A security management system and method for electronic archive data

Abstract

The present invention belongs to the field of data management technology, and specifically relates to a security management system and method for electronic archive data. The system includes an electronic archive security management data acquisition module, an encryption and performance optimization module, a regional blockchain storage function improvement module, and an access rights rapid linkage adjustment module. The encryption and performance optimization module is divided into an encryption monitoring unit, a performance monitoring unit, an encryption performance evaluation unit, and a security management performance optimization unit. By integrating electronic archive security management data acquisition technology, machine learning algorithms, encryption and performance optimization technology, blockchain storage capacity evaluation and improvement technology, knowledge graph technology, intelligent permission rule engine technology, and automated workflow technology, the present invention achieves real-time and comprehensive monitoring of the electronic archive encryption process, blockchain storage status, and changes in job permissions, significantly improving the intelligent level of electronic archive data security management.

Description

Technical Field

[0001] The present invention belongs to the technical field of data management, and in particular relates to a security management system and method for electronic archive data. Background Art

[0002] With the widespread adoption of digital office work, the volume of electronic archival data has exploded, posing numerous challenges to its security management. On the one hand, cyberattacks are escalating, with hackers attempting to illegally obtain, tamper with, or destroy electronic archival data through malware, phishing, and data theft. On the other hand, internal personnel misuse, abuse of authority, storage device failures, and natural disasters can also threaten the integrity, confidentiality, and availability of electronic archival data.

[0003] Existing technologies still have shortcomings in the security management of electronic archival data. Traditional technologies rely heavily on manual management. Faced with massive amounts of electronic archival data, the complexity of encryption leads to a significant decline in the performance of electronic archival data security management systems. As the number of electronic archives continues to grow, blockchain storage capacity will rapidly increase, further increasing the storage and processing pressure on nodes. Furthermore, existing technologies lack detailed analysis of employee job transfer scenarios, making it difficult to achieve accurate and intelligent permission management and timely adjust security management permissions for electronic archival data. Summary of the Invention

[0004] The purpose of the present invention is to provide a security management system and method for electronic archive data, which realizes accurate monitoring and efficient adjustment of electronic archive data security through intelligent encryption and performance optimization, blockchain storage improvement and dynamic permission management.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is a security management system for electronic archive data, comprising:

[0006] The electronic archive security management data collection module is used to collect electronic archive security management data, including encryption data, performance monitoring data, regional chain monitoring data, employee position data, and access permission data;

[0007] The encryption and performance optimization module is divided into the following units:

[0008] The encryption monitoring unit calculates the total amount of encrypted electronic archive data through encryption data, builds an encryption monitoring model by combining the random forest algorithm, and outputs the corresponding electronic archive data encryption index;

[0009] The performance monitoring unit uses the performance monitoring data to calculate the remaining amount of memory after encryption processing, combines the decision tree algorithm to build a performance monitoring model, and outputs the corresponding electronic archive data security management performance index;

[0010] The encryption performance evaluation unit, based on the output results of the encryption monitoring model and the performance monitoring model, uses a multivariate linear regression algorithm to construct a security encryption performance constraint analytical model and outputs the electronic archive encryption performance trade-off evaluation coefficient;

[0011] The security management performance optimization unit divides the relevant intervals of electronic archive data encryption and security management performance based on the electronic archive encryption performance trade-off evaluation coefficient, and takes improvement measures to optimize the relevant intervals of electronic archive data encryption and security management performance according to each electronic archive data encryption and security management performance;

[0012] The blockchain storage function improvement module combines performance monitoring data, blockchain monitoring data, and neural network algorithms to build a blockchain storage capacity monitoring model, output the blockchain storage capacity rating coefficient, and improve blockchain storage functions by combining data optimization technology, storage architecture technology, and blockchain mechanism technology.

[0013] The access rights rapid linkage adjustment module uses natural language processing technology and supervised learning algorithms to identify employee job transfer scenarios based on employee job data and access rights data, and combines knowledge graph technology, intelligent permission rule engine and automated workflow technology to make corresponding adjustments to electronic file data access rights in different employee job transfer scenarios.

[0014] Preferably, in the electronic archive security management data collection module, the electronic archive security management data collection process is:

[0015] Deploy different types of collection equipment, combined with data entry technology, to collect electronic archive security management data. The collection equipment includes password compliance testers, network protocol analyzers, encryption performance testers, HSM built-in timers, encryption accelerator card built-in counters, hardware encryption card memory probes, PMU probes, CPU frequency sensors, PCIe interrupt capture cards, hardware-level storage protocol analyzers, hardware-level network damage analyzers, blockchain node devices, blockchain-specific traffic analyzers, mixed-precision memory analyzers, and Hyperledger Caliper.

[0016] Encryption data includes key length, amount of electronic archive data encrypted per second, encryption start time, encryption end time, and real-time length of the encryption queue; performance monitoring data includes total memory and memory usage during the encryption process, as well as CPU usage, clock frequency and number of interruptions, disk read and write throughput when storing encrypted electronic archive data, disk response time for encryption operations, and network packet loss rate; regional chain monitoring data includes total storage capacity of regional chain ledgers, hourly regional chain capacity growth rate, memory read and write speed to regional chain, and time required for newly joined nodes and fault recovery nodes to synchronize regional chains; employee position data includes employee positions before and after scheduling, employee work numbers, and real-time scheduling time of employee positions; access rights data includes access rights to electronic archive data corresponding to each employee position, among which access rights to electronic archive data include viewing, downloading, modifying, deleting, and sharing;

[0017] The collected encrypted data, performance monitoring data, regional chain monitoring data, employee position data and access permission data are cleaned and standardized, and timestamps are assigned to each pre-processed electronic archive security management data. The timestamps are adjusted to achieve the unification of each electronic archive security management data in the time dimension, and the encrypted data, performance monitoring data, regional chain monitoring data, employee position data and access permission data are integrated to generate an electronic archive security management data set.

[0018] Preferably, in the encryption monitoring unit of the encryption and performance optimization module, the process of outputting the corresponding electronic archive data encryption index includes:

[0019] Calculate the difference between the encryption start time and the encryption end time to obtain the encryption process time, then calculate the product of the amount of electronic archive data encrypted per second and the encryption process time to obtain the total amount of encrypted electronic archive data, and integrate the total amount of encrypted electronic archive data into the electronic archive security management data set;

[0020] Extracting the key length, the real-time length of the encryption queue, and the total amount of encrypted electronic archive data from the electronic archive security management data set, and dividing the extracted data into a first training set and a first test set;

[0021] The random forest algorithm is used to take the first training set data as input and the electronic archive data encryption index as output to learn the nonlinear relationship between the first training set data and the electronic archive data encryption index and train the encryption monitoring model.

[0022] Inputting the first test set data into the encrypted monitoring model, adjusting the parameters of the encrypted monitoring model, and then obtaining the final encrypted monitoring model;

[0023] The current key length, real-time length of the encryption queue and the total amount of encrypted electronic archive data are input into the encryption monitoring model, the corresponding electronic archive data encryption index is output, and the electronic archive data encryption index is integrated into the electronic archive security management data set.

[0024] Preferably, in the performance monitoring unit of the encryption and performance optimization module, the process of outputting the corresponding electronic archive data security management performance index includes:

[0025] Calculate the difference between the total memory and the memory usage during the encryption process, obtain the remaining memory after encryption, and integrate the remaining memory after encryption into the electronic archive security management data set;

[0026] Extract the CPU usage, clock frequency and interruption times, disk read and write throughput when storing encrypted electronic archive data, disk response time for encryption operations, network packet loss rate, and remaining memory after encryption from the electronic archive security management dataset, and convert the extracted data into the second training set and the second test set;

[0027] A decision tree algorithm is used, with the second training set data as input and the electronic archive data security management performance index as output, to learn the nonlinear relationship between each second training set data and the electronic archive data security management performance index, and train the performance monitoring model;

[0028] Inputting the second test set data into the performance monitoring model, adjusting the performance monitoring model parameters, and obtaining the final performance monitoring model;

[0029] Combined with the current CPU usage, clock frequency and number of interrupts, the disk read and write throughput when storing encrypted electronic archive data, the disk's response time to encryption operations, the network packet loss rate and the remaining memory after encryption processing, the corresponding electronic archive data security management performance index is output and integrated into the electronic archive data security management data set.

[0030] Preferably, in the encryption performance evaluation unit of the electronic archive security management data acquisition module, the process of outputting the electronic archive encryption performance trade-off evaluation coefficient includes:

[0031] Extracting the electronic archive data encryption index and the electronic archive data security management performance index from the electronic archive security management data set, and dividing the extracted data into a third training set and a third test set;

[0032] A multivariate linear regression algorithm is used, with the third training set data as input and the electronic archive encryption performance trade-off evaluation coefficient as output. The linear relationship between each item of the third training set data and the electronic archive encryption performance trade-off evaluation coefficient is learned, and a security encryption performance constraint analytical model is trained.

[0033] Inputting the third test set data into the security encryption performance constraint analytical model, adjusting the intercept term and regression coefficient of the security encryption performance constraint analytical model, and then obtaining the final security encryption performance constraint analytical model;

[0034] The current electronic archive data encryption index and electronic archive data security management performance index are input into the security encryption performance constraint analytical model, and the corresponding electronic archive encryption performance trade-off evaluation coefficient is output.

[0035] Preferably, in the security management performance optimization unit of the electronic archive security management data collection module, the process of dividing the relevant intervals of electronic archive data encryption and security management performance, and taking improvement measures to optimize according to the relevant intervals of each electronic archive data encryption and security management performance includes:

[0036] Based on the electronic archive encryption performance trade-off evaluation coefficient, the correlation interval between electronic archive data encryption and security management performance is divided into strong negative correlation interval, moderate negative correlation interval and weak correlation interval;

[0037] For strong negative correlation intervals, the encryption algorithm is replaced with a lightweight solution, the number of server CPU cores is increased, memory capacity is expanded, high-speed storage devices are deployed, and load balancing technology is used to redistribute encryption tasks;

[0038] For moderate negative correlation intervals, a hybrid encryption method is used, and non-real-time encryption tasks are set to asynchronous execution;

[0039] For weakly correlated intervals, establish a real-time monitoring dashboard, strengthen real-time monitoring of various performance monitoring data, and formulate emergency plans.

[0040] Preferably, in the regional chain storage function improvement module, the process of outputting the regional chain storage capacity level coefficient includes:

[0041] Extracting the memory usage and regional chain monitoring data during the encryption process from the electronic archive security management dataset, and converting the extracted data into a fourth training set and a fourth test set;

[0042] A neural network algorithm is used, with the fourth training set data as input and the regional chain storage capacity level coefficient as output, to learn the nonlinear relationship between each fourth training set data and the regional chain storage capacity level coefficient, and train the regional chain storage capacity monitoring model;

[0043] Inputting the fourth test set data into the regional chain storage capacity monitoring model, adjusting the regional chain storage capacity monitoring model parameters, and obtaining the final regional chain storage capacity monitoring model;

[0044] The corresponding regional chain storage capacity level coefficient is output based on the memory usage during the current encryption process, the total storage capacity of the regional chain ledger, the hourly regional chain capacity growth rate, the memory read and write speed to the regional chain, and the time required for newly joined nodes and fault recovery nodes to synchronize the regional chain.

[0045] Preferably, in the regional chain storage function improvement module, the process of improving the blockchain storage function includes:

[0046] Based on the output results of the regional chain storage capacity monitoring model, the regional chain storage capacity level is divided into low regional chain storage capacity level, medium regional chain storage capacity level and high regional chain storage capacity level;

[0047] For low regional chain storage capacity levels, regularly monitor regional chain monitoring data and changes in its corresponding regional chain storage capacity level coefficient, use a lightweight data compression algorithm, and reserve regional chain storage capacity space;

[0048] For the medium-range blockchain storage capacity level, a tiered storage strategy is introduced. Different storage devices are used to store hot electronic archive data whose access frequency exceeds the set high-frequency threshold and cold electronic archive data whose access frequency is lower than the set low-frequency threshold, and the regional blockchain monitoring data is stored in shards.

[0049] For high regional chain storage capacity levels, the consensus mechanism of the regional chain is evaluated and optimized, and sharding technology is used to divide the regional chain network into several sub-chains to achieve the reconstruction of the regional chain architecture.

[0050] Preferably, in the access rights rapid linkage adjustment module, the process of identifying employee job transfer scenarios and adjusting corresponding electronic file data access rights in different employee job transfer scenarios includes:

[0051] Access rights to electronic archive data include viewing, downloading, modifying, deleting and sharing, setting job levels, which are divided into low job levels, middle job levels and high job levels;

[0052] Based on the access permission data, low-level positions are granted access permissions to view and download electronic archive data, mid-level positions are granted access permissions to view, download and modify electronic archive data, and high-level positions are granted access permissions to view, download, modify, delete and share electronic archive data;

[0053] Based on employee positions before and after scheduling, real-time scheduling time, and access rights to electronic archive data at different job levels, natural language processing technology and supervised learning algorithms are combined to identify employee job transfer scenarios and associate them with employee IDs. These scenarios include horizontal transfers, promotion scheduling, and demotion scheduling.

[0054] Based on knowledge graph technology, the access rights to electronic archive data corresponding to employee positions before and after scheduling are compared, and the intelligent rule permission engine is used to automatically calculate the difference in access rights to electronic archive data before and after scheduling. Real-time adjustment of access rights to electronic archive data is achieved through automated workflows.

[0055] A method for securely managing electronic archive data, based on the above system, comprises the following steps:

[0056] Collect electronic archive security management data, including encryption data, performance monitoring data, regional chain monitoring data, employee position data, and access permission data;

[0057] By encrypting data, the total amount of encrypted electronic archive data is calculated. In combination with the random forest algorithm, an encryption monitoring model is constructed to output the corresponding electronic archive data encryption index. By using performance monitoring data, the remaining amount of memory after encryption processing is calculated. In combination with the decision tree algorithm, a performance monitoring model is constructed to output the corresponding electronic archive data security management performance index.

[0058] Using the multivariate linear regression algorithm, the correlation between the electronic archive data encryption index and the electronic archive data security management performance index is analyzed, a security encryption performance constraint analytical model is constructed, and the electronic archive encryption performance trade-off evaluation coefficient is output;

[0059] Based on the electronic archive encryption performance trade-off evaluation coefficient, the relevant intervals of electronic archive data encryption and security management performance are divided, and improvement measures are taken according to the relevant intervals of each electronic archive data encryption and security management performance to optimize them;

[0060] Combining performance monitoring data, regional chain monitoring data and neural network algorithms, a regional chain storage capacity monitoring model is constructed to output the regional chain storage capacity grade coefficient;

[0061] Improve the blockchain storage function by combining the output of the regional chain storage capacity monitoring model with data optimization technology, storage architecture technology and blockchain mechanism technology;

[0062] Based on employee position data and access permission data, natural language processing technology and supervised learning algorithms are used to identify employee position transfer scenarios. In combination with knowledge graph technology, intelligent permission rule engine and automated workflow technology, corresponding adjustments are made to the access permissions of electronic archive data in different employee position transfer scenarios.

[0063] Beneficial effects of the present invention:

[0064] This invention integrates electronic archive security management data collection technology, machine learning algorithms, encryption and performance optimization techniques, blockchain storage capacity assessment and improvement technology, knowledge graph technology, intelligent permission rule engine technology, and automated workflow technology to achieve real-time and comprehensive monitoring of the electronic archive encryption process, blockchain storage status, and changes in job permissions. The system accurately captures encrypted data, performance monitoring data, blockchain monitoring data, employee position data, and access permission data. By constructing encryption monitoring models, performance monitoring models, security encryption performance constraint analysis models, and blockchain storage capacity monitoring models, it obtains key indicators such as the electronic archive data encryption index, security management performance index, encryption performance trade-off evaluation coefficient, and blockchain storage capacity rating coefficient, significantly improving the intelligent level of electronic archive data security management.

[0065] This invention effectively addresses issues such as the degradation of system performance caused by encryption complexity in traditional technologies, excessive node pressure caused by the growth of blockchain storage capacity, and the difficulty in timely adjusting access rights to electronic archive data after employee transfers. Through dynamic monitoring and optimization, the system ensures the security of electronic archive data while achieving continuous performance improvements and efficient resource utilization, providing more accurate and dynamic monitoring standards and optimization strategies for the secure management of electronic archive data. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a principle block diagram of the system of the present invention;

[0067] Figure 2 A correlation diagram between the electronic archive data encryption index and the electronic archive data security management performance index during the verification process of the present invention;

[0068] Figure 3 It is a dynamic distribution diagram of the electronic archive data encryption index, the electronic archive data security management performance index, and the related intervals of various electronic archive data encryption and security management performances during the verification process of the present invention;

[0069] Figure 4 This is a graph showing the regional chain storage capacity level coefficient and regional chain storage level change trend during the verification process of the present invention;

[0070] Figure 5A dynamic display diagram of employee position transfers during the verification process of the present invention;

[0071] Figure 6 A dynamic display diagram for adjusting access rights to electronic archive data during the verification process of the present invention;

[0072] Figure 7 Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0073] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0074] Example 1: Figure 1 As shown, a security management system for electronic archive data includes:

[0075] The electronic archive security management data collection module is used to collect electronic archive security management data, including encryption data, performance monitoring data, regional chain monitoring data, employee position data, and access permission data;

[0076] The encryption and performance optimization module is divided into the following units:

[0077] The encryption monitoring unit calculates the total amount of encrypted electronic archive data through encryption data, builds an encryption monitoring model by combining the random forest algorithm, and outputs the corresponding electronic archive data encryption index;

[0078] The performance monitoring unit uses the performance monitoring data to calculate the remaining amount of memory after encryption processing, combines the decision tree algorithm to build a performance monitoring model, and outputs the corresponding electronic archive data security management performance index;

[0079] The encryption performance evaluation unit, based on the output results of the encryption monitoring model and the performance monitoring model, uses a multivariate linear regression algorithm to construct a security encryption performance constraint analytical model and outputs the electronic archive encryption performance trade-off evaluation coefficient;

[0080] The security management performance optimization unit divides the relevant intervals of electronic archive data encryption and security management performance based on the electronic archive encryption performance trade-off evaluation coefficient, and takes improvement measures to optimize the relevant intervals of electronic archive data encryption and security management performance according to each electronic archive data encryption and security management performance;

[0081] The blockchain storage function improvement module combines performance monitoring data, blockchain monitoring data, and neural network algorithms to build a blockchain storage capacity monitoring model, output the blockchain storage capacity rating coefficient, and improve blockchain storage functions by combining data optimization technology, storage architecture technology, and blockchain mechanism technology.

[0082] The access rights rapid linkage adjustment module uses natural language processing technology and supervised learning algorithms to identify employee job transfer scenarios based on employee job data and access rights data, and combines knowledge graph technology, intelligent permission rule engine and automated workflow technology to make corresponding adjustments to electronic file data access rights in different employee job transfer scenarios.

[0083] Communication connection between each module.

[0084] In the electronic archive security management data collection module, the collection process of electronic archive security management data is as follows:

[0085] Deploy different types of collection equipment, combined with data entry technology, to collect electronic archive security management data. The collection equipment includes cryptographic compliance testers, network protocol analyzers, cryptographic performance testers, HSM built-in timers, cryptographic accelerator card built-in counters, hardware cryptographic card memory probes, PMU probes, CPU frequency sensors, PCIe interrupt capture cards, hardware-level storage protocol analyzers, hardware-level network damage analyzers, blockchain node devices, blockchain-specific traffic analyzers, mixed-precision memory analyzers, and Hyperledger Caliper (blockchain testing framework);

[0086] Encryption data includes key length, amount of electronic archive data encrypted per second, encryption start time, encryption end time, and real-time length of the encryption queue; performance monitoring data includes total memory and memory usage during the encryption process, as well as CPU usage, clock frequency and number of interruptions, disk read and write throughput when storing encrypted electronic archive data, disk response time for encryption operations, and network packet loss rate; regional chain monitoring data includes total storage capacity of regional chain ledgers, hourly regional chain capacity growth rate, memory read and write speed to regional chain, and time required for newly joined nodes and fault recovery nodes to synchronize regional chains; employee position data includes employee positions before and after scheduling, employee work numbers, and real-time scheduling time of employee positions; access rights data includes access rights to electronic archive data corresponding to each employee position, among which access rights to electronic archive data include viewing, downloading, modifying, deleting, and sharing;

[0087] Combine the password compliance tester and network protocol analyzer to collect the key length; use the encryption performance tester to collect the amount of encrypted electronic archive data per second; use the HSM built-in timer to collect the encryption start time and encryption end time; use the built-in counter of the encryption accelerator card to collect the real-time length of the encryption queue; use the hardware encryption card memory probe to collect the total memory and memory usage during the encryption process; combine the PMU probe, CPU frequency sensor and PCIe interrupt capture card to collect the CPU usage, clock frequency and number of interrupts during the encryption process; use the hardware-level storage protocol analyzer to collect the disk read and write throughput and the disk response time for encryption operations when storing encrypted electronic archive data; use the hardware-level network damage analyzer to collect the network packet loss rate; use the blockchain node device to collect the total storage capacity of the regional chain ledger; combine the blockchain-specific traffic analyzer and the mixed-precision memory analyzer to collect the hourly regional chain capacity growth rate and the memory to regional chain read and write speed respectively; through Hyperledger Caliper collects the time required for newly joined nodes and fault recovery nodes to synchronize with the regional chain; using data entry technology, it obtains employee positions, employee work numbers, and real-time scheduling times before and after scheduling from the employee archive. Similarly, through data entry technology, it obtains the access rights to electronic file data corresponding to each employee position from the rights management server.

[0088] The collected encrypted data, performance monitoring data, regional chain monitoring data, employee position data and access permission data are cleaned and standardized, and timestamps are assigned to each pre-processed electronic archive security management data. The timestamps are adjusted to achieve the unification of each electronic archive security management data in the time dimension, and the encrypted data, performance monitoring data, regional chain monitoring data, employee position data and access permission data are integrated to generate an electronic archive security management data set.

[0089] In the encryption monitoring unit of the encryption and performance optimization module, the process of outputting the corresponding electronic archive data encryption index includes:

[0090] Calculate the difference between the encryption start time and the encryption end time to obtain the encryption process time, then calculate the product of the amount of electronic archive data encrypted per second and the encryption process time to obtain the total amount of encrypted electronic archive data, and integrate the total amount of encrypted electronic archive data into the electronic archive security management data set;

[0091] Extract the key length, encryption queue real-time length and total encrypted electronic archive data volume from the electronic archive security management data set, and divide the extracted data into the first training set and the first test set, with the ratio of the first training set to the first test set being 7:3;

[0092] The random forest algorithm is used to train the encryption monitoring model by taking the first training set data as input and the electronic archive data encryption index as output. The nonlinear relationship between the key length, the real-time length of the encryption queue, the total amount of encrypted electronic archive data and the electronic archive data encryption index is learned.

[0093] Input the first test set data into the encryption monitoring model, use the SGD optimizer to adjust the parameters of the encryption monitoring model to optimize the performance of the encryption monitoring model, and then obtain the final encryption monitoring model;

[0094] The current key length, real-time length of the encryption queue and the total amount of encrypted electronic archive data are input into the encryption monitoring model, the corresponding electronic archive data encryption index is output, and the electronic archive data encryption index is integrated into the electronic archive security management data set.

[0095] In the performance monitoring unit of the encryption and performance optimization module, the process of outputting the corresponding electronic archive data security management performance index includes:

[0096] Calculate the difference between the total memory and the memory usage during the encryption process, obtain the remaining memory after encryption, and integrate the remaining memory after encryption into the electronic archive security management data set;

[0097] The CPU usage, clock frequency and interruption times, disk read and write throughput when storing encrypted electronic archive data, disk response time for encryption operations, network packet loss rate, and remaining memory after encryption processing were extracted from the electronic archive security management dataset. The extracted data were converted into the second training set and the second test set in a ratio of 7:3.

[0098] A decision tree algorithm is used, with the second training set data as input and the electronic archive data security management performance index as output. The nonlinear relationship between the CPU usage, clock frequency and number of interruptions, disk read and write throughput when storing encrypted electronic archive data, disk response time for encryption operations, network packet loss rate, and the remaining memory after encryption processing and the electronic archive data security management performance index is learned to train a performance monitoring model.

[0099] Input the second test set data into the performance monitoring model, use the SGD optimizer to adjust the performance monitoring model parameters, optimize the performance monitoring model function, and obtain the final performance monitoring model;

[0100] Combined with the current CPU usage, clock frequency and number of interrupts, the disk read and write throughput when storing encrypted electronic archive data, the disk's response time to encryption operations, the network packet loss rate and the remaining memory after encryption processing, the corresponding electronic archive data security management performance index is output and integrated into the electronic archive data security management data set.

[0101] In the encryption performance evaluation unit of the electronic archive security management data acquisition module, the process of outputting the electronic archive encryption performance trade-off evaluation coefficient includes:

[0102] Extract the electronic archive data encryption index and the electronic archive data security management performance index from the electronic archive security management data set, and divide the extracted data into the third training set and the third test set in a ratio of 8:2;

[0103] A multivariate linear regression algorithm is used, with the third training set data as input and the electronic archive encryption performance trade-off evaluation coefficient as output. The linear relationship between the electronic archive data encryption index, the electronic archive data security management performance index and the electronic archive encryption performance trade-off evaluation coefficient is learned, and the security encryption performance constraint analytical model is trained.

[0104] Input the third test set data into the security encryption performance constraint analytical model, use the Adam optimizer to adjust the intercept term and regression coefficient of the security encryption performance constraint analytical model, optimize the performance of the security encryption performance constraint analytical model, and then obtain the final security encryption performance constraint analytical model;

[0105] The current electronic archive data encryption index and electronic archive data security management performance index are input into the security encryption performance constraint analytical model, and the corresponding electronic archive encryption performance trade-off evaluation coefficient is output.

[0106] The expression of the security encryption performance constraint analytical model is:

[0107] ;

[0108] Where R is the electronic archive encryption performance trade-off evaluation coefficient, and They are the electronic archive data encryption index and the electronic archive data security management performance index. and are the regression coefficients of the electronic archive data encryption index and the electronic archive data security management performance index, To secure the encryption performance, the intercept term of the analytical model is constrained. The error term of the analytical model is constrained for secure encryption performance.

[0109] In the security management performance optimization unit of the electronic archive security management data collection module, the process of dividing the relevant intervals between electronic archive data encryption and security management performance and taking improvement measures to optimize them according to the relevant intervals of electronic archive data encryption and security management performance includes:

[0110] Based on the electronic archive encryption performance trade-off evaluation coefficient, the correlation interval between electronic archive data encryption and security management performance is divided into strong negative correlation interval, moderate negative correlation interval and weak correlation interval;

[0111] When the electronic archive encryption performance trade-off evaluation coefficient satisfies the condition of -1≤R<0, it corresponds to the strong negative correlation interval, indicating that encryption complexity is strongly negatively correlated with system performance, and the encryption operation has a great negative impact on the system; when the electronic archive encryption performance trade-off evaluation coefficient satisfies the condition of 0≤R<0.5, it corresponds to the moderate negative correlation interval, indicating that the encryption operation has a significant impact on system performance, but has not yet reached the level of seriously hindering its operation; when the electronic archive encryption performance trade-off evaluation coefficient satisfies the condition of 0.5≤R≤1, it corresponds to the weak correlation interval, indicating that the correlation between the encryption operation and system performance is weak and does not affect the basic operation of the system;

[0112] For strong negative correlation intervals, high-intensity encryption algorithms are replaced with lightweight solutions, prioritizing basic system performance. The number of server CPU cores is increased, memory capacity is expanded, and high-speed storage devices are deployed to quickly alleviate computing and storage pressures. Load balancing technology is used to redistribute encryption tasks and reduce the load on individual nodes.

[0113] For moderate negative correlation intervals, a hybrid encryption method is used, and non-real-time encryption tasks are set to asynchronous execution to avoid affecting the normal operation of the system;

[0114] For weakly correlated intervals, establish a real-time monitoring dashboard, strengthen real-time monitoring of various performance monitoring data, and formulate emergency plans.

[0115] In the regional chain storage function improvement module, the process of outputting the regional chain storage capacity level coefficient includes:

[0116] Extract the memory usage during encryption, memory usage during encryption, total storage capacity of the blockchain ledger, hourly blockchain capacity growth rate, memory read and write speed to the blockchain, and the time required for newly joined nodes and fault recovery nodes to synchronize with the blockchain from the electronic archive security management dataset. This extracted data is converted into the fourth training set and fourth test set in a 6:4 ratio.

[0117] Using a neural network algorithm, with the fourth training set data as input and the blockchain storage capacity rating coefficient as output, the algorithm learns the nonlinear relationship between memory usage during encryption, the total blockchain ledger storage capacity, the hourly blockchain capacity growth rate, the speed at which memory reads and writes to the blockchain, and the time required for newly joined nodes and fault recovery nodes to synchronize with the blockchain, and the blockchain storage capacity rating coefficient, thereby training a blockchain storage capacity monitoring model.

[0118] Input the fourth test set data into the blockchain storage capacity monitoring model, use the Adam optimizer to adjust the parameters of the blockchain storage capacity monitoring model, optimize the performance of the blockchain storage capacity monitoring model, and obtain the final blockchain storage capacity monitoring model;

[0119] The corresponding regional chain storage capacity level coefficient is output based on the memory usage during the current encryption process, the total storage capacity of the regional chain ledger, the hourly regional chain capacity growth rate, the memory read and write speed to the regional chain, and the time required for newly joined nodes and fault recovery nodes to synchronize the regional chain.

[0120] In the regional chain storage function improvement module, the process of improving the blockchain storage function includes:

[0121] Based on the output results of the regional chain storage capacity monitoring model, the regional chain storage capacity level is divided into low regional chain storage capacity level, medium regional chain storage capacity level and high regional chain storage capacity level;

[0122] When the regional chain storage capacity level coefficient (X) satisfies 0≤X<0.3, it corresponds to a low regional chain storage capacity level, indicating that the corresponding regional chain storage capacity is growing slowly and the node storage and processing pressure is relatively small; when the regional chain storage capacity level coefficient satisfies 0.3≤X<0.6, it corresponds to a medium regional chain storage capacity level, indicating that the regional chain storage capacity is growing rapidly, a certain proportion of the regional chain storage capacity has been occupied, and the nodes are beginning to show signs of storage and processing pressure; when the regional chain storage capacity level coefficient satisfies 0.6≤X≤1, it corresponds to a high regional chain storage capacity level, indicating that the regional chain storage capacity is growing rapidly, storage resources are saturated, and the node storage and processing pressure has increased significantly;

[0123] For low-area chain storage capacity levels, regularly monitor the changes in the area chain monitoring data and its corresponding area chain storage capacity level coefficient, and use lightweight data compression algorithms to reduce data storage space usage without affecting data reading and verification, thereby reserving area chain storage capacity space. Lightweight data compression algorithms fall under the category of data optimization technology.

[0124] For the medium-area blockchain storage capacity level, a tiered storage strategy is introduced. Different storage devices are used to store hot electronic archive data whose access frequency exceeds the set high-frequency threshold and cold electronic archive data whose access frequency is lower than the set low-frequency threshold (hot electronic archive data with high frequency access is stored in high-performance storage devices, and cold electronic archive data with low frequency access is transferred to large-capacity and low-cost storage devices). The regional chain monitoring data is stored in shards, and electronic archive data of different time or types are allocated to different storage nodes to improve the reading and writing efficiency of electronic archive data. Among them, the response measures for the medium-area blockchain storage capacity level mainly involve storage architecture technology;

[0125] For high regional chain storage capacity levels, the consensus mechanism of the regional chain is evaluated and optimized. For example, the system can gradually transition from the resource-intensive proof-of-work mechanism to more efficient consensus mechanisms such as proof-of-stake or delegated proof-of-stake, thereby reducing the large amount of data storage required by the consensus process and the storage and processing pressure on nodes. Sharding technology can be used to divide the regional chain network into several sub-chains, with each sub-chain responsible for processing a portion of transactions and storing related data, thereby reducing the storage and processing pressure on a single regional chain and achieving reconstruction of the regional chain architecture.

[0126] In the Access Rights Quick Linkage Adjustment module, the process of identifying employee job transfer scenarios and adjusting corresponding electronic file data access rights in different employee job transfer scenarios includes:

[0127] Access rights to electronic archive data include viewing, downloading, modifying, deleting and sharing, setting job levels, which are divided into low job levels, middle job levels and high job levels;

[0128] Based on the access permission data, low-level positions are granted access permissions to view and download electronic archive data, mid-level positions are granted access permissions to view, download and modify electronic archive data, and high-level positions are granted access permissions to view, download, modify, delete and share electronic archive data;

[0129] Based on employee positions before and after scheduling, real-time scheduling time, and access rights to electronic archive data at different job levels, natural language processing technology and supervised learning algorithms are combined to identify employee job transfer scenarios and associate them with employee IDs. These scenarios include horizontal transfers, promotion scheduling, and demotion scheduling.

[0130] Based on knowledge graph technology, the access rights to electronic archive data corresponding to employee positions before and after scheduling are compared, and the intelligent rule permission engine is used to automatically calculate the difference in the access rights to electronic archive data before and after scheduling. Through automated workflows, real-time adjustment of the access rights to electronic archive data is achieved. The entire process does not require human intervention, and the adjustment of access rights to electronic archive data can be achieved in seconds.

[0131] The verification process includes:

[0132] like Figure 2 As shown in the figure, the process of visualizing the correlation diagram between the electronic archive data encryption index and the electronic archive data security management performance index is as follows:

[0133] Figure 2 In the figure, the blue scatter points are historical data, describing the distribution trend of the encryption index and the performance index. In most cases, the higher the encryption index, the lower the performance index, indicating that the encryption complexity is negatively correlated with the system performance. The historical data corresponding to the blue scatter points circled in red are the current data points. The electronic archive data encryption index and the security management performance index are quantitatively associated through a machine learning model. The negative correlation characteristic is the core basis for system optimization. By dynamically dividing the relevant intervals and matching the improvement strategy, this system solves the contradiction between encryption complexity and performance in traditional technologies and realizes the intelligent and automated security management of electronic archives.

[0134] like Figure 3 As shown in FIG, the visualization process of the dynamic distribution of the electronic archive data encryption index, the electronic archive data security management performance index, and the related intervals of various electronic archive data encryption and security management performance includes:

[0135] Fluctuations of indicators in the time dimension: the horizontal axis reflects the system operation cycle (such as 1-100 time steps), and each time step corresponds to an encryption task or performance monitoring cycle; the vertical axis reflects the electronic archive data encryption index and the electronic archive data security management performance index, which are represented by blue and red lines respectively. The ranges of the electronic archive data encryption index and the electronic archive data security management performance index are both [0, 1]. The higher the value of the electronic archive data encryption index, the higher the encryption complexity, and the higher the value of the electronic archive data security management performance index, the better the system performance; the electronic archive encryption performance trade-off evaluation coefficient output by the security encryption performance constraint analytical model is used to analyze the correlation between the output results of the encryption monitoring model and the output results of the performance monitoring model. Figure 3 Indicated by blue, green and red dots.

[0136] like Figure 4 As shown in the figure, the regional chain storage capacity level coefficient and the regional chain storage level change trend diagram are described as follows:

[0137] The data records on the horizontal axis represent the increment of the number of archives, and the vertical axis describes the storage level coefficient. The green line is used to describe the historical trend curve, reflecting long-term changes, and the red icon is used to describe the current regional chain storage capacity level, indicating the latest status;

[0138] Linear growth stage: When the blockchain is at a low storage level, the slope of the curve is gentle, indicating that the storage pressure of the blockchain is controllable and the system can adopt a lightweight compression algorithm;

[0139] Exponential growth stage: When the regional chain enters the medium storage level, the slope becomes steeper. Corresponding to the batch upload scenario of electronic archives, the system automatically triggers the tiered storage strategy, storing hot data in SSD and cold data in HDD. Hot data refers to frequently accessed data, while cold data refers to infrequently accessed data.

[0140] Saturation stage: When the regional chain enters a high storage level, the curve tends to be vertical. At this time, the system starts to optimize the consensus mechanism and implement sharding technology, splitting the main chain into 4 sub-chains to disperse the storage pressure of the regional chain.

[0141] like Figure 5 As shown, the dynamic display process of employee position transfer is as follows:

[0142] Using natural language processing technology and supervised learning algorithms, we identify three types of transfer scenarios: horizontal transfers, where the job level remains unchanged before and after the transfer, i.e., from a low position to a low position, a medium position to a medium position, or a high position to a high position; promotion transfers, where the job level is increased, i.e., from a low position to a medium position or a high position, or from a medium position to a high position; and demotion transfers, where the job level is decreased, i.e., from a medium position to a low position, or from a high position to a medium position or a low position.

[0143] Figure 5 The horizontal axis in the middle is the employee number, which corresponds to the specific employee's work number; the vertical axis is the job level, and the larger the value, the higher the job level; gray represents the initial job level, which reflects the employee's access rights to electronic file data before the job transfer; blue represents the final job level, which reflects the employee's access rights to electronic file data after the job transfer. Among them, the vertical spacing intuitively displays the extent of the change in job level, with the promotion time interval being positive and the demotion time interval being negative. This dynamic display process provides a basis for solving the problems of delayed authority adjustment and high manual intervention costs after job transfers in traditional technologies, and significantly improves the intelligence and real-time nature of electronic file security management.

[0144] like Figure 6 As shown, the dynamic display process of adjusting the access rights of electronic archive data is as follows:

[0145] Using knowledge graph technology, we build a knowledge graph of positions and permissions, and clearly define the permission sets corresponding to each position level. Low-level positions can view and download; mid-level positions can view, download, and modify; high-level positions can view, download, modify, delete, and share.

[0146] The intelligent rule-based permission engine compares the differences in employee permissions before and after job reassignment. For promotion scenarios, a permission increment list is generated; for demotion scenarios, a permission deletion list is generated; and for horizontal transfers, no permission changes are made, only logs are recorded. Figure 6 The horizontal axis is the employee number, and the vertical axis is the extent of authority adjustment. Positive values ​​indicate an increase in authority, and negative values ​​indicate a decrease in authority. The color coding in the figure can be understood as the height of the bar corresponding to each employee reflects the extent of the change in authority.

[0147] Example 2, as Figure 7 As shown, a method for secure management of electronic archive data, based on the system in Example 1, includes the following steps:

[0148] Collect electronic archive security management data, including encryption data, performance monitoring data, regional chain monitoring data, employee position data, and access permission data;

[0149] By encrypting data, the total amount of encrypted electronic archive data is calculated. In combination with the random forest algorithm, an encryption monitoring model is constructed to output the corresponding electronic archive data encryption index. By using performance monitoring data, the remaining amount of memory after encryption processing is calculated. In combination with the decision tree algorithm, a performance monitoring model is constructed to output the corresponding electronic archive data security management performance index.

[0150] Using the multivariate linear regression algorithm, the correlation between the electronic archive data encryption index and the electronic archive data security management performance index is analyzed, a security encryption performance constraint analytical model is constructed, and the electronic archive encryption performance trade-off evaluation coefficient is output;

[0151] Based on the electronic archive encryption performance trade-off evaluation coefficient, the relevant intervals of electronic archive data encryption and security management performance are divided, and improvement measures are taken according to the relevant intervals of each electronic archive data encryption and security management performance to optimize them;

[0152] Combining performance monitoring data, regional chain monitoring data and neural network algorithms, a regional chain storage capacity monitoring model is constructed to output the regional chain storage capacity grade coefficient;

[0153] Improve the blockchain storage function by combining the output of the regional chain storage capacity monitoring model with data optimization technology, storage architecture technology and blockchain mechanism technology;

[0154] Based on employee position data and access permission data, natural language processing technology and supervised learning algorithms are used to identify employee position transfer scenarios. In combination with knowledge graph technology, intelligent permission rule engine and automated workflow technology, corresponding adjustments are made to the access permissions of electronic archive data in different employee position transfer scenarios.

Claims

1. A security management system for electronic archive data, characterized in that: include: The electronic archive security management data collection module is used to collect electronic archive security management data, including encryption data, performance monitoring data, regional chain monitoring data, employee position data, and access permission data; The encryption and performance optimization module is divided into the following units: The encryption monitoring unit calculates the total amount of encrypted electronic archive data through encryption data, builds an encryption monitoring model by combining the random forest algorithm, and outputs the corresponding electronic archive data encryption index; The performance monitoring unit uses the performance monitoring data to calculate the remaining amount of memory after encryption processing, combines the decision tree algorithm to build a performance monitoring model, and outputs the corresponding electronic archive data security management performance index; The encryption performance evaluation unit, based on the output results of the encryption monitoring model and the performance monitoring model, uses a multivariate linear regression algorithm to construct a security encryption performance constraint analytical model and outputs the electronic archive encryption performance trade-off evaluation coefficient; The security management performance optimization unit divides the relevant intervals of electronic archive data encryption and security management performance based on the electronic archive encryption performance trade-off evaluation coefficient, and takes improvement measures to optimize the relevant intervals of electronic archive data encryption and security management performance according to each electronic archive data encryption and security management performance; The regional chain storage function improvement module combines performance monitoring data, regional chain monitoring data and neural network algorithms to build a regional chain storage capacity monitoring model, output regional chain storage capacity level coefficients, and combines data optimization technology, storage architecture technology and blockchain mechanism technology to improve blockchain storage functions. The improvement process includes: Based on the output results of the regional chain storage capacity monitoring model, the regional chain storage capacity level is divided into low regional chain storage capacity level, medium regional chain storage capacity level and high regional chain storage capacity level; For low regional chain storage capacity levels, regularly monitor regional chain monitoring data and changes in its corresponding regional chain storage capacity level coefficient, use a lightweight data compression algorithm, and reserve regional chain storage capacity space; For the medium-range blockchain storage capacity level, a tiered storage strategy is introduced. Different storage devices are used to store hot electronic archive data whose access frequency exceeds the set high-frequency threshold and cold electronic archive data whose access frequency is lower than the set low-frequency threshold, and the regional blockchain monitoring data is stored in shards. For high blockchain storage capacity levels, the consensus mechanism of the blockchain is evaluated and optimized. Sharding technology is used to divide the blockchain network into several sub-chains to achieve the reconstruction of the blockchain architecture. The access rights rapid linkage adjustment module uses natural language processing technology and supervised learning algorithms to identify employee job transfer scenarios based on employee position data and access rights data. It then combines knowledge graph technology, an intelligent rights rule engine, and automated workflow technology to adjust access rights to electronic archive data in different employee job transfer scenarios. The process includes: Access rights to electronic archive data include viewing, downloading, modifying, deleting and sharing, setting job levels, which are divided into low job levels, middle job levels and high job levels; Based on the access permission data, low-level positions are granted access permissions to view and download electronic archive data, mid-level positions are granted access permissions to view, download and modify electronic archive data, and high-level positions are granted access permissions to view, download, modify, delete and share electronic archive data; Based on employee positions before and after scheduling, real-time scheduling time, and access rights to electronic archive data at different job levels, natural language processing technology and supervised learning algorithms are combined to identify employee job transfer scenarios and associate them with employee IDs. These scenarios include horizontal transfers, promotion scheduling, and demotion scheduling. Based on knowledge graph technology, the access rights to electronic archive data corresponding to employee positions before and after scheduling are compared, and the intelligent rule permission engine is used to automatically calculate the difference in access rights to electronic archive data before and after scheduling. Real-time adjustment of access rights to electronic archive data is achieved through automated workflows.

2. The electronic archive data security management system according to claim 1, characterized in that: In the electronic archive security management data collection module, the collection process of electronic archive security management data is as follows: Deploy different types of collection equipment, combined with data entry technology, to collect electronic archive security management data. The collection equipment includes password compliance testers, network protocol analyzers, encryption performance testers, HSM built-in timers, encryption accelerator card built-in counters, hardware encryption card memory probes, PMU probes, CPU frequency sensors, PCIe interrupt capture cards, hardware-level storage protocol analyzers, hardware-level network damage analyzers, blockchain node devices, blockchain-specific traffic analyzers, mixed-precision memory analyzers, and Hyperledger Caliper. Encryption data includes key length, amount of electronic archive data encrypted per second, encryption start time, encryption end time, and real-time length of the encryption queue; performance monitoring data includes total memory and memory usage during the encryption process, as well as CPU usage, clock frequency and number of interruptions, disk read and write throughput when storing encrypted electronic archive data, disk response time for encryption operations, and network packet loss rate; regional chain monitoring data includes total storage capacity of regional chain ledgers, hourly regional chain capacity growth rate, memory read and write speed to regional chain, and time required for newly joined nodes and fault recovery nodes to synchronize regional chains; employee position data includes employee positions before and after scheduling, employee work numbers, and real-time scheduling time of employee positions; access rights data includes access rights to electronic archive data corresponding to each employee position, among which access rights to electronic archive data include viewing, downloading, modifying, deleting, and sharing; The collected encrypted data, performance monitoring data, regional chain monitoring data, employee position data and access permission data are cleaned and standardized, and timestamps are assigned to each pre-processed electronic archive security management data. The timestamps are adjusted to achieve the unification of each electronic archive security management data in the time dimension, and the encrypted data, performance monitoring data, regional chain monitoring data, employee position data and access permission data are integrated to generate an electronic archive security management data set.

3. The electronic archive data security management system according to claim 2, characterized in that: In the encryption monitoring unit of the encryption and performance optimization module, the process of outputting the corresponding electronic archive data encryption index includes: Calculate the difference between the encryption start time and the encryption end time to obtain the encryption process time, then calculate the product of the amount of electronic archive data encrypted per second and the encryption process time to obtain the total amount of encrypted electronic archive data, and integrate the total amount of encrypted electronic archive data into the electronic archive security management data set; Extracting the key length, the real-time length of the encryption queue, and the total amount of encrypted electronic archive data from the electronic archive security management data set, and dividing the extracted data into a first training set and a first test set; The random forest algorithm is used to take the first training set data as input and the electronic archive data encryption index as output to learn the nonlinear relationship between the first training set data and the electronic archive data encryption index and train the encryption monitoring model. Inputting the first test set data into the encrypted monitoring model, adjusting the parameters of the encrypted monitoring model, and then obtaining the final encrypted monitoring model; The current key length, real-time length of the encryption queue and the total amount of encrypted electronic archive data are input into the encryption monitoring model, the corresponding electronic archive data encryption index is output, and the electronic archive data encryption index is integrated into the electronic archive security management data set.

4. The electronic archive data security management system according to claim 2, characterized in that: In the performance monitoring unit of the encryption and performance optimization module, the process of outputting the corresponding electronic archive data security management performance index includes: Calculate the difference between the total memory and the memory usage during the encryption process, obtain the remaining memory after encryption, and integrate the remaining memory after encryption into the electronic archive security management data set; Extract the CPU usage, clock frequency and interruption times, disk read and write throughput when storing encrypted electronic archive data, disk response time for encryption operations, network packet loss rate, and remaining memory after encryption from the electronic archive security management dataset, and convert the extracted data into the second training set and the second test set; A decision tree algorithm is used, with the second training set data as input and the electronic archive data security management performance index as output, to learn the nonlinear relationship between each second training set data and the electronic archive data security management performance index, and train the performance monitoring model; Inputting the second test set data into the performance monitoring model, adjusting the performance monitoring model parameters, and obtaining the final performance monitoring model; Combined with the current CPU usage, clock frequency and number of interrupts, the disk read and write throughput when storing encrypted electronic archive data, the disk's response time to encryption operations, the network packet loss rate and the remaining memory after encryption processing, the corresponding electronic archive data security management performance index is output and integrated into the electronic archive data security management data set.

5. The electronic archive data security management system according to claim 2, characterized in that: In the encryption performance evaluation unit of the electronic archive security management data acquisition module, the process of outputting the electronic archive encryption performance trade-off evaluation coefficient includes: Extracting the electronic archive data encryption index and the electronic archive data security management performance index from the electronic archive security management data set, and dividing the extracted data into a third training set and a third test set; A multivariate linear regression algorithm is used, with the third training set data as input and the electronic archive encryption performance trade-off evaluation coefficient as output. The linear relationship between each item of the third training set data and the electronic archive encryption performance trade-off evaluation coefficient is learned, and a security encryption performance constraint analytical model is trained. Inputting the third test set data into the security encryption performance constraint analytical model, adjusting the intercept term and regression coefficient of the security encryption performance constraint analytical model, and then obtaining the final security encryption performance constraint analytical model; The current electronic archive data encryption index and electronic archive data security management performance index are input into the security encryption performance constraint analytical model, and the corresponding electronic archive encryption performance trade-off evaluation coefficient is output.

6. The electronic archive data security management system according to claim 2, characterized in that: In the security management performance optimization unit of the electronic archive security management data collection module, the process of dividing the relevant intervals between electronic archive data encryption and security management performance and taking improvement measures to optimize them according to the relevant intervals of electronic archive data encryption and security management performance includes: Based on the electronic archive encryption performance trade-off evaluation coefficient, the correlation interval between electronic archive data encryption and security management performance is divided into strong negative correlation interval, moderate negative correlation interval and weak correlation interval; For strong negative correlation intervals, the encryption algorithm is replaced with a lightweight solution, the number of server CPU cores is increased, memory capacity is expanded, high-speed storage devices are deployed, and load balancing technology is used to redistribute encryption tasks; For moderate negative correlation intervals, a hybrid encryption method is used, and non-real-time encryption tasks are set to asynchronous execution; For weakly correlated intervals, establish a real-time monitoring dashboard, strengthen real-time monitoring of various performance monitoring data, and formulate emergency plans.

7. The electronic archive data security management system according to claim 2, characterized in that: In the regional chain storage function improvement module, the process of outputting the regional chain storage capacity level coefficient includes: Extracting the memory usage and regional chain monitoring data during the encryption process from the electronic archive security management dataset, and converting the extracted data into a fourth training set and a fourth test set; A neural network algorithm is used, with the fourth training set data as input and the regional chain storage capacity level coefficient as output, to learn the nonlinear relationship between each fourth training set data and the regional chain storage capacity level coefficient, and train the regional chain storage capacity monitoring model; Inputting the fourth test set data into the regional chain storage capacity monitoring model, adjusting the regional chain storage capacity monitoring model parameters, and obtaining the final regional chain storage capacity monitoring model; The corresponding regional chain storage capacity level coefficient is output based on the memory usage during the current encryption process, the total storage capacity of the regional chain ledger, the hourly regional chain capacity growth rate, the memory read and write speed to the regional chain, and the time required for newly joined nodes and fault recovery nodes to synchronize the regional chain.

8. A method for secure management of electronic archive data, based on the system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Collect electronic archive security management data, including encryption data, performance monitoring data, regional chain monitoring data, employee position data, and access permission data; By encrypting data, the total amount of encrypted electronic archive data is calculated. In combination with the random forest algorithm, an encryption monitoring model is constructed to output the corresponding electronic archive data encryption index. By using performance monitoring data, the remaining amount of memory after encryption processing is calculated. In combination with the decision tree algorithm, a performance monitoring model is constructed to output the corresponding electronic archive data security management performance index. Using the multivariate linear regression algorithm, the correlation between the electronic archive data encryption index and the electronic archive data security management performance index is analyzed, a security encryption performance constraint analytical model is constructed, and the electronic archive encryption performance trade-off evaluation coefficient is output; Based on the electronic archive encryption performance trade-off evaluation coefficient, the relevant intervals of electronic archive data encryption and security management performance are divided, and improvement measures are taken according to the relevant intervals of each electronic archive data encryption and security management performance to optimize them; Combining performance monitoring data, regional chain monitoring data and neural network algorithms, a regional chain storage capacity monitoring model is constructed to output the regional chain storage capacity grade coefficient; Improve the blockchain storage function by combining the output of the regional chain storage capacity monitoring model with data optimization technology, storage architecture technology and blockchain mechanism technology; Based on employee position data and access permission data, natural language processing technology and supervised learning algorithms are used to identify employee position transfer scenarios. In combination with knowledge graph technology, intelligent permission rule engine and automated workflow technology, corresponding adjustments are made to the access permissions of electronic archive data in different employee position transfer scenarios.

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