Data security access system based on big data

Abstract

The invention discloses a data security access system based on big data. The data security access system comprises a security management module for performing omnibearing control on system access authority; the firewall module is used for detecting network activities and capturing, analyzing and processing all data packets trying to pass through a firewall; the data encryption module is used for encrypting document contents by adopting an encryption algorithm; the data backup and recovery module is used for backing up data regularly; and the asynchronous processing module is responsible for putting the operations with relatively high non-real-time requirements into a background task queue, processing the operations by a background thread or process, receiving asynchronous tasks from the data backup and recovery module and the personalized service module, and sending the asynchronous tasks to the data backup and recovery module. The asynchronous tasks are put into a task queue to wait for processing; and the personalized service module is responsible for analyzing an access mode of the user and providing personalized data access suggestions and services according to historical access records and preferences of the user.

Description

Technical Field

[0001] The present invention relates to the technical field of data security access, and in particular to a data security access system based on big data. Background Art

[0002] Big data has the characteristics of huge data volume, diverse data types, and fast processing speed, providing enterprises and institutions with unprecedented data analysis and processing capabilities. The primary purpose of the data security access system based on big data is to protect data security. By adopting advanced technical means and management measures, the confidentiality, integrity and availability of data during transmission, storage, processing and use are ensured. On the premise of ensuring data security, improving data access efficiency is also an important purpose of the system. By optimizing the data access process and improving the data processing speed, users can access the required data more quickly and conveniently, thereby improving work efficiency and user experience.

[0003] In the prior art, operations with non-real-time requirements may occupy a large amount of system resources, resulting in unbalanced system load and affecting the execution efficiency of other operations with high real-time requirements. Therefore, a data security access system based on big data is proposed. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art that operations with non-high real-time requirements may occupy a large amount of system resources, resulting in unbalanced system load and affecting the execution efficiency of other operations with high real-time requirements, and to propose a data security access system based on big data.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A data security access system based on big data, comprising:

[0007] Security management module: comprehensively control system access rights, strictly classify user rights, and periodically evaluate information security-related factors within the system;

[0008] Firewall module: detects network activities, captures, analyzes and processes all data packets that attempt to pass through the firewall, stores and updates the communication status of the network and applications, combines it with the implemented custom rules, captures security violations, and ensures data integrity and security;

[0009] Data encryption module: Use encryption algorithms to encrypt document contents to prevent unauthorized access;

[0010] Data backup and recovery module: Back up data regularly to prevent data loss or damage. At the same time, establish a disaster recovery plan to ensure that data can be quickly restored in the event of data loss or system failure;

[0011] Cache management module: introduces a cache mechanism to store data or query results that users frequently access, so as to reduce the number of direct accesses to the database and thus improve data access speed. When a user initiates a data access request, it first checks whether the required data exists in the cache. If so, it directly returns the data in the cache. Otherwise, it accesses the database again, monitors the cache hit rate, adjusts the cache size or strategy based on the hit rate, and regularly cleans up expired or invalid cache data to maintain the accuracy and effectiveness of the cache.

[0012] Asynchronous processing module: For operations with high non-real-time requirements, such as data backup and batch data processing, the asynchronous processing module is responsible for placing these operations into the background task queue, which is processed by a dedicated background thread or process. This can reduce system response time, improve the fluency of user operations, and provide a task status query interface so that users can understand the progress and results of asynchronous tasks. The asynchronous processing module will receive asynchronous tasks from the data backup and recovery module and the personalized service module, and place the asynchronous tasks into the task queue for processing;

[0013] Personalized service module: Based on the user's historical access records and preferences, the personalized service module is responsible for analyzing the user's access patterns and providing personalized data access suggestions and services. For example, based on the user's search history and click behavior, it recommends relevant data or resources.

[0014] Behavior pattern analysis module: responsible for monitoring and analyzing user access behavior, using behavior analysis algorithms to identify abnormal access behavior or potential security risks, and generating early warning signals or taking corresponding security measures based on the identification results.

[0015] The above technical solution further includes:

[0016] Furthermore, the cache management module includes a cache storage unit, a cache policy management unit, a cache monitoring unit and a cache cleaning unit. The cache storage unit is responsible for actually storing cache data and adopts efficient storage structures, such as hash tables, red-black trees, etc., to quickly locate and access cache data. The cache policy management unit uses LRU to optimize the use efficiency of the cache and dynamically adjusts the policy parameters according to the cache access mode and business needs. The cache monitoring unit collects and analyzes cache access logs and calculates key indicators such as hit rate to provide a basis for adjusting the cache policy. The cache cleaning unit regularly cleans up expired or invalid cache data and regularly scans and deletes expired or invalid cache data according to the set cleaning strategy (such as timestamp, version number, etc.).

[0017] Furthermore, the cache policy management unit optimizes the use efficiency of the cache using LRU and dynamically adjusts the policy parameters according to the cache access mode and business requirements, including the following steps:

[0018] Initialize the LRU cache: set the size of the cache (i.e. the amount of data that the cache can store), create a doubly linked list and a hash table (or dictionary), the doubly linked list is used to maintain the order of data access, and the hash table is used to quickly locate the position of data in the linked list;

[0019] Data access: When a user requests to access a certain data, first check whether the data is already in the cache. If so, remove the data from the linked list and reinsert it to the head of the linked list (indicating that the data has been recently accessed). If not in the cache, load the data from the database and check whether the cache is full. If not, insert the new data to the head of the linked list and record the location of the data in the hash table. If the cache is full, eliminate the data at the end of the linked list (i.e., the least recently used data) according to the LRU algorithm, and then insert the new data to the head of the linked list.

[0020] Dynamically adjust policy parameters: Dynamically adjust parameters such as the size of the LRU cache and the capacity of the hash table based on business needs and cache access patterns. If the system detects a surge in data access during certain time periods, temporarily increase the cache size to cope with peak access needs. If it is found that some data is frequently accessed while other data is rarely accessed, dynamically adjust the cache allocation strategy based on the access frequency of these data.

[0021] Furthermore, the asynchronous processing module includes a task queue management unit, a background task processing unit, a task status monitoring unit and a task scheduling and optimization unit. The task queue management unit is responsible for storing and managing asynchronous tasks to be processed, which may include data backup, log analysis, batch data processing, etc. The background task processing unit is responsible for taking out tasks from the task queue and processing them. The background task processing unit is composed of a special background thread or process, which continuously takes out tasks from the task queue and executes them. The background task processing unit selects a processing strategy according to the type and priority of the task to ensure that the task can be completed efficiently. The task status monitoring unit is responsible for monitoring the processing status of the task and providing a query interface for task progress and results. The task scheduling and optimization unit is responsible for scheduling the execution of tasks according to the system's resource conditions and task requirements, and optimizing the processing efficiency of tasks. The task scheduling and optimization unit will formulate scheduling strategies based on factors such as task priority and resource usage, and dynamically adjust them according to actual conditions.

[0022] Furthermore, the background task processing unit is responsible for taking out tasks from the task queue and processing them, including the following steps:

[0023] Task queue monitoring and task extraction: The background task processing unit checks the task queue regularly or in real time. Once a new task is found, it will be extracted immediately and prepared for processing. The task queue is a buffer for storing tasks to be processed, and is sorted according to the priority and arrival time of the tasks.

[0024] Task type and priority determination: After extracting the task, the background task processing unit determines the processing strategy to be adopted according to the type and priority of the task, and the processing strategy includes immediate processing, delayed processing and batch processing;

[0025] Task processing and resource allocation: After the processing strategy is determined, the background task processing unit will start processing the task and allocate the corresponding system resources;

[0026] Task result feedback and log recording: After the task processing is completed, the background task processing unit sends the task result to the requester through a predefined interface or message queue. At the same time, the task execution process, results, exception information, etc. are recorded in the log system.

[0027] Furthermore, the personalized service module includes a user data collection unit, a data analysis and mining unit, a personalized recommendation engine unit and a user feedback collection and processing unit. The user data collection unit is responsible for collecting the user's historical access data, including access time, access frequency, access content, etc., and collects user data in real time or periodically through log records, user behavior tracking, etc., and stores it in a secure data warehouse. The data analysis and mining unit uses cluster analysis to analyze user data to identify user access patterns and preferences. The personalized recommendation engine unit generates personalized data access recommendations and services based on the results of the data analysis and mining unit. The user feedback collection and processing unit is responsible for collecting user feedback on personalized services, including satisfaction, improvement suggestions, etc., and optimizes recommendation algorithms and services based on feedback.

[0028] Furthermore, the data analysis and mining unit analyzes the user data using cluster analysis to identify the user's access patterns and preferences, and the personalized recommendation engine unit generates personalized data access suggestions and services based on the results of the data analysis and mining unit. The specific steps are:

[0029] Data collection and preprocessing: Collect user data, including user behavior data, user attribute data, etc., and preprocess the data, such as data cleaning, data conversion, data reduction, etc., to improve the quality and availability of the data;

[0030] Cluster analysis:

[0031] Determine the number of clusters: Use the elbow rule to determine the number of clusters k;

[0032] Initialization: Randomly select k initial centroids;

[0033] Assign data points: Assign each data point to the cluster with the nearest centroid;

[0034] Update the centroid: Calculate the average of all data points in each cluster as the new centroid;

[0035] Repeat the distribution of data points and the update of the centroid until the centroid no longer changes or the maximum number of iterations is reached. The update formula of the centroid is: Among them, C i is the centroid of the ith cluster, S i is the set of data points in the i-th cluster, and x is a data point;

[0036] Pattern recognition and preference analysis: Analyze clustering results, identify user access patterns and preferences, and use association rule mining to discover the correlation and regularity between user behaviors;

[0037] Receive data analysis results: receive cluster analysis results and user access patterns, preferences and other information;

[0038] Generate personalized recommendations: Based on the user's interests and behaviors, consider the user's historical behavior, current needs, and potential interests, and use collaborative filtering to generate personalized data access suggestions and services;

[0039] Evaluation and optimization of recommendation results: Evaluate the quality of recommendation results, and adjust collaborative filtering and parameters based on the evaluation results to optimize the recommendation effect.

[0040] The present invention has the following beneficial effects:

[0041] In the present invention, the asynchronous processing module allows the system to allocate resources to different types of tasks more efficiently. Operations with high real-time requirements can continue to be processed by the main thread or foreground process with priority, ensuring the system's response speed and user experience. Non-real-time operations are executed in the background and will not compete for resources with foreground operations, thereby avoiding resource contention and load imbalance. By making non-real-time operations asynchronous, the system can handle these tasks more flexibly, avoiding system crashes or performance degradation caused by long task execution time or excessive resource usage. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a system block diagram of a data security access system based on big data proposed by the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 As shown, the present invention is a data security access system based on big data, comprising:

[0045] Security management module: comprehensively control system access rights, strictly classify user rights, and periodically evaluate information security-related factors within the system;

[0046] Firewall module: detects network activities, captures, analyzes and processes all data packets that attempt to pass through the firewall, stores and updates the communication status of the network and applications, combines it with the implemented custom rules, captures security violations, and ensures data integrity and security;

[0047] Data encryption module: Use encryption algorithms to encrypt document contents to prevent unauthorized access;

[0048] Data backup and recovery module: Back up data regularly to prevent data loss or damage. At the same time, establish a disaster recovery plan to ensure that data can be quickly restored in the event of data loss or system failure;

[0049] Cache management module: introduces a cache mechanism to store data or query results that users frequently access, so as to reduce the number of direct accesses to the database and thus improve data access speed. When a user initiates a data access request, it first checks whether the required data exists in the cache. If so, it directly returns the data in the cache. Otherwise, it accesses the database again, monitors the cache hit rate, adjusts the cache size or strategy based on the hit rate, and regularly cleans up expired or invalid cache data to maintain the accuracy and effectiveness of the cache.

[0050] Asynchronous processing module: For operations with high non-real-time requirements, such as data backup and batch data processing, the asynchronous processing module is responsible for placing these operations into the background task queue, which is processed by a dedicated background thread or process. This can reduce system response time, improve the fluency of user operations, and provide a task status query interface so that users can understand the progress and results of asynchronous tasks. The asynchronous processing module will receive asynchronous tasks from the data backup and recovery module and the personalized service module, and place the asynchronous tasks into the task queue for processing;

[0051] Personalized service module: Based on the user's historical access records and preferences, the personalized service module is responsible for analyzing the user's access patterns and providing personalized data access suggestions and services. For example, based on the user's search history and click behavior, it recommends relevant data or resources.

[0052] Behavior pattern analysis module: responsible for monitoring and analyzing user access behavior, using behavior analysis algorithms to identify abnormal access behavior or potential security risks, and generating early warning signals or taking corresponding security measures based on the identification results.

[0053] In one embodiment, for the above-mentioned data acquisition module, the data acquisition module includes a traffic flow data acquisition unit, a traffic accident data acquisition unit and a meteorological data acquisition unit. The traffic flow data acquisition unit is responsible for collecting traffic flow data in real time from sensors installed at key positions. The traffic accident data acquisition unit is responsible for data interaction with the traffic police department to obtain detailed information on traffic accidents. The meteorological data acquisition unit is responsible for integrating real-time weather information provided by the meteorological department. A data sharing mechanism is established between the various acquisition units to synchronize the collected data to the system's central database in real time. The central database is set in the data storage and management module. During the data sharing process, each acquisition unit performs data verification and validation.

[0054] In one embodiment, for the above-mentioned cache management module, the cache management module includes a cache storage unit, a cache policy management unit, a cache monitoring unit and a cache cleaning unit. The cache storage unit is responsible for actually storing cache data and adopts efficient storage structures, such as hash tables, red-black trees, etc., to quickly locate and access cache data. The cache policy management unit uses LRU to optimize the use efficiency of the cache and dynamically adjusts the policy parameters according to the cache access mode and business needs. The cache monitoring unit collects and analyzes cache access logs and calculates key indicators such as hit rate to provide a basis for adjusting the cache policy. The cache cleaning unit regularly cleans up expired or invalid cache data and regularly scans and deletes expired or invalid cache data according to the set cleaning strategy (such as timestamp, version number, etc.).

[0055] In one embodiment, for the cache policy management unit, the cache policy management unit optimizes the use efficiency of the cache using LRU, and dynamically adjusts the policy parameters according to the cache access mode and business requirements, including the following steps:

[0056] Initialize the LRU cache: set the size of the cache (i.e. the amount of data that the cache can store), create a doubly linked list and a hash table (or dictionary), the doubly linked list is used to maintain the order of data access, and the hash table is used to quickly locate the position of data in the linked list;

[0057] Data access: When a user requests to access a certain data, first check whether the data is already in the cache. If so, remove the data from the linked list and reinsert it to the head of the linked list (indicating that the data has been recently accessed). If not in the cache, load the data from the database and check whether the cache is full. If not, insert the new data to the head of the linked list and record the location of the data in the hash table. If the cache is full, eliminate the data at the end of the linked list (i.e., the least recently used data) according to the LRU algorithm, and then insert the new data to the head of the linked list.

[0058] Dynamically adjust policy parameters: Dynamically adjust parameters such as the size of the LRU cache and the capacity of the hash table based on business needs and cache access patterns. If the system detects a surge in data access during certain time periods, temporarily increase the cache size to cope with peak access needs. If it is found that some data is frequently accessed while other data is rarely accessed, dynamically adjust the cache allocation strategy based on the access frequency of these data.

[0059] In one embodiment, for the above-mentioned asynchronous processing module, the asynchronous processing module includes a task queue management unit, a background task processing unit, a task status monitoring unit and a task scheduling and optimization unit. The task queue management unit is responsible for storing and managing asynchronous tasks to be processed, which may include data backup, log analysis, batch data processing, etc. The background task processing unit is responsible for taking out tasks from the task queue and processing them. The background task processing unit is composed of a special background thread or process, which continuously takes out tasks from the task queue and executes them. The background task processing unit selects a processing strategy according to the type and priority of the task to ensure that the task can be completed efficiently. The task status monitoring unit is responsible for monitoring the processing status of the task and providing a query interface for task progress and results. The task scheduling and optimization unit is responsible for scheduling the execution of tasks according to the system's resource conditions and task requirements, and optimizing the processing efficiency of tasks. The task scheduling and optimization unit will formulate scheduling strategies based on factors such as task priority and resource usage, and dynamically adjust them according to actual conditions.

[0060] In one embodiment, for the background task processing unit, the background task processing unit is responsible for taking out tasks from the task queue and processing them, including the following steps:

[0061] Task queue monitoring and task extraction: The background task processing unit checks the task queue regularly or in real time. Once a new task is found, it will be extracted immediately and prepared for processing. The task queue is a buffer for storing tasks to be processed, and is sorted according to the priority and arrival time of the tasks.

[0062] Task type and priority determination: After extracting the task, the background task processing unit determines the processing strategy to be adopted according to the type and priority of the task, and the processing strategy includes immediate processing, delayed processing and batch processing;

[0063] Task processing and resource allocation: After the processing strategy is determined, the background task processing unit will start processing the task and allocate the corresponding system resources;

[0064] Task result feedback and log recording: After the task processing is completed, the background task processing unit sends the task result to the requester through a predefined interface or message queue. At the same time, the task execution process, results, exception information, etc. are recorded in the log system.

[0065] In one embodiment, for the above-mentioned personalized service module, the personalized service module includes a user data collection unit, a data analysis and mining unit, a personalized recommendation engine unit and a user feedback collection and processing unit. The user data collection unit is responsible for collecting the user's historical access data, including access time, access frequency, access content, etc., and collects user data in real time or regularly through log records, user behavior tracking, etc., and stores it in a secure data warehouse. The data analysis and mining unit uses cluster analysis to analyze user data to identify user access patterns and preferences. The personalized recommendation engine unit generates personalized data access recommendations and services based on the results of the data analysis and mining unit. The user feedback collection and processing unit is responsible for collecting user feedback on personalized services, including satisfaction, improvement suggestions, etc., and optimizes recommendation algorithms and services based on feedback.

[0066] In one embodiment, for the above-mentioned data analysis and mining unit, the data analysis and mining unit analyzes user data using cluster analysis to identify user access patterns and preferences, and the personalized recommendation engine unit generates personalized data access suggestions and services based on the results of the data analysis and mining unit. The specific steps are:

[0067] Data collection and preprocessing: Collect user data, including user behavior data, user attribute data, etc., and preprocess the data, such as data cleaning, data conversion, data reduction, etc., to improve the quality and availability of the data;

[0068] Cluster analysis:

[0069] Determine the number of clusters: Use the elbow rule to determine the number of clusters k;

[0070] Initialization: Randomly select k initial centroids;

[0071] Assign data points: Assign each data point to the cluster with the nearest centroid;

[0072] Update the centroid: Calculate the average of all data points in each cluster as the new centroid;

[0073] Repeat the distribution of data points and the update of the centroid until the centroid no longer changes or the maximum number of iterations is reached. The update formula of the centroid is: Among them, C i is the centroid of the ith cluster, S i is the set of data points in the i-th cluster, and x is a data point;

[0074] Pattern recognition and preference analysis: Analyze clustering results, identify user access patterns and preferences, and use association rule mining to discover the correlation and regularity between user behaviors;

[0075] Receive data analysis results: receive cluster analysis results and user access patterns, preferences and other information;

[0076] Generate personalized recommendations: Based on the user's interests and behaviors, consider the user's historical behavior, current needs, and potential interests, and use collaborative filtering to generate personalized data access suggestions and services;

[0077] Evaluation and optimization of recommendation results: Evaluate the quality of recommendation results, and adjust collaborative filtering and parameters based on the evaluation results to optimize the recommendation effect.

[0078] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data security access system based on big data, characterized in that: include: Security management module: comprehensively control system access rights, strictly classify user rights, and periodically evaluate information security-related factors within the system; Firewall module: detects network activities, captures, analyzes and processes all data packets that attempt to pass through the firewall, stores and updates the communication status of the network and applications, combines it with the implemented custom rules, and captures security violations; Data encryption module: Use encryption algorithms to encrypt document contents to prevent unauthorized access; Data backup and recovery module: regularly back up data and establish a disaster recovery plan; Cache management module: introduces a cache mechanism to store data or query results that users frequently access. When a user initiates a data access request, it first checks whether the required data exists in the cache. If so, it directly returns the data in the cache. Otherwise, it accesses the database again, monitors the cache hit rate, adjusts the cache size or strategy based on the hit rate, and regularly cleans up expired or invalid cache data. Asynchronous processing module: For operations with high non-real-time requirements, the asynchronous processing module is responsible for placing these operations into the background task queue, which is processed by the background thread or process, and provides a task status query interface. The asynchronous processing module receives asynchronous tasks from the data backup and recovery module and the personalized service module, and places the asynchronous tasks into the task queue for processing; Personalized service module: Based on the user's historical access records and preferences, the personalized service module is responsible for analyzing the user's access patterns and providing personalized data access suggestions and services; Behavior pattern analysis module: responsible for monitoring and analyzing user access behavior, using behavior analysis algorithms to identify abnormal access behavior or potential security risks, and generating early warning signals or taking corresponding security measures based on the identification results.

2. According to the data security access system based on big data in claim 1, it is characterized in that: The data acquisition module includes a traffic flow data acquisition unit, a traffic accident data acquisition unit and a meteorological data acquisition unit. The traffic flow data acquisition unit is responsible for real-time acquisition of traffic flow data from sensors installed at key locations. The traffic accident data acquisition unit is responsible for data interaction with the traffic police department to obtain detailed information on traffic accidents. The meteorological data acquisition unit is responsible for integrating real-time weather information provided by the meteorological department. A data sharing mechanism is established between the various acquisition units to synchronize the collected data to the system's central database in real time. The central database is set in the data storage and management module. During the data sharing process, each acquisition unit performs data verification and validation.

3. According to the data security access system based on big data in claim 1, it is characterized in that: The cache management module includes a cache storage unit, a cache policy management unit, a cache monitoring unit and a cache cleaning unit. The cache storage unit is responsible for actually storing cache data. The cache policy management unit uses LRU to optimize the use efficiency of the cache and dynamically adjusts the policy parameters according to the cache access mode and business needs. The cache monitoring unit provides a basis for adjusting the cache policy by collecting and analyzing key indicators. The cache cleaning unit regularly cleans up expired or invalid cache data and regularly scans and deletes expired or invalid cache data according to the set cleaning policy.

4. A data security access system based on big data according to claim 3, characterized in that: The cache policy management unit optimizes the use efficiency of the cache using LRU and dynamically adjusts the policy parameters according to the cache access mode and business requirements, including the following steps: Initialize the LRU cache: set the cache size, create a doubly linked list and a hash table. The doubly linked list is used to maintain the access order of data, and the hash table is used to quickly locate the position of data in the linked list. Data access: When a user requests to access a certain data, first check whether the data is already in the cache. If so, remove the data from the linked list and reinsert it to the head of the linked list. If not, load the data from the database and check whether the cache is full. If not, insert the new data to the head of the linked list and record the location of the data in the hash table. If the cache is full, eliminate the data at the end of the linked list according to the LRU algorithm, and then insert the new data to the head of the linked list. Dynamically adjust policy parameters: Dynamically adjust LRU parameters based on business needs and cache access patterns. If the system detects a surge in data access during certain time periods, temporarily increase the cache size to cope with peak access needs. If it is found that some data is frequently accessed while other data is rarely accessed, dynamically adjust the cache allocation strategy based on the access frequency of these data.

5. The data security access system based on big data according to claim 1 is characterized in that: The asynchronous processing module includes a task queue management unit, a background task processing unit, a task status monitoring unit and a task scheduling and optimization unit. The task queue management unit is responsible for storing and managing asynchronous tasks to be processed. The background task processing unit is responsible for taking out tasks from the task queue and processing them. The background task processing unit is composed of a special background thread or process, which continuously takes out tasks from the task queue and executes them. The background task processing unit selects a processing strategy according to the type and priority of the task. The task status monitoring unit is responsible for monitoring the processing status of the task and providing a query interface for task progress and results. The task scheduling and optimization unit is responsible for scheduling the execution of tasks according to the system's resource conditions and task requirements, and optimizing the processing efficiency of tasks.

6. A data security access system based on big data according to claim 4, characterized in that: The background task processing unit is responsible for taking tasks from the task queue and processing them, including the following steps: Task queue monitoring and task extraction: The background task processing unit checks the task queue regularly or in real time. Once a new task is found, it will be extracted immediately and prepared for processing. The task queue is a buffer for storing tasks to be processed, and is sorted according to the priority and arrival time of the tasks. Task type and priority determination: After extracting the task, the background task processing unit determines the processing strategy to be adopted according to the type and priority of the task, and the processing strategy includes immediate processing, delayed processing and batch processing; Task processing and resource allocation: After the processing strategy is determined, the background task processing unit will start processing the task and allocate the corresponding system resources; Task result feedback and log recording: After the task processing is completed, the background task processing unit sends the task result to the requester through a predefined interface or message queue, and records the task in the log system.

7. A data security access system based on big data according to claim 1, characterized in that: The personalized service module includes a user data collection unit, a data analysis and mining unit, a personalized recommendation engine unit and a user feedback collection and processing unit. The user data collection unit is responsible for collecting the user's historical access data. The data analysis and mining unit uses cluster analysis to analyze the user data and identify the user's access patterns and preferences. The personalized recommendation engine unit generates personalized data access suggestions and services based on the results of the data analysis and mining unit. The user feedback collection and processing unit is responsible for collecting user feedback on personalized services and optimizing recommendation algorithms and services based on the feedback.

8. A data security access system based on big data according to claim 7, characterized in that: The data analysis and mining unit analyzes user data using cluster analysis to identify user access patterns and preferences. The personalized recommendation engine unit generates personalized data access suggestions and services based on the results of the data analysis and mining unit. The specific steps are: Data collection and preprocessing: Collect user data and preprocess the data; Cluster analysis: Determine the number of clusters: Use the elbow rule to determine the number of clusters k; Initialization: Randomly select k initial centroids; Assign data points: Assign each data point to the cluster with the nearest centroid; Update the centroid: Calculate the average of all data points in each cluster as the new centroid; Repeat the distribution of data points and the update of the centroid until the centroid no longer changes or the maximum number of iterations is reached. The update formula of the centroid is: Among them, C i is the centroid of the ith cluster, S i is the set of data points in the i-th cluster, and x is a data point; Pattern recognition and preference analysis: Analyze clustering results, identify user access patterns and preferences, and use association rule mining to discover the correlation and regularity between user behaviors; Receive data analysis results: receive cluster analysis results and user information; Generate personalized recommendations: Based on the user's interests and behaviors, consider the user's historical behavior, current needs, and potential interests, and use collaborative filtering to generate personalized data access suggestions and services; Evaluation and optimization of recommendation results: Evaluate the quality of recommendation results, and adjust collaborative filtering and parameters based on the evaluation results to optimize the recommendation effect.

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