeper, Eureka, or Nacos). Each microservice registers its own metadata (such as service name, IP address, port, health status, etc.) with the registry center upon startup. Other services obtain the address information of the target service through the registry center when making invocations, enabling dynamic communication between services. Furthermore, the system employs load balancing technology (such as Ribbon, Nginx, or Spring Cloud LoadBalancer) to evenly distribute requests across multiple service instances, improving system throughput and availability. Load balancing strategies may include Round Robin, Weighted Round Robin, Least Connections, etc., with the specific selection dynamically adjusted based on the performance metrics of the service instances (such as response time, concurrency, CPU utilization, etc.).

[0060] In fact, the system incorporates a service circuit breaker mechanism (such as Hystrix or Sentinel). When a microservice experiences an abnormal response or its timeout rate exceeds a preset threshold (e.g., five consecutive timeouts or an error rate exceeding 50%), the circuit breaker strategy is automatically triggered, isolating the service instance from the call chain to prevent the failure from spreading. Simultaneously, the system supports service fallback and retry strategies to ensure that basic functionality is still provided or service calls are restored when some services are unavailable.

[0061] It's easy to see that in service registration and discovery mechanisms, the interval for service health checks is typically set between 10 and 30 seconds, with a timeout threshold of three consecutive failures determining the service as unavailable. The load balancer's request allocation algorithm can be dynamically adjusted based on the load status of service instances, for example, using a weighted response time algorithm, the formula of which is:

[0062] in, Indicates the first The weight of each service instance, This indicates the average response time. The higher the weight, the greater the proportion of requests allocated, thus achieving optimal resource scheduling.

[0063] This step is widely used in core processes of life insurance business, including application, underwriting, claims, and policy maintenance. For example, when processing application applications in the contract center, the system uses a service discovery mechanism to call services from the underwriting center, decoupling risk assessment from underwriting decisions. When processing claims in the claims center, the system calls the data service center to obtain policy and customer information, ensuring data consistency and processing efficiency.

[0064] The technical benefits of this step are a significant improvement in the system's scalability, fault tolerance, and operational efficiency. Through the independent deployment and dynamic isolation of microservices, the system can quickly respond to changes in business needs, reducing maintenance costs caused by coupling between modules. Simultaneously, service circuit breaking and load balancing mechanisms effectively ensure system stability under high concurrency or partial service failures, thereby enhancing customer experience and business continuity.

[0065] Furthermore, S3 includes: S31, the service registration and discovery mechanism adopts the Nacos registry center. Each microservice automatically registers its service name, IP address and port number when it starts up, and maintains the service availability status through heartbeat detection.

[0066] Specifically, the service registration and discovery mechanism utilizes the Nacos registry center. As a key component in the microservice architecture, its core function is to achieve dynamic service registration, discovery, and health status management, thereby ensuring the stability and scalability of the system in high-concurrency, distributed environments. In some implementations, each microservice sends a registration request to the Nacos registry center via HTTP upon startup, carrying service metadata information, including service name, IP address, port number, health check path, and service group parameters. The registration process typically involves calling the RESTful API provided by Nacos to write service instance information into the registry center's memory or persistent storage, achieving global visibility of the service.

[0067] In this embodiment of the invention, key parameters for service registration include: a unique identifier for the service instance (service_name:ip:port), a health check interval (check_interval, typically set to 5-10 seconds), a timeout threshold (check_timeout, typically 3-5 seconds), and the number of retries. Service discovery is achieved by the client SDK or the service caller actively querying the Nacos registry to obtain a list of currently available service instances. Load balancing strategies (such as round-robin, weighted round-robin, and consistent hashing) are supported to improve system response efficiency and resource utilization. Nacos supports switching between AP (High Availability) and CP (Strong Consistency) modes, allowing users to select a suitable registry consistency model based on business needs.

[0068] This mechanism is widely applied to various system units of this invention (such as the product center, contract center, and insurance center) to ensure that services can automatically join or leave the service cluster during deployment, expansion, or fault recovery. For example, when the contract center processes an insurance application request, it may need to call the underwriting center's service. The available instances of the underwriting service are dynamically discovered through the Nacos registry, and the optimal node is selected for the call through a load balancing strategy, thereby achieving decoupling and elastic scaling between services.

[0069] This mechanism significantly improves the system's service discovery efficiency and availability management capabilities. Through a heartbeat mechanism, each service instance periodically sends a liveness signal to Nacos (e.g., every 5 seconds). If no heartbeat signal is received for three consecutive times (typically set to 3), Nacos automatically marks the instance as unhealthy or offline, preventing call failures. This mechanism effectively avoids the "zombie node" problem in service calls, improves the system's fault tolerance and stability, and is one of the key supporting technologies for achieving a highly available and scalable life insurance core business system in this invention.

[0070] S32, the load balancing technology uses a weighted round-robin algorithm to dynamically adjust the request allocation weights based on the real-time load metrics of the microservices (including CPU utilization, memory usage, and request response time).

[0071] Specifically, the load balancing technology employs a weighted round-robin algorithm, dynamically adjusting request allocation weights based on real-time load metrics of the microservices (including CPU utilization, memory usage, and request response time), thereby achieving efficient utilization of system resources and rational scheduling of business requests. This step is a crucial guarantee for the stability and scalability of the core life insurance business system built on a microservice architecture.

[0072] In this embodiment of the invention, the weighted round-robin algorithm assigns an initial weight to each microservice instance and dynamically adjusts this weight based on its runtime load status to determine the allocation order of subsequent requests. Specifically, the system uses a monitoring module to collect metrics such as CPU utilization, memory usage, and request response time for each microservice instance in real time, and maps these metrics to weight adjustment factors. For example, if the CPU utilization of an instance exceeds a preset threshold... If the load is high, its weight will be reduced to decrease request allocation; conversely, if the load is low, its weight will be increased to attract more requests. The weight adjustment formula can be expressed as:

[0073] in, Indicates the first Each microservice instance at time The weight, This indicates the load value (such as CPU utilization, memory usage, or response time) at that point in time. This represents the maximum threshold for the indicator. This formula ensures that the weights are dynamically adjusted according to load changes, thereby achieving a rational allocation of resources.

[0074] In practical applications, this algorithm is deployed in the system's unified operation and maintenance monitoring platform. By integrating DevOps and AIOPS technologies, it enables real-time monitoring and intelligent scheduling of microservice instances. For example, in high-concurrency business units such as the contract center and claims center, this algorithm can effectively avoid single-point overload and improve system throughput and response speed.

[0075] The technical value of this step lies in the fact that, through a dynamic weight adjustment mechanism, it significantly improves the system's load handling capacity and resource utilization, reduces service latency and failure rate, thereby enhancing the stability of the life insurance core business system and the customer experience.

[0076] Step S4: Integrate DevOps and AIOPS intelligent operation and maintenance systems to achieve continuous delivery through automated deployment, testing and integration technologies. Utilize machine learning algorithms to perform real-time analysis of system operation data and predict potential failures. Establish a unified operation and maintenance monitoring platform to perform visual management of each microservice.

[0077] Specifically, in this embodiment of the invention, integrating DevOps and AIOPS intelligent operation and maintenance systems is a key step in achieving continuous system delivery and intelligent operation and maintenance management. This step utilizes automated deployment, testing, and integration technologies to build a continuous integration / continuous delivery (CI / CD) pipeline, automating the entire code development and deployment process, thereby significantly improving system iteration efficiency and delivery quality. In some implementations, this pipeline can be based on tools such as Jenkins, GitLabCI, and ArgoCD, supporting code commit-triggered builds, unit tests, integration tests, static code analysis, security scanning, container image building and deployment, ensuring that each change is rigorously verified before deployment.

[0078] Furthermore, AIOPS technology uses machine learning algorithms to perform real-time analysis and fault prediction of system operation data. Specifically, the system collects operational metrics of each microservice (such as CPU utilization, memory usage, request latency, and error rate) and inputs them into a prediction model based on time series analysis, such as LSTM (Long Short-Term Memory) or the Prophet algorithm. In practical applications, a threshold alarm mechanism can be set up; when the predicted value exceeds a preset threshold (e.g., request latency exceeds a certain threshold), an alarm will be triggered. When an anomaly occurs, the system automatically triggers an alarm and pushes it to the operations and maintenance platform. In addition, AIOPS can also identify abnormal patterns through cluster analysis (such as K-means) to help operations and maintenance personnel quickly locate the root cause of the fault.

[0079] Optionally, this invention constructs a unified operation and maintenance monitoring platform, employing a technology stack including Prometheus, Grafana, and ELK (Elasticsearch, Logstash, Kibana) to achieve visualized management of various microservices. This platform supports multi-dimensional monitoring metric display, log aggregation and analysis, service dependency topology diagrams, and alarm rule configuration, enabling operation and maintenance personnel to monitor system operation status in real time and perform rapid response and troubleshooting through a visual interface.

[0080] This step is widely applicable in practical applications to multi-channel deployment environments of core life insurance business systems, such as PICC E-Pass, Bancassurance, and e-commerce websites, ensuring high availability and stability of services across all channels. By automating operations and maintenance processes through DevOps and combining them with the intelligent analysis capabilities of AIOPS, the system not only improves operational efficiency but also reduces the error rate caused by manual intervention, enhances the system's self-repair and predictive capabilities, thereby significantly improving the overall operational quality and business continuity of the system.

[0081] Furthermore, S4 includes: S41 uses Docker containerization technology for automated deployment and implements canary releases of microservices through Kubernetes cluster management. The canary release process includes: building images, creating canary release groups, and gradually replacing old version service instances.

[0082] Specifically, automated deployment utilizes Docker containerization technology and leverages Kubernetes cluster management to achieve canary releases of microservices. The canary release process includes building images, creating canary release groups, and gradually replacing older version service instances. This step is a crucial component of the continuous delivery and continuous deployment (CI / CD) process, aiming to reduce the risk of launching new versions and improve system stability and business continuity.

[0083] In this embodiment of the invention, Docker containerization technology packages each microservice into an independent image, ensuring consistent service operation across different environments. The image building process is typically based on a Dockerfile, generating a deployable container image through steps such as defining a base image, installing dependencies, compiling code, and injecting configurations. After building, the image is pushed to a private image repository (such as Harbor or AWSECR) for use by the Kubernetes cluster.

[0084] Kubernetes clusters implement canary releases through Deployments and Service resource objects. First, a canary release group is created; this involves defining a new Deployment in Kubernetes with the image version to be released, identified as a canary version using a label or annotation. The canary release group typically coexists with the older service instance in the same namespace. By using routing rules in the Service's Endpoints or Ingress, a portion of traffic is redirected to the canary group, achieving a gradual traffic switchover.

[0085] In one embodiment of the present invention, the traffic allocation ratio for canary releases can be controlled through Kubernetes traffic management tools (such as Istio or Linkerd), for example, setting the initial traffic to 5% (i.e., The initial capacity is then gradually increased to 50% and then 100%. The service instance replacement process typically employs a rolling update strategy, controlling instance scaling behavior during the update process by setting the maxSurge and maxUnavailable parameters. For example, maxSurge=25% means that a maximum of 25% of new instances can be added, and maxUnavailable=20% means that a maximum of 20% of old instances can be made unavailable, thereby ensuring service availability during the update process.

[0086] Specifically, this canary release mechanism is widely used in the version iteration process of core life insurance business systems, such as launching new products in the product center, updating insurance rules in the contract center, and optimizing case processing logic in the claims center. Through canary release, the system can verify the stability and compatibility of the new version without affecting the overall business operation, reducing the risk of business interruption due to version defects.

[0087] The technical benefits of this step are multifaceted: Firstly, the combination of containerization and Kubernetes enables rapid service deployment and elastic scaling. Secondly, the canary release mechanism effectively controls the risks of version switching, improving system fault tolerance and operational efficiency. Furthermore, this method supports consistent deployment across multiple environments, meeting the requirements of DevOps and AIOPS intelligent operations and maintenance systems, and providing reliable assurance for the continuous evolution of life insurance business systems.

[0088] S42, the machine learning algorithm uses a Long Short-Term Memory (LSTM) network to perform time-series analysis on the system's operational data, through the formula... Predict potential failures, among which For activation function, , This is the weight matrix. This is a bias term.

[0089] Specifically, in this embodiment of the invention, the machine learning algorithm uses a Long Short-Term Memory (LSTM) network to perform time-series analysis on system operation data to predict potential faults. LSTM is a special type of recurrent neural network (RNN) whose core advantage lies in its ability to effectively capture long-term dependencies in time-series data, thus performing excellently in system monitoring and fault early warning scenarios.

[0090] In its implementation, LSTM controls the flow and storage of information through its gating mechanism (input gate, forget gate, and output gate), enabling the model to identify time-related anomaly patterns when processing time-series data such as operation logs, service response times, and abnormal events from the core life insurance business system. In one implementation of this invention, the output of the prediction model is calculated using the following formula:

[0091] in, Indicates at time step The predicted output, The input feature vector at the current time step. This is the hidden state from the previous time step. and These are the weight matrices for the hidden state and the input features, respectively. For bias terms, The activation function is typically the Sigmoid or Softmax function, used to map linear combinations to a probability space.

[0092] This step is primarily used by the AIOPS intelligent operations and maintenance module. It constructs a multi-dimensional time-series dataset by collecting real-time metrics such as the running status, request latency, and error rate of each microservice in the system. The model input dimension is typically [missing information]. to This covers key performance indicators such as CPU utilization, memory usage, request response time, and service call frequency. During training, the Adam optimizer is used, with a learning rate set to... The loss function is cross-entropy loss, which aims to improve the prediction accuracy of the model in binary classification (normal / abnormal) tasks.

[0093] The technical advantage of this step lies in enabling the system to provide early warnings before faults occur, leveraging the time-series modeling capabilities of LSTM, thus achieving proactive operation and maintenance. For example, in high-concurrency business modules such as policy management centers or claims centers, this method can identify potential problems such as abnormal service response delays and database connection timeouts in advance, significantly improving system stability and business continuity. Furthermore, this method supports dynamically adjusting model parameters to adapt to the operational characteristics of different business units, enhancing the system's intelligent operation and maintenance capabilities.

[0094] Step S5, the steps for building the data service center, include: Specifically, the construction of a data service center is one of the key steps in realizing the modular architecture of the system in this invention. Its technical implementation principle is based on a unified mechanism for data access, aggregation, and service-oriented management, aiming to improve the system's ability to process business data and the efficiency of data asset reuse. In some implementations, the data service center achieves unified management and efficient utilization of structured and unstructured data by constructing a hybrid architecture that combines a data warehouse and a data lake.

[0095] In this embodiment of the invention, the data service center first defines a unified data access interface that supports access from multiple data sources (such as relational databases, NoSQL databases, file systems, API interfaces, etc.). During the data access process, ETL (Extract, Transform, Load) tools are used to clean, transform, and standardize the raw data to ensure data quality and consistency. Data transformation follows predefined mapping rules to unify heterogeneous data into a standard data model, such as using JSONSchema or XMLSchema for structure validation.

[0096] In one embodiment of the present invention, the data service center supports high-concurrency data access, with a throughput of over 10,000 records per second (TPS≥10,000) and latency controlled in milliseconds (<500ms). Data storage adopts a distributed architecture, supporting petabyte-level data capacity, and the data retention period can be configured from 30 days to 5 years according to business needs. Furthermore, the system supports multi-dimensional data querying and analysis, with a response time of no more than 2 seconds (QPS≥500).

[0097] It is clear that the data service center provides unified data support services for the product center, contract center, policy maintenance center, and claims center. For example, in the product analysis module, the system can make trend predictions based on historical sales data; in claims calculation, it can call policy and customer data for quick matching and compensation calculation.

[0098] The technical benefits of this step lie in effectively eliminating information silos and improving data reuse and system response speed through a unified data access and aggregation mechanism. Simultaneously, it provides a high-quality, standardized data foundation for subsequent business analysis, intelligent decision-making, and AI model training, significantly enhancing the overall intelligence level and business support capabilities of the system.

[0099] S51 uses data warehouse and data lake technologies to classify and store business data generated by various system units. The data warehouse is used for OLAP analysis of structured data, and the data lake is used for raw storage of unstructured data.

[0100] Specifically, classifying and storing business data generated by various system units through data warehouse and data lake technologies is a key step in achieving efficient data management and business analysis. This step is technically implemented based on modern data architecture design principles, combining the processing needs of both structured and unstructured data to construct a flexible and scalable data storage system.

[0101] In this embodiment of the invention, system units (such as product center, contract center, policy maintenance center, claims center, etc.) generate various types of data during business processing, including structured data (such as policy information, customer data, transaction records, etc.) and unstructured data (such as scanned documents uploaded by customers, claims investigation reports, voice recordings, etc.). Structured data is extracted, cleaned, and transformed through an ETL (Extract, Transform, Load) process before being loaded into a data warehouse to support OLAP (Online Analytical Processing) analysis. The data warehouse is modeled using a star or snowflake schema, with a fact table at its core, around which multiple dimension tables are built, such as time dimension, customer dimension, product dimension, etc., thereby enabling multidimensional data analysis. In some implementations, the data warehouse can be based on Hive in the Hadoop ecosystem or column-oriented storage technologies such as ClickHouse and Redshift, supporting high-concurrency queries and complex analysis requirements.

[0102] For unstructured data, the system employs data lake technology for raw storage. Data lakes are typically built on distributed storage systems (such as HDFS, S3, and Azure Data Lake Storage) to support the storage, processing, and analysis of raw data. In this invention, unstructured data undergoes metadata extraction and the addition of classification tags before entering the data lake to facilitate subsequent retrieval and processing. For example, scanned documents uploaded by the claims center will be assigned metadata fields such as claim_id, file_type, and upload_time to facilitate feature extraction and image recognition processing in subsequent machine learning models.

[0103] In this embodiment of the invention, the OLAP analysis performance of a data warehouse is typically measured by metrics such as query latency, concurrent processing capability, and data update frequency. For example, the system can be configured to execute daily ETL tasks during off-peak business hours (such as 2:00 AM to 4:00 AM) to ensure data timeliness and consistency. Storage capacity, data access latency, and data format compatibility are also key metrics for a data lake. In some implementations, the data lake supports multiple data formats such as Parquet, ORC, and JSON, and implements data governance through a unified metadata management platform (such as Apache Atlas).

[0104] This step is widely used in life insurance business scenarios such as multi-dimensional analysis, risk modeling, customer profiling, and product optimization. For example, the underwriting center can train a risk scoring model based on historical underwriting data in the data warehouse, while the claims center can use unstructured data in the data lake for fraud detection and analysis. By separating and storing structured and unstructured data, the system can meet the needs of both real-time business processing and long-term data analysis, improving overall business response efficiency and decision-making quality.

[0105] The technical benefits of this step lie in the fact that, through a categorized storage mechanism, the system achieves efficient data management and flexible analysis, reduces the complexity of data processing, and improves the system's scalability and stability. Simultaneously, it provides a solid data foundation for subsequent AI model training, business intelligence (BI) analysis, and regulatory compliance.

[0106] S52, establish a data cleaning and transformation mechanism, use ETL tools to standardize the raw data, ensure that the data quality meets the requirements, and achieve full-link traceability of business data through data lineage tracing.

[0107] Specifically, establishing a data cleaning and transformation mechanism is one of the key steps to achieve efficient system operation and data quality assurance. This step uses ETL (Extract, Transform, Load) tools to standardize the raw data, ensuring consistency, integrity, and accuracy before it enters the core business units of the system. Specifically, ETL tools first extract data from multiple heterogeneous data sources (such as relational databases, unstructured files, external API interfaces, etc.), then perform cleaning, formatting, deduplication, and missing field completion operations on the data according to predefined transformation rules, and finally load the processed data into a unified data warehouse or data lake for use by various system units.

[0108] In this embodiment of the invention, the data cleaning process includes operations such as field mapping, data type conversion, outlier detection and correction, and data denoising. For example, for insurance data in the contract center, the system will verify whether the insured's age is within a reasonable range. Does the premium amount comply with the product pricing rules? Data transformation includes standardizing field naming, unifying time formats (such as YYYY-MM-DD), and unifying currency units (such as RMB "CNY") to ensure consistency across data units.

[0109] This invention requires that the quality of the data after cleaning and transformation reaches a certain level. This means that for every 1000 data entries, only one data entry is allowed to have a quality issue. To achieve this goal, the system introduces a data lineage tracking mechanism, recording the complete flow path of each data entry from its source to the target system, including metadata information such as data source, transformation rules, processing time, and operators. This mechanism is implemented based on a distributed log system and a metadata management platform, supporting visual traceability and auditing functions.

[0110] This step is widely used in the data processing workflow of business units such as the contract center, claims center, and policy maintenance center. For example, in claims reporting, the system needs to access customer information from multiple channels (such as mobile terminals, call centers, and counter systems), and after ETL cleaning, load it uniformly into the claims center database to support subsequent case calculation and compensation decisions. Data lineage tracing is used for regulatory compliance, data quality auditing, and tracing the source of abnormal data.

[0111] The technical benefits of this step are that it significantly improves the accuracy and traceability of system data processing, reduces business processing delays and error rates caused by data quality issues, and provides a high-quality data foundation for subsequent AI model training, business analysis, and compliance review, thereby enhancing the overall stability and business responsiveness of the system.

[0112] Example 2 This invention relates to a life insurance core business system based on system modularization and its implementation method. Figure 2 This is a flowchart illustrating a technical solution for a life insurance core business system based on system unitization and its implementation method, provided as an embodiment of the present invention. Figure 2 As shown, the life insurance core business system proposed in this invention consists of multiple independent system units. These units communicate with each other through standardized interfaces, forming a highly modular and unitized system architecture. Specific units include: (I) Product Center.

[0113] The Product Center is a core component of the life insurance core business system, responsible for the management and maintenance of life insurance products. Its main functions include: Product Design: Design insurance products that align with the company's strategic direction, based on market demand and regulatory requirements.

[0114] Product Development: Complete product pricing, rate setting, and terms and conditions drafting to ensure product compliance and competitiveness.

[0115] Product Management: Continuously track and manage products already on the market, including product upgrades, rate adjustments, and terms and conditions revisions.

[0116] Product analysis: Analyze and evaluate the market performance of products to provide a basis for product optimization and new product development.

[0117] Product definition and configuration: Define and configure various attributes of life insurance products, such as product type, insurance period, and premium rate, based on market demand and business strategy.

[0118] Product pricing and sales strategy: Based on market research and cost analysis, formulate product pricing and sales strategies to ensure product competitiveness.

[0119] Product Inquiry and Analysis: Provides product inquiry services and supports analysis of sales data, customer feedback, etc., to provide a basis for product optimization and strategy adjustment.

[0120] (ii) Contract Center.

[0121] The Contract Center is responsible for handling the signing and management of life insurance contracts. Its main functions include: Insurance application processing: Receive insurance applications from customers, conduct preliminary review and organize relevant documents.

[0122] Underwriting support: The insurance application information is transmitted to the underwriting center for risk assessment, and a decision is made on whether to underwrite based on the underwriting results.

[0123] Underwriting Management: Complete the generation, printing, and distribution of policies after underwriting to ensure that customers can receive their policies in a timely manner.

[0124] Contract file management: Archive and manage customer insurance application materials, underwriting records, and underwriting results for easy subsequent inquiry and review.

[0125] Underwriting rule verification: Verify the customer's underwriting information to ensure it complies with the company's underwriting rules and regulatory requirements.

[0126] Contract generation and signing: Generate life insurance contracts based on the customer's insurance information and product configuration, and support online signing function.

[0127] Contract Inquiry and Modification: Provides contract inquiry services and supports modification operations for contracts, such as adjusting the insurance period and premium rate.

[0128] (III) Underwriting Center.

[0129] The Underwriting Center is responsible for underwriting life insurance products, ensuring that the insured risks of clients meet the company's underwriting requirements. Its main functions include: Risk assessment: Based on the client's insurance information and risk model, conduct risk assessment and prediction.

[0130] Underwriting decision: Based on the risk assessment results, decide whether to underwrite, underwrite with additional premiums, or refuse coverage.

[0131] Underwriting records: Record all information and decision results during the underwriting process, providing a basis for subsequent underwriting and claims settlement.

[0132] Underwriting strategy optimization: Continuously optimize underwriting strategies and risk assessment models based on market changes and business development needs.

[0133] Underwriting decision service: Based on the client's health information, financial situation, etc., we assess the risk of insurance and make an underwriting decision.

[0134] Underwriting record viewing and blacklist maintenance: Provides underwriting record query function and supports blacklist management of high-risk customers.

[0135] Underwriting Shared Pool Service: Share underwriting information with other departments to improve underwriting efficiency and quality.

[0136] (iv) Security Center.

[0137] The policy maintenance center is responsible for policy maintenance services for life insurance contracts, including contract modifications, renewals, and surrenders. Its main functions include: Preservation entry and approval: Supports customers to submit preservation applications online and manages the approval process. Approvals and decisions are made on preservation applications in accordance with the company's preservation policies and regulations.

[0138] Policy maintenance refund calculation: Based on the customer's policy maintenance needs, a refund calculation is performed to ensure the customer's rights are protected.

[0139] Canceling or deleting preservation orders: This involves canceling or deleting completed preservation operations.

[0140] Preservation application processing: Receive preservation applications from clients, conduct preliminary review and organize relevant documents.

[0141] Policy maintenance operations: Complete policy maintenance operations, such as policy changes, renewal payments, policy cancellations, etc., and update policy information.

[0142] Preservation file management: Archive and manage preservation applications, approval results, operation records, and other files to facilitate subsequent inquiries and audits.

[0143] (v) Claims Center.

[0144] The claims center is responsible for handling life insurance claims, ensuring that customers receive timely compensation in the event of an insured event. Its main functions include: Claims reporting and processing: Receive customer claims reports, conduct preliminary review and organize relevant documents.

[0145] Claims investigation: Conduct a detailed investigation of the reported incident, including on-site inspection and data collection, to determine the authenticity of the accident and the extent of the loss.

[0146] Claims assessment decision: Based on the investigation results and the company's claims policy, decide whether to pay compensation, the amount of compensation, and the method of compensation.

[0147] Claims Management: Complete claims processing, including fund transfers, payouts, and updating policy information.

[0148] Case acceptance and approval: Receive customer claims and manage the case acceptance and approval process.

[0149] Case settlement and investigation: Based on the client's claim application and contract terms, we settle cases and support on-site investigations of cases.

[0150] Benefit distribution service: After a successful claim, the benefit distribution will be processed according to the contract terms and the customer's needs.

[0151] (vi) Group insurance core.

[0152] The group insurance core team is responsible for the core processing of group insurance business, including the definition, configuration, sales, and claims settlement of group insurance products. Its main functions include: Group insurance product definition and configuration: Based on the characteristics of group insurance business and market demand, define and configure the various attributes of group insurance products. Design insurance products that meet the needs of group insurance customers based on the demands of the group insurance market and the company's strategic direction.

[0153] Group insurance underwriting management: Reviewing and underwriting applications from group insurance clients, and generating group insurance policies.

[0154] Group insurance policy maintenance management: Handling policy maintenance applications from group insurance clients, such as adding or removing personnel, and changing policies.

[0155] Group insurance claims management: Handling claims reports and payouts for group insurance customers to ensure their rights and interests are protected.

[0156] Group insurance sales: Supports the sales process management of group insurance products and provides after-sales service for group insurance.

[0157] Group insurance data analysis: Analyze group insurance business data to provide a basis for adjusting business strategies.

[0158] (vii) Policy Management Center.

[0159] The policy management center is responsible for the management of life insurance contracts, including policy generation, inquiry, modification, and loss reporting. Its main functions include: Policy generation and inquiry: Based on the customer's insurance information and contract terms, policies are generated and policy inquiry services are provided.

[0160] Policy information entry: Enter the customer's insurance information into the system to generate policy information.

[0161] Policy information maintenance: Continuously update and maintain policy information, including policy status, customer information, payment records, etc.

[0162] Policy Information Inquiry: Supports customers and company staff to inquire about policy information, including policy details, payment records, claims records, etc.

[0163] Policy information statistics: To collect and analyze policy information to support the company's business decisions.

[0164] Policy changes and loss reporting: Supports customers to submit policy change applications or loss reporting applications online and process them.

[0165] Policy locking and status changes: Lock policies to prevent unauthorized operations and support policy status change management.

[0166] (viii) General Center.

[0167] The General Services Center provides common services required by the core life insurance business systems, such as log management, access control, and configuration management. Its main functions include: Dictionary service: Provides various dictionary data required by the system, such as product type, insurance period, etc., to ensure the accuracy and consistency of the data.

[0168] Portal: As the entry point to the system, it provides basic functions such as user login and permission management.

[0169] Job platform: Supports the scheduling and management of daily system jobs, such as batch processing tasks.

[0170] Personnel Management: Managing and maintaining system users, including the entry, modification, and deletion of user information.

[0171] Organizational structure management: Manage the company's organizational structure and departmental information to ensure that the system's organizational structure matches the actual business operations.

[0172] Process management: Define, monitor, and optimize business processes within the system to improve the efficiency and quality of business processing.

[0173] Log management: Records various log information during system operation, providing a basis for troubleshooting and system optimization.

[0174] (ix) Front-end applications and channel units.

[0175] Core Front-End: Responsible for displaying information on various life insurance products and providing a user interface to enable operations such as purchasing and consultation.

[0176] Channel Platform: PICC E-Pass: A mobile sales channel for individual users, providing convenient life insurance product purchase and consultation services.

[0177] Yinbaotong: Partnering with banks to sell life insurance products through their channels.

[0178] PICC E-Learning: Provides an online learning platform for life insurance products, helping users understand product features and the application process.

[0179] Life Insurance Butler: Provides personalized life insurance product recommendations and services to enhance user experience.

[0180] E-Service: Integrates various life insurance services to provide users with a one-stop service experience.

[0181] PICC Micro-Connect: Provides life insurance product purchase and consultation services through platforms such as WeChat official accounts.

[0182] E-commerce website: As an online sales channel, it provides a wealth of information on life insurance products and purchase services.

[0183] E-Mobile Counter: Combining with offline outlets, it provides face-to-face life insurance product purchase and consultation services.

[0184] Call Center: Provides services such as answering users' questions about life insurance products and offering purchase guidance via telephone customer service.

[0185] In addition, the system also includes an infrastructure layer responsible for data processing, storage, backup and recovery, ensuring stable system operation.

[0186] Implementation method: 1. System unit division and functional definition.

[0187] In embodiments of the present invention, the life insurance core business system is first divided into multiple independent system units, including but not limited to product center, contract center, policy maintenance center, claims center, customer center, etc.

[0188] Front-end Application Center: Responsible for providing user interfaces and interactive functions, including various channel platforms (such as PICC E-Pass, Bank-Insurance Pass, etc.) and core front-end applications (such as e-commerce websites, E-stop counters, etc.). The front-end application center realizes functions such as product display, sales, and inquiry by calling back-end services.

[0189] Channel Sales Configuration Center: Responsible for product sales configuration and channel management, including product definition, pricing, and sales rules. This center allows for the configuration and management of products across different channels to meet the needs of various markets and customers.

[0190] General Center: Provides a range of general services and functions, such as dictionary services, portal, job platform, personnel management, organizational structure management, process management, log management, etc., to support the operation and management of the entire system.

[0191] Operations Support Center: As a back-end support department, it is responsible for providing support services such as customer service, operations monitoring, and business operations. Through this center, rapid response to customer needs and continuous system optimization can be achieved.

[0192] Product Factory Center: Responsible for product design and development, including the entire process of new product conception, design, testing, and launch. Through collaboration with the channel sales and configuration center, ensure the market adaptability and customer demand satisfaction of new products. Responsible for integrating the product capabilities of various systems and providing a unified product service interface, including functions for product creation, modification, and querying.

[0193] Account Clearing Center: Responsible for handling various clearing and settlement transactions related to accounts, such as premium payments, dividend distributions, and fee generation. Through connections with banks and other financial institutions, it achieves rapid clearing and settlement of funds.

[0194] Risk Control and Compliance Center: Responsible for the system's risk control and compliance assurance. This involves timely detection and prevention of potential risks through real-time monitoring and analysis of business data. Simultaneously, it ensures that the system's operation complies with relevant laws, regulations, and regulatory requirements.

[0195] Data Service Center: Responsible for data access, aggregation, management, and service. Through the construction of data warehouses and data lakes, as well as information aggregation technologies, it achieves comprehensive management and efficient utilization of business data. Simultaneously, it provides support services such as data querying, analysis, and mining to provide a basis for business decision-making and optimization.

[0196] Contract Center: Responsible for handling contract business processes such as insurance application and underwriting, and supports flexible configuration of contract rules, such as obtaining application number, verifying application rules, and underwriting decision services.

[0197] Policy Maintenance Center: Responsible for handling policy maintenance business processes such as policy changes and cancellations, including services such as policy entry, approval, cancellation, and refund calculation.

[0198] Claims Center: Responsible for handling claims business processes, including case acceptance, approval, calculation, investigation and other services, as well as anti-fraud functions for claims.

[0199] Customer Center: Responsible for managing customer information, including functions such as entering, modifying, and querying customer data, as well as providing customer service and support.

[0200] Each system unit has its own independent database, runtime environment, and code library, achieving a high degree of modular and unitized design.

[0201] 2. Interface protocol design and standardization.

[0202] To ensure smooth and efficient data interaction and communication between system units, this invention has developed a set of standardized interface protocols. These protocols define key elements such as data format, transmission method, and request and response processing flow.

[0203] Data format: Data is encapsulated using a unified JSON or XML format to ensure readability and parsability.

[0204] Transmission method: HTTPS protocol is used for data transmission to ensure the security and reliability of data transmission.

[0205] Requests and Responses: Define clear request and response structures, including request parameters, response codes, response data, etc., to ensure seamless integration between various system units.

[0206] In practice, the development team will strictly follow these protocols in interface design and conduct thorough testing to ensure the stability and reliability of data interaction and communication between system units.

[0207] 3. Microservice architecture setup.

[0208] Microservice architecture is one of the key technologies for implementing this invention. When building a microservice architecture, we first implement each system unit as an independent microservice, with each microservice having its own independent database, runtime environment, and code repository.

[0209] Service decomposition: Based on business needs and system architecture, each system unit is decomposed into independent microservices, ensuring that each microservice has a clear function and responsibility.

[0210] Service registration and discovery: A service registration and discovery mechanism is adopted to ensure that microservices can dynamically find each other and communicate.

[0211] Load balancing: By employing load balancing technology, requests are evenly distributed across various microservices to ensure the scalability and stability of the system.

[0212] Service governance and circuit breaking: By introducing service governance and circuit breaking mechanisms, when a microservice fails, it can be automatically isolated and recovered to avoid affecting the entire system.

[0213] 4. Building intelligent operations and maintenance using DevOps and AIOPS.

[0214] To achieve automated deployment, monitoring, alarming, and troubleshooting of the system, this invention establishes a unified operation and maintenance monitoring platform and integrates DevOps and AIOPS technologies.

[0215] DevOps: Employing automated deployment, testing, and integration technologies enables rapid code iteration and continuous system integration. Simultaneously, establishing continuous delivery and continuous deployment processes ensures the system can quickly respond to changes in business requirements.

[0216] AIOPS: Utilizes machine learning algorithms to perform real-time analysis of system data, predict potential faults, and intervene proactively. Simultaneously, it establishes intelligent alarm and fault diagnosis mechanisms to improve system stability and reliability.

[0217] Unified Operations and Maintenance Monitoring: A unified operations and maintenance monitoring platform is established to enable real-time monitoring and alerting of the operational status, performance metrics, and log information of each microservice. Simultaneously, a visual operations and maintenance management interface is provided to facilitate quick problem location and resolution by operations and maintenance personnel.

[0218] 5. Business function development and integration.

[0219] After determining the functions and interface protocols of each system unit, we began developing specific business functions.

[0220] Feature Development: The development team for each system unit strictly adheres to business requirements and technical specifications in writing and testing code. Simultaneously, a code review and quality assurance mechanism is established to ensure code quality and maintainability.

[0221] Functional Integration: After completing the functional development of each system unit, system integration and testing are performed. By simulating real-world business scenarios and processes, the overall performance of the system and the correctness of the business processes are verified. Simultaneously, a continuous integration and continuous deployment mechanism is established to ensure the system can quickly respond to changes in business requirements.

[0222] 6. Data access and aggregation.

[0223] To ensure that the system can process and analyze data efficiently, this invention establishes a unified data access and aggregation mechanism.

[0224] Data Access: A unified data access interface and protocol are used to enable the access and integration of various data sources (such as databases, files, APIs, etc.). Simultaneously, a data cleaning and transformation mechanism is established to ensure data quality and consistency.

[0225] Data aggregation: The collected data is summarized and integrated to form a unified data asset. Simultaneously, a data warehouse and data mart are established to provide the system with efficient data storage and query services.

[0226] 7. Continuous iteration and optimization.

[0227] The life insurance core business system of this invention is a continuous iteration and optimization process, aiming to continuously improve the system's performance and user experience.

[0228] Requirements gathering and feedback: Continuously collect user requirements and feedback through user surveys, business analysis, and other methods. Simultaneously, establish a requirements management mechanism to prioritize and track requirements.

[0229] System optimization and upgrades: Based on collected requirements and feedback, the system is regularly optimized and upgraded. This includes fixing known issues, optimizing system performance, and adding new features.

[0230] Version Management and Release: Version management tools are used to manage system versions, ensuring that every modification is recorded and traceable. At the same time, a standardized version release process is established to ensure a smooth transition and successful deployment of new versions.

[0231] Continuous monitoring and evaluation: Establish a continuous monitoring and evaluation mechanism to monitor and evaluate the system's operational status, performance indicators, and user satisfaction in real time. Through data analysis, identify potential problems and areas for improvement, providing a basis for subsequent iterations and optimizations.

[0232] 8. Security and compliance guarantees.

[0233] Security and compliance are of paramount importance during the implementation of core life insurance business systems.

[0234] Network security: Employ technologies such as firewalls, intrusion detection, and data encryption to ensure system network security. Simultaneously, establish network security management systems and emergency response plans to enhance the system's resistance to attacks and its resilience.

[0235] Data privacy protection: We strictly adhere to relevant laws, regulations, and industry standards to protect users' personal information and privacy. We employ technologies such as data anonymization and encrypted storage to ensure data security and privacy.

[0236] Compliance Review: Conduct regular compliance reviews of the system to ensure it complies with relevant laws, regulations, and industry standards. Simultaneously, establish a compliance management mechanism to continuously monitor and evaluate the system's compliance.

[0237] 9. Training and promotion.

[0238] To ensure the smooth implementation and widespread application of the core life insurance business system, systematic training and promotion are necessary.

[0239] User Training: Personalized training plans are developed for different types of users (such as business personnel, technical personnel, etc.). Training courses, user manuals, and online help are used to improve users' skills and system operation capabilities.

[0240] Technology Promotion: Actively promote the technical features and advantages of the core life insurance business system through industry conferences, technology forums, and partner channels. Simultaneously, establish a technology exchange and cooperation mechanism to jointly promote the development and innovation of the life insurance business with industry peers.

[0241] 10. Project Management and Risk Control.

[0242] In the implementation of core life insurance business systems, project management and risk control are of paramount importance.

[0243] Project Management: Establish standardized project management processes, including project planning, schedule tracking, and risk management. Utilize project management tools and methodologies to ensure smooth project implementation and on-time delivery.

[0244] Risk control: Identify and analyze potential risks during project implementation (such as technical risks, business risks, compliance risks, etc.), and formulate corresponding risk response strategies and measures. Simultaneously, establish a risk monitoring and reporting mechanism to continuously monitor and assess risks.

[0245] Example 3 This invention also provides a modular construction device for a life insurance core business system. Figure 3 This is a schematic diagram of a modular construction device for a core life insurance business system. Figure 3 As shown, the device includes: The system unit division module 100 is used to divide the life insurance core business system into multiple independent system units. Each system unit contains an independent database, operating environment and code library. The system units include product center, contract center, policy maintenance center, claims center, group insurance core, policy management center and general center. The standardized interface communication protocol definition module 200 is used to define a standardized interface communication protocol based on the system unit. The protocol includes a unified data format, HTTPS transmission method, and request and response processing flow to ensure the efficiency and accuracy of data interaction and communication between system units. The microservice architecture implementation module 300 is used to implement each system unit using a microservice architecture. Through service registration and discovery mechanisms, load balancing technology, and service governance and circuit breaker mechanisms, each system unit runs as an independent microservice and dynamically isolates faults. The DevOps and AIOPS Integration Module 400 is used to integrate the DevOps and AIOPS intelligent operation and maintenance system. It achieves continuous delivery through automated deployment, testing and integration technologies, and uses long short-term memory networks to perform time-series analysis on system operation data and predict potential failures.

[0246] Example 4 To implement the methods of the above embodiments, the present invention also provides an electronic device, which includes a memory and a processor; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the various steps of the methods described above.

[0247] 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 the method described in the foregoing embodiments.

[0248] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0249] 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.

[0250] 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 life insurance core business system unitization construction method, characterized by, The method comprises the following steps: S1, dividing the life insurance core business system into multiple independent system units, each system unit containing an independent database, running environment and code library, the system units including a product center, a contract center, an insurance center, a claim center, a group insurance core, a policy management center and a general center; S2, defining a standardized interface communication protocol based on the system units, the protocol including a unified data format, an HTTPS transmission method and a processing flow of requests and responses, ensuring the efficiency and accuracy of data interaction and communication between the system units; S3, implementing each system unit using a micro-service architecture, making each system unit run as an independent micro-service and dynamically isolating faults through a service registration and discovery mechanism, load balancing technology and service governance and circuit breaking mechanism; S4, integrating DevOps and AIOPS intelligent operation and maintenance system, realizing continuous delivery through automated deployment, testing and integration technology, predicting potential faults by using machine learning algorithms to analyze system operation data in real time, and establishing a unified operation and maintenance monitoring platform for visual management of each micro-service.

2. The method of claim 1, wherein, The life insurance core business system is divided into multiple independent system units, each system unit containing an independent database, running environment and code library, the system units including a product center, a contract center, an insurance center, a claim center, a group insurance core, a policy management center and a general center, further comprising: S11, dividing the product center into a product definition module, a pricing strategy module and a configuration management module according to business requirements, wherein the product definition module is used to define the basic attributes of insurance products, the pricing strategy module generates dynamic pricing schemes based on market research data and cost models, and the configuration management module supports rapid adjustment of product rules through parameterized configuration; S12, dividing the contract center into a policy application rule verification module and an underwriting decision module, wherein the policy application rule verification module verifies the policy application information in real time through a pre-set compliance verification rule set, and the underwriting decision module generates an underwriting suggestion based on a risk assessment model and a customer credit score.

3. The method of claim 1, wherein, A standardized interface communication protocol is defined based on the system units, the protocol including a unified data format, an HTTPS transmission method and a processing flow of requests and responses, ensuring the efficiency and accuracy of data interaction and communication between the system units, further comprising: S21, using JSON format as the data interaction format between the contract center and the underwriting center, wherein the JSON data structure includes a policyholder basic information field, a risk assessment parameter field and an underwriting result field; S22, when defining the request response structure, the response data includes a business status code field, an operation log identification field and a data verification result field, wherein the business status code field uses a three-digit coding rule, and the operation log identification field is associated with the log management system of the general center.

4. The method of claim 1, wherein, Each system unit is implemented using a micro-service architecture, making each system unit run as an independent micro-service and dynamically isolating faults through a service registration and discovery mechanism, load balancing technology and service governance and circuit breaking mechanism, further comprising: S31, the service registration and discovery mechanism adopts Nacos registration center, each microservice automatically registers its service name, IP address and port number when starting, and maintains service availability state through heartbeat detection; S32, the load balancing technology adopts weighted round robin algorithm, and dynamically adjusts request distribution weight according to real-time load index (including CPU utilization, memory occupancy and request response time) of microservice.

5. The method of claim 1, wherein, Integrate DevOps and AIOPS intelligent operation and maintenance system, realize continuous delivery through automatic deployment, testing and integration technology, use machine learning algorithm to analyze system running data in real time and predict potential faults, establish unified operation and maintenance monitoring platform for visual management of each microservice, including: S41, automatic deployment adopts Docker container technology, and realizes gray release of microservice through Kubernetes cluster management, wherein the gray release process includes: building image, creating gray release group, and gradually replacing old version service instance; S42, the machine learning algorithm employs a long short-term memory network (LSTM) to perform time series analysis on the system operation data, through the formula to predict potential failures, wherein is an activation function, , is a weight matrix, is a bias term.

6. The method of claim 1, wherein, Further comprising: S5, the steps of building a data service center, including: S51, classify and store business data generated by each system unit through data warehouse and data lake technology, wherein the data warehouse is used for OLAP analysis of structured data, and the data lake is used for raw storage of unstructured data; S52, establish data cleaning and conversion mechanism, use ETL tool to standardize raw data, ensure that data quality meets the requirements, and realize full-link traceability of business data through data blood relationship tracking.

7. A life insurance core business system unitization construction apparatus characterized by comprising: Including: The system unit division module is used for dividing the life insurance core business system into a plurality of independent system units, each system unit comprising an independent database, a running environment and a code library, and the system units include a product center, a contract center, an insurance center, a claim center, a group insurance core, a policy management center and a general center; The standardized interface communication protocol definition module is used for defining a standardized interface communication protocol based on the system units, the protocol comprising a unified data format, an HTTPS transmission mode and a request and response processing flow, ensuring the efficiency and accuracy of data interaction and communication between the system units; The microservice architecture implementation module is used for implementing each system unit by using a microservice architecture, and each system unit is run as an independent microservice and dynamically isolated from faults through a service registration and discovery mechanism, a load balancing technology and a service governance and fuse mechanism; The DevOps and AIOPS integration module is used for integrating a DevOps and AIOPS intelligent operation and maintenance system, realizing continuous delivery through automatic deployment, testing and integration technology, and using a long short-term memory network to analyze system running data in time sequence and predict potential faults.

8. The apparatus of claim 7, wherein, The system unit division module is also used for: According to business requirements, the product center is divided into a product definition module, a pricing strategy module and a configuration management module, wherein the product definition module is used for defining the basic attributes of insurance products, the pricing strategy module generates dynamic pricing schemes based on market research data and cost models, and the configuration management module supports rapid adjustment of product rules through parameterized configuration; The contract center is divided into an insurance application rule verification module and an underwriting decision module, wherein the insurance application rule verification module performs real-time verification on the insurance application information through a preset compliance verification rule set, and the underwriting decision module generates an underwriting suggestion based on a risk assessment model and a customer credit score.

9. An electronic device, comprising: comprises a processor and a memory; The processor executes the executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method in any one of claims 1-6.

10. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method in any one of claims 1-6.

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