Archive management system and method based on block chain technology

Abstract

The invention belongs to the technical field of block chain archive management, and discloses an archive management system and method based on the block chain technology, and the system comprises an archive circulation monitoring module, a block boundary dynamic adjustment module, a consensus node load balancing module, a cross-chain archive transaction priority regulation and control module, and a storage index optimization module. According to the method, the file circulation difficulty index is dynamically calculated, redundant evidence storage is reduced, the storage utilization rate is improved, the block evidence storage boundary is adjusted in combination with the circulation trend change, the evidence storage rule is adaptively adjusted along with the data state change, the block storage utilization rate is optimized, the evidence storage redundancy is reduced, and the data is adjusted according to the evidence storage boundary; the method can be used for calculating node storage utilization rate, task processing delay and data synchronization rate, reducing storage bottleneck, calculating transaction priority threshold, screening high-priority storage paths, matching low-delay storage nodes, improving data access efficiency, analyzing data access activeness, reducing occupation of high-performance storage resources and improving overall storage efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of blockchain archive management, and specifically, relates to an archive management system and method based on blockchain technology. Background Art

[0002] The field of blockchain archive management technology includes related technologies that use blockchain technology to store, manage, verify and trace archival information. Its core content involves decentralized storage architecture, multi-node consensus mechanism, data encryption technology, smart contract execution and authority management. The systematic content of this technology field includes archive data fingerprint generation based on hash algorithm, distributed ledger storage structure, timestamp authentication mechanism and data non-tamperability guarantee. Overall, blockchain archive management technology can ensure the authenticity, security and traceability of archival data, and provide multi-party collaborative management capabilities, which is suitable for government, enterprise and personal archive management scenarios.

[0003] Among them, an archive management system and method based on blockchain technology refers to a management method that uses blockchain technology to store, access control and share archive data; the patent subject covers archive data storage methods, access permission allocation strategies, data consistency assurance mechanisms and trusted records of archive circulation processes; specifically, the archive management system generates a unique identifier for archive data through blockchain hash value calculation, and combines asymmetric encryption technology to achieve data encryption storage, while using smart contracts to define archive access permissions and operating rules to ensure data integrity and security during the archive management process; in addition, the method uses a consensus algorithm to maintain distributed storage ledger data, ensures the consistency of archive information at each node, and combines timestamp technology to achieve traceability of archive circulation.

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

[0005] The above-mentioned existing archive storage methods can ensure the authenticity, integrity and privacy of electronic archive data, but the existing blockchain storage technology adopts a fixed strategy for evidence triggering, and fails to make dynamic adjustments according to the complexity of the archive circulation process, resulting in redundant evidence of some data, increasing the storage burden on the chain, and at the same time, some high-frequency circulation archives fail to obtain priority evidence, affecting the effectiveness of evidence; the block evidence boundary is set to a fixed range, lacks adaptive adjustment to the evidence demand, limits the block storage utilization rate, and increases the volume of on-chain data unnecessarily, increasing storage and retrieval costs; the consensus node task allocation does not consider the storage utilization rate and data synchronization rate, and some nodes are overloaded due to task overload, resulting in reduced evidence efficiency and even affecting the consensus stability of the entire system; the archive data access adopts a unified storage strategy, and fails to perform differentiated storage according to access frequency, permission level and storage urgency, so that high-frequency access archives and low-frequency access archives 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 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 evidence, improve storage utilization, reduce the occupancy of high-performance storage resources, and improve overall storage efficiency.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: A file management system based on blockchain technology, the system comprising: The archive circulation monitoring module obtains archive circulation path data, analyzes the permission intersection of evidence storage nodes and the change of evidence storage continuity, calculates and classifies the complexity data of evidence storage paths, identifies differential circulation patterns and analyzes the change trend of archive circulation patterns, and obtains circulation trend records; The block boundary dynamic adjustment module reads the flow trend record, calculates and adjusts the block boundary adjustment parameters and ranges based on the current block storage utilization and the distribution characteristics of the evidence data, and obtains the adjusted block evidence boundary data; The consensus node load balancing module evaluates the node load score based on the adjusted block evidence boundary data, combined with the node storage utilization, task processing delay and data synchronization rate, 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 archive transaction priority control module calculates and screens the archive transaction priority according to the consensus task node allocation plan, combined with the archive access frequency, user authority level and storage request urgency, matches the storage nodes with low latency and high bandwidth, adjusts the cross-chain consensus process, and generates the transaction priority evidence path.

[0008] The following is a further optimization of the above technical solution by the present invention: The circulation trend record includes classification data of complexity of evidence path, differential circulation mode data, and archive circulation mode change data; the adjusted block evidence boundary data includes block storage utilization data, data block generation interval data, and evidence data distribution characteristic data; the consensus task node allocation plan includes node load score data, node group data with high adaptation threshold, and consensus task allocation result data; the transaction priority evidence path includes archive access frequency data, user authority level data, and storage request urgency data.

[0009] Further optimization: The file circulation monitoring module includes: The path data collection submodule obtains the file circulation path data, extracts the evidence node authority information, data link connection status information and evidence storage time interval data, calculates the number of evidence node interactions, and obtains the file circulation path parameter set; The permission intersection calculation submodule obtains the file circulation path parameter set, calculates the permission intersection degree of the evidence storage node, calculates the continuity change value of the evidence storage in combination with the data link connection status information, calculates the classification proportion of the permission subject according to the evidence storage trigger time interval and the number of node interactions, and obtains the permission intersection and evidence storage change parameter set; The circulation pattern trend identification submodule calls the permission intersection and evidence change parameter set to classify the circulation path types, count the number and proportion of paths, analyze the change trend of path categories, screen path categories with large trend changes, analyze the characteristics of path categories, and obtain circulation trend records.

[0010] Further optimization: The block boundary dynamic adjustment module includes: The file circulation trend analysis submodule obtains the circulation trend record, detects the circulation quantity in the different time periods, calculates the change ratio of the circulation quantity between adjacent time periods, counts the increase and decrease of the change ratio in the different time periods, captures the change trend characteristics, and obtains the circulation trend change ratio; The storage utilization rate calculation submodule calls the circulation trend change ratio based on the data occupancy of the current storage block, determines the usage ratio of the storage space, calculates the mean value of the storage utilization rate relative to the block evidence storage amount, and obtains the storage utilization rate change amplitude data; The boundary adjustment optimization submodule calculates the block boundary adjustment parameters based on the storage utilization change amplitude data and the flow offset distribution interval, screens the boundary areas where the adjustment parameters meet the adjustment benchmark values, calculates the adjusted block boundary position based on the distribution of the evidence data, and obtains the adjusted block evidence boundary data.

[0011] Further optimization: The specific calculation formula for the storage utilization rate relative to the mean change of the block storage volume is: ; in, Representative Storage utilization at a point in time, Represents the storage utilization at the previous point in time, Represents the total number of storage utilization data points, Representative The amount of evidence in a block, Represents the average value of all block proofs. The total number of data points representing the amount of evidence stored, Representative The time interval between data block generation, Represents the total number of data points in the time interval between data block generation.

[0012] Further optimization: The consensus node load balancing module includes: The storage utilization evaluation submodule obtains the storage utilization rate of the consensus node based on the adjusted block evidence boundary data, calculates the available storage ratio of the node, compares the available storage ratio of the node with the storage utilization threshold, selects nodes whose available storage ratio is greater than or equal to the storage utilization threshold, calculates the storage balance degree and storage utilization deviation of the selected nodes, and obtains the storage load score; The task processing evaluation submodule calls the storage load score, obtains the task processing delay and data synchronization rate of the node, calculates the impact of the two data on the load, and obtains the overall load score; The node allocation optimization submodule calls the overall load score, compares the node overall load score with the adaptation threshold, selects 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 sorting.

[0013] Further optimization: The cross-chain archive transaction priority control module includes: The transaction priority threshold calculation submodule obtains the file access frequency, user authority level and storage request urgency based on the consensus task node allocation scheme, calculates the access frequency value, authority level value and urgency weight, analyzes the file transaction priority value, and compares the storage threshold to filter files with high transaction priority values ​​to obtain a filtered file set; The storage node matching submodule calls the screening archive set, obtains the storage node bandwidth value and delay value, calculates the low delay and high bandwidth score, sorts and matches the storage node with the highest score according to the score, and obtains the archive storage matching relationship; The transaction evidence path determination submodule calls the archive storage matching relationship, obtains the consensus process corresponding to the storage node, calculates the transaction priority evidence path weight, and generates the transaction priority evidence path.

[0014] Further optimization: The specific calculation formula of the archive transaction priority value is: ; Calculate the archive transaction priority parameter, compare it with the storage threshold to filter out archives with high transaction priority values, and obtain a filtered archive set; in, Represents the archive transaction priority value, Representative The frequency of access to the archive, represents the access frequency weight, The total number of data representing the frequency of archive access, Representative The user's permission level, Represents the authority level weight, The total number of data representing the user's permission level, Representative The urgency of the storage request. Represents the urgency weight, The total number of data representing the urgency of storage requests, Represents the maximum value of the current transaction priority parameter. Represents the average value of the current transaction priority parameter.

[0015] Further optimization: the system further includes: The storage index optimization module reads the transaction priority storage path, combines the archive access record, storage level division and index query response time, calculates the data access activity, adjusts the storage level, and generates a storage index optimization plan; The storage index optimization scheme includes data access activity analysis results, storage level adjustment scheme, and index structure optimization scheme; The storage index optimization module includes: The access activity calculation submodule calculates the number of archive accesses and the time interval value according to the transaction priority evidence storage path and based on the archive access record, extracts the archive access frequency value, compares the access frequency value with the cold storage threshold, filters the data with the access frequency value 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; The index query optimization submodule 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, screens the archive indexes whose query response time is higher than the set standard, and obtains the optimized index query time; The storage level division submodule calculates the data storage capacity under the differential index query time according to the optimized index query time and the storage level standard, sets the storage level attribution range according to the average of the data storage capacity and the query time, adjusts the data attribution of the storage level, and obtains the storage index optimization plan.

[0016] The present invention also provides a file management method based on blockchain technology, which is implemented by using the above-mentioned file management system based on blockchain technology, and includes the following steps: S1: Obtain archive circulation path data, call the authority distribution of evidence storage nodes, data link connection status and evidence storage continuity data, calculate the complexity parameter of the evidence storage path, classify the circulation mode based on the complexity parameter, calculate the change trend of the circulation mode, and obtain the circulation trend record; S2: calling the circulation trend record, obtaining the block storage utilization rate, data block generation interval and evidence data distribution characteristics, calculating the block evidence range adjustment parameters, changing the block evidence range based on the adjustment parameters, and obtaining the adjusted block evidence boundary data; S3: Based on the adjusted block evidence boundary data, the consensus node storage utilization, task processing delay and data synchronization rate are obtained, the node load score is calculated, the adaptation node group is selected based on the score threshold, the adaptation node group is called to calculate the consensus task allocation strategy, the consensus task is allocated according to the strategy, and the consensus task node allocation plan is obtained; S4: Call the consensus task node allocation plan, obtain the archive access frequency, user authority level and storage request urgency, calculate the archive transaction priority, filter high-priority archive transactions based on the transaction priority, call the storage node network delay and bandwidth parameters, match low-latency and high-bandwidth storage nodes, adjust the cross-chain consensus process, and obtain the transaction priority evidence storage path; S5: Based on the transaction priority notarization path, obtain archive access records, storage level division and index query response time, calculate data access activity, adjust the storage level based on the activity, optimize the index structure, and obtain a storage index optimization plan.

[0017] The present invention adopts the above technical solution, which has at least the following beneficial effects: The present invention dynamically calculates the archive circulation difficulty index, reduces redundant evidence, improves storage utilization, adjusts the block evidence boundary in combination with the circulation trend change, makes the evidence rule adaptively adjusted with the data status change, optimizes block storage utilization, reduces evidence redundancy, adjusts data according to the evidence boundary, calculates node storage utilization, task processing delay and data synchronization rate, reduces storage bottlenecks, calculates transaction priority thresholds, screens high-priority storage paths, matches low-latency storage nodes, improves data access efficiency, analyzes data access activity, reduces high-performance storage resource usage, and improves overall storage efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 is a system flow chart of the present invention; Figure 2 It is a flow chart of the submodules of the present invention; Figure 3 The figure is a flow chart of the steps of the method of the present invention. DETAILED DESCRIPTION

[0020] In the embodiments of the present invention, words such as "example" and "for example" are used to indicate examples, illustrations or explanations; any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or advantageous than other embodiments or designs; to be precise, the use of the word "example" is intended to present concepts in a concrete way; in addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0021] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings they intend to express are consistent. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings they intend to express are consistent.

[0022] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0023] In order to make the technical problems to be solved, technical solutions and beneficial effects of the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0024] See also Figure 1 , an archive management system based on blockchain technology includes: The archive circulation monitoring module obtains archive circulation path data, analyzes the cross-authority situation of the evidence storage node, the data link connection status and the change of evidence storage continuity, calculates the complexity data of the evidence storage path, and classifies the complexity data, identifies the difference circulation mode, combines the historical circulation records, and analyzes the change trend of the archive circulation mode to obtain the circulation trend record; The block boundary dynamic adjustment module reads the flow trend record, combines the current block storage utilization, data block generation interval and evidence data distribution characteristics, calculates the block boundary adjustment parameters, adjusts the block evidence range, and generates the adjusted block evidence boundary data; The consensus node load balancing module evaluates the node load score based on the adjusted block evidence boundary data, combined with the node storage utilization, task processing delay and data synchronization rate, 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 archive transaction priority control module calculates the archive transaction priority according to the consensus task node allocation plan, combined with the archive access frequency, user authority level and storage request urgency, screens high-priority archive transactions, matches low-latency and high-bandwidth storage nodes, adjusts the cross-chain consensus process, and generates a transaction priority evidence path; The storage index optimization module reads the transaction priority storage path, combines the archive access 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.

[0025] The circulation trend records include classification data of evidence path complexity, differential circulation mode data, and archive circulation mode change data; the adjusted block evidence boundary data include block storage utilization data, data block generation interval data, and evidence data distribution feature data; the consensus task node allocation plan includes node load score data, node group data with high adaptation threshold, and consensus task allocation result data; the transaction priority evidence path includes archive access frequency data, user authority level data, and storage request urgency data; the storage index optimization plan includes data access activity analysis results, storage level adjustment plan, and index structure optimization plan.

[0026] See also Figure 2 , the file circulation monitoring module includes: The path data collection submodule obtains the archive circulation path data, extracts the evidence node permission information, data link connection status information and evidence time interval data, calculates the number of evidence node interactions, and obtains the archive circulation path parameter set.

[0027] The path data collection submodule obtains the archive flow path data. First, it extracts the permission information of the evidence nodes, records the access permission level of each evidence node, classifies and counts the number of evidence nodes with different permission levels, and parses the permission structure according to the permission classification model. For example, assuming that the permission classification is P1 (basic permission), P2 (intermediate permission), and P3 (advanced permission), of which P1 nodes account for 40%, P2 nodes account for 35%, and P3 nodes account for 25%. By counting the distribution of evidence nodes at different permission levels, the balance of permission distribution is judged; secondly, the data link connection status information is obtained, and the data flow between each evidence node is recorded, including parameters such as connection success rate, transmission delay, and link interruption number. For example, if the connection success rate between AB nodes is 9 8%, the transmission delay is 20ms, while the connection success rate of BC nodes is 85%, and the transmission delay is 50ms, which means that the link stability between BC is poor and needs further optimization; then, extract the evidence time interval data, calculate the evidence time difference of adjacent evidence nodes, assuming that the evidence time interval T is 15 seconds, 22 seconds, and 30 seconds, then the mean and variance of the evidence time interval can be analyzed. For example, if the mean is 22 seconds and the variance is 4 seconds, it means that the evidence time of the evidence node is relatively uniform; finally, calculate the number of evidence node interactions, and record the interaction between different evidence nodes. For example, if the number of AB interactions is 100 times and the number of BC interactions is 60 times, then the interaction frequency between each node can be calculated and the evidence interaction matrix can be established, and finally the archive flow path parameter set is obtained.

[0028] The permission intersection calculation submodule obtains the file circulation path parameter set, calculates the permission intersection degree of the evidence node, calculates the continuity change value of the evidence in combination with the data link connection status information, calculates the classification proportion of the permission subject according to the evidence trigger time interval and the number of node interactions, and obtains the permission intersection and evidence change parameter set.

[0029] The permission cross calculation submodule obtains the parameter set of the archive circulation path, first calculates the permission cross degree of the evidence storage nodes, and records the permission sharing between different evidence storage 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 = P2 permission item ∩ P3 permission item / total number of permission items. For example, if the permission item of P2 is {R, W} and the permission item of P3 is {R, W, X}, then C = 2 / 3≈0.67; then, combined with the data link connection status information, calculate the continuity change value of the evidence storage, and record Record the continuity of evidence between different evidence nodes, and set the continuity change value S=1-link instability index. For example, if the connection success rate of AB link is 95% and the connection success rate of BC link is 80%, then the instability index of BC link is higher and its S value is relatively lower; then, according to the evidence trigger time interval and the number of node interactions, calculate the classification proportion of authority subjects, and set the proportion P=number of evidences of each authority subject / total number of evidences. For example, if the number of evidences of P1 node is 200 times and the total number of evidences is 1000 times, then the classification proportion of P1 is 0.2; finally, the permission intersection and evidence change parameter set is obtained.

[0030] The circulation pattern trend identification submodule calls the permission intersection and evidence change parameter set to classify the circulation path types, count the number and proportion of paths, analyze the change trend of path categories, screen path categories with large trend changes, analyze the characteristics of path categories, and obtain circulation trend records.

[0031] The circulation pattern trend identification submodule calls the permission intersection and evidence change parameter set to classify the evidence path types. According to the permission level of the evidence node, the structure of the evidence path and the stability of the evidence time interval, the evidence path is divided into different categories. For example, the evidence path can be divided into three types: chain flow, star flow and ring flow. Chain flow refers to the pattern in which the evidence data flows gradually along a fixed order, star flow refers to the centralized flow of evidence data to a core evidence node, and ring flow refers to the circulation of evidence data between multiple evidence nodes. The system counts different types of evidence paths and calculates the proportion of each type of evidence path. For example, within a certain period of time, There are 100 evidence paths in total, including 50 chain circulations, 30 star circulations, and 20 ring circulations. The proportion of chain circulation is 50%. The system further analyzes the changing trends of different evidence path categories in different time periods. For example, if the proportion of chain circulation paths in the recent period has increased from 50% to 60%, it means that this type of path occupies a larger proportion in the evidence process. The system selects the evidence path categories with larger changes, and combines parameters such as evidence interaction frequency and authority intersection to analyze the main characteristics of the evidence path category. For example, if the evidence time interval of a certain evidence path category is significantly shortened, it means that the evidence operations on the path are more frequent. Finally, the flow trend record is obtained.

[0032] See also Figure 2 ,The block boundary dynamic adjustment module includes: The archive circulation trend analysis submodule obtains circulation trend records, detects the circulation quantity in different time periods, calculates the change ratio of the circulation quantity between adjacent time periods, counts the increase and decrease of the change ratio in different time periods, captures the change trend characteristics, and obtains the circulation trend change ratio.

[0033] The file circulation trend analysis submodule obtains the circulation trend records and extracts the file circulation quantity in different time periods within the set time period. First, the specified time period is divided, for example, into days, weeks, and months, and the file circulation data is divided according to the time dimension to form a time series data set. Then, the file circulation quantity in each time period is counted, and the change rate of the circulation quantity between two adjacent time periods in the time series is calculated; The calculation formula for the turnover quantity change rate is: ; in, Indicates time period The rate of change, and Respectively represent the number of file transfers in the current time period and the previous time period; Taking the weekly file circulation data as an example, assuming that the circulation volume in the first week is 120 copies, second week is 150 copies, then the rate of change ; At this point, it is necessary to further calculate the change rates of 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 in the change rates. The formula for calculating the increase or decrease is: ; in, For the The increase or decrease in a certain period of time; like ,but , the rate of change of the entire time series is calculated, and finally, the trend characteristics are extracted to determine whether there is a trend of continuous growth, increased or decreased volatility, so as to obtain the circulation trend change ratio.

[0034] The storage utilization calculation submodule calls the circulation trend change ratio based on the data occupancy of the current storage block, determines the usage ratio of the storage space, calculates the mean change of the storage utilization relative to the block evidence storage amount, and obtains the storage utilization change amplitude data.

[0035] The storage utilization calculation submodule is based on the data occupancy of the current storage block. First, the total capacity of the current storage block is obtained. and used capacity , calculate the usage ratio of storage space, the formula is as follows: ; Assume that the total capacity of a storage block is The storage capacity is 1TB and 600GB is used. ; Next, call the circulation trend change ratio to calculate the average change of storage utilization relative to the block proof amount. The calculation method of the proof amount change ratio is similar to the circulation trend change ratio. Define the proof amount change ratio : ; in, For time period The amount of evidence in the first time period is assumed to be 500 copies, the second time period is 550 copies, then ; Finally, calculate the change in storage utilization: ; Assume that the storage utilization in the first period is , the second time period ,but ; Finally, the storage utilization rate change data is obtained.

[0036] The boundary adjustment optimization submodule calculates the block boundary adjustment parameters based on the storage utilization change range data and the flow offset distribution range, screens the boundary areas where the adjustment parameters meet the adjustment benchmark values, and calculates the adjusted block boundary position based on the distribution of the evidence data to obtain the adjusted block evidence boundary data.

[0037] Obtain historical storage utilization data and calculate the average change in storage utilization relative to the amount of block evidence. The specific calculation formula is as follows: ; in, Representative Storage utilization at a point in time, Represents the storage utilization at the previous point in time, Represents the total number of storage utilization data points, Representative The amount of evidence in a block, Represents the average value of all block proofs. The total number of data points representing the amount of evidence stored, Representative The time interval between data block generation, Represents the total number of data points in the time interval between data block generation.

[0038] The calculated block evidence boundary adjustment value is compared with the block evidence adjustment threshold to determine whether it exceeds the threshold range. If it exceeds the threshold range, the block evidence boundary is adjusted to finally obtain the adjusted block evidence boundary data; Parameter acquisition: Obtained by monitoring the storage usage of the system at different time points. Assuming that storage utilization is recorded once every hour in a day, there are 24 data points in total; : The total number of storage utilization data points, i.e. 24; Numerical example: Assume that the storage utilization 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; Calculation process: Calculate the sum of the absolute values ​​of the storage utilization differences at adjacent time points: .

[0039] Storage utilization change mean: .

[0040] Calculation of standard deviation of block proof volume: Parameter acquisition: : No. The amount of evidence stored in a block is obtained by the amount of evidence data recorded in each block by the blockchain system; assuming that 10 blocks are generated in one day; : The total number of data points of the evidence, i.e. 10; Numerical example: Assume that the amount of evidence in each block is as follows (unit: MB): 5, 6, 5.5, 6.5, 7, 6, 5.8, 6.2, 6.5, 7; Calculation process: Calculate the average value of the deposit amount: .

[0041] Calculate the sum of squares of the difference between each deposit and the average: .

[0042] Standard deviation of the amount of evidence: .

[0043] Calculation of the mean time interval for data block generation: Parameter acquisition: : No. The time interval for generating a data block is calculated through the block generation timestamp recorded by the blockchain system; assuming that 9 time intervals are recorded; : The total number of data points in the data block generation time interval, that is, 9; Numerical example: Assume that the time interval data is as follows (unit: minutes): 10,11,9,10,12,10,11,9,10; Calculation process: Compute the sum of a time interval: ; Time interval mean: ; Calculate the block evidence boundary adjustment value: Calculation process: Substitute the above calculation results into the formula: .

[0044] Result analysis: Storage utilization change mean: reflects the degree of fluctuation of storage utilization. A larger value indicates more frequent fluctuations. In this example, the mean is about 1.92%, indicating that the storage utilization changes by about 1.92% per hour on average during the monitoring period. Standard deviation of block proof: measures the degree of dispersion of block proof. The larger the value, the more dispersed the proof distribution. In this example, the standard deviation is about 0.685MB, which indicates the degree of fluctuation of the proof during the monitoring period. Mean time interval for data block generation: indicates the average time interval for block generation; in this case, the mean is about 10.22 minutes, which is close to the expected 10 minutes, indicating that the block generation speed is relatively stable; Block storage boundary adjustment value :Calculated based on the above indicators; in this example, Approximately -8.904. This value is used to determine whether the block storage boundary needs to be adjusted. If it exceeds the preset threshold range, the block storage boundary needs to be adjusted accordingly.

[0045] See also Figure 2 , the consensus node load balancing module includes: The storage utilization evaluation submodule obtains the storage utilization of the consensus node based on the adjusted block evidence boundary data, calculates the available storage ratio of the node, compares the available storage ratio of the node with the storage utilization threshold, filters out nodes whose available storage ratio is greater than and equal to the storage utilization threshold, calculates the storage balance degree and storage utilization deviation of the filtered nodes, and obtains the storage load score.

[0046] The storage utilization evaluation submodule is based on the adjusted block evidence boundary data. First, the evidence boundary data is parsed to obtain the storage distribution of the block. The block size, number of evidences, storage capacity and other information in the evidence boundary data are parsed to extract the distribution of block evidence and the occupancy rate of storage resources. The storage utilization calculation method of the consensus node is called to calculate the used storage capacity of each consensus node. and total storage capacity Calculate the ratio, i.e. storage utilization ,in Obtained through the accumulated data of block evidence, The storage upper limit configured for the node hardware. For example, the total storage capacity of a node , the current storage utilization is calculated and obtained ; Then calculate the available storage ratio of the node , that is, the remaining storage space ratio, such as the available storage ratio of the above node ; Then perform storage utilization threshold Settings, such as setting , filter to meet Nodes with available storage ratio greater than or equal to the storage utilization threshold. is excluded if Then keep it; calculate the storage balance degree for the selected nodes, using the storage load deviation formula ,in is the average storage utilization of the nodes after screening, is the total number of nodes. For example, if the storage utilization in a cluster is {0.7, 0.8, 0.6, 0.85}, then the average utilization , 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, the storage load score is determined based on the storage load deviation value, and the standardized score is used ,like , the storage load score calculation is completed.

[0047] The task processing evaluation submodule calls the storage load score, obtains the node's task processing delay and data synchronization rate, calculates the impact of the two data on the load, and obtains the overall load score.

[0048] The task processing evaluation submodule calls the storage load score to first obtain the task processing delay of the node and data synchronization rate , where task processing delay can be obtained through historical task records, such as the average execution time of the past 100 tasks of a node , the data synchronization rate can be calculated based on the actual bandwidth and block synchronization time. For example, if a node synchronizes 50MB of data per second, , calculate the impact of two data on the load, using the impact coefficient and Perform weighted summation to calculate the impact value of the node's task load ,in , ,but ; then calculate the overall load score ,in , , such as the storage load score of a node ,but ; Finally, the overall load score is obtained.

[0049] The node allocation optimization submodule calls the overall load score, compares the node overall load score with the adaptation threshold, selects 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 sorting.

[0050] The node allocation optimization submodule calls the overall load score and first compares the node overall load score with the adaptation threshold , for example, setting , filter out the Nodes, such as the overall load score of a node Meet the requirements; then select the node group with high adaptation threshold, for example, select from multiple nodes The top 10 nodes with higher values ​​are selected; in the filtered node group, they are sorted according to the overall load score. For example, if the node scores are {-25.6, -27.1, -29.7, -30.2, -32.0}, then the sorting is {-25.6, -27.1, -29.7, -30.2, -32.0}. Finally, the consensus task node allocation plan is generated according to the score sorting, that is, tasks are assigned to nodes with the highest storage load scores first, and the allocation order is executed in sequence.

[0051] See also Figure 2 , the cross-chain archive transaction priority control module includes: The transaction priority threshold calculation submodule obtains the archive access frequency, user authority level and storage request urgency based on the consensus task node allocation plan, calculates the access frequency value, authority level value and urgency weight, analyzes the archive transaction priority value, and compares the storage threshold to filter out archives with high transaction priority values ​​to obtain the filtered archive set.

[0052] The specific calculation formula for the archive affairs priority value is: ; Calculate the archive transaction priority parameter, compare it with the storage threshold to filter out archives with high transaction priority values, and obtain a filtered archive set; in, Represents the archive transaction priority value, Representative The frequency of access to the archive, represents the access frequency weight, The total number of data representing the frequency of archive access, Representative The user's permission level, Represents the authority level weight, The total number of data representing the user's permission level, Representative The urgency of the storage request. Represents the urgency weight, The total number of data representing the urgency of storage requests, Represents the maximum value of the current transaction priority parameter. Represents the average value of the current transaction priority parameter.

[0053] Access frequency value calculation: Parameter acquisition: : No. The access frequency of the archives is obtained through the access log statistics recorded by the archive management system; assuming that the statistical period is one month; : Access frequency weight, set to 0.5; the weight is set according to the archive management strategy. The higher the access frequency, the greater the importance of the archive, so an appropriate weight is given; Numerical example: Assume that there are 5 archives in a month, and their access times are: 20 times, 15 times, 30 times, 10 times, and 25 times respectively; Calculation process: Calculate the access frequency value for each archive: ; ; ; ; .

[0054] Permission level value calculation: Parameter acquisition: : No. The user's permission level is obtained through the user permission management system; permission levels are usually divided into multiple levels, such as: 1 (ordinary user), 2 (advanced user), 3 (administrator); : Permission level weight, set to 0.3; the weight is set according to the archive management strategy. The higher the permission level, the greater the user's authority to operate the archive, so an appropriate weight is given; Numerical example: Assume there are 3 users, whose permission levels are: 1, 2, 3; Calculation process: Calculate the permission level value for each user: ; ; .

[0055] Urgency weight calculation: Parameter acquisition: : No. The urgency of a storage request, which is obtained through the metadata of the storage request or the urgency mark when the user submits it; the urgency can be divided into multiple levels, such as: 1 (normal), 2 (urgent), 3 (very urgent); : Urgency weight, set to 0.2; the weight is set according to the archive management strategy. The higher the urgency of the storage request, the more urgent the need for the archive, so an appropriate weight is given; Numerical example: Assume there are 4 storage requests, with urgency levels of 1, 2, 3, and 1 respectively; Calculation process: Calculate the urgency weight for each storage request: ; ; ; .

[0056] Calculate archive transaction priority parameters: Parameter acquisition: : The total number of data on the frequency of file access, i.e. the number of files, assumed to be 5; : The total number of data of user permission levels, that is, the number of users, assumed to be 3; : The total number of data on the urgency of storage requests, that is, the number of storage requests, assumed to be 4.

[0057] Calculation process: Calculate the weighted sum of access frequency, permission level, and urgency: ; ; .

[0058] Calculate the weighted average: .

[0059] Calculate the dispersion adjustment factor for the priority parameter: Parameter acquisition: : The maximum value of the current transaction priority parameter, assumed to be 10; : The average value of the current transaction priority parameter, assumed to be 5; Calculation process: Calculate the dispersion adjustment factor: .

[0060] Calculate the final archive transaction priority parameters: Calculation process: Calculate the weighted average: ; .

[0061] Calculate the dispersion adjustment factor: .

[0062] Calculate the final archive transaction priority parameters: ; .

[0063] Result analysis: The results show that the final calculated value of the archive transaction priority parameter is 8.866, which reflects the comprehensive influence of archive access frequency, user authority level and storage request urgency, and is corrected by combining the discrete adjustment coefficient of priority data. When it is greater than the set storage threshold, the file will be included in the screening file set and stored or processed first, otherwise it will be delayed.

[0064] The storage node matching submodule calls the filtered archive set, obtains the storage node bandwidth value and latency value, calculates the low latency and high bandwidth score, sorts and matches the storage node with the highest score according to the score, and obtains the archive storage matching relationship.

[0065] The storage node matching submodule calls the screening archive set to obtain the storage node bandwidth value and latency value. First, the bandwidth information of the storage node is extracted. Assuming that the bandwidth of storage nodes A, B, and C are 100MB / s, 200MB / s, and 150MB / s respectively, and the latency values ​​are measured at the same time, which are 10ms, 5ms, and 8ms respectively, the low latency and high bandwidth scores are calculated, and the score calculation formula is set as follows: ; in, Score the storage nodes, is the storage node bandwidth, is the maximum bandwidth among all nodes, is the bandwidth weight, is the storage node latency, is the minimum delay among all nodes, is the delay weight.

[0066] Set the bandwidth weight to 0.6 and the latency weight to 0.4, and calculate the storage node score: Node A: ; Node B: ; Node C: ; According to the score sorting, the storage node with the highest score, that is, node B, is matched to obtain the archive storage matching relationship.

[0067] The transaction evidence path determination submodule calls the archive storage matching relationship, obtains the consensus process corresponding to the storage node, calculates the transaction priority evidence path weight, and generates the transaction priority evidence path.

[0068] The transaction evidence path determination submodule calls the archive 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), extracts the transaction processing process of the storage node, and calculates the transaction priority evidence path weight. Assume that the weight calculation method of the evidence path is as follows: ; in, The path weight of the transaction priority certificate, is the transaction priority value, To filter the maximum transaction priority value in the archive set, is the transaction priority value weight, Score the storage nodes, is the highest score of all storage nodes. Score weights for storage; Set the transaction priority value weight to 0.7 and the storage score weight to 0.3. Assuming 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, the calculated transaction priority evidence path weight is: ; Finally, a transaction priority evidence path is generated.

[0069] See also Figure 2 , the storage index optimization module includes: The access activity calculation submodule calculates the number of archive accesses and the time interval value according to the transaction priority storage path and based on the archive access records, extracts the access frequency value of the archive, compares the access frequency value with the cold storage threshold, filters the data with access frequency value lower than the cold storage threshold, calculates the access ratio under the access cycle, analyzes the changing trend of the access frequency, and obtains the access activity ratio.

[0070] The access activity calculation submodule counts the number of accesses to each file based on the transaction priority storage path and the file access records, and sets the time window. (e.g. 7 days, 30 days) Calculate the number of visits per unit time , where the access frequency value can be expressed as , if a file is accessed 50 times within 30 days, its access frequency value is times / day, after obtaining the access frequency values ​​of all archives, compare them with the cold storage threshold Compare and set times / day, all archives with access frequency less than 0.5 times / day are put into the cold storage screening list. Among the screened data, the access ratio under the access cycle is calculated. Indicates the number of days from the creation of the file to the last access. For example, if a file is accessed on the 120th day after its creation, the access cycle is 120 days, and the access ratio calculation formula is: , if the file is accessed 20 times, then its access ratio is After calculating the access ratio for all the screened archives, the access frequency change trend is analyzed, and the cold storage trend is judged by the decrease in the access frequency over time. The sliding mean is used to calculate the change trend. For example, the average access frequency of each archive in the last 7 days, 14 days, and 30 days is calculated and the changes are observed. For example, the average access frequency of a certain archive in the past 30 days is 0.8, the past 14 days is 0.6, and the past 7 days is 0.3. The downward trend is significant, and it is classified as a low-activity archive, and finally the access activity ratio is obtained.

[0071] The index query optimization submodule calls the access activity ratio, calculates the index query response time of high access activity data, extracts the matching relationship between query response time and access activity, filters archive indexes whose query response time is higher than the set standard, optimizes the index query path, and obtains the optimized index query time.

[0072] The index query optimization submodule calls the access activity ratio, extracts high access activity data, and calculates its index query response time and response time. Indicates the time required to query index data and return results. For example, if the index query time of a certain file is 200ms, then After calculating the index query response time for all files, extract the matching relationship between query response time and access activity, and set the access activity threshold If the access activity of a file is 0.6 and the index query time is 350ms, and the access activity of another file is 0.2 and the query time is 120ms, then the former needs to optimize the index path and filter out all file indexes whose query response time is higher than the set standard. For example, set the query time threshold ,all The archive index needs to be optimized. The optimization methods include index partitioning, index compression, etc. A hierarchical storage strategy is adopted for indexes that exceed the threshold. High-access active indexes are cached to the high-speed storage layer, and low-access active indexes are adjusted to the low-speed storage layer. For example, when an archive is migrated from HDD storage to SSD, its query time is reduced from 350ms to 180ms, and the optimized index query time is finally obtained.

[0073] The storage tier division submodule calculates the data storage capacity under the differential index query time according to the optimized index query time and the storage tier standard, sets the storage tier attribution range according to the average of the data storage capacity and the query time, adjusts the data attribution of the storage tier, and obtains the storage index optimization plan.

[0074] The storage tier division submodule calculates the data storage capacity under different index query times based on the optimized index query time and the storage tier standard. Indicates the total amount of data at different storage levels. For example, if the high-speed storage level stores 50 TB and the low-speed storage level stores 100 TB, then , set the storage tier range based on the storage volume and the average query time. Indicates the average query time for all data in a storage layer. For example, if the average query time for the high-speed storage layer is 180ms and the average query time for the low-speed storage layer is 450ms, then set ,According to the storage level standard, adjust the storage level data ownership, and migrate the data with 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, adjust its storage location and finally obtain the archive storage level division plan.

[0075] See also Figure 3 The present invention also provides a file management method based on blockchain technology, which is implemented by using the above-mentioned file management system based on blockchain technology, and includes the following steps: S1: Obtain archive circulation path data, call the authority distribution of evidence storage nodes, data link connection status and evidence storage continuity data, calculate the complexity parameter of the evidence storage path, classify the circulation mode based on the complexity parameter, calculate the change trend of the circulation mode, and obtain the circulation trend record; S2: Call the flow trend record to obtain the block storage utilization rate, data block generation interval and evidence data distribution characteristics, calculate the block evidence range adjustment parameters, change the block evidence range based on the adjustment parameters, and obtain the adjusted block evidence boundary data; S3: Based on the adjusted block evidence boundary data, obtain the consensus node storage utilization, task processing delay and data synchronization rate, calculate the node load score, select the adapter node group based on the score threshold, call the adapter node group to calculate the consensus task allocation strategy, allocate consensus tasks according to the strategy, and obtain the consensus task node allocation plan; S4: Call the consensus task node allocation plan, obtain the archive access frequency, user authority level and storage request urgency, calculate the archive transaction priority, filter high-priority archive transactions based on transaction priority, call the storage node network delay and bandwidth parameters, match low-latency and high-bandwidth storage nodes, adjust the cross-chain consensus process, and obtain the transaction priority evidence storage path; S5: Based on the transaction priority notarization path, obtain archive access records, storage level division and index query response time, calculate data access activity, adjust the storage level based on the activity, optimize the index structure, and obtain a storage index optimization plan.

[0076] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A file management system based on blockchain technology, characterized by: The system comprises: The archive circulation monitoring module obtains archive circulation path data, analyzes the permission intersection of evidence storage nodes and the change of evidence storage continuity, calculates and classifies the complexity data of evidence storage paths, identifies differential circulation patterns and analyzes the change trend of archive circulation patterns, and obtains circulation trend records; The block boundary dynamic adjustment module reads the flow trend record, calculates and adjusts the block boundary adjustment parameters and ranges based on the current block storage utilization and the distribution characteristics of the evidence data, and obtains the adjusted block evidence boundary data; The consensus node load balancing module evaluates the node load score based on the adjusted block evidence boundary data, combined with the node storage utilization, task processing delay and data synchronization rate, 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 archive transaction priority control module calculates and screens the archive transaction priority according to the consensus task node allocation plan, combined with the archive access frequency, user authority level and storage request urgency, matches the storage nodes with low latency and high bandwidth, adjusts the cross-chain consensus process, and generates the transaction priority evidence path.

2. The archive management system based on blockchain technology according to claim 1, characterized in that: The circulation trend record includes classification data of complexity of evidence path, differential circulation mode data, and archive circulation mode change data; the adjusted block evidence boundary data includes block storage utilization data, data block generation interval data, and evidence data distribution characteristic data; the consensus task node allocation plan includes node load score data, node group data with high adaptation threshold, and consensus task allocation result data; the transaction priority evidence path includes archive access frequency data, user authority level data, and storage request urgency data.

3. The archive management system based on blockchain technology according to claim 2 is characterized in that: The file circulation monitoring module includes: The path data collection submodule obtains the file circulation path data, extracts the evidence node authority information, data link connection status information and evidence storage time interval data, calculates the number of evidence node interactions, and obtains the file circulation path parameter set; The permission intersection calculation submodule obtains the file circulation path parameter set, calculates the permission intersection degree of the evidence storage node, calculates the continuity change value of the evidence storage in combination with the data link connection status information, calculates the classification proportion of the permission subject according to the evidence storage trigger time interval and the number of node interactions, and obtains the permission intersection and evidence storage change parameter set; The circulation pattern trend identification submodule calls the permission intersection and evidence change parameter set to classify the circulation path types, count the number and proportion of paths, analyze the change trend of path categories, screen path categories with large trend changes, analyze the characteristics of path categories, and obtain circulation trend records.

4. The archive management system based on blockchain technology according to claim 3 is characterized by: The block boundary dynamic adjustment module includes: The file circulation trend analysis submodule obtains the circulation trend record, detects the circulation quantity in the different time periods, calculates the change ratio of the circulation quantity between adjacent time periods, counts the increase and decrease of the change ratio in the different time periods, captures the change trend characteristics, and obtains the circulation trend change ratio; The storage utilization rate calculation submodule calls the circulation trend change ratio based on the data occupancy of the current storage block, determines the usage ratio of the storage space, calculates the mean value of the storage utilization rate relative to the block evidence storage amount, and obtains the storage utilization rate change amplitude data; The boundary adjustment optimization submodule calculates the block boundary adjustment parameters based on the storage utilization change amplitude data and the flow offset distribution interval, screens the boundary areas where the adjustment parameters meet the adjustment benchmark values, calculates the adjusted block boundary position based on the distribution of the evidence data, and obtains the adjusted block evidence boundary data.

5. The archive management system based on blockchain technology according to claim 4 is characterized in that: The specific calculation formula of the storage utilization rate relative to the mean change of the block evidence volume is: ； in, Representative Storage utilization at a point in time, Represents the storage utilization at the previous point in time, Represents the total number of storage utilization data points, Representative The amount of evidence in a block, Represents the average value of all block proofs. The total number of data points representing the amount of evidence stored, Representative The time interval between data block generation, Represents the total number of data points in the time interval between data block generation.

6. The archive management system based on blockchain technology according to claim 5 is characterized in that: The consensus node load balancing module includes: The storage utilization evaluation submodule obtains the storage utilization rate of the consensus node based on the adjusted block evidence boundary data, calculates the available storage ratio of the node, compares the available storage ratio of the node with the storage utilization threshold, selects nodes whose available storage ratio is greater than or equal to the storage utilization threshold, calculates the storage balance degree and storage utilization deviation of the selected nodes, and obtains the storage load score; The task processing evaluation submodule calls the storage load score, obtains the task processing delay and data synchronization rate of the node, calculates the impact of the two data on the load, and obtains the overall load score; The node allocation optimization submodule calls the overall load score, compares the node overall load score with the adaptation threshold, selects 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 sorting.

7. The archive management system based on blockchain technology according to claim 6 is characterized by: The cross-chain archive transaction priority control module includes: The transaction priority threshold calculation submodule obtains the file access frequency, user authority level and storage request urgency based on the consensus task node allocation scheme, calculates the access frequency value, authority level value and urgency weight, analyzes the file transaction priority value, and compares the storage threshold to filter files with high transaction priority values ​​to obtain a filtered file set; The storage node matching submodule calls the screening archive set, obtains the storage node bandwidth value and delay value, calculates the low delay and high bandwidth score, sorts and matches the storage node with the highest score according to the score, and obtains the archive storage matching relationship; The transaction evidence path determination submodule calls the archive storage matching relationship, obtains the consensus process corresponding to the storage node, calculates the transaction priority evidence path weight, and generates the transaction priority evidence path.

8. The archive management system based on blockchain technology according to claim 7 is characterized by: The specific calculation formula of the archive transaction priority value is: ； Calculate the archive transaction priority parameter, compare it with the storage threshold to filter out archives with high transaction priority values, and obtain a filtered archive set; in, Represents the archive transaction priority value, Representative The frequency of access to the archive, represents the access frequency weight, The total number of data representing the frequency of archive access, Representative The user's permission level, Represents the authority level weight, The total number of data representing the user's permission level, Representative The urgency of the storage request. Represents the urgency weight, The total number of data representing the urgency of storage requests, Represents the maximum value of the current transaction priority parameter. Represents the average value of the current transaction priority parameter.

9. The archive management system based on blockchain technology according to claim 8, characterized in that: The system further comprises: The storage index optimization module reads the transaction priority storage path, combines the archive access record, storage level division and index query response time, calculates the data access activity, adjusts the storage level, and generates a storage index optimization plan; The storage index optimization scheme includes data access activity analysis results, storage level adjustment scheme, and index structure optimization scheme; The storage index optimization module includes: The access activity calculation submodule calculates the number of archive accesses and the time interval value according to the transaction priority evidence storage path and based on the archive access record, extracts the archive access frequency value, compares the access frequency value with the cold storage threshold, filters the data with the access frequency value 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; The index query optimization submodule 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, screens the archive indexes whose query response time is higher than the set standard, and obtains the optimized index query time; The storage level division submodule calculates the data storage capacity under the differential index query time according to the optimized index query time and the storage level standard, sets the storage level attribution range according to the average of the data storage capacity and the query time, adjusts the data attribution of the storage level, and obtains the storage index optimization plan.

10. A file management method based on blockchain technology, characterized in that: According to any one of claims 1 to 9, the archive management system based on blockchain technology is implemented, comprising the following steps: S1: Obtain archive circulation path data, call the authority distribution of evidence storage nodes, data link connection status and evidence storage continuity data, calculate the complexity parameter of the evidence storage path, classify the circulation mode based on the complexity parameter, calculate the change trend of the circulation mode, and obtain the circulation trend record; S2: calling the circulation trend record, obtaining the block storage utilization rate, data block generation interval and evidence data distribution characteristics, calculating the block evidence range adjustment parameters, changing the block evidence range based on the adjustment parameters, and obtaining the adjusted block evidence boundary data; S3: Based on the adjusted block evidence boundary data, the consensus node storage utilization, task processing delay and data synchronization rate are obtained, the node load score is calculated, the adaptation node group is selected based on the score threshold, the adaptation node group is called to calculate the consensus task allocation strategy, the consensus task is allocated according to the strategy, and the consensus task node allocation plan is obtained; S4: Call the consensus task node allocation plan, obtain the archive access frequency, user authority level and storage request urgency, calculate the archive transaction priority, filter high-priority archive transactions based on the transaction priority, call the storage node network delay and bandwidth parameters, match low-latency and high-bandwidth storage nodes, adjust the cross-chain consensus process, and obtain the transaction priority evidence storage path; S5: Based on the transaction priority notarization path, obtain archive access records, storage level division and index query response time, calculate data access activity, adjust the storage level based on the activity, optimize the index structure, and obtain a storage index optimization plan.

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