An archive management system and method based on blockchain technology

Abstract

The present invention belongs to the technical field of blockchain file management, and discloses an archive management system and method based on blockchain technology. The system includes an archive transfer monitoring module, a block boundary dynamic adjustment module, a consensus node load balancing module, a cross-chain archive transaction priority regulation module, and a storage index optimization module. By dynamically calculating the archive transfer difficulty index, the present invention reduces redundant depositions, improves storage utilization rate, adjusts the block deposition boundary in combination with the change of transfer trend, enables the deposition rule to be adaptively adjusted according to the data state change, optimizes the block storage utilization rate, reduces deposition redundancy, adjusts data according to the deposition boundary, calculates the storage utilization rate of the computing node, the task processing delay and the data synchronization rate, reduces storage bottlenecks, calculates the transaction priority threshold, screens high-priority storage paths, matches low-latency storage nodes, improves data access and storage efficiency, analyzes data access activity, reduces the occupation of high-performance storage resources, and improves the overall storage efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of blockchain file management. Specifically, it relates to a file management system and method based on blockchain technology. Background Art

[0002] The technical field of blockchain file management includes technologies related to storing, managing, verifying, and tracing file information using blockchain technology. Its core content involves aspects such as decentralized storage architecture, multi-node consensus mechanism, data encryption technology, intelligent contract execution, and permission management. The systematic content of this technical field includes generating file data fingerprints based on hash algorithms, distributed ledger storage structures, timestamp authentication mechanisms, and ensuring data immutability. Generally speaking, blockchain file management technology can ensure the authenticity, security, and traceability of file data and provide multi-party collaborative management capabilities to be applicable to government, enterprise, and personal file management scenarios.

[0003] Among them, a file management system and method based on blockchain technology refers to a management method that uses blockchain technology to store, access control, and share file data. The patent theme covers content such as file data storage methods, access permission allocation strategies, data consistency guarantee mechanisms, and trustworthy records of the file transfer process. Specifically, this file management system generates a unique identifier for file data through blockchain hash value calculation, and combines asymmetric encryption technology to achieve encrypted data storage. At the same time, it uses intelligent contracts to define file access permissions and operation rules to ensure data integrity and security during the file management process. In addition, this method uses a consensus algorithm to maintain the ledger data of distributed storage, ensures the consistency of file information among all nodes, and combines timestamp technology to achieve the traceability of file transfer.

[0004] The Chinese invention patent with the patent application number: CN202110037518.2 discloses a blockchain storage method for electronic files. The electronic file node receives an electronic file data processing request, and divides it into a transaction data processing request and a status data processing request; determines whether the electronic file data processing request can be processed accordingly. If so, continue to process the request. If not, it is necessary to re-initiate an electronic file data processing request to the electronic file node; perform corresponding processing on the received electronic file data processing request, store the processed transaction data in the transaction tree, and store the processed status data in the status tree. The status tree is an MPT tree for storing status data, and the transaction tree is an MPT tree for storing transaction data; store the electronic file data to be stored according to the data mapping relationship, and query the electronic file data according to the corresponding mapping relationship when querying.

[0005] The existing file storage methods of this kind can guarantee the authenticity, integrity, and privacy of electronic file data. However, the existing blockchain storage technologies of this kind adopt a fixed strategy for deposit triggering, failing to dynamically adjust according to the complexity of the file circulation process, resulting in increased on-chain storage burden due to redundant deposit of some data, and at the same time, some frequently circulated files fail to obtain priority deposit, affecting the effectiveness of deposit; the block deposit boundary is set to a fixed range, lacking adaptive adjustment to the deposit demand, restricting the utilization rate of block storage, increasing the unnecessary volume of on-chain data, and increasing the storage and retrieval costs; the task allocation of consensus nodes does not consider the storage utilization rate and data synchronization rate, and some nodes have a decrease in deposit efficiency due to overloaded tasks, even affecting the consensus stability of the entire system; the access and storage of file data adopt a unified storage strategy, failing to perform differential storage according to the access frequency, permission level, and storage urgency, so that frequently accessed files and infrequently accessed files occupy the same storage resources, affecting the access efficiency of high-priority data; the storage index mechanism is not adjusted in combination with the data access activity, resulting in low-access-activity data occupying high-performance storage for a long time, increasing the storage overhead and reducing the overall storage performance of the system. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to solve the shortcomings existing in the prior art, and propose an archive management system and method based on blockchain technology, which can reduce redundant deposit, improve storage utilization rate, reduce the occupation of high-performance storage resources, and improve the overall storage efficiency.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] An archive management system based on blockchain technology, the system includes:

[0009] The file circulation monitoring module obtains file circulation path data, analyzes the permission intersection situation and the change situation of deposit continuity of deposit nodes, calculates and classifies the complexity data of deposit paths, identifies different circulation modes and analyzes the change trend of file circulation modes, and obtains a circulation trend record;

[0010] The block boundary dynamic adjustment module reads the circulation trend record, combines the current block storage utilization situation and the deposit data distribution characteristics, calculates and adjusts the block boundary adjustment parameters and range, and obtains the adjusted block deposit boundary data;

[0011] The consensus node load balancing module, according to the adjusted block deposit boundary data, combines the node storage utilization rate, task processing delay, and data synchronization rate, evaluates the node load score, selects a node group with a score higher than the adaptation threshold for consensus task allocation, and generates a consensus task node allocation plan;

[0012] The cross-chain file transaction priority regulation module calculates and filters the file transaction priorities according to the consensus task node allocation scheme, combines the file access frequency, user privilege levels, and the urgency of storage requests, matches storage nodes with low latency and high bandwidth, adjusts the cross-chain consensus process, and generates a transaction priority evidence storage path.

[0013] The following is a further optimization of the above technical solution by the present invention:

[0014] The transfer trend record includes data on the classification of the complexity of the evidence storage path, data on different transfer modes, and data on changes in the file transfer mode; the adjusted block evidence storage boundary data includes data on the utilization of block storage, data on the generation interval of data blocks, and data on the distribution characteristics of the evidence storage data; the consensus task node allocation scheme includes data on node load scores, data on node groups with high adaptation thresholds, and data on the results of consensus task allocation; the transaction priority evidence storage path includes data on file access frequency, data on user privilege levels, and data on the urgency of storage requests.

[0015] Further optimization: The file transfer monitoring module includes:

[0016] The path data acquisition sub-module obtains the file transfer path data, extracts the permission information of the evidence storage nodes, the data link connection status information, and the evidence storage time interval data, calculates the interaction times of the evidence storage nodes, and obtains a set of file transfer path parameters.

[0017] The permission cross-calculation sub-module obtains the set of file transfer path parameters, calculates the permission cross-degree of the evidence storage nodes, combines the data link connection status information, calculates the change value of evidence storage continuity, and calculates the proportion of permission subject classification for the evidence storage trigger time interval and the node interaction times, and obtains a set of permission cross and evidence storage change parameters.

[0018] The transfer mode trend recognition sub-module calls the set of permission cross and evidence storage change parameters, classifies the types of transfer paths, counts the number and proportion of the paths, analyzes the change trend of the path categories, filters the path categories with large change amplitudes, and analyzes the characteristics of the path categories, and obtains a transfer trend record.

[0019] Further optimization: The block boundary dynamic adjustment module includes:

[0020] The file transfer trend analysis sub-module obtains the transfer trend record, detects the transfer quantity within different time periods, calculates the change ratio of the transfer quantity between adjacent time periods, counts the increase and decrease amplitudes of the change ratio within different time periods, captures the change trend characteristics, and obtains a transfer trend change ratio.

[0021] Based on the data occupancy of the current storage block, the storage utilization calculation sub-module calls the change ratio of the transfer trend to determine the usage ratio of the storage space, calculates the average change of the storage utilization relative to the block deposit quantity, and obtains the storage utilization change amplitude data;

[0022] Based on the storage utilization change amplitude data, the boundary adjustment and optimization sub-module calculates the block boundary adjustment parameter according to the transfer offset distribution interval, screens the boundary areas where the adjustment parameter meets the adjustment reference value, and combines the deposit data distribution to calculate the adjusted block boundary position, and obtains the adjusted block deposit boundary data.

[0023] Further optimization: The specific calculation formula for the average change of the storage utilization relative to the block deposit quantity is:

[0024] ;

[0025] Where, represents the storage utilization at the th time point, represents the storage utilization at the previous time point, represents the total number of storage utilization data points, represents the deposit quantity of the th block, represents the average value of the deposit quantities of all blocks, represents the total number of deposit quantity data points, represents the time interval between the generations of the th data block, represents the total number of data points of the data block generation time interval.

[0026] Further optimization: The consensus node load balancing module includes:

[0027] Based on the adjusted block deposit boundary data, the storage utilization evaluation sub-module obtains the storage utilization of the consensus node, calculates the available storage ratio of the node, compares the node available storage ratio with the storage utilization threshold, screens the nodes whose available storage ratio is greater than or equal to the storage utilization threshold, and calculates the storage balance degree and storage utilization deviation of the screened nodes to obtain the storage load score;

[0028] The task processing evaluation sub-module calls the storage load score, obtains the task processing delay and data synchronization rate of the node, calculates the influence value of the two data on the load, and obtains the overall load score;

[0029] The node allocation optimization sub-module calls the overall load score, compares the overall load score of the node with the adaptation threshold, screens the node group with a high adaptation threshold, sorts the nodes in the node group according to the overall load score, and generates a consensus task node allocation plan according to the score ranking.

[0030] Further optimization: The cross-chain file transaction priority regulation module includes:

[0031] The transaction priority threshold calculation sub-module obtains the file access frequency, user permission level, and storage request urgency based on the consensus task node allocation scheme, calculates the access frequency value, permission level value, and urgency weight, analyzes the file transaction priority value, compares the storage threshold to screen for files with high transaction priority values, and obtains a screened file set;

[0032] The storage node matching sub-module calls the screened file set, obtains the storage node bandwidth value and latency value, calculates the low-latency and high-bandwidth score, sorts according to the score, and matches the storage node with the highest score to obtain a file storage matching relationship;

[0033] The transaction deposit path determination sub-module calls the file storage matching relationship, obtains the consensus process corresponding to the storage node, calculates the transaction priority deposit path weight, and generates a transaction priority deposit path.

[0034] Further optimization: The specific calculation formula for the file transaction priority value is:

[0035] ;

[0036] Calculate the file transaction priority parameter, compare the storage threshold to screen for files with high transaction priority values, and obtain a screened file set;

[0037] Among them, represents the file transaction priority value, represents the access frequency of the th file, represents the access frequency weight, represents the total number of data of the file access frequency, represents the permission level of the th user, represents the permission level weight, represents the total number of data of the user permission level, represents the urgency of the th storage request, represents the urgency weight, represents the total number of data of the storage request urgency, represents the maximum value in the current transaction priority parameter, represents the average value of the current transaction priority parameter.

[0038] Further optimization: The system further includes:

[0039] The storage index optimization module reads the transaction priority evidence storage path, combines the file access records, storage level division, and index query response time, calculates the data access activity, adjusts the storage level, and generates a storage index optimization plan;

[0040] The storage index optimization plan includes the analysis result of data access activity, the storage level adjustment plan, and the index structure optimization plan;

[0041] The storage index optimization module includes:

[0042] The access activity calculation sub-module calculates the file access times and time interval values according to the transaction priority evidence storage path and based on the file access records, extracts the access frequency values of the files, compares the access frequency values with the cold storage threshold, filters the data with access frequency values lower than the cold storage threshold, calculates the access ratio under the access cycle, analyzes the change trend of the access frequency, and obtains the access activity ratio;

[0043] The index query optimization sub-module calls the access activity ratio, calculates the index query response time of the high-access-activity data, extracts the matching relationship between the query response time and the access activity, filters the file indexes with query response times higher than the set standard, and obtains the optimized index query time;

[0044] The storage level division sub-module calculates the data storage amount under different index query times according to the optimized index query time and in combination with the storage level standard, determines the attribution range of the storage level based on the data storage amount and the average query time, adjusts the data attribution of the storage level, and obtains the storage index optimization plan.

[0045] The present invention also provides an archive management method based on blockchain technology, which is executed by using the above-mentioned archive management system based on blockchain technology, and includes the following steps:

[0046] S1: Obtain the file transfer path data, call the permission distribution of the evidence storage nodes, the data link connection status, and the evidence storage continuity data, calculate the complexity parameter of the evidence storage path, classify the transfer modes based on the complexity parameter, calculate the change trend of the transfer modes, and obtain the transfer trend record;

[0047] S2: Call the transfer trend record, obtain the block storage utilization rate, the data block generation interval, and the evidence data distribution characteristics, calculate the block evidence storage range adjustment parameter, change the block evidence storage range based on the adjustment parameter, and obtain the adjusted block evidence storage boundary data;

[0048] S3: Based on the adjusted block evidence storage boundary data, obtain the storage utilization rate of consensus nodes, task processing latency, and data synchronization rate of nodes, calculate the node load score, filter the adapted node group based on the score threshold, call the adapted node group to calculate the consensus task allocation strategy, and allocate the consensus task according to the strategy to obtain the consensus task node allocation plan;

[0049] S4: Call the consensus task node allocation plan, obtain the file access frequency, user permission level, and storage request urgency, calculate the file transaction priority, filter the high-priority file transactions based on the transaction priority, call the network latency and bandwidth parameters of the storage node, match the storage nodes with low latency and high bandwidth, and adjust the cross-chain consensus process to obtain the storage path for transactions with high priority;

[0050] S5: Based on the storage path for transactions with high priority, obtain the file access and storage records, storage level division, and index query response time, calculate the data access activity, adjust the storage level based on the activity, optimize the index structure, and obtain the storage index optimization plan.

[0051] The present invention adopts the above technical solutions and has at least the following beneficial effects:

[0052] In the present invention, by dynamically calculating the file transfer difficulty index, redundant evidence storage is reduced, and the storage utilization rate is improved. By adjusting the block evidence storage boundary in combination with the change of the transfer trend, the evidence storage rules are adaptively adjusted according to the change of the data state, the block storage utilization rate is optimized, and the redundant evidence storage is reduced. Adjust the data according to the evidence storage boundary, calculate the storage utilization rate of nodes, task processing latency, and data synchronization rate of nodes, reduce the storage bottleneck, calculate the transaction priority threshold, filter the storage path with high priority, match the storage nodes with low latency, improve the data access and storage efficiency, analyze the data access activity, reduce the occupation of high-performance storage resources, and improve the overall storage efficiency. Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0054] Figure 1 It is the system flowchart of the present invention;

[0055] Figure 2 It is the flowchart of the sub-module of the present invention;

[0056] Figure 3 It is the flowchart of the method steps of the present invention. Detailed Embodiments

[0057] In the embodiments of the present invention, words such as "example" and "for example" are used to represent examples, illustrations, or explanations; any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions; rather, the use of the word "example" is intended to present concepts in a specific manner; in addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0058] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same; "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0059] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0060] To make the technical problems to be solved, technical solutions, and beneficial effects of the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0061] Please refer to Figure 1 , an archive management system based on blockchain technology includes:

[0062] The archive transfer monitoring module obtains archive transfer path data, analyzes the permission cross situation of the evidence storage nodes, the data link connection status, and the change situation of the evidence storage continuity, calculates the complexity data of the evidence storage path, classifies the complexity data, identifies different transfer modes, combines the historical transfer records, and analyzes the change trend of the archive transfer mode to obtain the transfer trend record;

[0063] The block boundary dynamic adjustment module reads the transfer trend record, combines the current block storage utilization situation, the data block generation interval, and the evidence storage data distribution characteristics, calculates the block boundary adjustment parameter, adjusts the block evidence storage range, and generates the adjusted block evidence storage boundary data;

[0064] The consensus node load balancing module, according to the adjusted block evidence storage boundary data, combines the node storage utilization rate, task processing delay, and data synchronization rate, evaluates the node load score, selects the node group with a score higher than the adaptation threshold for consensus task allocation, and generates the consensus task node allocation plan;

[0065] The cross-chain file transaction priority regulation module calculates the file transaction priority according to the consensus task node allocation plan, combines the file access frequency, user privilege level, and storage request urgency, filters high-priority file transactions, matches storage nodes with low latency and high bandwidth, adjusts the cross-chain consensus process, and generates a transaction priority proof-of-existence path;

[0066] The storage index optimization module reads the transaction priority proof-of-existence path, combines the file access and storage records, storage level division, and index query response time, calculates the data access activity, adjusts the storage level, optimizes the index structure, and generates a storage index optimization plan.

[0067] The transfer trend records include data on the classification of the complexity of the proof-of-existence path, data on different transfer modes, and data on changes in the file transfer mode; the adjusted block proof-of-existence boundary data includes data on the utilization of block storage, data on the generation interval of data blocks, and data on the distribution characteristics of the proof-of-existence data; the consensus task node allocation plan includes data on node load scores, data on node groups with high adaptation thresholds, and data on the results of consensus task allocation; the transaction priority proof-of-existence path includes data on file access frequency, user privilege level, and storage request urgency; the storage index optimization plan includes the analysis results of data access activity, the storage level adjustment plan, and the index structure optimization plan.

[0068] Please refer to Figure 2 , the file transfer monitoring module includes:

[0069] The path data collection sub-module obtains the file transfer path data, extracts the permission information of the proof-of-existence nodes, the data link connection status information, and the proof-of-existence time interval data, calculates the number of interactions between the proof-of-existence nodes, and obtains the file transfer path parameter set.

[0070] The path data acquisition sub-module obtains the file transfer path data. First, it extracts the permission information of the evidence storage nodes, records the access permission levels of each evidence storage node, classifies and counts the number of evidence storage nodes with different permission levels, and analyzes the permission structure according to the permission classification model. For example, assuming the permission classification is P1 (basic permission), P2 (intermediate permission), and P3 (advanced permission), where the proportion of P1 nodes is 40%, the proportion of P2 nodes is 35%, and the proportion of P3 nodes is 25%. By counting the distribution of evidence storage nodes with different permission levels, it judges the balance of permission distribution. Secondly, it obtains the data link connection status information, records the data transfer situation between each evidence storage node, including parameters such as connection success rate, transmission delay, and link interruption times. For example, if the connection success rate between nodes A - B is 98% and the transmission delay is 20ms, while the connection success rate between nodes B - C is 85% and the transmission delay is 50ms, it indicates that the link stability between B - C is poor and needs further optimization. Then, it extracts the evidence storage time interval data, calculates the time difference between the evidence storage of adjacent evidence storage nodes. Assuming the evidence storage time intervals T are 15 seconds, 22 seconds, and 30 seconds, the mean and variance of the evidence storage time intervals can be analyzed. For example, if the mean is 22 seconds and the variance is 4 seconds, it indicates that the evidence storage time of the evidence storage nodes is relatively uniform. Finally, it calculates the interaction times of the evidence storage nodes, records the interaction situation between different evidence storage nodes. For example, if the interaction times between A - B are 100 times and the interaction times between B - C are 60 times, the interaction frequency between each node can be calculated and an evidence storage interaction matrix can be established, and finally a file transfer path parameter set is obtained.

[0071] The permission cross - calculation sub-module obtains the file transfer path parameter set, calculates the permission cross - degree of the evidence storage nodes, combines the data link connection status information, calculates the change value of evidence storage continuity, and calculates the proportion of permission subject classification for the evidence storage trigger time interval and the node interaction times, and obtains the permission cross - and evidence storage change parameter set.

[0072] The permission cross - calculation sub - module obtains the archive transfer path parameter set. First, it calculates the permission cross - degree of the evidence - storing nodes, recording the permission sharing situation between different evidence - storing nodes. For example, if the permission level of node A is P2, the permission level of node B is P3, and node A needs to access the data of node B, then it is necessary to calculate the permission cross - degree between P2 and P3. Let the cross - degree C = the intersection of P2 permission items and P3 permission items / the total number of permission items. For example, if the P2 permission items are {R, W} and the P3 permission items are {R, W, X}, then C = 2 / 3≈0.67. Then, combined with the data - link connection status information, it calculates the evidence - storing continuity change value, recording the evidence - storing continuity situation between different evidence - storing nodes. Let the continuity change value S = 1 - the link instability index. For example, if the connection success rate of the A - B link is 95% and the connection success rate of the B - C link is 80%, then the B - C link has a higher instability index and its S value is relatively low. Next, for the evidence - storing trigger time interval and the number of node interactions, it calculates the classification proportion of permission subjects. Let the proportion P = the number of times each permission subject stores evidence / the total number of evidence - storing times. For example, if the number of times node P1 stores evidence is 200 times and the total number of evidence - storing times is 1000 times, then the classification proportion of P1 is 0.2. Finally, it obtains the permission cross - and evidence - storing change parameter set.

[0073] The transfer mode trend recognition sub - module calls the permission cross - and evidence - storing change parameter set to classify the transfer path types, count the quantity and proportion of the paths, analyze the change trend of the path categories, screen out the path categories with large trend change amplitudes, and analyze the characteristics of the path categories to obtain the transfer trend record.

[0074] The transfer mode trend recognition sub-module calls the permission cross and evidence preservation change parameter set to classify the types of evidence preservation paths. According to the permission level of the evidence preservation nodes, the structure of the evidence preservation paths, and the stability of the evidence preservation time interval, the evidence preservation paths are divided into different categories. For example, the evidence preservation paths can be divided into three types: chain transfer, star transfer, and circular transfer. Chain transfer refers to the mode in which the evidence preservation data gradually transfers along a fixed order. Star transfer means that the evidence preservation data centrally transfers to a core evidence preservation node, while circular transfer refers to the cyclic transfer of the evidence preservation data among multiple evidence preservation nodes. The system counts different types of evidence preservation paths and calculates the proportion of each type of evidence preservation path. For example, within a certain time period, there are 100 evidence preservation paths in total, including 50 chain transfer paths, 30 star transfer paths, and 20 circular transfer paths. Then the proportion of chain transfer is 50%. The system further analyzes the change trends of different evidence preservation path categories in different time periods. For example, if the proportion of chain transfer paths increases from 50% to 60% recently, it indicates that this type of path occupies a larger proportion in the evidence preservation process. The system screens out the evidence preservation path categories with large change amplitudes and analyzes the main characteristics of this evidence preservation path category in combination with parameters such as the evidence preservation interaction frequency and permission cross degree. For example, if the evidence preservation time interval of a certain evidence preservation path category is significantly shortened, it indicates that the evidence preservation operations on this path are more frequent. Finally, a transfer trend record is obtained.

[0075] Please refer to Figure 2 , the block boundary dynamic adjustment module includes:

[0076] The file transfer trend analysis sub-module obtains the transfer trend record, detects the transfer quantity in different time periods with differences, calculates the change ratio of the transfer quantity between adjacent time periods, counts the increase and decrease amplitude of the change ratio in the time periods with differences, captures the change trend characteristics, and obtains the transfer trend change ratio.

[0077] The file transfer trend analysis sub-module obtains the transfer trend record, extracts the file transfer quantities in different time periods within the set time cycle. First, divide the specified time period, for example, by days, weeks, or months, and divide the file transfer data into a time series data set according to the time dimension. Then count the file transfer quantity in each time period and calculate the transfer quantity change rate between two adjacent time periods in the time series;

[0078] The calculation formula for the transfer quantity change rate is:

[0079] ;

[0080] Among them, represents the change rate of the time period , and respectively represent the file transfer quantities of the current time period and the previous time period;

[0081] Taking the weekly file transfer data as an example, assume the transfer quantity in the first week is 120 copies, and in the second week is 150 copies, then the change rate ;

[0082] At this time, it is necessary to further calculate the change rates for multiple time periods to obtain a complete change rate sequence. After that, analyze the change trends of these change rates and calculate the increase or decrease amplitude of the change rates.

[0083] The formula for calculating the increase or decrease amplitude is:

[0084] ;

[0085] Among them, is the increase or decrease amplitude of the th time period;

[0086] If , then , and by calculating the increase or decrease amplitude of the change rate for the entire time series, finally, extract the change trend characteristics to determine whether there are trends such as continuous growth, increasing volatility, or decline, so as to obtain the change ratio of the transfer trend.

[0087] The storage utilization rate calculation sub-module, based on the data occupancy of the current storage block, calls the change ratio of the transfer trend to determine the usage ratio of the storage space, calculates the average change of the storage utilization rate relative to the block evidence deposit volume, and obtains the data of the change amplitude of the storage utilization rate.

[0088] The storage utilization rate calculation sub-module, based on the data occupancy of the current storage block, first obtains the total capacity and the used capacity of the current storage block, and calculates the usage ratio of the storage space. The formula is as follows:

[0089] ;

[0090] Assume that the total capacity of a certain storage block is 1TB and 600GB has been used, then the storage utilization rate ;

[0091] Then, call the change ratio of the transfer trend to calculate the average change of the storage utilization rate relative to the block evidence deposit volume. The calculation method of the change ratio of the evidence deposit volume is similar to that of the change ratio of the transfer trend. Define the change rate of the evidence deposit volume :

[0092] ;

[0093] Among them, is the change rate of the evidence deposit volume for the time period The amount of evidence stored. Assume the amount of evidence stored in the first time period is 500 copies, and in the second time period is 550 copies, then ;

[0094] Finally, calculate the change range of storage utilization rate:

[0095] ;

[0096] Assume the storage utilization rate in the first time period , and in the second time period , then ;

[0097] Finally, obtain the data of the change range of storage utilization rate.

[0098] Based on the data of the change range of storage utilization rate, the boundary adjustment optimization sub-module calculates the block boundary adjustment parameters according to the distribution interval of the transfer offset, filters the boundary areas where the adjustment parameters meet the adjustment reference value, and combines the distribution of the evidence storage data to calculate the adjusted block boundary position, and obtains the adjusted block evidence storage boundary data.

[0099] Obtain the historical storage utilization rate data, and calculate the average change of the storage utilization rate relative to the block evidence storage amount. The specific calculation formula is as follows:

[0100] ;

[0101] Among them, represents the storage utilization rate at the th time point, represents the storage utilization rate at the previous time point, represents the total number of storage utilization rate data points, represents the evidence storage amount of the th block, represents the average value of the evidence storage amounts of all blocks, represents the total number of data points of the evidence storage amount, represents the time interval between the generations of the th data block, represents the total number of data points of the time interval between the generations of data blocks.

[0102] Compare the calculated block evidence storage boundary adjustment value with the block evidence storage adjustment threshold to determine whether it exceeds the threshold range. If it exceeds, adjust the block evidence storage boundary, and finally obtain the adjusted block evidence storage boundary data;

[0103] Parameter acquisition:

[0104] Obtained by monitoring the storage usage of the system at different time points. Assume that the storage utilization rate is recorded hourly within a day, resulting in a total of 24 data points.

[0105] : The total number of data points for the storage utilization rate, which is 24.

[0106] Numerical example:

[0107] Assume the storage utilization rate data is as follows (unit: %): 50, 52, 51, 53, 55, 54, 56, 58, 57, 59, 60, 62, 61, 63, 65, 64, 66, 68, 67, 69, 70, 72, 71, 73.

[0108] Calculation process:

[0109] Calculate the sum of the absolute values of the differences in storage utilization rates between adjacent time points:

[0110] .

[0111] Average change in storage utilization rate:

[0112] .

[0113] Calculation of the standard deviation of the block deposit quantity:

[0114] Parameter acquisition:

[0115] : The deposit quantity of the th block, obtained from the deposit data volume of each block recorded by the blockchain system; assume that 10 blocks are generated within a day.

[0116] : The total number of data points for the deposit quantity, which is 10.

[0117] Numerical example:

[0118] Assume the deposit quantity data for each block is as follows (unit: MB): 5, 6, 5.5, 6.5, 7, 6, 5.8, 6.2, 6.5, 7.

[0119] Calculation process:

[0120] Calculate the average value of the deposit quantity:

[0121] .

[0122] Calculate the sum of the squares of the differences between each deposit quantity and the average value:

[0123] .

[0124] Standard deviation of the number of certificates stored:

[0125] 。

[0126] Calculation of the average time interval for data block generation:

[0127] Parameter acquisition:

[0128] : The time interval for the generation of the th data block is obtained by calculating the block generation timestamp recorded by the blockchain system; assume that 9 time intervals are recorded;

[0129] : The total number of data points for the time interval of data block generation, i.e., 9;

[0130] Numerical example:

[0131] Assume the time interval data is as follows (unit: minutes): 10, 11, 9, 10, 12, 10, 11, 9, 10;

[0132] Calculation process:

[0133] Calculate the sum of the time intervals:

[0134] ;

[0135] Average time interval:

[0136] ;

[0137] Calculate the adjustment value of the block certificate storage boundary:

[0138] Calculation process:

[0139] Substitute the above calculation results into the formula:

[0140] 。

[0141] Result analysis:

[0142] Average change in storage utilization: Reflects the degree of fluctuation of storage utilization, and the larger the value, the more frequent the fluctuation; in this example, the average value is about 1.92%, indicating that the storage utilization changes by about 1.92% per hour on average during the monitoring period;

[0143] Standard deviation of the block certificate storage volume: Measures the degree of dispersion of the block certificate storage volume, and the larger the value, the more dispersed the certificate storage volume; in this example, the standard deviation is about 0.685 MB, indicating the degree of fluctuation of the certificate storage volume during the monitoring period;

[0144] Mean of data block generation time interval: It represents the average time interval for block generation; in this example, the mean is approximately 10.22 minutes, close to the expected 10 minutes, indicating that the block generation speed is relatively stable;

[0145] Block deposit boundary adjustment value : Calculated based on the above indicators; in this example, it is approximately -8.904, and this value is used to determine whether to adjust the block deposit boundary. If it exceeds the preset threshold range, the block deposit boundary needs to be adjusted accordingly.

[0146] Please refer to Figure 2 , the consensus node load balancing module includes:

[0147] The storage utilization evaluation sub-module, based on the adjusted block deposit boundary data, obtains the storage utilization rate of the consensus nodes, calculates the available storage ratio of the nodes, compares the available storage ratio of the nodes with the storage utilization threshold, filters out the nodes with the available storage ratio greater than or equal to the storage utilization threshold, and calculates the storage balance degree and storage utilization deviation of the filtered nodes to obtain the storage load score.

[0148] The storage utilization evaluation sub-module, based on the adjusted block deposit boundary data, first analyzes the deposit boundary data to obtain the storage distribution of the blocks, analyzes the information such as the block size, the number of deposits, and the storage capacity in the deposit boundary data, extracts the distribution of block deposits and the occupancy rate of storage resources, calls the calculation method of the storage utilization rate of the consensus nodes, and calculates the used storage volume and the total storage capacity of each consensus node for ratio calculation, that is, the storage utilization rate , where is obtained through the cumulative data volume of block deposits, is the storage upper limit value of the node's hardware configuration. For example, for a certain node, its total storage capacity , and the current storage utilization rate is calculated to be ; subsequently, calculate the available storage ratio of the node, that is, the proportion of the remaining storage space. For example, the available storage ratio of the above node ; then set the storage utilization threshold , for example, set , filter out the nodes that meet , that is, the nodes with the available storage ratio greater than or equal to the storage utilization threshold. If a certain node then it is excluded, if then it is retained; for the filtered nodes, calculate the storage balance degree, and use the storage load deviation formula , where is the average storage utilization rate of the filtered nodes, is the total number of nodes. For example, if the storage utilization rates in a certain cluster are {0.7, 0.8, 0.6, 0.85}, then the average utilization rate , calculate the storage load deviation D s =[(0.7 - 0.7375) 2 + (0.8 - 0.7375) 2 + (0.6 - 0.7375) 2 + (0.85 - 0.7375) 2 / 4 = 0.0089; Finally, determine the storage load score based on the storage load deviation value, and use the standardized score , such as , the calculation of the storage load score is completed.

[0149] The task processing evaluation sub-module calls the storage load score, obtains the task processing latency and data synchronization rate of the nodes, calculates the influence values of the two data on the load, and obtains the overall load score.

[0150] The task processing evaluation sub-module calls the storage load score, first obtains the task processing latency of the nodes and the data synchronization rate , where the task processing latency can be obtained through historical task records, such as the average execution time of the past 100 tasks of a certain node , the data synchronization rate can be calculated based on the actual bandwidth and block synchronization time, such as a certain node synchronizes 50MB of data per second , calculate the influence values of the two data on the load, and use the influence coefficients and to perform weighted summation to calculate the task load influence value of the node , where , , then ; Then calculate the overall load score , where , , such as the storage load score of a certain node , then ; Finally, obtain the overall load score.

[0151] The node allocation optimization sub-module calls the overall load score, compares the overall load score of the nodes with the adaptation threshold, filters out the node groups with a high adaptation threshold, sorts the nodes within the node groups according to the overall load score, and generates a consensus task node allocation scheme according to the score ranking.

[0152] The node allocation optimization sub-module calls the overall load score, first compares the overall load score of the nodes with the adaptation threshold , for example, set , filter out the ones that meet nodes, such as the overall load score of a certain node Meet the requirements; then screen the node groups with high adaptation thresholds, for example, select from multiple nodes The top 10 nodes with higher values; in the screened node group, sort according to the overall load score. For example, if the node scores are {-25.6, -27.1, -29.7, -30.2, -32.0} in sequence, then after sorting it is {-25.6, -27.1, -29.7, -30.2, -32.0}. Finally, generate a consensus task node allocation plan according to the score sorting, that is, give tasks to the node with the highest storage load score first, and execute the allocation order in sequence.

[0153] Please refer to Figure 2 , the cross-chain file transaction priority regulation module includes:

[0154] The transaction priority threshold calculation sub-module, based on the consensus task node allocation plan, obtains the file access frequency, user permission level, and storage request urgency, calculates the access frequency value, permission level value, and urgency weight, analyzes the file transaction priority value, and compares the storage threshold to screen the files with high transaction priority values to obtain a screened file set.

[0155] The specific calculation formula for the file transaction priority value is:

[0156] ;

[0157] Calculate the file transaction priority parameter, compare the storage threshold to screen the files with high transaction priority values to obtain a screened file set;

[0158] Among them, represents the file transaction priority value, represents the th file's access frequency, represents the access frequency weight, represents the total number of data of the file access frequency, represents the th user's permission level, represents the permission level weight, represents the total number of data of the user permission level, represents the th storage request's urgency, represents the urgency weight, represents the total number of data of the storage request urgency, represents the maximum value in the current transaction priority parameter, represents the average value of the current transaction priority parameter.

[0159] Calculation of access frequency value:

[0160] Parameter acquisition:

[0161] : The access frequency of the th file, which is obtained by counting the access logs recorded in the file management system; assume that the statistical period is one month;

[0162] : The access frequency weight, which is set to 0.5; the weight is set according to the file management strategy. The higher the access frequency, the greater the importance of the file may be, so an appropriate weight is assigned;

[0163] Numerical example:

[0164] Assume that there are 5 files in a month, and their access times are: 20 times, 15 times, 30 times, 10 times, 25 times;

[0165] Calculation process:

[0166] Calculate the access frequency value of each file:

[0167] ;

[0168] ;

[0169] ;

[0170] ;

[0171] .

[0172] Calculation of permission level value:

[0173] Parameter acquisition:

[0174] : The permission level of the th user, which is obtained through the user permission management system; the permission level is usually divided into multiple levels, for example: 1 (ordinary user), 2 (advanced user), 3 (administrator);

[0175] : The permission level weight, which is set to 0.3; the weight is set according to the file management strategy. The higher the permission level, the greater the operation permission of the user for the file, so an appropriate weight is assigned;

[0176] Numerical example:

[0177] Assume that there are 3 users, and their permission levels are: 1, 2, 3;

[0178] Calculation process:

[0179] Calculate the permission level value for each user:

[0180] ;

[0181] ;

[0182] 。

[0183] Calculation of the urgency weight:

[0184] Parameter acquisition:

[0185] : The urgency of the th storage request, obtained through the metadata of the storage request or the urgency identifier at the time of user submission; The urgency can be divided into multiple levels, for example: 1 (ordinary), 2 (urgent), 3 (very urgent);

[0186] : The urgency weight, set to 0.2; The setting of the weight is based on the file management strategy. The higher the urgency of the storage request, the more urgent the need for the file, so an appropriate weight is assigned;

[0187] Numerical example:

[0188] Suppose there are 4 storage requests, and their urgencies are: 1, 2, 3, 1;

[0189] Calculation process:

[0190] Calculate the urgency weight for each storage request:

[0191] ;

[0192] ;

[0193] ;

[0194] 。

[0195] Calculate the priority parameter of the file transaction:

[0196] Parameter acquisition:

[0197] : The total number of data on the file access frequency, that is, the number of files, assumed to be 5;

[0198] : The total number of data on the user permission level, that is, the number of users, assumed to be 3;

[0199] : The total number of data storing request urgencies, i.e., the number of storing requests, assumed to be 4.

[0200] Calculation process:

[0201] Calculate the weighted sum of access frequency, permission level, and urgency:

[0202] ;

[0203] ;

[0204] 。

[0205] Calculate the weighted average:

[0206] 。

[0207] Calculate the dispersion adjustment coefficient of the priority parameter:

[0208] Parameter acquisition:

[0209] : The maximum value in the current transaction priority parameter, assumed to be 10;

[0210] : The average value of the current transaction priority parameter, assumed to be 5;

[0211] Calculation process:

[0212] Calculate the dispersion adjustment coefficient:

[0213] 。

[0214] Calculate the final archival transaction priority parameter:

[0215] Calculation process:

[0216] Calculate the weighted average:

[0217] ;

[0218] 。

[0219] Calculate the dispersion adjustment coefficient:

[0220] 。

[0221] Calculate the final archival transaction priority parameter:

[0222] ;

[0223] 。

[0224] Result analysis:

[0225] The result shows that the final calculated value of the archive transaction priority parameter is 8.866. This value reflects the comprehensive influence of the archive access frequency, user permission level, and the urgency of the storage request, and is corrected by combining the dispersion adjustment coefficient of the priority data. When it is greater than the set storage threshold, the archive will be included in the screened archive set and stored or processed preferentially; otherwise, the processing will be postponed.

[0226] The storage node matching sub-module calls the screened archive set, obtains the bandwidth value and latency value of the storage node, calculates the low-latency and high-bandwidth score, sorts according to the score, and matches the storage node with the highest score to obtain the archive storage matching relationship.

[0227] The storage node matching sub-module calls the screened archive set, obtains the bandwidth value and latency value of the storage node. First, extracts the bandwidth information of the storage node. Assume that the bandwidths of storage nodes A, B, and C are 100MB / s, 200MB / s, and 150MB / s respectively, and measures the latency values, which are 10ms, 5ms, and 8ms respectively. Calculate the low-latency and high-bandwidth score. The score calculation formula is set as follows:

[0228] ;

[0229] Among them, is the storage node score, is the bandwidth of this storage node, is the maximum bandwidth among all nodes, is the bandwidth weight, is the storage node latency, is the minimum latency among all nodes, is the latency weight.

[0230] Set the bandwidth weight to 0.6 and the latency weight to 0.4, and calculate the storage node score:

[0231] Node A: ;

[0232] Node B: ;

[0233] Node C: ;

[0234] Sort according to the score, match the storage node with the highest score, that is, Node B, to obtain the archive storage matching relationship.

[0235] The transaction deposit path determination sub-module calls the archive storage matching relationship, obtains the consensus process corresponding to the storage node, calculates the transaction priority deposit path weight, and generates the transaction priority deposit path.

[0236] The transaction evidence storage path determination sub-module calls the file storage matching relationship to obtain the consensus process corresponding to the storage node. First, it searches for the consensus process adopted by storage node B, such as PBFT (Practical Byzantine Fault Tolerance Algorithm), extracts the transaction processing process of the storage node, and calculates the weight of the transaction priority evidence storage path. Assume the weight calculation method of the evidence storage path is as follows:

[0237] ;

[0238] Among them, is the weight of the transaction priority evidence storage path, is the transaction priority value, is the maximum transaction priority value in the screened file set, is the weight of the transaction priority value, is the storage node score, is the highest score of all storage nodes, is the storage score weight;

[0239] Set the weight of the transaction priority value to 0.7 and the storage score weight to 0.3. Assume the maximum transaction priority value is 20, the current transaction priority value is 15.8, the maximum storage score is 1.0, and the current storage score is 1.0. Then the calculated weight of the transaction priority evidence storage path is:

[0240] ;

[0241] Finally, generate the transaction priority evidence storage path.

[0242] Please refer to Figure 2 , the storage index optimization module includes:

[0243] The access activity calculation sub-module calculates the file access times and time interval values according to the transaction priority evidence storage path and based on the file access and storage records, extracts the access frequency value of the file, compares the access frequency value with the cold storage threshold, screens the data with the access frequency value lower than the cold storage threshold, calculates the access ratio under the access and storage cycle, analyzes the change trend of the access frequency, and obtains the access activity ratio.

[0244] The access activity calculation sub-module counts the access times of each file according to the transaction priority evidence storage path and based on the file access and storage records, sets a time window (such as 7 days, 30 days) to calculate the access times within the unit time , where the access frequency value can be expressed as , if a file is accessed 50 times within 30 days, then its access frequency value is times per day. After obtaining the access frequency values of all files, compare them with the cold storage threshold Compare and set times per day. Then all files with an access frequency lower than 0.5 times per day enter the cold storage screening list. Among the screened data, calculate the access ratio under its access cycle. The access cycle represents the number of days from the creation of the file to its most recent access. For example, if a file is accessed on the 120th day after its creation, the access cycle is 120 days. The formula for calculating its access ratio is . If the number of accesses to this file is 20 times, then its access ratio is . After calculating the access ratio for all screened files, analyze the trend of access frequency changes. Determine its cold storage trend by the decline rate of access frequency over time. Use the moving average to calculate the change trend. For example, calculate the average access frequency for the most recent 7 days, 14 days, and 30 days for each file and observe the changes. For example, if the average access frequency of a file in the past 30 days is 0.8, in the past 14 days is 0.6, and in the past 7 days is 0.3, then its downward trend is significant, and it is classified as a low-activity file. Finally, obtain the access activity ratio.

[0245] The index query optimization sub-module calls the access activity ratio, calculates the index query response time of high-access-activity data, extracts the matching relationship between the query response time and access activity, filters the file indexes with a query response time higher than the set standard, optimizes the index query path, and obtains the optimized index query time.

[0246] The index query optimization sub-module calls the access activity ratio, extracts high-access-activity data, calculates its index query response time. The response time represents the time required to return the query index data result. For example, if the index query time of a file is 200ms, then . After calculating the index query response time for all files, extract the matching relationship between the query response time and access activity, and set the access activity threshold to 0.4. If the access activity of a file is 0.6 and the index query time is 350ms, while the access activity of another file is 0.2 and the query time is 120ms, then the former needs to optimize the index path. Filter out all file indexes with a query response time higher than the set standard. For example, set the query time threshold . All file indexes need to be optimized. The optimization methods include index partitioning, index compression, etc. Adopt a hierarchical storage strategy for indexes exceeding the threshold. Cache high-access-activity indexes to the high-speed storage layer and adjust low-access-activity indexes to the low-speed storage layer. For example, if a file is migrated from HDD storage to SSD, its query time is reduced from 350ms to 180ms. Finally, obtain the optimized index query time.

[0247] The storage - level division sub - module calculates the data storage amount under different differential index query times according to the optimized index query time and in combination with the storage - level standard, sets the attribution range of the storage level based on the data storage amount and the average query time, adjusts the data attribution of the storage level, and obtains the storage - index optimization scheme.

[0248] The storage - level division sub - module calculates the data storage amount under different index query times according to the optimized index query time and in combination with the storage - level standard. The storage amount represents the total amount of data in different storage levels. For example, if the high - speed storage layer stores 50TB and the low - speed storage layer stores 100TB, then , sets the storage - level attribution range based on the storage amount and the average query time. The average query time represents the average query time of all data in a certain storage layer. For example, if the average query time of the high - speed storage layer is 180ms and the average query time of the low - speed storage layer is 450ms, then set , adjusts the data attribution of the storage level according to the storage - level standard, migrates the data with a longer query time to the low - speed storage layer. For example, if the current query time of a certain data is 400ms, which meets the low - speed storage - layer standard, then adjusts its storage location, and finally obtains the file - storage - level division scheme.

[0249] Please refer to Figure 3 , the present invention also provides an archive management method based on blockchain technology, which is executed by using the above - mentioned archive management system based on blockchain technology, and includes the following steps:

[0250] S1: Obtain the data of the archive transfer path, call the permission distribution of the certification nodes, the data - link connection status and the certification continuity data, calculate the complexity parameter of the certification path, classify the transfer mode based on the complexity parameter, calculate the change trend of the transfer mode, and obtain the transfer - trend record;

[0251] S2: Call the transfer - trend record, obtain the block - storage utilization rate, the data - block generation interval and the certification - data distribution characteristics, calculate the block - certification range adjustment parameter, change the block - certification range based on the adjustment parameter, and obtain the adjusted block - certification boundary data;

[0252] S3: Based on the adjusted block - certification boundary data, obtain the storage utilization rate of the consensus nodes, the task - processing delay and the data - synchronization rate, calculate the node - load score, screen the adapted node group based on the score threshold, call the adapted node group to calculate the consensus - task allocation strategy, and allocate the consensus task according to the strategy to obtain the consensus - task node - allocation scheme;

[0253] S4: Invoke the consensus task node allocation scheme, obtain the file access frequency, user privilege level, and storage request urgency, calculate the file transaction priority, filter high-priority file transactions based on the transaction priority, invoke the storage node network latency and bandwidth parameters, match low-latency and high-bandwidth storage nodes, and adjust the cross-chain consensus process to obtain the transaction priority evidence storage path;

[0254] S5: Based on the transaction priority evidence storage path, obtain the file access and storage records, storage level division, and index query response time, calculate the data access activity, adjust the storage level based on the activity, optimize the index structure, and obtain the storage index optimization scheme.

[0255] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as the technical solution content of the present invention is not departed from, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An archive management system based on blockchain technology, characterized in that: The system includes: The file transfer monitoring module obtains file transfer path data, analyzes the permission intersection situation and the change situation of the continuity of evidence preservation at the evidence preservation nodes, calculates and classifies the complexity data of the evidence preservation path, identifies different transfer modes and analyzes the change trend of the file transfer mode, and obtains a transfer trend record; The block boundary dynamic adjustment module reads the transfer trend record, combines the current block storage utilization situation and the distribution characteristics of the evidence preservation data, calculates and adjusts the block boundary adjustment parameters and range, and obtains the adjusted block evidence preservation boundary data; The block boundary dynamic adjustment module includes: The file transfer trend analysis sub-module obtains the transfer trend record, detects the transfer quantity within different time periods, calculates the change ratio of the transfer quantity between adjacent time periods, counts the increase and decrease amplitude of the change ratio within different time periods, captures the change trend characteristics, and obtains the transfer trend change ratio; The storage utilization rate calculation sub-module, based on the data occupancy situation of the current storage block, calls the transfer trend change ratio, determines the usage ratio of the storage space, calculates the change average value of the storage utilization rate relative to the block evidence preservation quantity, and obtains the storage utilization rate change amplitude data; The boundary adjustment optimization sub-module, based on the storage utilization rate change amplitude data, calculates the block boundary adjustment parameters according to the transfer offset distribution interval, screens the boundary areas where the adjustment parameters meet the adjustment reference value, combines the distribution situation of the evidence preservation data, calculates the adjusted block boundary position, and obtains the adjusted block evidence preservation boundary data; The consensus node load balancing module, according to the adjusted block evidence preservation boundary data, combines the node storage utilization rate, task processing delay and data synchronization rate, evaluates the node load score, selects the node group with a score higher than the adaptation threshold for consensus task allocation, and generates a consensus task node allocation plan; The cross-chain file transaction priority regulation module, according to the consensus task node allocation plan, combines the file access frequency, user permission level and storage request urgency, calculates and screens the file transaction priority, matches the storage nodes with low latency and high bandwidth, adjusts the cross-chain consensus process, and generates a transaction priority evidence preservation path.

2. The archival management system based on blockchain technology according to claim 1, wherein: The transfer trend record is generated based on the classified data of the evidence preservation path complexity, different transfer mode data and the analysis of the change of the file transfer mode; the adjusted block evidence preservation boundary data is calculated based on the block storage utilization situation data, data block generation interval data and evidence preservation data distribution characteristic data; the consensus task node allocation plan is determined based on the node load score data, the node group data with a high adaptation threshold and the consensus task allocation result data; the transaction priority evidence preservation path is determined according to the file access frequency data, user permission level data and storage request urgency data analysis.

3. The archival management system based on blockchain technology according to claim 1, wherein: The file transfer monitoring module includes: The path data acquisition sub-module obtains file transfer path data, extracts the permission information of the evidence preservation nodes, the data link connection status information and the evidence preservation time interval data, calculates the interaction times of the evidence preservation nodes, and obtains the file transfer path parameter set; The permission cross - calculation sub - module obtains the above - mentioned archive transfer path parameter set, calculates the permission cross - degree of the evidence - depositing nodes, combines the data - link connection status information, calculates the change value of evidence - depositing continuity, and calculates the classification proportion of permission subjects for the evidence - depositing trigger time interval and the number of node interactions, to obtain the permission cross and evidence - depositing change parameter set; The transfer mode trend recognition sub - module calls the above - mentioned permission cross and evidence - depositing change parameter set, classifies the types of transfer paths, counts the quantity and proportion of paths, analyzes the change trend of path categories, screens the path categories with large change amplitudes of trends, and analyzes the characteristics of path categories to obtain transfer trend records.

4. The archival management system based on blockchain technology according to claim 1, wherein: The specific calculation formula for the average change of the storage utilization rate relative to the block evidence - depositing volume is: ； Among them, represents the storage utilization rate at the th time point, represents the storage utilization rate at the previous time point, represents the total number of data points of the storage utilization rate, represents the th block's deposit volume, represents the average value of the deposit volumes of all blocks, represents the total number of data points of the deposit volume, represents the th data block generation time interval, represents the total number of data points of the data block generation time interval.

5. The archival management system based on blockchain technology according to claim 1, wherein: The consensus node load - balancing module includes: The storage utilization evaluation sub - module obtains the storage utilization rate of the consensus nodes based on the adjusted block evidence - depositing boundary data, calculates the available storage ratio of the nodes, compares the available storage ratio of the nodes with the storage utilization threshold, screens the nodes with available storage ratios greater than or equal to the storage utilization threshold, and calculates the storage balance degree and storage utilization deviation of the screened nodes to obtain the storage load score; The task - processing evaluation sub - module calls the above - mentioned storage load score, obtains the task - processing delay and data - synchronization rate of the nodes, calculates the influence value of the two data on the load, and obtains the overall load score; The node - allocation optimization sub - module calls the above - mentioned overall load score, compares the overall load score of the nodes with the adaptation threshold, screens the node groups with high adaptation thresholds, sorts the nodes within the node groups according to the overall load score, and generates a node - allocation scheme for the consensus task according to the score ranking.

6. The archival management system based on blockchain technology according to claim 1, wherein: The cross - chain archive transaction priority regulation module includes: The transaction priority threshold calculation sub - module obtains the archive access frequency, user permission level, and storage request urgency degree based on the above - mentioned node - allocation scheme for the consensus task, calculates the access - frequency value, permission - level value, and urgency - degree weight, analyzes the archive transaction priority value, and compares with the storage threshold to screen the archives with high transaction priority values to obtain the screened archive set; The storage - node matching sub - module calls the above - mentioned screened archive set, obtains the bandwidth value and delay value of the storage nodes, calculates the low - delay and high - bandwidth score, sorts according to the score and matches the storage node with the highest score to obtain the archive - storage matching relationship; The transaction evidence - depositing path determination sub - module calls the above - mentioned archive - storage matching relationship, obtains the consensus process corresponding to the storage node, calculates the weight of the transaction - priority evidence - depositing path, and generates the transaction - priority evidence - depositing path.

7. The archival management system based on blockchain technology according to claim 6, wherein: The specific calculation formula for analyzing the archive transaction priority value is: ； Compare with the storage threshold to screen the archives with high transaction priority values to obtain the screened archive set; Among them, represents the file transaction priority value, represents the th file access frequency, represents the access frequency weight, represents the total number of data on file access frequency, represents the th user's permission level, represents the permission level weight, represents the total number of data on user permission level, represents the nd storage request urgency, represents the urgency weight, represents the total number of data on storage request urgency, represents the maximum value in the current transaction priority parameter, represents the average value of the current transaction priority parameter.

8. The archival management system based on blockchain technology according to claim 1, wherein: The system further includes: The storage index optimization module reads the above - mentioned transaction - priority evidence - depositing path, combines the archive access and storage records, storage - level division, and index - query response time, calculates the data - access activity, adjusts the storage level, and generates a storage index optimization scheme; The above - mentioned storage index optimization scheme includes the data - access activity analysis result, storage - level adjustment scheme, and index - structure optimization scheme; The storage index optimization module includes: The access activity calculation sub-module calculates the number of file accesses and the time interval value according to the priority evidence storage path and based on the file access and storage records, extracts the file access frequency value, compares the access frequency value with the cold storage threshold, filters the data with an access frequency value lower than the cold storage threshold, calculates the access ratio under the access and storage cycle, analyzes the change trend of the access frequency, and obtains the access activity ratio; The index query optimization sub-module calls the access activity ratio, calculates the index query response time of the high-access-activity data, extracts the matching relationship between the query response time and the access activity, filters the file indexes with a query response time higher than the set standard, and obtains the optimized index query time; The storage layer division sub-module calculates the data storage volume under the different index query times according to the optimized index query time and in combination with the storage layer standard, sets the attribution range of the storage layer based on the average value of the storage volume and the query time, adjusts the data attribution of the storage layer, and obtains the storage index optimization scheme.

9. A file management method based on blockchain technology, characterized in that, The execution of an archive management system based on blockchain technology according to any one of claims 1-8 includes the following steps: S1: Obtain the file transfer path data, call the permission distribution of the evidence storage nodes, the data link connection status, and the evidence storage continuity data, calculate the complexity parameter of the evidence storage path, classify the transfer modes based on the complexity parameter, and calculate the change trend of the transfer modes to obtain the transfer trend record; S2: Call the transfer trend record, obtain the block storage utilization rate, the data block generation interval, and the evidence storage data distribution characteristics, calculate the block evidence storage range adjustment parameter, and change the block evidence storage range based on the adjustment parameter to obtain the adjusted block evidence storage boundary data; S3: Based on the adjusted block evidence storage boundary data, obtain the consensus node storage utilization rate, the task processing delay, and the data synchronization rate, calculate the node load score, filter the adapted node group based on the score threshold, call the adapted node group to calculate the consensus task allocation strategy, and allocate the consensus tasks according to the strategy to obtain the consensus task node allocation scheme; S4: Call the consensus task node allocation scheme, obtain the file access frequency, the user permission level, and the urgency of the storage request, calculate the file transaction priority, filter the high-priority file transactions based on the transaction priority, call the storage node network delay and bandwidth parameters, match the low-latency and high-bandwidth storage nodes, and adjust the cross-chain consensus process to obtain the transaction priority evidence storage path; S5: Based on the transaction priority evidence storage path, obtain the file access and storage records, the storage layer division, and the index query response time, calculate the data access activity, adjust the storage layer based on the activity, and optimize the index structure to obtain the storage index optimization scheme.

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