A data synchronization and update optimization method and system for universal cards

Abstract

The present invention discloses a data synchronization and update optimization method and system for universal card types, which belongs to the field of information technology, and specifically includes: integrating Kafka and RabbitMQ to realize real-time synchronization of universal card type data with social security and bank systems, and capturing universal card type data from the source through event triggering; the system adopts a machine learning model to perform efficient data verification and preprocessing, mark incremental data and assign timestamps; uses machine learning to predict data trends, dynamically adjusts data update strategies, and combines intelligent workflows to realize real-time analysis of incremental data; constructs user portraits, and provides personalized recommendation services based on natural language processing and cluster analysis; the system implements TLS / SSL encryption, multi-factor authentication and zero-trust security models to ensure data access security, and is equipped with an automated data backup and rapid recovery mechanism to comprehensively guarantee the security and efficiency of data processing.

Description

Technical Field

[0001] The invention belongs to the field of information technology, and in particular to a method and system for optimizing data synchronization and updating of universal cards. Background Art

[0002] As an important part of the implementation of the national unified labor and social security information system project, the universal card has been widely used throughout the country. It not only carries personal social security information, but also serves as an important certificate for remote and online services, and is interoperable in various places. With the national networking of the Golden Insurance Project application system, the universal card plays an increasingly important role in promoting the convenience and efficiency of social security services. In the operation of the universal card system, data synchronization and updating are crucial links. Since personal social security information will change over time, it is necessary to synchronize these changes to the universal card system in a timely manner to ensure that individuals can enjoy accurate and timely social security services. At the same time, data synchronization is also the basis for realizing the interoperability of universal cards in different places, which is of great significance for promoting the sharing of social security services across regions and departments. Although the universal card system has made some progress in data synchronization, it still faces some technical challenges, including the lack of real-time data synchronization, data leakage and illegal access, and poor system compatibility and scalability.

[0003] For example, the patent with publication number CN112270527A discloses a processing method for social security card data information management, including: after the management service terminal obtains the data information, it processes the data information and stores it in the corresponding layer according to different data storage layer processing methods; creates and organizes the mapping relationship of each layer, and establishes a mapping function; applies the mapping function to the data query kernel interface, and the terminal encapsulates the data information into a metadata package after receiving the data information, and then stores it in the database. When the server needs to call the original information, it first opens the routing information, and then searches for the corresponding terminal location through the terminal address information, and searches for the corresponding metadata package and original data information through the address information of the assigned number. In this technical solution, distributed storage technology is used to integrate the scattered terminal storage space through the network, improve the utilization rate of the equipment, and at the same time improve the search response speed of data during query.

[0004] The above existing technologies all have the following problems: 1) They focus on the hierarchical processing of data information, the establishment of mapping relationships, and the query process of data, and lack a real-time synchronization and update mechanism; 2) The degree of automation of data processing and verification is low; 3) They are relatively traditional in data processing and query, and do not mention the use of machine learning or intelligent algorithms to predict data change trends or optimize data processing processes, and lack intelligence and predictive capabilities; 4) They are weak in security and data governance. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a data synchronization and update optimization method and system for universal card types, which integrates Kafka and RabbitMQ to realize real-time synchronization of universal card data with social security and bank systems, and captures universal card data from the source through event triggering; the system adopts machine learning models for efficient data verification and preprocessing, marks incremental data and assigns timestamps; uses machine learning to predict data trends, dynamically adjusts data update strategies, and combines intelligent workflows to realize real-time analysis of incremental data; constructs user portraits, and provides personalized recommendation services based on natural language processing and cluster analysis; the system implements TLS / SSL encryption, multi-factor authentication and zero-trust security models to ensure data access security, and is equipped with automated data backup and rapid recovery mechanisms to comprehensively ensure the security and efficiency of data processing.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A data synchronization and update optimization method for universal cards, comprising:

[0008] Step S1: Integrate the message queue services of Kafka and RabbitMQ, establish a real-time synchronization channel between the universal card type and the external system, read the universal card type data from the database of the external system in real time through the event trigger mechanism, and push it to the RabbitMQ queue through the interface. The universal card type refers to the social security card;

[0009] Step S2: according to the set data verification rules and verification logic, use the machine learning model to verify the universal card data one by one, and record the verification results. If the universal card data is verified to be correct, the universal card data that is verified to be correct is adaptively preprocessed. At the same time, a timestamp is added to each universal card data in the data table, and the data recognition logic is used to mark the changed universal card data as incremental data;

[0010] Step S3: Use machine learning models to predict the change trend of universal card data and formulate dynamic universal card data update strategies. At the same time, configure an intelligent workflow engine to automatically adjust the update logic and process according to business needs and data status, extract incremental data from the pre-processed universal card data, and use streaming processing methods to process and analyze the incremental data in real time;

[0011] Step S4: Set up online, offline and third-party data sources, use natural language processing methods to parse user interaction texts, combine behavioral data, use cluster analysis methods to build user portraits, and use intelligent recommendation strategies to recommend update items based on user portraits and current universal card data status;

[0012] Step S5: Configure TLS / SSL encryption and adopt a multi-factor authentication mechanism. At the same time, implement a zero-trust security model, authenticate and control all access to external systems, establish a data governance framework, adopt an automated full and incremental data backup strategy, and establish a rapid data recovery mechanism;

[0013] Specifically, the specific steps of step S4 include:

[0014] S4.1: Collect online, offline and third-party data to generate user data ,in, represents the hth user data, where h represents the number of user data;

[0015] S4.2: Based on the collected , construct the user-update item interaction matrix R, where each row in R represents a user and each column represents an update item or service;

[0016] S4.3: Use cluster analysis to Group users based on the characteristics of their interactive texts to form different user portraits;

[0017] Specifically, the specific steps of step S4 also include:

[0018] S4.4: Decompose the user-update item interaction matrix R into a user factor matrix Q and an item factor matrix D using singular value decomposition, and , where T represents transpose and S represents a diagonal matrix;

[0019] S4.5: Use the loss function to find the optimal Q and D. The formula is:

[0020]

[0021] Among them, Loss represents the loss value, represents the actual rating or interaction strength of user u on item i, represents the predicted score based on the user factor matrix and the item factor matrix, represents the elements in the user factor matrix Q, represents the elements in the project factor matrix D, represents the time decay factor, , A control parameter that indicates the degree of time influence, It represents the time interval between the last time user u interacted with item i and now. represents the social network impact factor, represents the similarity between user u and his friend v to item i, represents the weight of controlling social influence, and represents the newly added regularization term, H represents the time decay factor matrix, G represents the social influence matrix, represents the F-norm, represents the regularization coefficient, K represents the dataset with known user-update item interactions;

[0022] S4.6: Use A / B testing methods to evaluate the performance of intelligent recommendation strategies.

[0023] Specifically, the specific formula for verifying the universal card type data in step S2 is:

[0024]

[0025] in, represents the probability that the universal card data is valid given the feature vector x and time variable t of the universal card data, Indicates that the universal card data is valid. represents the i-th insurance characteristic, represents the intercept term of the machine learning model, Indicates insurance characteristics The coefficient of represents the linear term of the machine learning model, represents the cross term of insurance characteristics in the machine learning model, represents the coefficient of the interaction term between the insurance characteristics, represents the L2 regularization term in the machine learning model, represents the regularization strength parameter, W represents the weight vector of the machine learning model, represents the 2-norm, represents the amplitude parameter of the time periodic effect, w represents the angular frequency of the time periodic effect, Represents the kth The associated coefficient, represents the nonlinear transformation function, represents the sine function, n represents the number of insurance characteristics, m represents the number of nonlinear transformation functions, Represents an exponential function.

[0026] Specifically, the specific steps of implementing the zero-trust security model in step S5 include:

[0027] S5.1: Develop a zero-trust strategy, introduce a dynamic policy adjustment mechanism, automatically adjust the policy based on threat intelligence, user behavior analysis, and security events, and implement strong authentication and fine-grained access control mechanisms;

[0028] S5.2: Divide the network into l isolated areas and limit the traffic between areas. Use micro-segmentation technology to further limit lateral movement within the internal network.

[0029] S5.3: Integrate security information and event management systems with security orchestration, automation, and response platforms to use artificial intelligence and machine learning models to analyze network traffic and user behavior, identify abnormal patterns and threats, and set up automated response measures;

[0030] S5.4: Regularly audit and risk assess the zero-trust security model, and optimize the zero-trust strategy, network architecture, and monitoring response system based on the audit results and risk assessment.

[0031] A data synchronization and update optimization system for universal cards, comprising: a data acquisition module, a data verification module, a data prediction module, an intelligent recommendation module, and a data security module;

[0032] The data collection module is used to integrate the message queue services of Kafka and RabbitMQ, establish a real-time synchronization channel with external systems, and collect universal card data from different sources;

[0033] The data verification module is used to verify the universal card data according to the set data verification rules and verification logic, and compare it with the existing database records to verify whether the newly collected data is consistent;

[0034] The data prediction module is used for machine learning models to predict the change trend of universal card data based on historical universal card data trends and formulate dynamic universal card data update strategies;

[0035] The intelligent recommendation module is used to construct a user portrait and recommend update items using an intelligent recommendation algorithm based on the user portrait and the current universal card data status;

[0036] The data security module is used to implement a zero-trust security model and perform identity authentication and permission control on all access to external systems.

[0037] Specifically, the data verification module includes a data marking unit and an adaptive preprocessing unit;

[0038] The data marking unit is used to mark the problematic data in the verified universal card data, distinguish valid data from data to be processed, and obtain incremental data;

[0039] The adaptive preprocessing unit is used to dynamically adjust the preprocessing process according to data quality feedback.

[0040] Specifically, the data prediction module includes: a trend prediction unit, a workflow engine unit, and a stream processing unit;

[0041] The trend prediction unit is used to use a machine learning model to analyze historical universal card data and predict future development trends;

[0042] The workflow engine unit is used to automatically manage the training, testing and deployment processes of the machine learning model;

[0043] The stream processing unit is used to process the data stream in real time and perform instant analysis on the newly incoming data.

[0044] Specifically, an electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a data synchronization and update optimization method for universal cards when executing the computer program.

[0045] Specifically, a computer-readable storage medium stores computer instructions, which, when executed, execute the steps of a data synchronization and update optimization method for a universal card.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. The present invention proposes a data synchronization and update optimization system for universal cards, and optimizes and improves the architecture, operation steps and processes. The system has the advantages of simple processes, low investment and operation costs, and low production work costs.

[0048] 2. The present invention proposes a data synchronization and update optimization method for universal card types. By integrating Kafka and RabbitMQ message queue services, a real-time synchronization channel between universal card data and external systems is established, ensuring real-time update and efficient transmission of data, shortening the delay in data synchronization, and improving the response speed and overall efficiency of the system; a machine learning model is used for data verification to automatically identify and correct data errors, ensuring the accuracy and completeness of universal card data; a machine learning model is used to predict the changing trend of universal card data, and a dynamic update strategy is formulated accordingly, so that the system can flexibly respond to data changes and business needs; an intelligent workflow engine is configured to realize the automatic adjustment of update logic and processes, thereby improving the intelligence level and processing efficiency of the system.

[0049] 3. The present invention proposes a data synchronization and update optimization method for universal cards, configures TLS / SSL encryption and multi-factor identity authentication mechanism, and implements a zero-trust security model, which improves the security of external systems and ensures the confidentiality, integrity and availability of data during transmission and storage. At the same time, a data governance framework is established, an automated full and incremental data backup strategy is adopted, and a rapid data recovery mechanism is established to provide guarantees for the security and recoverability of data; data processing efficiency is improved through distributed storage technology and intelligent data processing procedures. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of a data synchronization and update optimization method for a universal card type of the present invention;

[0051] Figure 2 This is a principle flow chart of a data synchronization and update optimization method for a universal card of the present invention;

[0052] Figure 3 It is a flowchart of the implementation process of the zero-trust security model of a data synchronization and update optimization method for a universal card type of the present invention;

[0053] Figure 4 This is a diagram of the data synchronization and update optimization system architecture of a universal card of the present invention. DETAILED DESCRIPTION

[0054] Example 1

[0055] See also Figure 1-Figure 3 The present invention provides an embodiment: a data synchronization and update optimization method for universal cards, which is applicable to the field of information technology and also to the field of social security cards. In the present invention, the universal card refers to a social security card, and includes the following steps:

[0056] Step S1: Integrate the message queue services of Kafka and RabbitMQ, establish a real-time synchronization channel between the universal card type data and the external system, read the universal card type data from the database of the external system in real time through the event trigger mechanism, and push it to the RabbitMQ queue through the interface. The universal card type refers to the social security card;

[0057] In the present invention, universal card type data refers to insurance data, external system refers to banking system and social security system, and feature vector of universal card type data refers to insurance feature vector, which is obtained by feature extraction of insurance data.

[0058] Step S2: according to the set data verification rules and verification logic, use the machine learning model to verify the universal card data one by one, and record the verification results. If the universal card data is verified to be correct, the universal card data that is verified to be correct is adaptively preprocessed. At the same time, a timestamp is added to each universal card data in the data table, and the data recognition logic is used to mark the changed universal card data as incremental data;

[0059] Among them, in the present invention, the data verification rules and verification logic include:

[0060] (1) ID card number verification: Check whether the ID card number is 18 digits and complies with the ID card number coding rules;

[0061] (2) Date of birth verification: Check whether the date of birth is a valid date and is consistent with the date of birth in the ID card number;

[0062] (3) Gender verification: Calculate gender based on ID number and compare it with the gender filled in by the user;

[0063] (4) Required fields verification: Ensure that all required fields, such as name, ID number, and date of birth, are filled in;

[0064] (5) Logical relationship verification: Check the consistency between age and date of birth to ensure that the age calculation is correct.

[0065] Furthermore, the specific steps of step S2 include:

[0066] S2.1: Define data validation rules: clarify which fields are required, set the range or format of field values, and define the logical relationship between data;

[0067] S2.2: Configure the machine learning model: Select the Isolation Forest anomaly detection algorithm to identify data that does not conform to the normal pattern, and use the historical general card data to train the machine learning model so that the machine learning model learns the characteristics of normal data;

[0068] S2.3: Verify universal card data one by one: Read the universal card data to be verified, perform basic verification according to the defined data verification rules, use machine learning models to further verify the data that has passed the basic verification, identify potential abnormal data, and record the verification results, including which data has passed the verification and which data has been marked as abnormal;

[0069] S2.4: Adaptive preprocessing:

[0070] (1) Condition judgment: Perform adaptive preprocessing on the data that has passed the verification, which involves dynamically adjusting the preprocessing steps according to the data characteristics, such as dynamic field selection and conversion rules;

[0071] (2) Timestamp addition: Add the current timestamp to each preprocessed data record as the time basis for data update;

[0072] S2.5: Add timestamp and mark incremental data: Record a timestamp for each verified universal card type data in the data table to indicate the last update time of the data. Use data recognition logic, such as comparing the difference between the current data and the last record, to mark the changed universal card type data as incremental data.

[0073] Step S3: Use the machine learning model to predict the change trend of universal card data, formulate a dynamic universal card data update strategy, and configure an intelligent workflow engine to automatically adjust the update logic and process according to business needs and data status, and extract incremental data from the pre-processed universal card data, and use the streaming processing method to process and analyze the incremental data in real time, wherein the machine learning model prediction process and streaming processing method are the prior art content in this field, and are not the inventive solution of this application, and will not be described in detail here;

[0074] Furthermore, the dynamic universal card data update strategy includes:

[0075] (1) Update frequency: Dynamically adjust the frequency of data updates based on the speed of change and importance of universal card data. For example, if it is predicted that a certain universal card data will change dramatically, the update frequency may be increased to obtain the latest data.

[0076] (2) Update scope: Determine which universal card data needs to be updated based on the confidence level of the prediction results, the importance of the data, or the relevance to business needs;

[0077] (3) Prioritization: When there are multiple data that need to be updated, prioritize the data based on business urgency and the impact of the forecast results to ensure that the most important data is updated first;

[0078] (4) Data integration and verification: During the update process, data from different data sources needs to be integrated and a series of verification rules need to be applied to ensure the quality and accuracy of the data;

[0079] (5) Exception handling: During the data update process, when encountering abnormal situations, such as data inconsistency and missing values, formulate corresponding processing strategies to deal with these problems.

[0080] Step S4: Set up online, offline and third-party data sources, use natural language processing methods to parse user interaction texts, combine behavioral data, use cluster analysis methods to build user portraits, and use intelligent recommendation strategies to recommend update items based on user portraits and current universal card data status;

[0081] Step S5: Configure TLS / SSL encryption and adopt a multi-factor authentication mechanism. At the same time, implement a zero-trust security model, authenticate and control permissions for all access to external systems, establish a data governance framework, adopt automated full and incremental data backup strategies, and establish a rapid data recovery mechanism.

[0082] The specific formula for verifying the universal card type data in step S2 is:

[0083]

[0084] in, represents the probability that the universal card data is valid given the feature vector x and time variable t of the universal card data, Indicates that the universal card data is valid. represents the i-th insurance characteristic, Represents the intercept term of the machine learning model, reflecting the effect of no feature or time consideration. The probability of occurrence, Indicates insurance characteristics The coefficient is the linear influence of the feature on the predicted probability. If it is a positive value, it means that the probability increases, and if it is a negative value, it means that the probability decreases. represents the linear term of the machine learning model, represents the cross term of insurance characteristics in the machine learning model, represents the coefficient of the interaction term between the insurance characteristics, represents the L2 regularization term in the machine learning model, represents the regularization strength parameter, W represents the weight vector of the machine learning model, represents the 2-norm, represents the amplitude parameter of the time periodic effect, which is used to capture the periodicity in the data, and w represents the angular frequency of the time periodic effect. Represents the kth The associated coefficient, represents the nonlinear transformation function, represents the sine function, n represents the number of insurance characteristics, m represents the number of nonlinear transformation functions, Represents an exponential function.

[0085] The specific steps of step S4 include:

[0086] S4.1: Collect online, offline and third-party data to generate user data ,in, represents the hth user data, where h represents the number of user data;

[0087] S4.2: Based on the collected , construct the user-update item interaction matrix R, where each row in R represents a user and each column represents an update item or service;

[0088] S4.3: Use cluster analysis to The characteristics of the user interaction text are grouped to form different user portraits, wherein cluster analysis is the prior art content in this field and is not the inventive solution of the present application, and will not be described in detail here;

[0089] S4.4: Decompose the user-update item interaction matrix R into a user factor matrix Q and an item factor matrix D using singular value decomposition, and , where T represents transpose and S represents a diagonal matrix;

[0090] S4.5: Use the loss function to find the optimal Q and D. The formula is:

[0091]

[0092] Among them, Loss represents the loss value, represents the actual rating or interaction strength of user u on item i, represents the predicted score based on the user factor matrix and the item factor matrix, represents the elements in the user factor matrix Q, represents the elements in the project factor matrix D, represents the time decay factor, , A control parameter that indicates the degree of time influence, It represents the time interval between the last time user u interacted with item i and now. represents the social network impact factor, represents the similarity between user u and his friend v to item i, represents the weight of controlling social influence, and represents the newly added regularization term, H represents the time decay factor matrix, G represents the social influence matrix, represents the F-norm, represents the regularization coefficient, K represents the dataset with known user-update item interactions;

[0093] S4.6: Use A / B testing methods to evaluate the performance of intelligent recommendation strategies.

[0094] The specific steps for implementing the zero trust security model in step S5 include:

[0095] S5.1: Develop a zero-trust strategy, introduce a dynamic policy adjustment mechanism, automatically adjust the policy based on threat intelligence, user behavior analysis, and security events, and implement strong authentication and fine-grained access control mechanisms;

[0096] S5.2: Divide the network into l isolated areas and limit the traffic between areas. Use micro-segmentation technology to further limit lateral movement within the internal network.

[0097] S5.3: Integrate security information and event management systems with security orchestration, automation, and response platforms to use artificial intelligence and machine learning models to analyze network traffic and user behavior, identify abnormal patterns and threats, and set up automated response measures;

[0098] S5.4: Regularly audit and risk assess the zero-trust security model, and optimize the zero-trust strategy, network architecture, and monitoring response system based on the audit results and risk assessment.

[0099] Example 2

[0100] See also Figure 4 Another embodiment provided by the present invention is a data synchronization and update optimization system for universal cards, comprising:

[0101] Data collection module, data verification module, data prediction module, intelligent recommendation module, data security module;

[0102] The data collection module is used to integrate the message queue services of Kafka and RabbitMQ, establish a real-time synchronization channel with external systems, and collect universal card data from different sources;

[0103] Among them, the collection of universal card data from different sources involves interfacing with multiple external systems such as government databases, medical institutions, and banking systems, as well as collecting data from smart terminals or self-service devices using wired or wireless communication technologies;

[0104] The data verification module is used to verify the universal card data according to the set data verification rules and verification logic, including checking whether the data format is correct, whether the data is missing, whether there is a logical error, and comparing with the existing database records to verify whether the newly collected data is consistent;

[0105] The data prediction module is used for machine learning models to predict the change trend of universal card data based on historical universal card data trends and formulate dynamic universal card data update strategies, which helps social security management departments to plan resource allocation in advance, optimize policy formulation, and improve service efficiency and response speed;

[0106] The intelligent recommendation module is used to construct a user portrait and recommend update items using an intelligent recommendation algorithm based on the user portrait and the current universal card data status;

[0107] The data security module is used to implement a zero-trust security model, authenticate and control all access to external systems, and ensure information security throughout the entire data processing process, including data encryption storage, access control, data transmission encryption, and anti-tampering mechanisms, aiming to protect the privacy of participants, prevent data leakage, illegal access or malicious attacks, and ensure the compliance and credibility of the universal card system.

[0108] The data acquisition module includes: data integration unit and event triggering unit;

[0109] Data integration unit, used to integrate data from social security systems, hospital information systems, and bank systems, and to realize centralized collection of universal card data through interfaces and file import;

[0110] The event trigger unit is used to monitor internal and external events of the system and automatically trigger the data collection process to ensure the immediacy and timeliness of the data.

[0111] The data verification module includes: a data verification unit, a data marking unit, a preprocessing unit, and an adaptive preprocessing unit;

[0112] The data verification unit is used to check the collected universal card data, verify the format, integrity and consistency of the data, and ensure the quality of the universal card data;

[0113] A data marking unit is used to mark problematic data in the verified universal card data, distinguish valid data from data to be processed, and obtain incremental data;

[0114] The pre-processing unit is used to perform preliminary processing on the incremental data, such as removing duplicate records, processing null values, and processing abnormal values;

[0115] The adaptive preprocessing unit is used to dynamically adjust the preprocessing process according to data quality feedback, such as automatically optimizing the processing rules for specific errors that occur frequently.

[0116] The data prediction module includes: a trend prediction unit, a workflow engine unit, and a stream processing unit;

[0117] Trend prediction unit, which uses machine learning models to analyze historical universal card data and predict future development trends;

[0118] Workflow engine unit, which is used to automatically manage the training, testing, and deployment processes of machine learning models to ensure the continuous optimization and operation of prediction models;

[0119] The stream processing unit is used to process data streams in real time, conduct instant analysis on new incoming data, and support real-time prediction and decision-making needs.

[0120] The intelligent recommendation module includes: data integration unit, image building unit, and intelligent recommendation unit;

[0121] Data integration unit, used to integrate multi-source data and build a unified user view;

[0122] A portrait building unit, used to build a user portrait based on user data;

[0123] The intelligent recommendation unit is used to use user portraits and algorithm models to recommend suitable social security services, health plans, and preferential policies to insured persons.

[0124] The data security module includes: encryption and authentication unit, zero trust implementation unit, and data governance unit;

[0125] Encryption and authentication unit, used to protect sensitive data by encryption, and to implement user identity authentication to ensure the confidentiality of data during transmission and storage;

[0126] Zero Trust Implementation Unit, which follows the zero trust principle and is used to strictly verify all access requests, regardless of their source, to enhance system security protection;

[0127] The data governance unit is responsible for data lifecycle management, including data classification, permission management, and compliance auditing, to ensure the legality and compliance of data use.

[0128] Example 3

[0129] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a data synchronization and update optimization method for a universal card are implemented. For details, please refer to the above method embodiment, which will not be repeated here.

[0130] A computer-readable storage medium stores computer instructions, which, when executed, execute the steps of a data synchronization and update optimization method for a universal card, wherein the storage medium may be a volatile or non-volatile computer-readable storage medium.

[0131] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation modes, which are merely illustrative rather than restrictive. Under the guidance of the present invention, ordinary technicians in the field may also change, modify, replace and modify the above-mentioned embodiments without departing from the purpose and scope of protection of the present invention, and all of these are within the protection of the present invention.

[0132] If the disclosed technical solution involves personal information, the product using the disclosed technical solution has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the disclosed technical solution involves sensitive personal information, the product using the disclosed technical solution has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A data synchronization and update optimization method for universal cards, characterized in that: include: Step S1: Integrate the message queue services of Kafka and RabbitMQ, establish a real-time synchronization channel between the universal card type and the external system, read the universal card type data from the database of the external system in real time through the event trigger mechanism, and push it to the RabbitMQ queue through the interface. The universal card type refers to the social security card; Step S2: according to the set data verification rules and verification logic, use the machine learning model to verify the universal card data one by one, and record the verification results. If the universal card data is verified to be correct, the universal card data that is verified to be correct is adaptively preprocessed. At the same time, a timestamp is added to each universal card data in the data table, and the data recognition logic is used to mark the changed universal card data as incremental data; Step S3: Use machine learning models to predict the change trend of universal card data and formulate dynamic universal card data update strategies. At the same time, configure an intelligent workflow engine to automatically adjust the update logic and process according to business needs and data status, extract incremental data from the pre-processed universal card data, and use streaming processing methods to process and analyze the incremental data in real time; Step S4: Set up online, offline and third-party data sources, use natural language processing methods to parse user interaction texts, combine behavioral data, use cluster analysis methods to build user portraits, and use intelligent recommendation strategies to recommend update items based on user portraits and current universal card data status; Step S5: Configure TLS / SSL encryption and adopt a multi-factor authentication mechanism. At the same time, implement a zero-trust security model, authenticate and control all access to external systems, establish a data governance framework, adopt an automated full and incremental data backup strategy, and establish a rapid data recovery mechanism; The specific steps of step S4 include: S4.1: Collect online, offline and third-party data sources to generate user data ,in, represents the hth user data, where h represents the number of user data; S4.2: Based on the collected , construct the user-update item interaction matrix R, where each row in R represents a user and each column represents an update item or service; S4.3: Based on The characteristics of the user interaction text are clustered using cluster analysis methods to form different user portraits; The specific steps of step S4 also include: S4.4: Decompose the user-update item interaction matrix R into a user factor matrix Q and an item factor matrix D using singular value decomposition, and , where T represents transpose and S represents a diagonal matrix; S4.5: Use the loss function to find the optimal Q and D. The formula is: ； Among them, Loss represents the loss value, represents the actual rating or interaction strength of user u on item i, represents the predicted score based on the user factor matrix and the item factor matrix, represents the elements in the user factor matrix Q, represents the elements in the project factor matrix D, represents the time decay factor, , A control parameter that indicates the degree of time influence, It represents the time interval between the last time user u interacted with item i and now. represents the social network impact factor, represents the similarity between user u and his friend v to item i, represents the weight of controlling social influence, and represents the newly added regularization term, H represents the time decay factor matrix, G represents the social influence matrix, represents the F-norm, represents the regularization coefficient, K represents the dataset with known user-update item interactions; S4.6: Use A / B testing methods to evaluate the performance of intelligent recommendation strategies.

2. A data synchronization and update optimization method for universal cards as claimed in claim 1, characterized in that: The specific formula for verifying the universal card type data in step S2 is: ； in, represents the probability that the universal card data is valid given the feature vector x and time variable t of the universal card data, Indicates that the universal card data is valid. represents the i-th insurance characteristic, represents the intercept term of the machine learning model, Indicates insurance characteristics The coefficient of represents the linear term of the machine learning model, represents the cross term of insurance characteristics in the machine learning model, represents the coefficient of the interaction term between the insurance characteristics, represents the L2 regularization term in the machine learning model, represents the regularization strength parameter, W represents the weight vector of the machine learning model, represents the 2-norm, represents the amplitude parameter of the time periodic effect, w represents the angular frequency of the time periodic effect, Represents the kth The associated coefficient, represents the nonlinear transformation function, represents the sine function, n represents the number of insurance characteristics, m represents the number of nonlinear transformation functions, Represents an exponential function.

3. A data synchronization and update optimization method for universal cards as claimed in claim 2, characterized in that: The specific steps of implementing the zero trust security model in step S5 include: S5.1: Develop a zero-trust strategy, introduce a dynamic policy adjustment mechanism, automatically adjust the policy based on threat intelligence, user behavior analysis, and security events, and implement strong authentication and fine-grained access control mechanisms; S5.2: Divide the network into l isolated areas and limit the traffic between areas. Use micro-segmentation technology to further limit lateral movement within the internal network. S5.3: Integrate security information and event management systems with security orchestration, automation, and response platforms to use artificial intelligence and machine learning models to analyze network traffic and user behavior, identify abnormal patterns and threats, and set up automated response measures; S5.4: Regularly audit and risk assess the zero-trust security model, and optimize the zero-trust strategy, network architecture, and monitoring response system based on the audit results and risk assessment.

4. A data synchronization and update optimization system for universal cards, which is used to implement a data synchronization and update optimization method for universal cards as claimed in any one of claims 1 to 3, characterized in that: include: Data collection module, data verification module, data prediction module, intelligent recommendation module, data security module; The data collection module is used to integrate the message queue services of Kafka and RabbitMQ, establish a real-time synchronization channel with external systems, and collect universal card data from different sources; The data verification module is used to verify the universal card data according to the set data verification rules and verification logic, and compare it with the existing database records to verify whether the newly collected data is consistent; The data prediction module is used to use a machine learning model to predict the change trend of universal card data based on historical universal card data trends and formulate a dynamic universal card data update strategy; The intelligent recommendation module is used to construct a user portrait and recommend update items using an intelligent recommendation algorithm based on the user portrait and the current universal card data status; The data security module is used to implement a zero-trust security model and perform identity authentication and permission control on all access to external systems.

5. A data synchronization and update optimization system for universal cards as claimed in claim 4, characterized in that: The data verification module includes a data marking unit and an adaptive preprocessing unit; The data marking unit is used to identify and mark the changed universal card type data, i.e., incremental data; The adaptive preprocessing unit is used to dynamically adjust the preprocessing process according to the quality feedback of the verified data.

6. A data synchronization and update optimization system for universal cards as claimed in claim 5, characterized in that: The data prediction module includes: a trend prediction unit, a workflow engine unit, and a stream processing unit; The trend prediction unit is used to use a machine learning model to analyze historical universal card data and predict future development trends; The workflow engine unit is used to automatically manage the training, testing and deployment processes of the machine learning model; The stream processing unit is used to process the data stream in real time and perform instant analysis on the newly incoming data.

7. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the data synchronization and update optimization method for a universal card type described in any one of claims 1-3 are implemented.

8. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, the steps of a data synchronization and update optimization method for a universal card type as described in any one of claims 1-3 are executed.

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