Business product processing method and device based on data integration, equipment and medium

Abstract

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a business product processing method, device, equipment and medium based on data integration. Connecting a service system by using a data integration mechanism, and collecting and analyzing multi-dimensional data; and optimizing an operation strategy of the product based on an analysis result, and synchronizing the optimized strategy to a service system through a data integration mechanism. Through centralized management of key information and real-time data acquisition and analysis, the transparency and integration efficiency of information are improved, it is ensured that product design can quickly respond to market changes, product strategies are optimized, the cross-department data sharing capacity is improved, and finally efficient and flexible product adjustment is achieved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a business product processing method, device, equipment and storage medium based on data integration. Background Art

[0002] Insurance product design and management have always been a core business within the insurance industry. However, traditional insurance product design and management models suffer from numerous issues, severely impacting product flexibility and market adaptability. Currently, information such as insurance product terms, pricing rules, and coverage is often dispersed across disparate systems, resulting in a lack of transparency and difficulty in unified management and query. This fragmented information not only increases management complexity but also limits the ability to flexibly adjust insurance products and respond promptly to market changes. Furthermore, traditional design and management approaches often lack systematic management tools, making it difficult to achieve rapid and precise market adjustments. The various information components involved in the insurance product design process are difficult to effectively integrate, hindering data sharing and collaboration between different departments. This creates the so-called data silo problem, which directly impacts product design and operational efficiency.

[0003] In other fintech business areas, the design and management of financial products face similar challenges. Financial products, such as loans and investments, involve a vast amount of rules and data, often scattered across disparate systems and lacking effective integration. Existing financial product management methods often rely on traditional manual operations and lack flexible system support. This often results in delayed product adjustments when markets fluctuate or customer needs change, failing to meet the demand for rapid response to market changes. Furthermore, data silos remain prevalent, limiting collaboration and data sharing between different business departments. This leads to inefficient decision-making, hinders product innovation, and hinders the ability to quickly meet personalized customer needs.

[0004] Similar challenges exist in healthcare. The design and management of medical products or services often rely on data support from multiple departments, such as insurance companies, healthcare providers, and regulatory agencies. However, this data is often scattered across disparate systems, resulting in information discontinuity and difficulty in quickly responding to patients' personalized needs. During the product design process, a lack of real-time data feedback on patient needs and health status leads to rigid product designs, hindering timely adjustments to changing market demands and technological advances. Furthermore, healthcare services require a high level of personalization and flexibility, and traditional design models are unable to quickly adapt to these changing needs, relying instead on manual operations and information input.

[0005] Current technical solutions primarily focus on information integration and data sharing. While some systems have been integrated through data platforms, existing solutions often fail to effectively break down traditional information silos and lack flexibility, responsiveness, and cross-departmental collaboration in product design. Therefore, the industry urgently needs a more efficient and flexible information management and product design solution to improve product market adaptability, design efficiency, and customer responsiveness. Summary of the Invention

[0006] The main purpose of the present invention is to provide a business product processing method, device, equipment and storage medium based on data integration, aiming to solve the technical problems in the existing technology that product design and management information is scattered and lacks systematic management, resulting in low information transparency, slow response to market changes, difficulty in cross-departmental data sharing, and inability to efficiently and flexibly adjust products to meet needs.

[0007] To achieve the above object, the present invention provides a business product processing method based on data integration, comprising:

[0008] Build a unified detailed information layer for business products;

[0009] Connecting the unified detailed information layer with the interface of the business system by building a data integration mechanism;

[0010] Collect multi-dimensional data from the business system based on the data integration mechanism;

[0011] Analyzing the operating status of the business product through the multi-dimensional data;

[0012] The operation strategy of the business product is optimized based on the operation status, and the optimized operation strategy is synchronized to the business system through the data integration mechanism.

[0013] Furthermore, to achieve the above-mentioned purpose, the present invention provides a business product processing device based on data integration, comprising:

[0014] Information layer building module, used to build a unified detailed information layer for business products;

[0015] A data integration module, configured to connect the unified detailed information layer with the interface of the business system by building a data integration mechanism;

[0016] A data collection module, used to collect multi-dimensional data from the business system based on the data integration mechanism;

[0017] A data analysis module, configured to analyze the operating status of the business product through the multi-dimensional data;

[0018] A strategy optimization module is used to optimize the operation strategy of the business product based on the operation status, and synchronize the optimized operation strategy to the business system through the data integration mechanism.

[0019] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer device, which includes a memory, a processor, and a business product processing program based on data integration stored in the memory and executable on the processor. When the business product processing program based on data integration is executed by the processor, the steps of the business product processing method based on data integration as described above are implemented.

[0020] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a business product processing program based on data integration is stored. When the business product processing program based on data integration is executed by a processor, the steps of the business product processing method based on data integration as described above are implemented.

[0021] Beneficial effects: The present invention relates to the field of data processing technology and can be applied to business scenarios such as financial technology and medical health. It discloses a business product processing method, device, equipment and medium based on data integration, including: building a unified detailed information layer to centrally manage key product information; using a data integration mechanism to connect business systems, collect multi-dimensional data and analyze it; optimizing product operation strategies based on analysis results, and synchronizing the optimized strategies to the business system through a data integration mechanism. The present invention improves information transparency and integration efficiency by centrally managing key information and real-time data collection and analysis, ensuring that product design can quickly respond to market changes, optimizing product strategies, and improving cross-departmental data sharing capabilities, ultimately achieving efficient and flexible product adjustments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0023] Figure 1 A schematic diagram of an application environment of a business product processing method based on data integration in one embodiment of the present invention;

[0024] Figure 2 This is a flow chart of an embodiment of a method for processing business products based on data integration according to the present invention;

[0025] Figure 3 This is a functional module diagram of a preferred embodiment of a business product processing device based on data integration according to the present invention;

[0026] Figure 4 A schematic diagram of the structure of a computer device according to an embodiment of the present invention;

[0027] Figure 5 FIG. 2 is another structural diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0029] The data integration-based business product processing method provided by the embodiment of the present invention can be applied in Figure 1 in an application environment, wherein the user end communicates with the server end through the network. The server end can build a unified detailed information layer through the user end to centrally manage the key information of the product; use the data integration mechanism to connect to the business system, collect multi-dimensional data and analyze it; optimize the product's operating strategy based on the analysis results, and synchronize the optimized strategy to the business system through the data integration mechanism. The present invention improves the transparency and integration efficiency of information by centrally managing key information and real-time data collection and analysis, ensuring that product design can quickly respond to market changes, optimize product strategies, enhance cross-departmental data sharing capabilities, and ultimately achieve efficient and flexible product adjustments. Among them, the user end can be but is not limited to various personal computers, laptops, smart phones, tablet computers and portable wearable devices. The server end can be implemented with an independent server or a server cluster consisting of multiple servers. The present invention is described in detail below through specific embodiments.

[0030] See also Figure 2 , Figure 2 This is a flow chart of an embodiment of a business product processing method based on data integration provided by the present invention. It should be noted that although a logical sequence is shown in the flow chart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0031] like Figure 2 As shown, the business product processing method based on data integration proposed by the present invention includes the following steps:

[0032] S10, building a unified detailed information layer for business products;

[0033] In this embodiment, the purpose of building a unified detailed information layer for business products is to address the issue of fragmented and difficult-to-manage information by centrally managing key business product information. Key business product information typically includes product attributes, rules, and policies. Systematically organizing and storing this information not only improves data transparency but also enables rapid query and effective management. The construction of a unified detailed information layer, through standardized and structured information storage, makes data sharing and synchronization across multiple systems more efficient.

[0034] Building a unified detailed information layer first requires classifying and organizing business product information. Key business product information typically includes attribute information, rule information, and policy information. Attribute information primarily relates to basic product characteristics, such as product identifiers and coverage periods. Rule information primarily includes pricing and underwriting rules, covering aspects of product pricing and underwriting policies. Policy information includes sales channel adaptation, clause revisions, and other strategic information related to sales strategies and how to adapt to market demand.

[0035] When building a unified details layer, this information must undergo data validation to ensure the integrity and accuracy of each type of information. For example, attribute information must include product identification and coverage period fields, rule information must include pricing rule and underwriting rule fields, and policy information must include sales channel adaptation fields. The presence of these fields is a prerequisite for ensuring the effective use and sharing of information. After validation, the data must be stored in a structured data format in the unified details layer, and a corresponding version identifier must be generated for each type of information to facilitate subsequent data backtracking and historical version queries.

[0036] The key to implementing a unified detail layer is supporting data access from external systems by configuring a queryable interface service. This interface service enables external systems to access and manipulate data in the unified detail layer in a standardized manner, enabling data sharing and integration, and further promoting collaboration between systems.

[0037] In practical applications, the specific implementation of a unified detailed information layer requires different technical adaptations based on different business scenarios. In some scenarios, a distributed storage system may be required to handle large amounts of business data, while in other scenarios, a cloud platform may be needed for data storage and management to meet elastic scalability requirements. For example, in the management of financial products, key product information such as insurance terms, pricing rules, and sales strategies may change frequently, so the unified detailed information layer must be able to quickly adapt to these changes. In this case, using a cloud platform for data storage and management provides greater flexibility and scalability, enabling real-time updates and synchronization across various business systems. In the healthcare sector, the unified detailed information layer may need to integrate with hospital information systems, drug management systems, and other systems to ensure that all patient health-related data is accurately recorded and shared. In this scenario, data verification is particularly important, especially for sensitive patient information, which must be rigorously verified and protected to comply with relevant regulations.

[0038] By building a unified detailed information layer, this embodiment enables centralized management of key business product information, standardized data storage, and information transparency, eliminating the fragmented information and complex management issues inherent in traditional product design. The structured storage and standardized management of information improves query efficiency and avoids the creation of data silos between different systems. Furthermore, the unified detailed information layer supports seamless integration with external systems through data interfaces, enabling data sharing and synchronization between business systems, further improving the efficiency and flexibility of business operations.

[0039] S20, connecting the unified detailed information layer with the interface of the business system by building a data integration mechanism;

[0040] In this embodiment, a data integration mechanism is constructed to connect the unified detailed information layer with the interfaces of the business systems. The core purpose of the data integration mechanism is to ensure data flow between various business systems, breaking down the traditional data silos and enabling effective collaboration between different business systems. Traditional business systems are often isolated, each processing only its own data. This data is often difficult to share and integrate, resulting in information duplication, query difficulties, and delayed data updates. Through the data integration mechanism, data dispersed across different systems can be aggregated into a unified detailed information layer, enabling information sharing and unified management.

[0041] The specific implementation process of the data integration mechanism includes multiple steps, including defining interface protocols, configuring data transmission channels, implementing data format conversion, and supporting data synchronization. First, defining the interface protocol is the foundation for effective communication between the unified detailed information layer and various business systems. The interface protocol specifies the format and communication protocol for data exchange, as well as how to call, store, and access data. This protocol ensures that data between different systems can be exchanged according to unified rules, avoiding misoperations and data loss caused by format incompatibilities.

[0042] Secondly, implementing a data integration mechanism also requires configuring data transmission channels to ensure that data from various business systems can be efficiently transferred to a unified detailed information layer. The configuration of data transmission channels must consider the real-time, reliability, and security of data transmission. Appropriate technologies (such as APIs and message queues) are typically used to ensure data fluidity and consistency.

[0043] When it comes to data format conversion, data integration mechanisms need to perform appropriate format conversion based on the data structures and requirements of different business systems. Different systems may use different data storage and transmission formats. The conversion capabilities of data integration mechanisms can transform this data into a unified format, facilitating subsequent operations and management.

[0044] Finally, data integration mechanisms must also support data synchronization. This includes not only regular data updates but also immediate synchronization when data changes occur, ensuring data consistency across all relevant business systems. This mechanism can significantly reduce decision-making errors caused by data asynchrony and improve the efficiency of business system collaboration.

[0045] The specific implementation methods of data integration mechanisms can vary across different implementation scenarios. For example, in a cloud computing environment, data integration can be achieved through cloud-based APIs and services, enabling rapid data access and system integration. In this scenario, data transmission channels utilize the secure transmission mechanisms provided by cloud services to ensure data privacy and security.

[0046] In traditional enterprise applications, data integration mechanisms may employ an internal ESB (Enterprise Service Bus) architecture, leveraging the ESB's message routing and data conversion capabilities to ensure data flow and conversion between systems. The ESB architecture can handle a variety of data sources and formats, offering strong scalability and flexibility, making it suitable for integrating large-scale enterprise systems.

[0047] Furthermore, in demanding scenarios like finance and healthcare, data integration mechanisms must support real-time data transmission and high-frequency updates. To ensure the timeliness and accuracy of data, message queue technologies like Kafka and RabbitMQ can be used to achieve efficient and reliable real-time data transmission.

[0048] This embodiment effectively resolves the problem of information silos by building a data integration mechanism and connecting a unified detailed information layer with the interfaces of the business system, ensuring data sharing and collaboration between business systems. The data integration mechanism provides a unified data format and standardized communication protocols, enabling seamless integration and interoperability of data from different systems. Furthermore, real-time data synchronization and efficient data transmission channels improve system response speed and data accuracy, helping to enhance the efficiency of overall business processes and supporting flexible adjustments and rapid responses to business systems.

[0049] S30, collecting multi-dimensional data from the business system based on the data integration mechanism;

[0050] In this embodiment, a data integration mechanism breaks down the data barriers between traditional business systems, enabling the integration and flow of data across different systems. This provides data support for subsequent data analysis, decision-making optimization, and business adjustments. Multi-dimensional data collection involves collecting various types of data from multiple business systems. Each type of data reflects different aspects of business product performance, including market dynamics, customer behavior, and operational status, ensuring comprehensive and accurate analysis of product operational status.

[0051] Data integration mechanisms serve as a bridge in this process, ensuring that data from diverse business systems flows into a unified, detailed information layer. Generally speaking, data integration mechanisms encompass not only the technical means of data collection but also functions such as data transmission, format conversion, and synchronization updates. Their core goal is to ensure that data collected from diverse business systems is promptly and accurately transferred to a unified, detailed information layer, and that all data is processed and stored in a unified format.

[0052] Specifically, the implementation of data integration relies on a variety of technical means. First, configuring "interface protocols" ensures data from different systems is compatible and can be correctly extracted. Second, event monitoring services help capture data changes in real time, such as product attribute changes and user feedback, ensuring the timeliness and accuracy of collected data. The data collection module converts various multi-dimensional data into a unified format for storage and management. Ultimately, this data provides the raw material for subsequent data analysis, generating key insights such as market response, customer demand, and risk distribution.

[0053] In actual application scenarios, data integration mechanisms can be adapted to meet the needs of different industries and systems. For example, in the financial industry, data integration mechanisms can collect data from various transaction systems, insurance claims systems, and customer management systems. This data may include customer transaction behavior, claims records, and account information. To ensure the real-time and accuracy of data, the data collection process may utilize real-time data streaming processing technologies, such as using message queues (such as Kafka) to transmit data streams, ensuring that data is updated instantly when changes occur.

[0054] In the healthcare industry, data integration mechanisms require collecting data from multiple systems, including electronic medical records, drug management systems, and insurance claims systems. This data may include patient medical records, drug inventory, and insurance policy terms. In this scenario, data collection and integration may require the use of the FHIR (Fast Healthcare Interoperability Resources) standard to ensure interoperability between different systems. Through standardized interface protocols, data can be unified into a detailed information layer, making it easier for healthcare providers, insurance companies, and others to share and use it.

[0055] In the e-commerce sector, data integration mechanisms can integrate multi-dimensional data from inventory management systems, order processing systems, and distribution management systems. For example, order data, logistics and delivery information, and inventory levels need to be updated and integrated in real time. By building standardized data interfaces and integrating data transmission mechanisms, all real-time data can be collected and managed uniformly, ensuring the real-time and accuracy of inventory management and order processing.

[0056] This embodiment collects multi-dimensional data from multiple business systems through a data integration mechanism, which can break the silos of traditional business systems and ensure that all types of data can flow and share in real time and accurately. The multi-dimensional collection of data provides comprehensive data support for subsequent data analysis, strategy optimization, and decision-making. At the same time, the standardized processing and integration of data can ensure the uniformity and consistency of data, providing a foundation for collaboration and information exchange between systems. Through real-time data collection and flow, business responsiveness and the real-time nature of decision-making can be significantly improved, thereby improving overall business operation efficiency.

[0057] S40, analyzing the operating status of the business product through the multi-dimensional data;

[0058] In this embodiment, comprehensive analysis of multi-dimensional data allows for in-depth exploration and understanding of the overall operational performance of business products, providing robust data support for optimizing product strategies, improving operational efficiency, and meeting customer needs. Multi-dimensional data, collected from multiple business systems, encompasses information on product attributes, market dynamics, customer behavior, and risk management. Analysis of this data is fundamental to optimizing the operational status of business products.

[0059] The core of data analysis lies in processing and interpreting multi-dimensional data using specific analytical models. Data from different dimensions corresponds to different aspects of business product performance. For example, the market response efficiency model assesses a product's adaptability in the market, the customer demand priority model identifies customer priorities, and the risk distribution characteristic model helps analyze potential product risks. These models work together to comprehensively assess product performance from multiple perspectives and provide a reliable basis for subsequent product optimization.

[0060] In practice, multi-dimensional data preprocessing is first required, converting data from various sources into a unified format suitable for analysis. This includes data cleaning, standardization, and the removal of abnormal data. Next, appropriate analytical methods and algorithms are used for data analysis. These methods may include regression analysis, classification models, and time series analysis, depending on the type of data and the analysis objectives. Finally, the analysis results are converted into easy-to-understand metrics or labels to form a comprehensive report on the operational status of the business product.

[0061] During implementation, the specific implementation methods of data analysis can be flexibly adjusted based on different scenarios and needs. For example, in the financial industry, a market response efficiency model might focus on analyzing fluctuations in market demand for financial products (such as loans and insurance), while a customer demand priority model might focus on evolving customer demand for different financial services. In this scenario, machine learning algorithms can be used to analyze customer transaction behavior data, identify potential customer demand for certain products, and predict future demand trends.

[0062] In the healthcare sector, analytical models may focus more on the connection between a patient's health data and medical services. For example, a customer demand prioritization model can identify a patient's need for specific medical services based on their medical and treatment histories. A market response efficiency model assesses market acceptance and demand fluctuations for medical products (such as insurance services or medical devices). Data analysis can be combined with patient medical data, payment records, and other information to provide more precise services and optimization solutions.

[0063] In the e-commerce sector, data analysis models can assess product market performance and popularity based on user purchase history, browsing history, and feedback data. Market response efficiency models can analyze the sales of different products over different time periods, while customer demand priority models can predict future demand for products based on user feedback and purchasing behavior. Risk distribution feature models are used to identify potential product inventory issues, logistics problems, or product returns, ensuring smooth product flow in the market.

[0064] This embodiment, through the analysis of multi-dimensional data, can comprehensively and accurately assess the operational status of business products. This not only improves the transparency of product design and management, but also provides a scientific basis for dynamic product adjustments. Through the comprehensive utilization of multi-dimensional data, potential problems can be promptly identified, product strategies can be optimized, and the accuracy and real-time nature of business decisions can be improved. Furthermore, data analysis can help companies maintain their competitive advantage in an ever-changing market, ensuring that products can quickly respond to customer needs and market changes, thereby improving overall operational efficiency and customer satisfaction.

[0065] S50: Optimize the operation strategy of the business product based on the operation status, and synchronize the optimized operation strategy to the business system through the data integration mechanism.

[0066] In this example, product performance issues or improvement opportunities are identified through analysis of product operational status. Based on these analysis results, the operational strategy for the business product is adjusted to improve market performance and operational efficiency. Ultimately, the optimized strategy is synchronized with the business system through data integration mechanisms to ensure that all relevant systems operate according to the latest strategy.

[0067] First, operational status assessment results (including market responsiveness, customer demand prioritization, and risk distribution) provide data support for optimizing operational strategies. Specifically, these indicators help business managers identify the reasons for poor product performance in the market, shifts in customer demand, and potential risks. This information is typically provided by data analysis models, which may be generated through multiple steps, such as market trend analysis, customer feedback processing, and risk assessment.

[0068] Second, based on these analysis results, existing operational strategies need to be optimized. This optimization strategy can involve multiple aspects, such as adjusting product pricing, modifying terms and conditions, improving service processes, or enhancing user experience. Strategy optimization methods may include redefining product pricing rules, adjusting sales strategies, or revising product market positioning. This process can be assisted by automated tools, which, based on data analysis results, generate specific optimization measures and formulate strategic adjustment instructions.

[0069] Finally, optimized policies need to be synchronized with business systems through data integration mechanisms to ensure that all relevant departments (such as product management, sales, customer service, and risk management) can execute operations based on the latest policies. Data integration mechanisms push these optimized policies to various systems through interface protocols, ensuring real-time policy updates and unified execution, preventing issues caused by inconsistent policies across different systems.

[0070] During implementation, data integration mechanisms must support real-time data exchange and policy synchronization. For example, in the financial industry, banks can adjust loan product interest rate strategies by analyzing market responsiveness and shifting customer priorities. If demand for a particular loan product grows, banks might lower its interest rate to attract more customers. Conversely, when certain products present high risk, they might raise interest rates or tighten approval requirements. This optimization process is updated in real time via data integration mechanisms to loan approval, risk management, and customer service systems, ensuring that all relevant departments are operating in accordance with the latest policies.

[0071] In healthcare, providers can optimize care plans based on patient feedback and treatment outcomes. For example, if a medication isn't as effective as expected, they might need to adjust the regimen or recommend an alternative treatment. Data integration mechanisms ensure that these optimized treatment plans are instantly synchronized with the hospital's electronic medical record system, medication management system, and patient management system, ensuring that all providers are providing services based on the latest strategies.

[0072] In the e-commerce sector, after analyzing customer purchasing behavior and market demand, e-commerce platforms may decide to adjust prices or optimize promotional strategies for certain products. These policy adjustments are synchronized in real time to inventory management systems, sales systems, and customer service systems through data integration mechanisms, ensuring consistency in inventory adjustments, promotional activities, and customer communication strategies.

[0073] This embodiment optimizes the operational strategies of business products based on operational status analysis and synchronizes these strategies to business systems through a data integration mechanism, effectively improving business flexibility and responsiveness. All relevant business systems receive the latest policy adjustments in real time, avoiding operational deviations caused by information lags or inconsistencies between different systems. This real-time policy update and unified execution not only improves business operational efficiency but also enables rapid strategy adjustments based on market changes and customer needs, thereby strengthening the company's advantage in the face of fierce competition.

[0074] The present invention relates to the field of data processing technology and can be applied to business scenarios such as financial technology and medical health. It discloses a business product processing method, device, equipment and medium based on data integration, including: building a unified detailed information layer to centrally manage key product information; using a data integration mechanism to connect business systems, collect multi-dimensional data and analyze it; optimizing product operation strategies based on analysis results, and synchronizing the optimized strategies to the business system through a data integration mechanism. By centrally managing key information and real-time data collection and analysis, the present invention improves information transparency and integration efficiency, ensures that product design can quickly respond to market changes, optimizes product strategies, and enhances cross-departmental data sharing capabilities, ultimately achieving efficient and flexible product adjustments.

[0075] In one embodiment, the above step S10 includes:

[0076] S101, classify the key information of the business product into attribute information, rule information and policy information;

[0077] S102, performing data verification on the attribute information, rule information, and policy information;

[0078] S103, storing the verified attribute information, rule information, and policy information in a structured data format in a unified detailed information layer, and generating a data version identifier corresponding to each type of information;

[0079] S104: configuring a queryable interface service in the unified detailed information layer that can trace back historical version information through the data version identifier.

[0080] In this embodiment, the construction of the unified detailed information layer integrates key information of business products to achieve centralized storage, standardized management and version control of information, thereby improving the efficiency and flexibility of product management.

[0081] First, the unified detailed information layer manages key product information through classification. This key information can be divided into attribute information, rule information, and policy information. Attribute information typically includes basic product characteristics, such as product identification fields (e.g., product number, name, etc.) and coverage period fields (e.g., coverage period, policy start and end times, etc.). Rule information includes terms and underwriting rules related to product pricing, which specify the basis and conditions for product pricing. Policy information includes sales channel adaptation fields to ensure that the product's sales channels and marketing strategies match market demand and customer behavior.

[0082] When building a unified detail layer, data validation is essential. This process ensures the integrity and compliance of each piece of information. For example, it verifies that attribute information includes the correct product identification and coverage period fields, that rule information includes the necessary pricing and underwriting fields, and that policy information includes sales channel adaptation fields. This validation step prevents missing or incorrectly formatted data from impacting subsequent operations or decisions.

[0083] Verified information is converted into a structured data format and stored in a unified detailed information layer. This structured data format ensures data uniformity and readability, facilitating sharing and querying across different systems and modules. Furthermore, a corresponding data version identifier is generated for each type of information to facilitate tracking of historical data changes. Version identifiers support historical retrieval, ensuring that product information at a specific moment can be retrieved when needed.

[0084] Finally, a queryable interface service is configured in the unified detail layer, allowing other business systems to trace back historical version information based on data version identifiers. Through this interface service, relevant personnel or systems can access historical versions of the product at any time, ensuring data transparency and traceability.

[0085] During implementation, data classification, verification, and storage can be managed using a relational or distributed database. Attribute information, such as product identification and coverage period fields, can be stored using a standardized database table structure. For example, a "Product Information" table could be created containing fields such as product number, product name, coverage start date, and coverage end date, with strict data type validation.

[0086] Rule information, such as pricing and underwriting rule fields, can be dynamically managed through the rule engine. The rule engine performs calculations and judgments based on pre-set rules, supporting flexible pricing and underwriting strategies. Furthermore, the rule engine can validate rule data to ensure consistency and accuracy.

[0087] Strategic information, such as sales channel adaptation fields, can be dynamically configured and managed through a multi-channel management system. For example, product sales strategies can be dynamically adjusted based on different customer groups, sales models, and market demand. Through interface services, sales channel adaptation information can be shared with other business systems (such as sales management systems and customer service systems), ensuring real-time updates and unified execution of sales strategies.

[0088] This embodiment significantly improves the transparency, flexibility, and efficiency of business product design and management by building a unified detailed information layer and classifying, verifying, storing, and versioning key information. Standardized information storage and management ensures data consistency and real-time performance across different departments and business systems, avoiding information silos. Furthermore, the configuration of data version control and query interface services facilitates the retrieval of historical versions, ensuring the traceability of product information. These optimization measures not only improve the efficiency of product management but also enable faster response to market changes and customer needs.

[0089] In one embodiment, the above step S20 includes:

[0090] S201, defining an interface protocol to standardize the data interaction format between the unified detailed information layer and multiple business systems;

[0091] S202, configuring an event monitoring service, and capturing data change requests from the multiple business systems through the event monitoring service;

[0092] S203, based on the interface protocol, converting the data change request into a standardized instruction recognizable by the unified detailed information layer;

[0093] S204: Synchronize the standardized instructions to the unified detailed information layer through the data synchronization module, and update the business product data stored in the unified detailed information layer according to the standardized instructions.

[0094] In this embodiment, by establishing a standardized data interaction interface and a real-time event monitoring mechanism, efficient data sharing and collaboration between different business systems are achieved. This process ensures data consistency between the unified detailed information layer and other business systems, avoids data silos, and improves the flexibility and accuracy of data processing.

[0095] First, the data integration mechanism defines an interface protocol to standardize the data exchange format between the unified detail layer and multiple business systems. The definition of the interface protocol is crucial, ensuring that different business systems can communicate using the same data format and protocol. The interface protocol typically includes data format specifications, field mapping rules, transmission methods (such as RESTful APIs and SOAP), and error handling mechanisms. Through this interface protocol, the unified detail layer can effectively exchange data with other systems (such as sales management systems, inventory management systems, and customer service systems).

[0096] Secondly, configuring an event listening service is key to achieving real-time data synchronization. This service monitors business system events (such as data changes and status updates) and captures corresponding data change requests, enabling timely data updates. Specifically, the event listening service can be deployed within the business system to monitor specific event sources (such as database tables, files, and message queues). When data changes, it automatically triggers corresponding actions, ensuring that the unified detail layer receives updated data in a timely manner.

[0097] After receiving a data change request, the data change request is converted into standardized instructions that can be recognized by the unified detailed information layer based on the interface protocol. The key to this process lies in the conversion and standardization of data formats. Data change requests may come from different systems or business scenarios, and their data formats and structures may vary. Therefore, after receiving a data change request, it must be processed according to the pre-defined interface protocol and converted into standardized instructions that can be understood and processed by the unified detailed information layer. These standardized instructions include specific operation types (such as add, modify, delete) and related field data, thereby ensuring seamless connection and efficient exchange of data between different systems.

[0098] Finally, the data synchronization module synchronizes standardized instructions to the unified detailed information layer and updates the business product data stored there based on the standardized instructions. The data synchronization module is responsible for accurately transmitting standardized instructions converted via the interface protocol to the unified detailed information layer and ensuring their correct execution within the unified detailed information layer. Specifically, the data synchronization module adds, deletes, and modifies data in the unified detailed information layer based on the contents of the standardized instructions, ensuring consistent data status across all business systems.

[0099] In actual implementation, standard protocols such as RESTful API or SOAP can be used to define interface protocols to ensure efficient and stable data transmission between different systems. For example, in the financial industry, a bank's loan product management system can share loan product data (such as loan interest rates, approval conditions, etc.) with other systems (such as customer information management systems and risk assessment systems) through interface protocols. When the interest rate of a loan product changes, the event monitoring service can capture this change and transmit the changed data to a unified detailed information layer, which can then update the loan interest rate data in other related systems through the data synchronization module.

[0100] In the healthcare sector, a hospital's information management system may involve multiple business systems, including patient information, medical products, and drug management. Through data integration mechanisms, hospitals can store all of this information in a unified detailed information layer and exchange data with other systems through interface protocols. For example, when a hospital's drug inventory changes, the event monitoring service can capture inventory change data in real time and synchronize this data with other related systems (such as the drug supply chain management system and patient prescription system), ensuring data synchronization and consistency across all systems.

[0101] In the e-commerce world, product inventory, pricing, promotional information, and other information must be shared across multiple systems (e.g., inventory management systems, sales systems, and customer service systems). By defining standardized interface protocols and configuring event monitoring services, e-commerce platforms can capture real-time changes in information such as product prices and inventory quantities. These changes are then synchronized with other related systems through data synchronization modules, ensuring that sales and inventory management systems remain consistent.

[0102] This embodiment effectively resolves the data silo problem between different business systems by establishing a data integration mechanism. Real-time data synchronization ensures data consistency across all relevant systems. This not only improves the efficiency of data sharing but also significantly enhances the system's flexibility and responsiveness, enabling business systems to more quickly respond to market changes and customer needs. By using a unified detailed information layer and standardized interface protocols, collaboration between different business systems becomes more efficient and reliable, helping to improve overall operational efficiency.

[0103] In one embodiment, the above step S30 includes:

[0104] S301, obtaining attribute change records of the business product from the product management business system through the event monitoring service in the data integration mechanism;

[0105] S302, collecting market trend data and channel sales indicators from the product sales business system through the interface protocol in the data integration mechanism;

[0106] S303, monitoring user feedback data and service request logs of the customer service system through the event monitoring service;

[0107] S304, subscribing to the service response rate data and product termination rate data stream of the service processing system through the interface protocol;

[0108] S305 , integrating the attribute change records, market trend data, channel sales indicators, user feedback data, service request logs, service response rate data, and product termination rate data streams into the multi-dimensional data.

[0109] In this embodiment, multiple types of data are acquired from different business systems to support comprehensive business product management. This process utilizes data integration mechanisms to establish data sharing and real-time update channels between multiple business systems, thereby providing real-time and accurate information support for subsequent data analysis, decision optimization, and strategy adjustment.

[0110] First, the attribute change records of the business products are obtained from the product management business system through the event monitoring service in the data integration mechanism. The event monitoring service is a key component for achieving real-time data capture. It can monitor and capture attribute changes (such as product specifications, terms and conditions changes, etc.) from the product management business system. When the attributes of a product change, the event monitoring service will automatically trigger and record these change data. This process ensures the timely update of product information and avoids business decision-making errors caused by data lag. In specific implementation, the event monitoring service can be deployed in the product management system and use technologies such as database triggers or message queues to monitor data changes in real time.

[0111] Secondly, market trend data and channel sales metrics are collected from the product sales business system through the interface protocol within the data integration mechanism. This interface protocol ensures standardized data transmission and efficient interaction between different business systems. Product sales business systems typically contain extensive market and sales data, which is crucial for analyzing product market performance and adjusting sales strategies. Through standardized interface protocols, sales data can be seamlessly transferred to the data integration mechanism, providing a foundation for subsequent analysis. For example, market trend data includes market demand changes and consumer preference analysis, while channel sales metrics include sales revenue and sales volume information for each sales channel.

[0112] Next, the event monitoring service monitors user feedback data and service request logs from the customer service system. Data in the customer service system, including user feedback and service request logs, reflects customer satisfaction with products or services, as well as potential complaints. The event monitoring service captures this feedback data in real time and transmits it to the data integration mechanism, providing timely information for product optimization and strategy adjustments. For example, customer feedback data includes product quality issues and service response speed, while service request logs reflect the problems and needs encountered by customers during product use.

[0113] Then, the service processing system's service response rate and product termination rate data streams are subscribed to through the interface protocol. Data from the service processing system, such as the service response rate and product termination rate data streams, are important indicators for assessing the operational effectiveness of business products and customer retention. Service response rate data reflects the efficiency of processing service requests, while product termination rate data streams reflect customer terminations or cancellations of product subscriptions. Subscribing to these data streams through the interface protocol allows for real-time access and analysis of business product performance. For example, through technologies such as message queues, product termination rate data can be transmitted instantly, providing a basis for subsequent risk assessment and optimization.

[0114] Finally, the attribute change records, market trend data, channel sales indicators, user feedback data, service request logs, service response rate data and product termination rate data streams are integrated into the multi-dimensional data. All collected data needs to be integrated through a data integration mechanism to aggregate data from different business systems into multi-dimensional data in a unified format. The integrated multi-dimensional data can provide rich information support for the operation status analysis, strategy optimization and decision support of business products. These data can be structured (such as numbers, text) or unstructured (such as images, audio). Through data processing and standardization, they can provide a reliable data foundation for further data analysis and model training.

[0115] This embodiment, through a data integration mechanism, can efficiently collect and integrate multi-dimensional data from multiple business systems, avoiding information silos and ensuring data consistency across systems. This not only improves information transparency but also accelerates the decision-making process. Real-time data collection and processing enables business products to flexibly respond to market demand and customer feedback, further optimizing product design, sales strategies, and customer service. At the same time, cross-system data sharing improves collaboration efficiency among business departments, reduces duplicate data processing and manual intervention, and promotes the development of business automation and intelligence.

[0116] In one embodiment, the above step S40 includes:

[0117] S401, inputting the multi-dimensional data into a market response efficiency model, and generating a market response efficiency index for the business product through the market response efficiency model;

[0118] S402, inputting the multi-dimensional data into a customer demand priority model, and generating a customer demand priority label for the business product through the customer demand priority model;

[0119] S403: Input the multi-dimensional data into a risk distribution characteristic model, and generate a risk distribution characteristic value of the business product through the risk distribution characteristic model;

[0120] S404: Generate an operating status report of the business product according to the market response efficiency index, the customer demand priority label, and the risk distribution characteristic value.

[0121] In this example, data from multiple business systems is integrated and analyzed using data analysis models to assess product market performance, customer demand, and risk profiles, providing data support for subsequent strategy optimization. This process relies on multiple analytical models, the output of which directly influences the operational strategy adjustments and optimization of business products.

[0122] First, the multi-dimensional data is input into a market response efficiency model, which is then used to generate a market response efficiency index for the business product. The purpose of the market response efficiency model is to assess a product's performance in the market, specifically how quickly and effectively the product adapts to market changes. By inputting multi-dimensional data, such as market trends and channel sales data, the model generates a "market response efficiency index," which typically reflects factors such as market demand satisfaction and product sales growth rate. Through the market response efficiency model, companies can accurately understand whether their products meet current market demand and adjust product design or marketing strategies based on the analysis results.

[0123] Next, the multi-dimensional data is input into a customer needs prioritization model, which generates customer needs priority labels for the business products. The customer needs prioritization model utilizes inputs such as user feedback, market research, and customer behavior data to assess the importance and urgency of different customer needs and generate customer needs priority labels. These labels help companies identify the most critical customer needs, prioritize changes that will have the greatest impact on products, and ensure that product designs and services are responsive to customer needs, thereby improving customer satisfaction and market competitiveness.

[0124] This multi-dimensional data is then input into a risk distribution characteristic model, which generates a risk distribution characteristic value for the business product. The primary task of the risk distribution characteristic model is to assess potential risks during product operations. These risks may stem from factors such as product market performance, customer feedback, and changes in industry regulations. By inputting multi-dimensional data, the model generates characteristic values that reflect risk levels, such as market risk, legal risk, and operational risk. Enterprises can use these characteristic values to assess the risk profile of their products and, when appropriate, adjust relevant management or pricing strategies to mitigate potential business risks.

[0125] Finally, a business product operational status report is generated based on the market response efficiency indicator, customer demand priority tags, and risk distribution characteristic values. This operational status report is a synthesis of all the above analysis results, combining market response efficiency, customer demand priority, and risk distribution characteristics to comprehensively reflect the operational status of the business product. This report provides product managers with clear data support, enabling more accurate and timely product optimization, adjustments, and risk response.

[0126] Through multi-dimensional data analysis, this embodiment enables enterprises to obtain accurate assessments of product operational status and adjust and optimize product strategies based on market responsiveness, customer demand priorities, and risk profiles. This data-driven dynamic management model not only improves a company's responsiveness and market adaptability, but also significantly enhances the precision of product design and sales strategies. Ultimately, enterprises can improve their market competitiveness, reduce product operational risks, and optimize customer experience and satisfaction.

[0127] In one embodiment, the above step S50 includes:

[0128] S501, when the market response efficiency index in the operating state is lower than a preset efficiency threshold or the risk distribution characteristic value exceeds a preset risk threshold, generating a strategy adjustment instruction;

[0129] S502: extracting a corresponding identification value configuration template or clause revision template from a preset policy library according to the policy adjustment instruction;

[0130] S503, performing a difference comparison between the identification value configuration template or the clause revision template and the current policy data stored in the unified detailed information layer, and marking the difference content fields;

[0131] S504, generating a policy adjustment plan based on the difference content field;

[0132] S505, updating the policy adjustment plan to the unified detailed information layer;

[0133] S506: Synchronize the policy adjustment plan to the corresponding business system through the interface protocol in the data integration mechanism.

[0134] In this embodiment, by analyzing the real-time operational status of business products, timely adjustments are made to different market environments and risk levels. This step relies on the collaborative work of multiple sub-steps, including triggering policy adjustments, extracting adjustment templates, comparing differences, and generating adjustment plans.

[0135] First, when the market response efficiency index in the operating state is lower than the preset efficiency threshold or the risk distribution characteristic value exceeds the preset risk threshold, a strategy adjustment instruction is generated. The core of this step lies in two key indicators in the operating state: market response efficiency and risk distribution characteristic value. The market response efficiency index measures the response speed of the product to changes in market demand, while the risk distribution characteristic value measures the potential risk of the product in different environments. If these indicators reach or exceed the preset threshold (such as slow market response or increased risk level), the strategy adjustment instruction is triggered. These thresholds are pre-set by business strategies and market demand changes, and will be continuously updated as the external environment changes.

[0136] Then, based on the policy adjustment instruction, the corresponding identification value configuration template or clause revision template is extracted from the preset policy library. When the policy adjustment is triggered, the system extracts the appropriate adjustment template from the preset policy library. These templates may include identification value configuration templates (such as adjustment templates for prices, points, quotas, etc.) or clause revision templates (such as modification templates for sales terms and service terms). The template is a standardized definition of policy adjustment that can ensure the consistency and standardization of content during the adjustment process.

[0137] The identification value configuration template or clause revision template is then compared with the current policy data stored in the unified detailed information layer, and the difference content fields are marked. In this step, the system compares the template extracted from the policy library with the current policy data stored in the unified detailed information layer. The purpose of this comparison is to identify differences between the current policy and the template, including differences in price, terms, sales strategy, and other aspects. The difference content fields are marked for subsequent adjustment operations. This process ensures that adjustments are only made to the parts that need to be updated, avoiding unnecessary modifications.

[0138] Next, a policy adjustment plan is generated based on the difference content fields. Based on the comparison results, the system generates a specific policy adjustment plan, specifying the required changes, such as price fluctuations, modified terms, and changes to service content. This plan, a processing of the comparison results, details each adjustment, including the adjustment amount, modified parts, and effective date. The generated policy adjustment plan is a standardized output that can be directly used in subsequent update operations.

[0139] Next, the policy adjustment plan is updated to the unified detailed information layer. After generating the policy adjustment plan, the system stores it in the unified detailed information layer. This layer contains the core information for all business products, including policies, rules, and terms. Therefore, updated policy adjustment plans directly affect the information in this layer. This step ensures that all business systems and departments use the latest policies and consistent information.

[0140] Finally, the policy adjustment plan is synchronized with the corresponding business system through the interface protocol in the data integration mechanism. After the policy adjustment plan is updated, the system synchronizes the updated plan with various relevant business systems (such as sales, claims, and product management systems) through the interface protocol in the data integration mechanism. This synchronization ensures data consistency between business systems and ensures that each system can execute operations such as pricing, sales, and underwriting based on the latest policy.

[0141] This embodiment, through real-time analysis and optimization of operational status, enables business products to flexibly respond to market changes and risk fluctuations, ensuring that product strategies consistently align with market demands and the business environment. This process not only improves the company's responsiveness and market adaptability, but also provides data support and decision-making basis for the dynamic management of business products, effectively enhancing product market competitiveness and customer satisfaction.

[0142] In one embodiment, after the above step S504, the method further includes:

[0143] S5041, assigning a role tag to the operating subject through the user management system;

[0144] S5042, verifying the operating authority of the operating subject for the policy adjustment scheme, executing the update operation if the verification passes, and terminating the operation and recording an exception log if the verification fails;

[0145] S5043, when the policy adjustment plan is synchronized to the corresponding business system through the data integration mechanism, the operation time, operation subject identifier and synchronization content of the policy adjustment plan are recorded in the operation log module;

[0146] S5044: Back up the log data through the operation log module, and generate a log summary report based on the backed up log data.

[0147] In this embodiment, when adjusting the business product policy, the legality and compliance of the operation are ensured, and complete audit and logging are provided. The entire process involves multiple aspects such as operation permission verification, logging, and backup, ensuring the transparency and traceability of the adjustment process. The specific implementation process is as follows:

[0148] The operating subject is assigned a role label through the user management system, which is accessed through the queryable interface service of the unified detailed information layer. The role label corresponds to different data access permission levels, and the operating subject includes a user account or an automated service program. In this link, first, the user management system assigns a role label based on the identity of the operating subject (such as a manual operation or an automated service program). Each role label corresponds to a different level of data access permission, which means that different operating subjects can access data at different levels. For example, some administrators can access all business product data, while ordinary employees can only access product information or certain specific terms for which they are responsible. This process is completed through the queryable interface service in the unified detailed information layer to ensure information sharing and permission management.

[0149] Verify the operating entity's authority to operate the policy adjustment plan. If the verification passes, perform the update operation. If the verification fails, terminate the operation and record an exception log. This step ensures that only authorized operating entities can perform policy adjustment operations. After receiving an update request, the user management system will first verify the operating entity's role label to ensure that it has the corresponding authority. For example, ordinary employees may not be able to directly modify important pricing rules or terms; only operating entities with administrator authority can perform such operations. If the authority verification fails, the system will terminate the operation and record an exception log for future review. This mechanism helps prevent unauthorized operations and erroneous data modifications, ensuring the security and compliance of operations.

[0150] When the policy adjustment plan is synchronized to the corresponding business system through the data integration mechanism, the operation time, operation subject identification and synchronization content of the policy adjustment plan are recorded in the operation log module. After the policy adjustment plan is completed, the system will synchronize it to each relevant business system (such as the sales system, product management system, etc.). Every operation in the synchronization process will be recorded, including the operation time, operation subject identification (that is, the user or service program that performs the operation), and the synchronization content (such as adjusted terms, pricing, etc.). This information will be written to the operation log module for subsequent audits and inspections to ensure that the operation is traceable.

[0151] The operation log module backs up log data and generates log summary reports based on the backed-up log data. To ensure data security and integrity, all operation logs are backed up regularly. Backed-up log data can not only be used for auditing purposes, but summary reports can also be generated through the operation log module, allowing managers to quickly understand key operations and events occurring in the system. These reports help identify potential issues, ensure business process compliance, and prevent data loss or improper operations due to human error or system failures.

[0152] In actual implementation, the permission management of operating entities can be achieved through a role-based access control (RBAC) model. Administrators can assign different permission levels to different operating entities through the user management system to ensure that only legally authorized personnel or systems can adjust product policies. The user management system is connected to the interface service of the unified detailed information layer through an API, allowing various business systems to share and process the latest policy adjustment information. During the data verification and permission verification process, multi-factor authentication (MFA) can be used to improve security and ensure that only strictly verified operating entities can perform sensitive policy adjustment operations. In terms of logging, a cloud-based data storage system is used to ensure the high availability and security of log data. A detailed log entry will be generated for each operation, including information such as the operating entity, operation time, and operation content, and the log will be backed up according to a predetermined period.

[0153] Example: In the financial sector, for example, traditional credit card product designs often suffer from fragmented information and rigid terms, making them difficult to flexibly respond to market changes and customer needs. By building a unified detailed product layer, all key information (such as product attributes, pricing rules, and sales strategies) is stored in the system. This system seamlessly integrates with the bank's various business systems (such as risk assessment, customer service, and sales) through data integration mechanisms, enabling information sharing and real-time synchronization. This allows banks to access real-time market trend data, customer feedback, and service request data. Using data analysis modules to generate reports on market response efficiency, customer demand priorities, and risk distribution characteristics, banks can quickly identify deficiencies in product design and implement timely policy adjustments. For example, if the market response efficiency of a particular credit card falls below a preset threshold, the bank's policy adjustment mechanism automatically generates adjustment instructions, extracts the corresponding adjustment template from a pre-set policy library, identifies any discrepancies (such as adjustments to points reward policies or credit limits), and synchronizes the updated terms with the bank's business systems through data integration mechanisms, ensuring that all relevant departments are using the latest credit card terms. Through this approach, banks can respond to market changes and customer needs more quickly and flexibly, while ensuring data transparency, compliance and traceability.

[0154] In the healthcare sector, particularly in the management of health insurance products, product terms and coverage require dynamic adjustments based on evolving medical needs and customer feedback. Taking health insurance products as an example, insurance companies build a unified detailed information layer to centrally manage various product data (such as coverage, underwriting rules, and pricing). This layer integrates data with internal business systems (such as underwriting, claims, and customer service) through data integration mechanisms. Whenever new customer needs arise or market changes occur, insurance companies can analyze product performance using real-time data collected (such as customer feedback, claims rates, and market trends). For example, if the claims rate for a health insurance product exceeds a preset risk threshold, the product's dynamic adjustment mechanism automatically generates policy adjustment instructions. It extracts relevant adjustment templates (such as modifying policy terms or adding exemptions) from a pre-set policy library, compares them with the current product policy data, and generates a policy adjustment plan. This plan is synchronized with the insurance company's relevant business systems, and every step of the operation is recorded in an operation log, ensuring traceability and transparency of the product change process. Through this approach, insurance companies can respond promptly to changes in market and customer demand, optimize product structure, and enhance market competitiveness while ensuring compliance and data security.

[0155] This embodiment, through precise permission control, operation logging, and backup mechanisms, provides a more secure, transparent, and traceable operating environment for the dynamic management of business products. Through permission verification and exception logging, unauthorized operations and potential business risks are avoided. Furthermore, detailed operation logs and regular backup mechanisms ensure the traceability and compliance of all adjustment processes, improving the flexibility of product strategy adjustments and market responsiveness.

[0156] In one embodiment, a business product processing device based on data integration is provided, and the business product processing device based on data integration corresponds one-to-one to the business product processing method based on data integration in the above embodiment. Figure 3 , Figure 3 This is a functional module diagram of a preferred embodiment of a data integration-based business product processing device of the present invention. It includes an information layer construction module 10, a data integration module 20, a data acquisition module 30, a data analysis module 40, and a strategy optimization module 50. Each functional module is described in detail below:

[0157] An information layer construction module 10 is used to construct a unified detailed information layer for business products;

[0158] A data integration module 20 is used to connect the unified detailed information layer with the interface of the business system by building a data integration mechanism;

[0159] A data collection module 30 is used to collect multi-dimensional data from the business system based on the data integration mechanism;

[0160] A data analysis module 40 is configured to analyze the operating status of the business product using the multi-dimensional data;

[0161] The strategy optimization module 50 is used to optimize the operation strategy of the business product based on the operation status, and synchronize the optimized operation strategy to the business system through the data integration mechanism.

[0162] In one embodiment, the information layer construction module 10 is specifically configured to:

[0163] Classify the key information of business products into attribute information, rule information and policy information;

[0164] Performing data verification on the attribute information, rule information, and policy information;

[0165] Store the verified attribute information, rule information, and policy information in a structured data format in a unified detailed information layer, and generate a data version identifier corresponding to each type of information;

[0166] A queryable interface service that can trace back historical version information through the data version identifier is configured in the unified detailed information layer.

[0167] In one embodiment, the data integration module 20 is specifically configured to:

[0168] Defining an interface protocol to standardize the data exchange format between the unified detailed information layer and multiple business systems;

[0169] Configuring an event monitoring service to capture data change requests from the multiple business systems through the event monitoring service;

[0170] Based on the interface protocol, converting the data change request into a standardized instruction recognizable by the unified detailed information layer;

[0171] The standardized instructions are synchronized to the unified detailed information layer through the data synchronization module, and the business product data stored in the unified detailed information layer is updated according to the standardized instructions.

[0172] In one embodiment, the data acquisition module 30 is specifically configured to:

[0173] Obtaining attribute change records of the business product from the product management business system through the event monitoring service in the data integration mechanism;

[0174] Collect market trend data and channel sales indicators from the product sales business system through the interface protocol in the data integration mechanism;

[0175] Monitor user feedback data and service request logs of the customer service system through the event monitoring service;

[0176] Subscribe to the service response rate data and product termination rate data stream of the service processing system through the interface protocol;

[0177] The attribute change records, market trend data, channel sales indicators, user feedback data, service request logs, service response rate data and product termination rate data streams are integrated into the multi-dimensional data.

[0178] In one embodiment, the data analysis module 40 is specifically configured to:

[0179] Inputting the multi-dimensional data into a market response efficiency model, and generating a market response efficiency index for the business product through the market response efficiency model;

[0180] Inputting the multi-dimensional data into a customer demand priority model, and generating a customer demand priority label for the business product through the customer demand priority model;

[0181] Inputting the multi-dimensional data into a risk distribution characteristic model, and generating a risk distribution characteristic value of the business product through the risk distribution characteristic model;

[0182] An operating status report of the business product is generated based on the market response efficiency index, customer demand priority label and risk distribution characteristic value.

[0183] In one embodiment, the policy optimization module 50 is specifically configured to:

[0184] When the market response efficiency index in the operating state is lower than a preset efficiency threshold or the risk distribution characteristic value exceeds a preset risk threshold, generating a strategy adjustment instruction;

[0185] Extracting a corresponding identification value configuration template or clause revision template from a preset policy library according to the policy adjustment instruction;

[0186] Comparing the identification value configuration template or the clause revision template with the current policy data stored in the unified detailed information layer, and marking the difference content fields;

[0187] generating a policy adjustment plan based on the difference content field;

[0188] Updating the policy adjustment plan to the unified detailed information layer;

[0189] The policy adjustment plan is synchronized to the corresponding business system through the interface protocol in the data integration mechanism.

[0190] In one embodiment, the policy optimization module 50 is specifically configured to:

[0191] Assign role labels to operating subjects through the user management system;

[0192] Verify the operation authority of the operation subject for the policy adjustment scheme, execute the update operation when the verification passes, and terminate the operation and record the exception log if the verification fails;

[0193] When the policy adjustment plan is synchronized to the corresponding business system through the data integration mechanism, the operation time, operation subject identifier and synchronization content of the policy adjustment plan are recorded in the operation log module;

[0194] The log data is backed up by the operation log module, and a log summary report is generated based on the backed up log data.

[0195] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4 As shown. The computer device includes a processor, memory, network interface and database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external user terminal via a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the service side of a business product processing method based on data integration.

[0196] In one embodiment, a computer device is provided. The computer device may be a user terminal, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, memory, network interface, display screen and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it realizes the functions or steps on the user side of a business product processing method based on data integration.

[0197] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the following steps are performed:

[0198] Build a unified detailed information layer for business products;

[0199] Connecting the unified detailed information layer with the interface of the business system by building a data integration mechanism;

[0200] Collect multi-dimensional data from the business system based on the data integration mechanism;

[0201] Analyzing the operating status of the business product through the multi-dimensional data;

[0202] The operation strategy of the business product is optimized based on the operation status, and the optimized operation strategy is synchronized to the business system through the data integration mechanism.

[0203] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0204] Build a unified detailed information layer for business products;

[0205] Connecting the unified detailed information layer with the interface of the business system by building a data integration mechanism;

[0206] Collect multi-dimensional data from the business system based on the data integration mechanism;

[0207] Analyzing the operating status of the business product through the multi-dimensional data;

[0208] The operation strategy of the business product is optimized based on the operation status, and the optimized operation strategy is synchronized to the business system through the data integration mechanism.

[0209] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can be found in the relevant descriptions of the server side and the user side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0210] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0211] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0212] It should be noted that if any software tools or components other than those of the Company appear in the embodiments of this application, they are merely for illustration and do not represent actual use. The above embodiments are intended only to illustrate the technical solutions of the present invention, not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein with equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A business product processing method based on data integration, characterized in that: The following steps are involved: Build a unified detailed information layer for business products; Connecting the unified detailed information layer with the interface of the business system by building a data integration mechanism; Collect multi-dimensional data from the business system based on the data integration mechanism; Analyzing the operating status of the business product through the multi-dimensional data; The operation strategy of the business product is optimized based on the operation status, and the optimized operation strategy is synchronized to the business system through the data integration mechanism.

2. The data integration-based business product processing method according to claim 1, characterized in that: Build a unified layer of detail for business products, including: Classify the key information of business products into attribute information, rule information and policy information; Performing data verification on the attribute information, rule information, and policy information; Store the verified attribute information, rule information, and policy information in a structured data format in a unified detailed information layer, and generate a data version identifier corresponding to each type of information; A queryable interface service that can trace back historical version information through the data version identifier is configured in the unified detailed information layer.

3. The data integration-based business product processing method according to claim 1, characterized in that: Connect the unified detailed information layer with the business system interface by building a data integration mechanism, including: Defining an interface protocol to standardize the data exchange format between the unified detailed information layer and multiple business systems; Configuring an event monitoring service to capture data change requests from the multiple business systems through the event monitoring service; Based on the interface protocol, converting the data change request into a standardized instruction recognizable by the unified detailed information layer; The standardized instructions are synchronized to the unified detailed information layer through the data synchronization module, and the business product data stored in the unified detailed information layer is updated according to the standardized instructions.

4. The business product processing method based on data integration according to claim 1, characterized in that: Collect multi-dimensional data from business systems based on the data integration mechanism, including: Obtaining attribute change records of the business product from the product management business system through the event monitoring service in the data integration mechanism; Collect market trend data and channel sales indicators from the product sales business system through the interface protocol in the data integration mechanism; Monitor user feedback data and service request logs of the customer service system through the event monitoring service; Subscribe to the service response rate data and product termination rate data stream of the service processing system through the interface protocol; The attribute change records, market trend data, channel sales indicators, user feedback data, service request logs, service response rate data and product termination rate data streams are integrated into the multi-dimensional data.

5. The business product processing method based on data integration according to claim 1, characterized in that: Analyzing the operating status of the business product through the multi-dimensional data includes: Inputting the multi-dimensional data into a market response efficiency model, and generating a market response efficiency index for the business product through the market response efficiency model; Inputting the multi-dimensional data into a customer demand priority model, and generating a customer demand priority label for the business product through the customer demand priority model; Inputting the multi-dimensional data into a risk distribution characteristic model, and generating a risk distribution characteristic value of the business product through the risk distribution characteristic model; An operating status report of the business product is generated based on the market response efficiency index, customer demand priority label and risk distribution characteristic value.

6. The data integration-based business product processing method according to claim 1, characterized in that: Optimizing the operation strategy of the business product based on the operation status, and synchronizing the optimized operation strategy to the business system through the data integration mechanism, including: When the market response efficiency index in the operating state is lower than a preset efficiency threshold or the risk distribution characteristic value exceeds a preset risk threshold, generating a strategy adjustment instruction; Extracting a corresponding identification value configuration template or clause revision template from a preset policy library according to the policy adjustment instruction; Comparing the identification value configuration template or the clause revision template with the current policy data stored in the unified detailed information layer, and marking the difference content fields; generating a policy adjustment plan based on the difference content field; Updating the policy adjustment plan to the unified detailed information layer; The policy adjustment plan is synchronized to the corresponding business system through the interface protocol in the data integration mechanism.

7. The business product processing method based on data integration according to claim 6, characterized in that: After generating a policy adjustment solution based on the difference content field, the method further includes: Assign role labels to operating subjects through the user management system; Verify the operation authority of the operation subject for the policy adjustment scheme, execute the update operation when the verification passes, and terminate the operation and record the exception log if the verification fails; When the policy adjustment plan is synchronized to the corresponding business system through the data integration mechanism, the operation time, operation subject identifier and synchronization content of the policy adjustment plan are recorded in the operation log module; The log data is backed up by the operation log module, and a log summary report is generated based on the backed up log data.

8. A business product processing device based on data integration, characterized in that: The business product processing device based on data integration includes: Information layer building module, used to build a unified detailed information layer for business products; A data integration module, configured to connect the unified detailed information layer with the interface of the business system by building a data integration mechanism; A data collection module, used to collect multi-dimensional data from the business system based on the data integration mechanism; A data analysis module, configured to analyze the operating status of the business product through the multi-dimensional data; A strategy optimization module is used to optimize the operation strategy of the business product based on the operation status, and synchronize the optimized operation strategy to the business system through the data integration mechanism.

9. A computer device, characterized in that: The computer device includes a memory, a processor, and a data integration-based business product processing program stored in the memory and executable on the processor. When the data integration-based business product processing program is executed by the processor, the steps of the data integration-based business product processing method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The storage medium stores a business product processing program based on data integration, which, when executed by a processor, implements the steps of the business product processing method based on data integration according to any one of claims 1 to 7.

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