Customer information calling method and system based on integrated information model

Abstract

The application discloses a customer information calling method and system based on an integrated information model, and belongs to the technical field of data processing. The method comprises the following steps: establishing a data warehouse; establishing a data management layer of the data warehouse, and performing self-adaptive updating of data of the data warehouse through a self-checking updating channel of the data management layer; establishing a record result; performing cache scoring through a cache evaluation network and the record result, and generating a hierarchical cache strategy according to cache load and cache scoring results; performing hierarchical cache of customer information through the hierarchical cache strategy, receiving user demand information through the integrated information model, performing linkage demand prediction according to the user demand information and information association identifiers, and performing customer information calling management according to linkage demand prediction results and the hierarchical cache strategy. The application solves the technical problems of non-uniform customer information management, non-timely data updating and slow system response speed in the prior art, thereby causing poor user experience and unreliable data.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a customer information calling method and system based on an integrated information model. Background Art

[0002] In today's digital age, enterprise informatization has become a key way to enhance competitiveness, optimize resource allocation, and enhance customer experience. With the rapid development of information technology, the amount of customer data accumulated by enterprises has exploded. This data not only includes basic customer information but also covers multi-dimensional information such as transaction records, behavioral preferences, and service requirements.

[0003] Although many companies have established customer information systems, most suffer from information silos, data inconsistencies, and delayed updates, making comprehensive and accurate access and analysis of customer information difficult. Traditional methods for accessing customer information often rely on manual intervention, which is inefficient and error-prone, making it difficult for companies to quickly respond to customer needs and optimize the customer experience. Furthermore, as customer data continues to grow, the storage and query pressures facing the system are also increasing. Therefore, effectively managing data warehouses and improving data access speed and accuracy have become urgent challenges. Summary of the Invention

[0004] This application provides a customer information calling method and system based on an integrated information model, aiming to solve the technical problems in the existing technology of inconsistent customer information management, untimely data updates and slow system response speed, which lead to poor user experience and unreliable data.

[0005] In view of the above problems, the present application provides a customer information calling method and system based on an integrated information model.

[0006] The first aspect disclosed in the present application provides a customer information calling method based on an integrated information model, the method comprising: reading customer information, establishing a customer information set, organizing information on the customer information set, and establishing a data warehouse, wherein the data warehouse is provided with an information association identifier of the customer information; establishing a data management layer of the data warehouse, the data management layer comprising a self-checking and updating channel and an integrated information model, and adaptively updating the data of the data warehouse through the self-checking and updating channel of the data management layer; recording information calls through the integrated information model of the data management layer, and establishing record results; activating a cache evaluation network of the integrated information model, performing cache scoring through the cache evaluation network and the record results, and generating a hierarchical cache strategy based on the cache load and the cache scoring results; performing hierarchical caching of customer information through the hierarchical cache strategy, receiving user demand information through the integrated information model, performing linkage demand prediction based on the user demand information and the information association identifier, and managing customer information calls based on the linkage demand prediction results and the hierarchical cache strategy.

[0007] Another aspect disclosed in the present application provides a customer information call system based on an integrated information model, the system including: a data warehouse establishment module: used to read customer information, establish a customer information set, organize information on the customer information set, and establish a data warehouse, wherein the data warehouse is provided with an information association identifier of the customer information; an adaptive update module: used to establish a data management layer of the data warehouse, the data management layer includes a self-check update channel and an integrated information model, and the data of the data warehouse is adaptively updated through the self-check update channel of the data management layer; an information call module: used to record information call through the integrated information model of the data management layer and establish record results; a cache scoring module: used to activate the cache evaluation network of the integrated information model, perform cache scoring through the cache evaluation network and the record results, and generate a hierarchical cache strategy based on the cache load and the cache scoring results; a demand prediction module: used to perform hierarchical caching of customer information through the hierarchical cache strategy, receive user demand information through the integrated information model, perform linkage demand prediction based on the user demand information and the information association identifier, and perform customer information call management based on the linkage demand prediction results and the hierarchical cache strategy.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By adopting the technical solutions of integrated information model, data management layer of data warehouse, self-checking update channel and cache evaluation network, the technical problems in existing technologies such as inconsistent customer information management, untimely data update and slow system response speed, which lead to poor user experience and unreliable data, are solved. The technical effects of efficiently integrating and managing customer information, updating data in a timely manner, and optimizing cache strategies to improve system performance and response speed are achieved.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A flowchart of a method for calling customer information based on an integrated information model is provided for an embodiment of the present application.

[0012] Figure 2 A structural diagram of a customer information calling system based on an integrated information model is provided for an embodiment of the present application.

[0013] Description of the accompanying drawings: data warehouse establishment module 11, adaptive update module 12, information calling module 13, cache scoring module 14, demand prediction module 15. DETAILED DESCRIPTION

[0014] The overall idea of ​​the technical solution provided by this application is as follows:

[0015] The embodiments of the present application provide a method and system for calling customer information based on an integrated information model. First, by reading customer information and establishing a data set, the information is organized and a data warehouse is created, and an information association identifier is set. Then, a data management layer is established, including a self-check update channel and an integrated information model, to achieve adaptive updates of data. Next, information calls are recorded through the integrated information model, and the cache evaluation network is activated to generate a hierarchical cache strategy based on the cache load and the scoring results. Finally, customer information is cached through a hierarchical cache strategy, and linkage demand forecasting is performed based on user demand information and information association identifiers to achieve efficient customer information call management. This method ensures unified management, timely updates, and efficient calls of data, thereby improving system performance and user experience.

[0016] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced in conjunction with the drawings in the specification.

[0017] Example 1, as Figure 1As shown, the embodiment of the present application provides a method for calling customer information based on an integrated information model, the method comprising:

[0018] Step S100: Read customer information, establish a customer information set, organize the customer information set, and establish a data warehouse, wherein the data warehouse is provided with an information association identifier of the customer information.

[0019] Specifically, accessing customer information involves obtaining relevant customer information from various data sources. These data sources can include customer relationship management systems (CRMs), enterprise resource planning systems (ERPs), sales records, customer feedback systems, social media platforms, and more. Specifically, this involves identifying all systems and databases containing customer information; obtaining the necessary permissions to access these data sources to ensure legal and secure access to the data; and extracting customer information from these data sources using appropriate tools and techniques (such as API calls, database queries, and ETL (Extract, Transform, Load) tools). The customer information extracted from these different data sources is then integrated. For example, customer contact information in the CRM can be merged with purchase records in the sales system to form a unified customer information set that contains all relevant customer information and is stored in a structured format.

[0020] Information organization involves further categorizing, sorting, and organizing existing customer information sets for subsequent use and analysis. Specifically, customer information is categorized by type (e.g., personal information, purchase history, feedback history, etc.). Indexes are created to facilitate quick retrieval. For example, indexes can be created based on customer ID, name, and contact information. Customer information is sorted as needed, such as by most recent purchase date or by customer value. Customers are grouped based on characteristics (e.g., location, purchasing behavior, etc.) to facilitate group analysis and targeted marketing.

[0021] Building a data warehouse involves storing organized customer information in a dedicated data warehouse for large-scale data analysis and querying. Specifically, the data warehouse architecture, including the design of fact tables and dimension tables, is designed to ensure efficient querying and analysis. The organized customer information is loaded into the data warehouse. This is typically done using ETL tools to ensure efficient data loading and transformation. Data warehouse performance optimizations, such as indexing, partitioning, and compression, are performed to improve query and analysis efficiency. The data warehouse is regularly backed up to ensure data security and reliability.

[0022] The data warehouse maintains information association identifiers for customer information. Specifically, a unique identifier (such as a customer ID) is generated for each customer, ensuring that all related information can be linked using this identifier. Associations between different types of customer information are defined. For example, a customer's personal information is associated with their purchase history using the customer ID. These association identifiers are indexed in the data warehouse to improve the efficiency of association queries. The consistency and accuracy of association identifiers are ensured during data updates and maintenance.

[0023] This step allows us to access customer information from multiple data sources, establish a unified customer information set, effectively organize this information, and efficiently store and manage it in the data warehouse. Furthermore, setting information association tags ensures data relevance and accessibility, laying a solid foundation for subsequent data retrieval and analysis.

[0024] Step S200: establishing a data management layer of the data warehouse, wherein the data management layer includes a self-checking and updating channel and an integrated information model, and adaptively updating the data of the data warehouse is performed through the self-checking and updating channel of the data management layer.

[0025] Specifically, the data management layer is an important part of the data warehouse, responsible for managing, maintaining, and updating the data in the data warehouse. Its main functions include adaptive data updates and information access and management through an integrated information model.

[0026] Among them, the self-check and update channel is an automated mechanism used to regularly check and update the data in the data warehouse to ensure the data is up-to-date and accurate. The self-check and update channel functions include: regular self-check: setting the time interval for regular data checks to ensure that the data remains up-to-date. Automatic update: automatically update the data when it is found that the data needs to be updated to reduce manual intervention. Specifically, according to business needs and the frequency of data changes, set an appropriate self-check cycle (such as daily, weekly or monthly). Regularly check the consistency of the data in the data warehouse with the source data, including data integrity, correctness and timeliness. When data is found to be inconsistent or outdated, automatically re-extract the data from the data source and update it. In the process of updating data, perform data cleaning to remove invalid or duplicate data.

[0027] The integrated information model is a unified data model used to integrate and manage data from different data sources, providing consistent and efficient data call and query capabilities. Its functions include: Data integration: integrating data from different data sources to form a unified data view. Data standardization: standardizing data of different formats and standards to ensure data consistency. Efficient query: providing efficient data query and call functions to support complex data analysis and mining. Specifically, based on business needs, a unified data model is designed, including data table structure, field types, and relationships. Data from different data sources are mapped to a unified data model to form a unified data view. The integrated data is standardized to ensure consistent data formats for easy query and analysis. Through the integrated information model, a unified data call interface is provided to support efficient data query and analysis.

[0028] Data adaptive update refers to the automatic checking and updating of data in the data warehouse through the self-checking update channel in the data management layer to ensure the timeliness and accuracy of the data.

[0029] Specifically, based on business needs and the frequency of data changes, set self-check and update rules and policies, including self-check cycles, update frequencies, and data cleaning standards. When the set self-check cycle arrives, the data self-check and update process is automatically triggered. Perform consistency checks on the data in the data warehouse to detect whether the data is inconsistent, outdated, or erroneous. When data inconsistencies or outdated data are detected, re-extract the latest data from the data source and update it. During the data update process, perform data cleaning to remove invalid or duplicate data, and verify the updated data to ensure the correctness and completeness of the data. Record a log for each self-check update, including the update time, update content, and update results, to facilitate subsequent tracking and auditing.

[0030] This step enables adaptive data updates in the data warehouse by establishing a data management layer, including a self-checking and updating channel and an integrated information model. The self-checking and updating channel regularly checks and automatically updates data, ensuring its timeliness and accuracy. The integrated information model provides unified data management and access capabilities, supporting efficient data query and analysis. The combination of these two effectively improves data warehouse management efficiency and data quality, providing reliable data support for business decisions.

[0031] Step S300: Record information calls through the integrated information model of the data management layer and establish record results.

[0032] Specifically, information call logging refers to the detailed recording of each data call request, including the time of the call, caller information, and call content, for subsequent analysis and auditing. Recording results refer to the integration and storage of information call records to form complete record entries for subsequent query and analysis.

[0033] Specifically, when a user or application initiates a data retrieval request, the system first receives the request and initiates a call session. The system generates a unique session ID for each call, which is used to identify and track the call. For example, if a user queries a customer's information through a CRM system, this query request is captured by the system and assigned a unique session ID. The system uses an integrated information model to retrieve relevant data from the data warehouse based on the user's query criteria. This model provides a unified data view, making data retrieval efficient and consistent. During the data retrieval and processing process, the system records each step, including retrieval criteria, processing steps, and processing time. After the data retrieval is complete, the system consolidates all recorded information into a complete call record. This includes the session ID, call time, caller information, retrieval criteria, processing steps, and processing time. The system stores these consolidated call records in a dedicated record database and creates indexes for subsequent query and analysis. Indexes can be created based on various dimensions, such as call time, user ID, and session ID.

[0034] This step uses an integrated information model at the data management level to record information and create detailed records, enabling a comprehensive understanding and analysis of user behavior, system performance, and data usage. This process not only improves system efficiency and user experience, but also ensures data security and compliance.

[0035] Step S400: activating the cache evaluation network of the integrated information model, performing cache scoring through the cache evaluation network and the recorded results, and generating a hierarchical cache strategy according to the cache load and the cache scoring results.

[0036] Specifically, cache scoring is the scoring of cached data based on a cache evaluation network, assessing its value in the cache. Cache load refers to the current usage of the cache system, including the amount of cached data and cache space utilization. A hierarchical cache strategy is a cache management strategy that allocates data to different cache tiers based on its importance and access frequency to optimize system performance and resource utilization.

[0037] Specifically, the cache evaluation network is a system that assesses the value of cached data. When the integrated information model is activated, the cache evaluation network is also activated. This network uses various algorithms and rules to assess the value of each data item in the cache. The system analyzes records of every information call made through the integrated information model. Records include call time, caller information, and call content. These records are used to assess data access frequency and patterns. The cache evaluation network combines these call records to assign a score to each data item. The score is based on factors such as data access frequency, data size, and update frequency. For example, data items with high access frequency and low update frequency receive a higher cache score because their presence in the cache can significantly improve system performance. Cache load is monitored in real time, including cache utilization and the amount of data currently cached. If cache space is nearing saturation, the system uses the cache score to determine which data should be retained and which should be evicted. Based on the cache load and cache score, the system generates a hierarchical caching strategy. This strategy allocates data to different cache tiers to optimize cache utilization and system performance. For example, high-scoring data is assigned to a fast cache tier (such as memory cache), while low-scoring data is assigned to a slower cache tier (such as disk cache).

[0038] This step activates the cache evaluation network of the integrated information model, enabling the system to evaluate and record detailed information about each data call. The cache evaluation network then analyzes these records to generate a cache score for each data item. Based on cache load and these scores, the system generates a hierarchical caching strategy, allocating data to different cache tiers to optimize cache utilization and system performance. This process ensures that frequently accessed and important data is quickly accessible, while infrequently accessed and less important data is properly managed, improving overall system efficiency and responsiveness.

[0039] Step S500: Perform hierarchical caching of customer information through the hierarchical caching strategy, receive user demand information through the integrated information model, perform linkage demand prediction based on the user demand information and information association identifier, and manage customer information calls based on the linkage demand prediction results and the hierarchical caching strategy.

[0040] Specifically, customer information data is allocated to different cache tiers based on the hierarchical caching strategy. For example, frequently accessed customer information is stored in the memory cache, while less frequently accessed information is stored in the disk cache. Data is actually stored in the corresponding cache tier to ensure efficient access and read efficiency. Cache management tools (such as Redis and Memcached) are used to implement hierarchical caching. Caching strategy algorithms (such as LFU and LRU) are employed for data hierarchical management. Data requests from users are received through an integrated information model to obtain the information they require. The content of the user request is parsed to determine the type and scope of the data to be retrieved.

[0041] Based on the information association identifiers in the user's request information, data items related to the current request are identified. Leveraging machine learning models or rule engines, the user's subsequent needs are predicted based on current needs and historical access patterns. Furthermore, based on the predicted linkage needs, relevant data is retrieved from the cache in advance to prepare for subsequent user requests. Combined with hierarchical caching strategies, data call paths and methods are optimized to ensure efficient data access and processing.

[0042] This step utilizes a hierarchical caching strategy to cache customer information at different levels, ensuring that data of varying importance and access frequency is appropriately stored and managed. After the integrated information model receives user demand information, the system performs a coordinated demand forecast based on information association identifiers to predict the user's subsequent needs. Ultimately, the system combines the coordinated demand forecast results with the hierarchical caching strategy to optimize the management of customer information access, ensuring efficient data access and user experience. This process, through the integration of multiple technologies and tools, effectively improves system performance and user satisfaction.

[0043] Furthermore, the cache evaluation network is as follows:

[0044] ;

[0045] ;

[0046] ;

[0047] ;

[0048] in, Representing data items In the time period The cache adaptability score of is the time period starting from the current time node, Representing data items In the time period The access frequency characteristics of For data items The size penalty feature, Representing data items In the time period The update frequency characteristics of is the time node index, For the time node The access frequency feature weight, Representing data items At the time node The frequency of visits, is the weight of data size, Representing data items The data size, Time node The update frequency weight of For data items At the time node Update frequency.

[0049] Specifically, a cache evaluation network is used to score the cache adaptability of data items. The scoring formula includes access frequency features , size penalty feature and update frequency characteristics The access frequency feature is calculated by weighted summing access frequencies, the size penalty feature is weighted by adjusting the data size, and the update frequency feature is the weighted sum of update frequencies. This method uses these features to evaluate the cache value of data items, thereby optimizing cache management and improving system performance.

[0050] Furthermore, the generation of a hierarchical cache strategy based on cache load and cache scoring results also includes: configuring hierarchical cache constraints, wherein the hierarchical cache constraints include local cache constraints, distributed cache constraints, and delayed cache constraints; performing sequential screening of customer information based on the cache load and cache scoring results, and establishing screening results; performing adaptation evaluation of the hierarchical cache constraints on the screening results, and generating a hierarchical cache strategy based on the adaptation evaluation selection results.

[0051] Specifically, cache constraints are set at different levels, including local cache constraints, distributed cache constraints, and delayed cache constraints. Local cache constraints refer to the conditions and restrictions for caching data in local memory. This data is typically frequently accessed and requires a fast response. Distributed cache constraints refer to the conditions and restrictions for caching data in a distributed cache system. This data can be stored and accessed on multiple nodes, making it suitable for large-scale and high-availability requirements. Delay cache constraints refer to the conditions and restrictions for using slower storage (such as disk cache) for data items that can tolerate a certain degree of delay. This data is typically infrequently accessed or updated.

[0052] According to the cache load and cache score results, customer information is sorted and filtered to generate filtering results. The filtering results are evaluated for adaptation to determine whether they meet the hierarchical cache constraints. Based on the adaptation evaluation results, the final hierarchical cache strategy is generated. Specifically, the load of the cache system is monitored in real time, including memory utilization, cache hit rate, etc. All customer information is sorted according to the cache score generated by the cache evaluation network. Based on the sorting results, data items that meet the cache load and score requirements are filtered out to generate filtering results. The filtering results are matched with the hierarchical cache constraints to evaluate whether the data items meet the corresponding cache level requirements. Based on the adaptation evaluation results, the cache level of the data items is determined, and a hierarchical cache strategy is generated. For example, frequently accessed data items are stored in the local cache, infrequently accessed but important data items are stored in the distributed cache, and other data items are stored in the delayed cache.

[0053] Through this method, the system can generate a hierarchical cache strategy based on the cache load and cache scoring results, and ensure that data items are stored in the most appropriate cache level by configuring hierarchical cache constraints, performing customer information sequence screening and adaptation evaluation, thereby optimizing cache utilization and system performance.

[0054] Furthermore, the linkage demand prediction based on the user demand information and the information association identifier also includes: establishing a user account and reading the user account characteristics; performing user behavior verification on the user account and establishing a behavior trust result; generating a call association preference through the behavior trust result and the user account characteristics; and performing linkage demand prediction based on the call association preference and the information association identifier.

[0055] Specifically, behavioral trust results are trust scores derived from user behavior verification, indicating the credibility of the user's behavior. Call association preferences are generated based on the user's behavioral trust results and account characteristics, indicating the associated data or operational preferences of interest to the user. Linked demand prediction predicts the user's subsequent demand based on their call association preferences and information association identifiers.

[0056] Specifically, create a separate account for each user in the system and record the user's basic information. Obtain relevant feature information of the user account, which may include the user's basic information (such as name, age, gender, etc.) and historical behavior data (such as past query records, purchase records, etc.). Collect user behavior data in the system, including login records, operation records, etc. Analyze the authenticity and consistency of user behavior to ensure the reliability of behavioral data. Based on the results of behavioral analysis, generate the user's behavioral trust score and evaluate its credibility. Combine the user's behavioral trust results with their account characteristics to generate the user's call preferences. Based on the user's historical behavior and trust score, generate related data items or operations that the user is interested in. Use the generated call association preferences to predict the user's subsequent needs. Based on the information association identifier, identify data items related to the user's needs, and prepare and cache related data in advance. Predict the user's subsequent operations or needs to ensure that the system can respond quickly.

[0057] Through this step, the system can comprehensively analyze and predict user needs, thereby optimizing data access and caching strategies. For example, on an e-commerce platform, the system will recommend relevant products based on the user's purchase history and behavioral trust score, and cache these product information in advance to ensure that users can quickly access these recommendations. In banking systems, by analyzing user account operations and behavioral trust scores, the system can predict the user's next needs, such as checking balances or making transfers, and prepare relevant data and operation interfaces in advance, improving user experience and system responsiveness.

[0058] Furthermore, the method also includes: establishing user call feedback, and using the call feedback to update features within the user account features, the feature update including time-based feature authentication enhancement and conflict feature replacement; and recording the user account according to the feature update results.

[0059] Specifically, call feedback refers to the feedback left by users after using the system. This information can help the system understand user satisfaction and behavior patterns. Time-series-based feature authentication enhancement refers to enhancing the authentication and understanding of user features by analyzing the time series data of user features. Conflicting feature replacement refers to the appropriate replacement of new features when they conflict with existing features to ensure data consistency.

[0060] Specifically, users provide feedback through the system interface, such as ratings, comments, and satisfaction surveys. The system automatically collects user behavior data, such as click-through rate, dwell time, and purchase history. Analyze time series data of user features to ensure the dynamic update and accuracy of feature information. When new feedback information conflicts with existing feature information, perform feature replacement. For example, when a user's preferences change, the system needs to update their preference features. Save the updated user feature information to the user account to ensure that the user data stored in the system is up-to-date and accurate. Record the history of each feature update for auditing and backtracking.

[0061] By establishing user call feedback and performing feature updates, the system can dynamically adjust the feature information in the user's account based on the user's latest behavior and feedback. This includes time-based feature authentication to ensure the timeliness of feature information, and conflicting feature replacement to resolve conflicts between old and new feature information. The updated feature information is recorded in the user's account to ensure the accuracy and integrity of the system data.

[0062] Furthermore, the adaptive updating of data in the data warehouse through the self-checking and updating channel of the data management layer also includes: reading storage throughput through the data management layer to establish storage traffic; adaptively updating the self-checking cycle of the self-checking and updating channel according to the storage traffic; when the time node meets the self-checking cycle, calling the data cleaning tool to perform data cleaning authentication of the data warehouse, the data cleaning authentication includes source trust authentication, invalid duplicate authentication, and linkage analysis authentication; and completing the adaptive updating of data according to the data cleaning authentication result.

[0063] Specifically, storage throughput refers to the speed and capacity of data read and written within a storage system, reflecting the performance of the storage system. Storage traffic refers to the flow of data within the storage system, indicating the frequency and scale of data read and write operations. The self-check cycle is the interval between system data checks and updates.

[0064] Specifically, the system monitors the storage and read rates of the data warehouse in real time, recording the amount of data read and written per unit time. Based on the monitored storage throughput, the system calculates the total amount of data stored and read within a certain time period and establishes storage traffic. The system analyzes storage traffic over a period of time to evaluate the load and data changes in the data warehouse. Based on changes in storage traffic, the system dynamically adjusts the self-test cycle of the self-test update channel. When storage traffic is high, the self-test cycle is shortened; when storage traffic is low, the self-test cycle is extended. When the time node meets the self-test cycle, the system automatically triggers the self-test update process. The system calls the data cleaning tool to clean and authenticate the data in the data warehouse.

[0065] Data cleansing certification includes trusted source certification, invalid duplicate certification, and linked analysis certification. Trusted source certification verifies the data's origin, ensuring it comes from a trusted and legitimate data source. Invalid duplicate certification detects and removes duplicate or invalid data to ensure its uniqueness and validity. Linked analysis certification analyzes the relationships between data to ensure its consistency and logic. The system analyzes the results of data cleansing certification to identify data items that require updating. Based on the analysis results, the data in the data warehouse is adaptively updated to ensure data freshness and accuracy.

[0066] Through the above steps, the system can adaptively update data in the data warehouse through the self-check and update channel of the data management layer. This includes monitoring storage throughput, calculating storage traffic, adaptively adjusting the self-check cycle, invoking data cleansing tools for data verification, and completing data updates based on the verification results. This process ensures the accuracy and real-time nature of data in the data warehouse, helping to optimize system performance and data quality.

[0067] Furthermore, the method further includes: when any customer information flows into the data warehouse, performing data format authentication on the customer information; performing formatting processing based on the data format authentication result, and storing the customer information according to the formatting processing result.

[0068] Specifically, the data warehouse entrance is monitored, and when any customer information flows in, the data format verification process is triggered. Data format standards are pre-defined, including requirements for field type, length, format, and so on. The incoming data is format-verified to check whether it meets the pre-defined standards.

[0069] Based on the results of data format verification, identify data items that require formatting. Adjust and convert data that does not conform to format standards to meet predetermined requirements. Define and implement formatting strategies, such as field standardization, data cleansing, and type conversion.

[0070] After formatting is complete, verify that the data meets storage requirements. Store the formatted and verified data in the data warehouse to ensure data consistency and integrity. Record detailed information about each data storage operation for auditing and backtracking.

[0071] This step allows the system to authenticate and format customer information after it enters the data warehouse, ensuring it complies with predefined standards before storing it in the warehouse. This process ensures data consistency and integrity, improves data quality, and optimizes data storage and management.

[0072] In summary, the customer information calling method based on the integrated information model provided in the embodiments of the present application has the following technical effects:

[0073] 1. A customer information retrieval method based on an integrated information model achieves effective data integration and management by establishing a data warehouse and data management layer. A self-checking update channel and cache evaluation network ensure timely data updates and efficient retrieval, improving system responsiveness and data reliability. A hierarchical caching strategy optimizes cache utilization and enhances system performance.

[0074] 2. By defining a cache evaluation network, data items are scored for cache adaptability. This method accurately assesses the cache value of data and, by combining characteristics such as access frequency, data size, and update frequency, optimizes caching strategies, improving data access efficiency and system performance.

[0075] 3. Linked demand forecasting based on user demand information and information association identifiers enhances system foresight. By establishing user accounts, verifying user behavior, and generating call association preferences, the system can prepare relevant data in advance, improving user experience and system responsiveness.

[0076] 4. Adaptively update the data warehouse through a self-checking update channel to ensure data freshness and accuracy. Steps such as storage throughput reading, self-checking cycle adjustment, and data cleansing and certification make data management more efficient and system maintenance more convenient.

[0077] The second embodiment is based on the same inventive concept as the method for calling customer information based on the integrated information model in the above embodiment. Figure 2 As shown, the embodiment of the present application provides a customer information calling system based on an integrated information model, the system comprising:

[0078] Data warehouse establishment module 11: used to read customer information, establish a customer information set, organize the customer information set, and establish a data warehouse, wherein the data warehouse is provided with information association identifiers of customer information;

[0079] Adaptive update module 12: used to establish a data management layer of the data warehouse, the data management layer includes a self-checking and updating channel and an integrated information model, and adaptively updates the data in the data warehouse through the self-checking and updating channel of the data management layer;

[0080] Information calling module 13: used to call and record information through the integrated information model of the data management layer and establish record results;

[0081] Cache scoring module 14: used to activate the cache evaluation network of the integrated information model, perform cache scoring through the cache evaluation network and record results, and generate a hierarchical cache strategy based on the cache load and cache scoring results;

[0082] Demand forecasting module 15: used to perform hierarchical caching of customer information through the hierarchical caching strategy, receive user demand information through the integrated information model, perform linkage demand forecasting based on the user demand information and information association identifier, and manage customer information calls based on the linkage demand forecasting results and the hierarchical caching strategy.

[0083] Furthermore, the system further comprises:

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] in, Representing data items In the time period The cache adaptability score of is the time period starting from the current time node, Representing data items In the time period The access frequency characteristics of For data items The size penalty feature, Representing data items In the time period The update frequency characteristics of is the time node index, For the time node The access frequency feature weight, Representing data items At the time node The frequency of visits, is the weight of data size, Representing data items The data size, Time node The update frequency weight of For data items At the time node Update frequency.

[0089] Furthermore, the system further comprises:

[0090] Configure hierarchical cache constraints, including local cache constraints, distributed cache constraints, and delayed cache constraints;

[0091] Performing sequential screening of customer information based on the cache load and cache scoring results, and establishing screening results;

[0092] The screening results are evaluated for adaptability of the hierarchical cache constraints, and a hierarchical cache strategy is generated based on the adaptation evaluation selection results.

[0093] Furthermore, the system further comprises:

[0094] Create a user account and read user account characteristics;

[0095] Performing user behavior verification on the user account to establish a behavior trust result;

[0096] generating a call association preference based on the behavior trust result and the user account characteristics;

[0097] A linkage demand forecast is performed based on the call association preference and the information association identifier.

[0098] Furthermore, the system further comprises:

[0099] Establishing user call feedback and using the call feedback to update features within the user account features, the feature updates include time-series-based feature authentication enhancement and conflicting feature replacement;

[0100] Account records are made for user accounts based on the feature update results.

[0101] Furthermore, the system further comprises:

[0102] Perform storage throughput reading through the data management layer to establish storage traffic;

[0103] Adaptively updating the self-test cycle of the self-test update channel according to the storage flow;

[0104] When the time node meets the self-check cycle, the data cleaning tool is called to perform data cleaning certification of the data warehouse. The data cleaning certification includes source trust certification, invalid duplication certification, and linkage analysis certification;

[0105] Complete data adaptive update based on data cleaning and certification results.

[0106] Furthermore, the system further comprises:

[0107] When any customer information flows into the data warehouse, the data format of the customer information is authenticated;

[0108] Formatting is performed based on the data format authentication result, and customer information is stored according to the formatting result.

[0109] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0110] Furthermore, the terms "first" or "second" as described above may not only represent an order relationship but may also represent a specific concept and / or refer to the selection of multiple elements individually or collectively. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, if such modifications and variations fall within the scope of this application and its equivalents, this application is intended to include such modifications and variations.

Claims

1. A customer information calling method based on an integrated information model, characterized in that: The method comprises: Reading customer information, establishing a customer information set, organizing the customer information set, and establishing a data warehouse, wherein the data warehouse is provided with an information association identifier of the customer information; Establishing a data management layer for the data warehouse, wherein the data management layer includes a self-checking and updating channel and an integrated information model, and adaptively updating the data in the data warehouse through the self-checking and updating channel of the data management layer; Recording information through the integrated information model of the data management layer and establishing record results; activating a cache evaluation network of the integrated information model, performing cache scoring using the cache evaluation network and recorded results, and generating a hierarchical cache strategy based on the cache load and the cache scoring results; Perform hierarchical caching of customer information through the hierarchical caching strategy, receive user demand information through the integrated information model, perform linkage demand prediction based on the user demand information and information association identifier, and manage customer information calls based on the linkage demand prediction results and the hierarchical caching strategy; The cache evaluation network is as follows: ； ； ； ； in, Representing data items In the time period The cache adaptability score of is the time period starting from the current time node, Representing data items In the time period The access frequency characteristics of For data items The size penalty feature, Representing data items In the time period The update frequency characteristics of is the time node index, For the time node The access frequency feature weight, Representing data items At the time node The frequency of visits, is the weight of data size, Representing data items The data size, Time node The update frequency weight of For data items At the time node Frequency of updates; The step of generating a hierarchical cache strategy based on the cache load and the cache scoring result further includes: Configure hierarchical cache constraints, including local cache constraints, distributed cache constraints, and delayed cache constraints; Performing sequential screening of customer information based on the cache load and cache scoring results, and establishing screening results; The screening results are evaluated for adaptability of the hierarchical cache constraints, and a hierarchical cache strategy is generated based on the adaptation evaluation selection results.

2. The method for calling customer information based on an integrated information model according to claim 1, characterized in that: The performing linkage demand forecasting according to the user demand information and the information association identifier further includes: Create a user account and read user account characteristics; Performing user behavior verification on the user account to establish a behavior trust result; generating a call association preference based on the behavior trust result and the user account characteristics; A linkage demand forecast is performed based on the call association preference and the information association identifier.

3. The method for calling customer information based on an integrated information model according to claim 2, characterized in that: The method further comprises: Establishing user call feedback and using the call feedback to update features within the user account features, the feature updates include time-series-based feature authentication enhancement and conflicting feature replacement; Account records are made for user accounts based on the feature update results.

4. The method for calling customer information based on an integrated information model according to claim 1, wherein: The self-adaptive updating of data in the data warehouse through the self-checking and updating channel of the data management layer also includes: Perform storage throughput reading through the data management layer to establish storage traffic; Adaptively updating the self-test cycle of the self-test update channel according to the storage flow; When the time node meets the self-check cycle, the data cleaning tool is called to perform data cleaning certification of the data warehouse. The data cleaning certification includes source trust certification, invalid duplication certification, and linkage analysis certification; Complete data adaptive update based on data cleaning and certification results.

5. The method for calling customer information based on an integrated information model according to claim 4, characterized in that: The method further comprises: When any customer information flows into the data warehouse, the data format of the customer information is authenticated; Formatting is performed based on the data format authentication result, and customer information is stored according to the formatting result.

6. The customer information calling system based on the integrated information model is characterized by: For executing the method according to any one of claims 1 to 5, the system comprises: Data warehouse establishment module: used to read customer information, establish a customer information set, organize the customer information set, and establish a data warehouse, wherein the data warehouse is provided with an information association identifier of the customer information; Adaptive update module: used to establish the data management layer of the data warehouse, which includes a self-checking and updating channel and an integrated information model. The self-checking and updating channel of the data management layer is used to perform adaptive data updates of the data warehouse. Information call module: used to call and record information through the integrated information model of the data management layer and establish record results; Cache scoring module: used to activate the cache evaluation network of the integrated information model, perform cache scoring based on the cache evaluation network and record results, and generate a hierarchical cache strategy based on the cache load and cache scoring results; Demand forecasting module: used to perform hierarchical caching of customer information through the hierarchical caching strategy, receive user demand information through the integrated information model, perform linkage demand forecasting based on the user demand information and information association identifier, and manage customer information calls based on the linkage demand forecasting results and the hierarchical caching strategy.

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