Customer information calling method and system based on integrated information model

Abstract

The invention discloses a customer information calling method and system based on an integrated information model, and belongs to the technical field of data processing, and the method comprises the steps: building a data warehouse; establishing a data management layer of the data warehouse, and performing data self-adaptive updating of the data warehouse through a self-checking updating channel of the data management layer; establishing a recording result; performing cache scoring through a cache evaluation network and a record result, and generating a hierarchical cache strategy according to a cache load and a cache scoring result; client information hierarchical caching is carried out through a hierarchical caching strategy, user demand information is received through an integrated information model, linkage demand prediction is carried out according to the user demand information and an information association identifier, and client information calling management is carried out according to a linkage demand prediction result and the hierarchical caching strategy. The technical problems of poor user experience and unreliable data caused by non-uniform customer information management, untimely data updating and low system response speed in the prior art are solved.

Description

Technical Field

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

[0002] In today's digital age, enterprise informatization has become a key way to improve 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. These data not only include basic customer information, but also cover multi-dimensional information such as transaction records, behavioral preferences, and service needs.

[0003] At present, although many companies have established customer information systems, most of them have problems such as information islands, inconsistent data, and delayed updates, which makes it difficult to fully and accurately call and analyze customer information. Traditional customer information calling methods often rely on manual intervention, which is inefficient and error-prone, and it is difficult to meet the needs of companies to quickly respond to customer needs and optimize customer experience. In addition, with the continuous increase in customer data, the storage and query pressure faced by the system is also increasing. How to effectively manage data warehouses and improve data access speed and accuracy has become an urgent problem to be solved. 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 prior art 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 method and system for calling customer information based on an integrated information model.

[0006] The first aspect disclosed in the present application provides a method for calling customer information based on an integrated information model, the method comprising: reading customer information, establishing a customer information set, organizing information of 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 recording results; activating a cache evaluation network of the integrated information model, performing cache scoring through the cache evaluation network and the recording 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 calling system based on an integrated information model, the system comprising: a data warehouse establishment module: used to read customer information, establish a customer information set, organize information of 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 comprises a self-check update channel and an integrated information model, and adaptively update the data of the data warehouse through the self-check update channel of the data management layer; an information calling module: used to record information calling through the integrated information model of the data management layer and establish a record result; 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 result, and generate a hierarchical cache strategy according to the cache load and the cache scoring result; a demand prediction module: used to perform hierarchical cache of customer information through the hierarchical cache strategy, receive user demand information through the integrated information model, perform linkage demand prediction according to the user demand information and the information association identifier, and perform customer information calling management according to the linkage demand prediction result and the hierarchical cache strategy.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: 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 the prior art 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 time and optimizing cache strategy to improve system performance and response speed are achieved.

[0009] 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

[0010] 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.

[0011] 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.

[0012] Explanation of the reference numerals: data warehouse establishment module 11, adaptive update module 12, information call module 13, cache scoring module 14, demand prediction module 15. DETAILED DESCRIPTION

[0013] The overall idea of ​​the technical solution provided by this application is as follows: 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 to create a data warehouse, 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 updating 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 updating, and efficient calling of data, and improves system performance and user experience.

[0014] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0015] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a method for calling customer information based on an integrated information model, the method comprising: Step S100: reading customer information, establishing a customer information set, sorting the customer information set, and establishing a data warehouse, wherein the data warehouse is provided with information association identifiers of the customer information.

[0016] Specifically, reading customer information means obtaining relevant information about customers from various data sources. These data sources can include customer relationship management systems (CRM), enterprise resource planning systems (ERP), sales records, customer feedback systems, social media platforms, etc. That is, identify all systems and databases that contain customer information; obtain the necessary permissions to access these data sources to ensure that the data can be read legally and securely; use appropriate tools and techniques (such as API calls, database queries, ETL (Extract, Transform, Load) tools, etc.) to extract customer information from these data sources. Then, integrate the customer information extracted from different data sources. For example, merge the customer contact information in the CRM with the 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 form.

[0017] Information collation refers to further classifying, sorting and organizing the established customer information set for subsequent use and analysis. Specifically, classify according to different types of customer information (such as personal information, purchase records, feedback records, etc.). Create indexes for quick retrieval. For example, create indexes based on customer ID, name, contact information, etc. Sort customer information as needed, such as sorting by the most recent purchase date, sorting by customer value, etc. Group customers according to customer characteristics (such as geographic location, purchase behavior, etc.) to facilitate group analysis and target marketing.

[0018] Building a data warehouse means storing the organized customer information set in a dedicated data warehouse for large-scale data analysis and query. Specifically, design the data warehouse architecture, including the design of fact tables and dimension tables, to ensure that the data warehouse can support efficient query and analysis. Load the organized customer information set into the data warehouse. This is usually done using ETL tools to ensure efficient loading and conversion of data. Optimize the performance of the data warehouse, such as indexing, partitioning, compression, etc., to improve the efficiency of query and analysis. Back up the data warehouse regularly to ensure data security and reliability.

[0019] The data warehouse is equipped with information association identifiers for customer information, that is, a unique identifier (such as customer ID) is generated for each customer to ensure that all related information can be associated through this identifier. Define the association relationship between different types of customer information. For example, a customer's personal information is associated with his or her purchase record through the customer ID. Indexes are created for these association identifiers in the data warehouse to improve the efficiency of association queries. Ensure the consistency and correctness of association identifiers during data update and maintenance.

[0020] Through this step, customer information can be read from multiple data sources, a unified customer information set can be established, effective information organization can be performed, and efficient storage and management can be performed in the data warehouse. At the same time, setting information association identifiers ensures the relevance and accessibility of data, laying a solid foundation for subsequent data calls and analysis.

[0021] Step S200: Establish 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 update the data of the data warehouse through the self-checking and updating channel of the data management layer.

[0022] 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 updating of data and information calling and management through an integrated information model.

[0023] Among them, the self-check update channel is an automated mechanism for regularly checking and updating the data in the data warehouse to ensure the data is up-to-date and accurate. The self-check 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 the 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.

[0024] 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, according to 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. Standardize the integrated data 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.

[0025] Data adaptive update refers to automatically checking and updating the 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.

[0026] Specifically, according to business needs and the frequency of data changes, set the rules and policies for self-checking and updating, including self-checking cycle, update frequency, and data cleaning standards. When the set self-checking cycle is reached, the data self-checking and updating 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 inconsistent or outdated data is 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 the log of each self-checking update, including the update time, update content, and update results, to facilitate subsequent tracking and auditing.

[0027] This step can achieve data adaptive update of data 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 is responsible for regularly checking and automatically updating data to ensure the timeliness and accuracy of data; the integrated information model provides unified data management and call functions to support efficient data query and analysis. The combination of the two can effectively improve the management efficiency and data quality of the data warehouse and provide reliable data support for business decisions.

[0028] Step S300: Record information calls through the integrated information model of the data management layer and establish record results.

[0029] Specifically, information call records refer to detailed records of each data call request, including the time of the call, caller information, call content, etc., for subsequent analysis and audit. Record results refer to the integration and storage of information call records to form complete record entries for subsequent query and analysis.

[0030] Specifically, when a user or application initiates a data call request, the system first receives the request and initializes a call session. The system generates a unique session ID for each call to identify and track the call. For example, if a user queries a customer's information through a CRM system, the query request will be captured by the system and assigned a unique session ID. The system retrieves relevant data from the data warehouse based on the query conditions provided by the user through an integrated information model. 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 of the operation, including retrieval conditions, processing steps, and processing time. After completing the data call, the system integrates all recorded information into a complete call record. This includes session ID, call time, caller information, retrieval conditions, processing steps, and processing time. The system stores the integrated call records in a dedicated record database and creates indexes for these records for subsequent query and analysis. The index can be established based on multiple dimensions such as call time, user ID, and session ID.

[0031] This step records information through the integrated information model of the data management layer and establishes detailed record results, which helps to fully understand and analyze user behavior, system performance and data usage. This process not only improves system efficiency and user experience, but also ensures the security and compliance of data use.

[0032] 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.

[0033] Specifically, cache scoring refers to the scoring of cache data based on the cache evaluation network to evaluate its value in the cache. Cache load refers to the current usage of the cache system, including the amount of cached data and the utilization of cache space. Hierarchical cache strategy refers to a cache management strategy that allocates data to different cache levels based on the importance and access frequency of the data to optimize system performance and resource utilization.

[0034] Specifically, the cache evaluation network is a system that evaluates 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 evaluate the value of each data item in the cache. The system analyzes each information call record made through the integrated information model. The record results include the time of the call, the caller information, the call content, etc. These records are used to evaluate the access frequency and pattern of the data. The cache evaluation network will score each data item based on the call record results. The basis for scoring includes data access frequency, data size, update frequency, etc. For example, data items with high access frequency and low update frequency will receive a higher cache score because these data can significantly improve system performance in the cache. Monitor the cache load in real time, including cache utilization and the amount of data currently cached. If the cache space is close to saturation, the system needs to decide which data should be retained and which data should be cleared based on the cache score. Based on the cache load and cache score results, the system will generate a hierarchical cache strategy. This strategy allocates data to different cache levels 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).

[0035] This step activates the cache evaluation network of the integrated information model, which enables the system to evaluate and record detailed information for each data call. Then, by analyzing these records, the cache evaluation network generates a cache score for each data item. Based on the cache load and these scores, the system generates a hierarchical cache strategy to allocate data to different cache levels to optimize cache utilization and system performance. This process ensures that high-frequency access and important data can be accessed quickly, while low-frequency access and less important data are managed reasonably, improving the efficiency and responsiveness of the overall system.

[0036] 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 result and the hierarchical caching strategy.

[0037] Specifically, customer information data is allocated to different cache layers according to the hierarchical cache strategy. For example, frequently accessed customer information is stored in the memory cache, and less frequently accessed information is stored in the disk cache. The data is actually stored in the corresponding cache layer to ensure efficient access and reading. Use cache management tools (such as Redis, Memcached) to implement hierarchical caching. Use cache strategy algorithms (such as LFU, LRU) for data hierarchical management. Receive data requests from users through an integrated information model to obtain user demand information. Parse the content of the user request to determine the type and range of data to be obtained.

[0038] According to the information association identifier in the user demand information, the data items related to the current demand are identified. Based on the current user demand and historical access patterns, the user's subsequent demand is predicted using a machine learning model or rule engine. Then, according to the linkage demand prediction results, relevant data is obtained from the cache in advance to prepare to respond to the user's subsequent requests. Combined with the hierarchical cache strategy, the data call path and method are optimized to ensure efficient data access and processing.

[0039] This step uses a hierarchical caching strategy to cache customer information at different levels, ensuring that data of different importance and access frequency are stored and managed reasonably. After the integrated information model receives user demand information, the system performs linkage demand forecasting based on information association identifiers to predict the user's subsequent needs. Finally, the system combines the linkage demand forecast results and hierarchical caching strategies to optimize the call management of customer information and ensure efficient data access and user experience. This process effectively improves system performance and user satisfaction through the combination of multiple technologies and tools.

[0040] Furthermore, the cache evaluation network is as follows: ; ; ; ; in, Characterizing data items In time cycle The cache adaptability score of is the time period starting from the current time node, Characterizing data items In time cycle The access frequency characteristics of For data items The size penalty feature, Characterizing data items In time cycle The update frequency characteristics of is the time node index, For the time node The access frequency feature weight, Characterizing data items At the time point The frequency of visits, is the weight of data size, Characterizing data items The data size, For time node The update frequency weight, For data items At the time point The update frequency.

[0041] 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 of access frequencies, the size penalty feature is weighted by 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.

[0042] Furthermore, the generating of the hierarchical cache strategy according to the cache load and the cache scoring results also includes: configuring hierarchical cache constraints, the 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 the cache scoring results, and establishing screening results; performing adaptation evaluation of the hierarchical cache constraints on the screening results, and generating the hierarchical cache strategy according to the adaptation evaluation selection results.

[0043] Specifically, cache constraints at different levels are set, including local cache constraints, distributed cache constraints, and delayed cache constraints. Among them, local cache constraints refer to the conditions and restrictions for caching data in local memory. These data are usually data with high access frequency and require fast response. Distributed cache constraints refer to the conditions and restrictions for caching data in a distributed cache system. These data can be stored and accessed on multiple nodes, which is 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 delay. These data are usually data with low access frequency or low update frequency.

[0044] According to the cache load and cache scoring 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. According to the sorting results, data items that meet the cache load and scoring 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 the 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.

[0045] 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.

[0046] Furthermore, the linkage demand prediction based on the user demand information and the information association identifier also includes: establishing a user account and reading 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.

[0047] Specifically, the behavior trust result is a trust score based on user behavior verification, which indicates the credibility of the user's behavior. The call association preference is the associated data or operation preference of the user's interest generated based on the user's behavior trust result and account characteristics. The linkage demand prediction is to predict the user's subsequent demand based on the user's call association preference and information association identifier.

[0048] 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 behavior data. Based on the results of behavior analysis, generate the user's behavior trust score and evaluate its credibility. Combine the user's behavior trust results with their account characteristics to generate the user's call preferences. Based on the user's historical behavior and trust score, generate associated data items or operations that the user is interested in. Use the generated call association preferences to predict the user's subsequent needs. According to the information association identifier, identify the data items related to the user's needs, and prepare and cache the relevant data in advance. Predict the user's subsequent operations or needs to ensure that the system can respond quickly.

[0049] Through this step, the system can comprehensively analyze and predict user needs, thereby optimizing data calls and caching strategies. For example, in an e-commerce platform, the system will recommend relevant products based on the user's historical purchase records and behavioral trust scores, and cache the information of these products in advance to ensure that users can quickly access these recommendations. In the banking system, by analyzing the user's account operations and behavioral trust scores, the system can predict the user's next needs, such as checking the balance or making a transfer, and prepare relevant data and operation interfaces in advance to improve user experience and system response speed.

[0050] Furthermore, the method also includes: establishing a user call feedback, and using the call feedback to update features within the user account features, wherein the feature update includes time-based feature authentication enhancement and conflict feature replacement; and recording the user account according to the feature update result.

[0051] Specifically, call feedback refers to the feedback information left by users after using the system, which can help the system understand the user's 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 performing reasonable feature replacement when new features conflict with existing features to ensure data consistency.

[0052] Specifically, feedback provided by users through the system interface, such as ratings, comments, satisfaction surveys, etc. User behavior data automatically collected by the system, such as click-through rate, dwell time, purchase history, etc. Analyze the 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 its preference features. Save the updated user feature information to the user account to ensure that the user data saved in the system is up-to-date and accurate. Record the history of each feature update for auditing and backtracking.

[0053] By establishing user call feedback and updating features, the system can dynamically adjust the feature information in the user account based on the user's latest behavior and feedback information. This includes time-based feature authentication enhancement to ensure the timeliness of feature information, and conflict feature replacement to resolve conflicts between new and old feature information. The updated feature information will be recorded in the user account to ensure the accuracy and integrity of the system data.

[0054] Furthermore, the adaptive updating of data in the data warehouse through the self-check update channel of the data management layer also includes: reading storage throughput through the data management layer to establish storage traffic; adaptively updating the self-check cycle of the self-check update channel according to the storage traffic; when the time node meets the self-check 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.

[0055] Specifically, storage throughput refers to the speed and capacity of data read and write in the storage system, reflecting the performance of the storage system. Storage traffic refers to the flow of data in the storage system, indicating the frequency and scale of data read and write operations. The self-check cycle is the time interval for the system to check and update data.

[0056] Specifically, the system monitors the storage and reading rates of the data warehouse in real time, and records the amount of data read and written per unit time. Based on the monitored storage throughput, the total amount of data storage and reading within a certain period of time is calculated to establish storage traffic. The storage traffic over a period of time is analyzed to evaluate the load and data changes of the data warehouse. According to the changes in storage traffic, the self-test cycle of the self-test update channel is dynamically adjusted. When the storage traffic is large, the self-test cycle is shortened; when the storage traffic is small, 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.

[0057] Among them, data cleaning certification includes trusted source certification, invalid duplicate certification, and linkage analysis certification. Trusted source certification: Verify the source of data to ensure that the data comes from a trusted and legal data source. Invalid duplicate certification: Detect and delete duplicate or invalid data to ensure the uniqueness and validity of the data. Linked analysis certification: Analyze the association between data to ensure the consistency and logic of data. The system analyzes the results of data cleaning certification and identifies the data items that need to be updated. Based on the analysis results, the data in the data warehouse is adaptively updated to ensure the latest and accuracy of the data.

[0058] Through the above steps, the system can adaptively update the data in the data warehouse through the self-check update channel of the data management layer. Specifically, it includes monitoring storage throughput, calculating storage traffic, adaptively adjusting the self-check cycle, calling data cleaning tools for data authentication, and completing data updates based on the authentication results. This process ensures the accuracy and real-time nature of the data in the data warehouse, and helps optimize system performance and data quality.

[0059] Furthermore, the method also includes: when any customer information flows into the data warehouse, authenticating the data format of the customer information; performing formatting processing based on the data format authentication result, and storing the customer information according to the formatting processing result.

[0060] Specifically, the entrance of the data warehouse is monitored, and when any customer information flows in, the data format authentication process is triggered. The data format standards are pre-defined, including field type, length, format and other requirements. The format of the incoming data is verified to check whether the data meets the predetermined standards.

[0061] Based on the results of data format authentication, identify the data items that need to be formatted. Adjust and convert data that does not meet the format standards to meet the predetermined requirements. Define and implement formatting strategies, such as field standardization, data cleaning, type conversion, etc.

[0062] After the formatting process is completed, verify again whether the data meets the storage requirements. Store the formatted and verified data in the data warehouse to ensure data consistency and integrity. Record the detailed information of each data storage operation for auditing and backtracking.

[0063] Through this step, the system can authenticate and format the data after the customer information flows into the data warehouse, ensure that the data meets the predetermined standards, and then store the data in the data warehouse. This process ensures the consistency and integrity of the data, improves data quality, and optimizes data storage and management.

[0064] In summary, the customer information calling method based on the integrated information model provided in the embodiment of the present application has the following technical effects: 1. The customer information calling method based on the integrated information model realizes effective data integration and management by establishing a data warehouse and data management layer. The self-checking update channel and cache evaluation network ensure timely data update and efficient calling, improve the system's response speed and data reliability. The hierarchical cache strategy optimizes cache utilization and improves system performance.

[0065] 2. By defining a cache evaluation network, the cache adaptability of data items is scored. This method can accurately evaluate the cache value of data, and optimize the cache strategy by combining features such as access frequency, data size, and update frequency, thereby improving data access efficiency and system performance.

[0066] 3. Linked demand forecasting based on user demand information and information association identifiers enhances the system's predictability. By establishing user accounts, verifying user behavior, and generating call association preferences, the system can prepare relevant data in advance to improve user experience and system responsiveness.

[0067] 4. Adaptive update of the data warehouse through the self-check update channel to ensure the latest and accuracy of the data. Steps such as storage throughput reading, self-check cycle adjustment and data cleaning certification make data management more efficient and system maintenance more convenient.

[0068] Embodiment 2 is based on the same inventive concept as the method for calling customer information based on an 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: 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; Adaptive update module 12: used to establish a data management layer of the data warehouse, the data management layer includes a self-checking update channel and an integrated information model, and the data of the data warehouse is adaptively updated through the self-checking update channel of the data management layer; Information calling module 13: used to record information calling through the integrated information model of the data management layer and establish the recording result; 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 the record results, and generate a hierarchical cache strategy according to the cache load and the cache scoring results; Demand forecasting module 15: used to perform hierarchical caching of customer information through the hierarchical caching strategy, receive user demand information through an 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.

[0069] Furthermore, the system also includes: ; ; ; ; in, Characterizing data items In time cycle The cache adaptability score of is the time period starting from the current time node, Characterizing data items In time cycle The access frequency characteristics of For data items The size penalty feature, Characterizing data items In time cycle The update frequency characteristics of is the time node index, For the time node The access frequency feature weight, Characterizing data items At the time point The frequency of visits, is the weight of data size, Characterizing data items The data size, For time node The update frequency weight, For data items At the time point The update frequency.

[0070] Furthermore, the system also includes: Configure hierarchical cache constraints, where the hierarchical cache constraints include local cache constraints, distributed cache constraints, and delayed cache constraints; Sequentially screen the customer information based on the cache load and the cache scoring result, and establish a screening result; The screening results are evaluated for adaptability of the hierarchical cache constraints, and a hierarchical cache strategy is generated according to the adaptation evaluation selection results.

[0071] Furthermore, the system also includes: Create a user account and read user account characteristics; Performing user behavior verification on the user account to establish a behavior trust result; Generate a call association preference based on the behavior trust result and the user account characteristics; The linkage demand forecast is performed based on the call association preference and the information association identifier.

[0072] Furthermore, the system also includes: Establishing a user's call feedback, and using the call feedback to update features in the user's account features, wherein the feature update includes time-series-based feature authentication enhancement and conflict feature replacement; Account records are made for user accounts based on feature update results.

[0073] Furthermore, the system also includes: Reading storage throughput through the data management layer to establish storage traffic; Adaptively update the self-check cycle of the self-check 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, which includes source trust certification, invalid duplication certification, and linkage analysis certification; Complete data adaptive update based on data cleaning and certification results.

[0074] Furthermore, the system also includes: 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.

[0075] 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.

[0076] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to multiple elements that can be selected individually or in full. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for calling customer information 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 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 through the self-checking and updating channel of the data management layer; Information call records are performed through the integrated information model of the data management layer, and record results are established; Activate the cache evaluation network of the integrated information model, perform cache scoring through the cache evaluation network and the recorded results, and generate a hierarchical cache strategy according to the cache load and the cache scoring results; The hierarchical caching strategy is used to cache customer information at the hierarchical level, user demand information is received through an integrated information model, linkage demand prediction is performed based on the user demand information and information association identifier, and customer information call management is performed based on the linkage demand prediction result and the hierarchical caching strategy.

2. The method for calling customer information based on an integrated information model according to claim 1, characterized in that: The cache evaluation network is as follows: ； ； ； ； in, Characterizing data items In time cycle The cache adaptability score of is the time period starting from the current time node, Characterizing data items In time cycle The access frequency characteristics of For data items The size penalty feature, Characterizing data items In time cycle The update frequency characteristics of is the time node index, For the time node The access frequency feature weight, Characterizing data items At the time point The frequency of visits, is the weight of data size, Characterizing data items The data size, For time node The update frequency weight, For data items At the time point The update frequency.

3. The method for calling customer information based on an integrated information model according to claim 2, characterized in that: The generating of the hierarchical cache strategy according to the cache load and the cache scoring result also includes: Configure hierarchical cache constraints, where the hierarchical cache constraints include local cache constraints, distributed cache constraints, and delayed cache constraints; Sequentially screen the customer information based on the cache load and the cache scoring result, and establish a screening result; The screening results are evaluated for adaptability of the hierarchical cache constraints, and a hierarchical cache strategy is generated according to the adaptation evaluation selection results.

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

5. The method for calling customer information based on an integrated information model according to claim 4, characterized in that: The method further comprises: Establishing a user's call feedback, and using the call feedback to update features in the user's account features, wherein the feature update includes time-series-based feature authentication enhancement and conflict feature replacement; Account records are made for user accounts based on feature update results.

6. The method for calling customer information based on an integrated information model according to claim 1, characterized in that: The self-adaptive updating of the 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 update the self-check cycle of the self-check 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, which includes source trust certification, invalid duplication certification, and linkage analysis certification; Complete data adaptive update based on data cleaning and certification results.

7. The method for calling customer information based on an integrated information model according to claim 6, 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.

8. 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 7, 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 information association identifiers of customer information; Adaptive update module: used to establish a data management layer of the data warehouse, the data management layer includes a self-checking update channel and an integrated information model, and the data of the data warehouse is adaptively updated through the self-checking update channel of the data management layer; Information calling module: used to record information calling through the integrated information model of the data management layer and establish the record results; Cache scoring module: 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; Demand forecasting module: used to perform hierarchical caching of customer information through the hierarchical caching strategy, receive user demand information through an 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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