A blockchain-based archive information analysis and management system and method

Abstract

The present invention discloses an archive information analysis and management system and method based on blockchain, which specifically relates to the field of archive management and is used to solve the problem of data consistency in the task update process. Through task dependency analysis, node execution evaluation and intelligent contract synchronization mechanism, the archive update task can be efficiently and stably executed to ensure the order of task execution and resource allocation optimization, thereby avoiding delays and errors caused by the complexity of task dependency paths or unstable node execution; by dynamically selecting the most suitable temporary transition node, combining path complexity and node stability, the appearance of bottleneck paths can be effectively reduced to ensure that key tasks are efficiently processed; at the same time, with the help of consensus mechanism and double hash matching, the consistency and integrity of on-chain and off-chain data are guaranteed, ultimately improving the overall execution efficiency of the system, data synchronization accuracy and operation security, and effectively reducing potential execution risks and bottlenecks.

Description

Technical Field

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

[0002] In traditional archive management systems, as the amount of archive data continues to increase, the synchronization of on-chain and off-chain data becomes particularly complex. Especially in a distributed storage environment, on-chain data (such as metadata and indexes) and off-chain data (such as archive file content) often need to be updated synchronously to ensure consistency and integrity between the two. However, due to the immutability of on-chain data and the distributed nature of off-chain storage, the synchronization of the two is easily affected by network delays, node failures, or execution order problems, resulting in data inconsistency or update failure. In order to solve this problem, there is an urgent need for a method that can ensure atomic consistency of on-chain and off-chain data operations while maintaining system efficiency. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an archive information analysis and management system and method based on blockchain. Through task dependency analysis, node execution evaluation and smart contract synchronization mechanism, the archive update task can be executed efficiently and stably to ensure the order of task execution and resource allocation optimization, thereby avoiding delays and errors caused by the complexity of task dependency paths or unstable node execution; by dynamically selecting the most suitable temporary transition node, combining path complexity and node stability, the emergence of bottleneck paths can be effectively reduced to ensure that key tasks are processed efficiently; at the same time, with the help of consensus mechanism and double hash matching, the consistency and integrity of on-chain and off-chain data are guaranteed, which ultimately improves the overall execution efficiency of the system, data synchronization accuracy and operation security, and effectively reduces potential execution risks and bottlenecks to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: A blockchain-based archival information analysis and management method comprises the following steps: S1: Receive user profile update requests, extract the index information and hash fingerprint of the target profile in the blockchain, obtain the current profile content and version information stored outside the chain, and generate atomic update tasks; S2: By analyzing the dependency and execution order of the update tasks, the complexity of each task path and the stability of node execution are evaluated, and a set of temporary transition nodes with corresponding processing capabilities are matched; S3: Perform hash comparison on archive integrity through temporary transition node set, implement multi-level rule verification of user operation permissions, and generate verification logs containing data difference and permission status; S4: Combined with the verification log, a preset fault tolerance threshold is used to trigger the consensus mechanism, and an executable token carrying the data version number and transaction tracking code is generated through dynamic weight voting decisions; S5: Based on the executable token, the synchronization processing unit of the smart contract is activated. When the blockchain updates the archive metadata fingerprint, it sends a double hash matching update instruction to the off-chain storage to complete the cross-chain data consistency solidification.

[0005] In a preferred embodiment, step S1 includes the following contents: First, the user's file update request is received, and the unique identifier of the target file is extracted based on the request content; then, the metadata of the corresponding file on the blockchain is queried and extracted through the blockchain smart contract; at the same time, the actual content and version information of the file are queried from the off-chain storage through the interface to ensure that the off-chain data is consistent with the on-chain index information; all the obtained file information is unified and constructed into atomic update tasks.

[0006] In a preferred embodiment, step S2 includes the following contents: When selecting a temporary transition node set, it is necessary to first build a task dependency graph; the task dependency graph construction process first decomposes the archive update request submitted by the user into multiple subtasks, each subtask represents a verification or execution link, and each subtask will be used as a node in the task dependency graph. The dependency relationship between subtasks will be connected by directed edges to indicate the order of task execution; based on the task dependency graph, the path complexity information and the node execution task stability information are calculated, and the resonance degree of the task and node pairing is obtained based on the combination of the path complexity information and the node execution task stability information. The task topology resonance degree is obtained by comprehensive calculation, and the temporary transition node set that is most suitable for executing the current task is screened out; the path complexity information includes the dependent path vortex density, and the node execution task stability information includes the node resonance frequency-amplitude ratio.

[0007] In a preferred embodiment, the vortex density of the dependent path is firstly: each edge in the task dependency graph is The weight is marked as ; Next, calculate the depth of each edge , whose depth is determined by the level of the edge; in addition, the global importance of each edge is determined by ranking Calculate the vortex contribution of each edge Calculated according to the following formula: ; Then, for each subtask, the vortex density of all its predecessor paths is calculated, which is the sum of the vortex contribution values ​​of all edges in the path.

[0008] In a preferred embodiment, the node resonance frequency ratio evaluates the degree of spectrum alignment between the node execution efficiency and the theoretical optimal execution time through frequency domain analysis; first, for each node, its recent actual execution time series is collected , and calculate its difference with the theoretical optimal time series The difference between the actual execution time of the node and the ideal execution time is quantified by performing frequency domain analysis on the two time series through fast Fourier transform and extracting the energy difference of the first five frequency bands: ;in and The actual and theoretical execution time series are The amplitude of the frequency domain component; then, the spectral gradient norm of the actual execution time series of the node is calculated; finally, the node resonance frequency amplitude ratio It is calculated as the ratio of the difference energy and the gradient norm.

[0009] In a preferred embodiment, for each task, the vortex density of its dependent path is first calculated, and based on the node resonance frequency-amplitude ratio of the node, the two are combined to obtain the resonance degree of the corresponding task and node pairing, and the temporary transition node set that is most suitable for executing the current task is screened out based on the calculation results; the candidate nodes are sorted according to the task topology resonance value, and the top M nodes with the highest task topology resonance value are selected. When the task topology resonance values ​​of multiple nodes are close to the standard, the node with the lowest consensus offset inertia factor value is preferentially selected to perform the task.

[0010] In a preferred embodiment, step S3 includes the following contents: First, the temporary transition node set will compare the hash value in the archive index stored on the blockchain with the hash value of the actual archive data in the off-chain storage. The hash comparison calculates the hash value of the current archive content and matches it with the hash fingerprint recorded on the blockchain. If the two are inconsistent, the temporary transition node set immediately marks the data as incomplete or invalid; if the data comparison, signature verification and permission verification are all passed, the temporary transition node set will continue to retain the update record and generate a verification log containing the data difference and permission status.

[0011] In a preferred embodiment, step S4 specifically includes the following contents: The temporary transition node set collectively votes on the verification results of each task according to the predetermined consensus algorithm to confirm whether the update conditions are met; the temporary transition node set analyzes the generated verification log and determines the acceptable range of the verification results based on the set fault tolerance threshold; when the temporary transition node set reaches a consensus through voting and all verification tasks pass the fault tolerance threshold test, the temporary transition node set will generate an executable token.

[0012] In a preferred embodiment, step S5 includes the following contents: The smart contract starts the data synchronization process by receiving and verifying the executable token. First, the smart contract verifies the legitimacy of the token, including checking whether the signature of the token is valid and whether the data in the token is consistent. At the same time, the contract sends an update instruction to the off-chain storage system, which includes the data to be updated and the hash value of its new version. After receiving the instruction, the off-chain storage system will actually update the archive data and generate an updated archive hash value. This hash value is then returned to the smart contract, which will verify the hash value returned from the off-chain again. If the verification is successful, the contract will solidify the new archive index and data hash on the blockchain, completing the data synchronization and solidification process to ensure the integrity and consistency of the archive data. If data inconsistency or verification errors occur at any stage, the contract will roll back the update operation.

[0013] A blockchain-based archive information analysis and management system, comprising: a task processing module, a dependency assessment module, a data verification module, a consensus decision module and a synchronization solidification module; Task processing module: receives the file update request submitted by the user, extracts the index information and hash fingerprint of the target file in the blockchain, obtains the current file content and version information in the off-chain storage, generates atomic update tasks and passes them to the dependency evaluation module; Dependency evaluation module: By analyzing the dependency and execution order of the update tasks, evaluating the complexity of each task path and the stability of node execution, matching the temporary transition node set with corresponding processing capabilities, and sending the temporary task transition node set to the data verification module; Data verification module: Perform hash comparison on the integrity of the archive through a temporary transition node set, implement multi-level rule verification of user operation permissions, generate a verification log containing data differences and permission status, and pass the verification log to the consensus decision module; Consensus decision module: Combined with the verification log, the preset fault tolerance threshold is used to trigger the consensus mechanism. Through dynamic weight voting, an executable token carrying the data version number and transaction tracking code is generated, and the executable token is passed to the synchronization and curing module. Synchronization and curing module: A synchronization processing unit based on executable tokens that activates smart contracts. When the blockchain updates the archive metadata fingerprint, it sends a double hash matching update instruction to the off-chain storage to complete cross-chain data consistency curing.

[0014] The technical effects and advantages of the blockchain-based archive information analysis and management system and method of the present invention are as follows: The present invention effectively improves the execution efficiency and stability of the archive update task through accurate task dependency graph construction, dynamic evaluation of path complexity and node stability. First, by refining the dependency relationship and execution order of the task, the critical path and potential bottlenecks in the task are accurately identified to avoid execution delays or task blocking caused by unstable nodes or improper resource allocation; secondly, combined with the dependent path vortex density and node resonance frequency-amplitude ratio, the temporary transition node that is most suitable for executing the current task can be dynamically screened to ensure the efficiency and stability of the task execution process, especially when dealing with tasks with complex paths or high-precision timing requirements, effectively reducing system delays and errors; in addition, the high reliability of the task execution process is ensured through consensus mechanisms and fault-tolerant mechanisms, and the consistency synchronization of on-chain and off-chain data is achieved using double hash matching and transaction tracking coding technology, thereby ensuring the integrity of archive data in multiple storage levels; finally, this solution improves the execution efficiency, data consistency and system reliability of the entire archive update process, maximizes task processing capabilities and resource utilization efficiency, and effectively reduces potential execution risks and bottlenecks. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flowchart of a blockchain-based archival information analysis and management method of the present invention; Figure 2 This is a structural schematic diagram of an archive information analysis and management system based on blockchain in the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1: Figure 1 The present invention provides a blockchain-based archival information analysis and management method, comprising: S1: Receive user profile update requests, extract the index information and hash fingerprint of the target profile in the blockchain, obtain the current profile content and version information stored outside the chain, and generate atomic update tasks; S2: By analyzing the dependency and execution order of the update tasks, the complexity of each task path and the stability of node execution are evaluated, and a set of temporary transition nodes with corresponding processing capabilities are matched; S3: Perform hash comparison on archive integrity through temporary transition node set, implement multi-level rule verification of user operation permissions, and generate verification logs containing data difference and permission status; S4: Combined with the verification log, a preset fault tolerance threshold is used to trigger the consensus mechanism, and an executable token carrying the data version number and transaction tracking code is generated through dynamic weight voting decisions; S5: Based on the executable token, the synchronization processing unit of the smart contract is activated. When the blockchain updates the archive metadata fingerprint, it sends a double hash matching update instruction to the off-chain storage to complete the cross-chain data consistency solidification.

[0018] In archival data management, the accuracy and consistency of update operations are crucial. To ensure that on-chain and off-chain data are synchronized during the update process and avoid inconsistencies or errors, it is necessary to comprehensively collect and pre-process update requests in the early stages of the update operation. Step S1 mainly analyzes user update requests and combines archival information in blockchain and off-chain storage to lay a solid foundation for subsequent verification, decision-making, and execution steps.

[0019] Step S1 includes the following contents: First, the user's file update request is received, and the unique identifier of the target file (such as the file number or ID) is extracted according to the request content. Then, through the blockchain smart contract, the metadata of the file on the blockchain is queried and extracted, including the file's index information and hash fingerprint, which are used for subsequent version verification and consistency verification. At the same time, the system queries and obtains the actual content and version information of the file from the off-chain storage through the interface to ensure that the off-chain data is consistent with the on-chain index information. All acquired file information, including the index, hash fingerprint, content and version information of the off-chain storage on the blockchain, will be uniformly summarized and constructed into an atomic update task, which includes the unique identifier of the task, data version, update requirements and related operation types. This atomic update task will serve as the input of subsequent steps to ensure the coherence and consistency of data updates.

[0020] Step S2 includes the following contents: When selecting a temporary transition node set, it is necessary to build a task dependency graph first, because the task dependency graph can clearly identify the execution order and dependency relationship between each subtask, thereby providing an accurate basis for subsequent node selection. Through the dependency graph, the system can identify which tasks are critical paths, which tasks have complex dependencies, and which paths may become bottlenecks. Only by understanding the topological structure and dependency relationships of the tasks first can the system reasonably allocate resources according to the complexity and importance of the tasks and select the most appropriate temporary transition nodes to execute different tasks. If the task dependency graph is not built first, the priority and resource requirements of task execution cannot be accurately identified, which may lead to improper task execution, path blocking or unstable nodes being assigned to critical tasks, thereby affecting the efficiency and stability of the entire task execution. Therefore, the task dependency graph not only provides hierarchical and structured task information for node selection, but also ensures the orderliness and efficiency of the task execution process.

[0021] The task dependency graph construction process first decomposes the archive update request submitted by the user into multiple subtasks, each of which represents a verification or execution link, such as data integrity verification, permission verification, hash matching, etc. Each subtask will be used as a node in the task dependency graph, and the dependencies between subtasks will be connected by directed edges to indicate the order of task execution. Next, a weight is assigned to each edge, and each edge weight is used to indicate the urgency of the task dependency and the importance of the task to the final update result. The weight is calculated based on the priority, complexity and historical verification success rate of the task. When constructing the task dependency graph, the edge weights between nodes directly affect the order of subsequent task execution and node selection, ensuring that complex and critical path tasks are executed first to avoid potential bottleneck risks. At the same time, the system topologically sorts the task dependency graph to ensure that each subtask is executed only after the predecessor task it depends on has been completed, thereby ensuring the orderliness and coherence of the update operation.

[0022] Based on the task dependency graph, the path complexity information and the node execution task stability information are calculated. The resonance degree of task and node pairing is obtained based on the combination of the path complexity information and the node execution task stability information. The task topology resonance degree is obtained by comprehensive calculation, and the temporary transition node set that is most suitable for executing the current task is screened out.

[0023] The path complexity information includes the dependent path vortex density, and the node execution task stability information includes the node resonance frequency-amplitude ratio.

[0024] Path-dependent vortex density It is used to evaluate the critical path complexity and potential bottleneck effect in the task dependency graph. First, each edge in the task dependency graph The weight is marked as Next, calculate the depth of each edge , whose depth is determined by the level of the edge, the root task level is 0, and as the level increases, the negative impact of the vortex effect also increases. In addition, the global importance of each edge is ranked Calculation, the top-ranked edges will be calculated through the hyperbolic cosine function in subsequent calculations The vortex contribution of each edge is enlarged to highlight the clustering effect of the critical path. Calculated according to the following formula: ; This formula shows that the greater the path depth and the higher the edge weight, the greater the contribution to the vortex effect of the task path. Then, for each subtask, the vortex density of all its predecessor paths is calculated, that is, the sum of the vortex contribution values ​​of all edges in the path: ; Paths with high dependency path vortex density usually indicate that there are more dependency overlaps or clustering effects of core task paths, which may form bottlenecks. Therefore, nodes with higher stability need to be assigned to execute these paths to avoid execution blocking.

[0025] The node resonance frequency ratio evaluates the degree of spectrum alignment between the node execution efficiency and the theoretical optimal execution time through frequency domain analysis. First, for each node, its recent actual execution time series is collected. , and calculate its difference with the theoretical optimal time series The two time series are analyzed in the frequency domain by fast Fourier transform, and the energy difference of the first five frequency bands is extracted to quantify the deviation between the actual execution time of the node and the ideal execution time: ;in and The actual and theoretical execution time series are Next, the spectral gradient norm of the actual execution time series of the node is calculated to suppress the influence of high-frequency noise on the execution effect: ; Finally, the node resonance frequency amplitude ratio It is calculated by the ratio of the difference energy to the gradient norm: ; Nodes with lower node resonance frequency-amplitude ratios indicate that their actual execution time distribution is close to the theoretical optimal time, have better execution stability and low-frequency noise, and are suitable for executing tasks with high timing requirements.

[0026] The task topology resonance evaluates the optimal execution node by dynamically coupling the complexity of the task path and the node execution stability. Specifically, the calculation of the task topology resonance takes into account the vortex density of the path and the resonance frequency amplitude ratio of the node. For each task, the vortex density of its dependent path is first calculated, and then the resonance degree of the task and node pairing is obtained by combining the two according to the node resonance frequency amplitude ratio: ; in is a smoothing factor, which is usually the mean of the vortex density of all task paths to prevent the denominator from being too small and causing numerical overflow. The node and path combination with a higher task topology resonance indicates that the node has higher stability and lower execution latency in the execution of the task path, and the system will give priority to these combinations for execution. This calculation process ensures that the path complexity and node execution stability are effectively balanced in node selection, avoiding interference from bottleneck paths and unstable nodes.

[0027] After the task topology resonance calculation is completed, the temporary transition node set that is most suitable for executing the current task is screened out based on the calculation results. First, hard condition filtering is performed to exclude candidate nodes with insufficient resources or low credibility. Then, the candidate nodes are sorted according to the task topology resonance value, and the top M nodes with the highest task topology resonance value are selected. These nodes are considered to be the most suitable nodes for executing the current task, with high path processing capabilities and execution stability. When the task topology resonance values ​​are close to the standard, dynamic tuning will be performed based on the consensus offset inertia factor, and nodes with the lowest consensus offset inertia factor values ​​will be given priority to avoid lags or errors during system execution. Through this screening process, the system can accurately select temporary transition nodes based on the complexity of the task and the stability of the node, maximize task execution efficiency and reduce potential execution delays and risks.

[0028] The consensus offset inertia factor acquisition process is to calculate the average deviation between the execution time of the node in the historical tasks and the synchronization time of other nodes, and combine it with the failure rate of the node to obtain the offset inertia of the node relative to the ideal execution state, reflecting its execution stability and the possible lag error.

[0029] Secondly, during dynamic tuning, when the task topology resonance values ​​of multiple nodes are close to the standard, the node with the lowest consensus offset inertia factor value is selected to perform the task first, to ensure the synchronization and stability of task execution and reduce potential lags and errors.

[0030] By screening the temporary transition node set, the system can ensure that those nodes with stable execution and small consensus synchronization offset are selected when executing tasks, thereby improving the accuracy and efficiency of the task execution process. Nodes with low consensus offset inertia factors have smaller execution deviations and lower latency risks, which help reduce lags and errors in task execution, especially when dealing with complex paths or critical tasks, and can avoid performance bottlenecks caused by unstable node execution. This screening mechanism not only optimizes the use of system resources by prioritizing the allocation of high-stability nodes, but also ensures the smoothness of the task execution process, improving the overall task processing capability and reliability of the system.

[0031] Step S3 includes the following contents: The temporary transition node set verifies the integrity, legitimacy and feasibility of the collected archive data. First, the temporary transition node set compares the hash value in the archive index stored on the blockchain with the hash value of the actual archive data in the off-chain storage to ensure that the off-chain data has not been tampered with or lost. Hash comparison calculates the hash value of the current archive content and matches it with the hash fingerprint recorded on the blockchain. If the two are inconsistent, the temporary transition node set immediately marks the data as incomplete or invalid and stops the subsequent process. Secondly, it verifies the digital signature attached to the archive update request, verifies whether the signature matches the identity of the requester, and confirms the legitimacy of the request. If the signature is invalid or does not match, the temporary transition node set will mark this operation as rejected. Next, the user's access rights are verified by multi-level rules to check whether the operation permissions in the update request meet the preset rules, such as whether there is update permission for a specific archive type, whether the update field matches the archive type, and whether it meets the role permissions. If the permissions are insufficient, the operation will also be rejected. If data comparison, signature verification, and permission verification are all passed, the temporary transition node set will continue to retain the update record and generate a verification log containing data differences and permission status. The log content includes data hash consistency verification results, signature verification status, and permission verification results. Finally, these verification results will be written into the verifiable log maintained by the temporary transition node set for reference in the subsequent decision-making consensus stage. If all verifications are passed, the temporary transition node set will pass the verification results to the subsequent steps for further decision-making.

[0032] Step S4 specifically includes the following contents: The temporary transition node set collectively votes on the verification results of each task according to a predetermined consensus algorithm (such as Raft or PBFT) to confirm whether the update conditions are met. First, the temporary transition node set analyzes the verification log generated in the previous step and determines the acceptable range of the verification results based on the set fault tolerance threshold. The threshold is used to determine the maximum error or inconsistency allowed in the verification process to ensure that the system can continue to execute when some nodes fail or there are small differences in the results. When the temporary transition node set reaches a consensus through voting and all verification tasks pass the fault tolerance threshold test, the temporary transition node set will generate an executable token. The token includes the following key information: a unique transaction identifier to distinguish each update operation; a data hash to ensure the data integrity and consistency of the update operation; operation permission information to confirm the legitimacy of the update operation; and security verification information to ensure the authenticity of the token and the legitimacy of the operation. The generated executable token serves as a credential for subsequent update operations to ensure that multiple nodes reach a consensus on the same operation and can execute it safely. If there is a disagreement during the consensus process or the verification result does not pass the fault tolerance threshold, the token will not be generated. At this point, the temporary transition node assembly will record the specific reasons for the failure, including the subtasks that failed to verify and the specific items that did not pass the fault tolerance threshold, and pass them to the relevant management modules for analysis through the log system. In this way, the generation of tokens ensures the security and effectiveness of the entire update process, avoiding potential risks caused by consensus disagreements or verification failures.

[0033] Step S5 includes the following contents: The smart contract starts the data synchronization process by receiving and verifying the executable token. First, the smart contract verifies the legitimacy of the token, including checking whether the token's signature is valid and whether the data in the token is consistent. For example, the contract verifies whether the data hash in the token matches the hash value stored on the blockchain to confirm that the update conditions of the task have been met. After verification, the contract updates the archive metadata on the blockchain according to the instructions in the token, including key information such as the archive's storage location and version number. At the same time, the contract sends an update instruction to the off-chain storage system, which includes the data to be updated and the hash value of its new version. After receiving the instruction, the off-chain storage system will actually update the archive data and generate an updated archive hash value. The off-chain storage system then returns this hash value to the smart contract, and the contract will verify the hash value returned from the off-chain again to ensure the consistency of the off-chain data with the on-chain data. The verification process includes comparing the off-chain hash value with the hash value stored in the blockchain. If the two are inconsistent, the contract will mark the update as failed and roll back the operation. If the verification is successful, the contract will solidify the new archive index and data hash on the blockchain, completing the data synchronization and solidification process to ensure the integrity and consistency of the archive data. If data inconsistency or verification errors occur at any stage, the contract will roll back the update operation to ensure the atomicity of the entire process and prevent erroneous data from being permanently recorded. Through this step, the on-chain and off-chain data are updated synchronously to ensure the consistency and integrity of the archive data in the blockchain and off-chain storage system.

[0034] Embodiment 2: Figure 2 The present invention provides an archive information analysis and management system based on blockchain, including: a task processing module, a dependency assessment module, a data verification module, a consensus decision module and a synchronization solidification module; Task processing module: receives the file update request submitted by the user, extracts the index information and hash fingerprint of the target file in the blockchain, obtains the current file content and version information in the off-chain storage, generates atomic update tasks and passes them to the dependency evaluation module; Dependency evaluation module: By analyzing the dependency and execution order of the update tasks, evaluating the complexity of each task path and the stability of node execution, matching the temporary transition node set with corresponding processing capabilities, and sending the temporary task transition node set to the data verification module; Data verification module: Perform hash comparison on the integrity of the archive through a temporary transition node set, implement multi-level rule verification of user operation permissions, generate a verification log containing data differences and permission status, and pass the verification log to the consensus decision module; Consensus decision module: Combined with the verification log, the preset fault tolerance threshold is used to trigger the consensus mechanism. Through dynamic weight voting, an executable token carrying the data version number and transaction tracking code is generated, and the executable token is passed to the synchronization and curing module. Synchronization and curing module: A synchronization processing unit based on executable tokens that activates smart contracts. When the blockchain updates the archive metadata fingerprint, it sends a double hash matching update instruction to the off-chain storage to complete cross-chain data consistency curing.

[0035] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0036] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0037] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0038] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A blockchain-based archival information analysis and management method, characterized in that: Includes steps: S1: Receive user profile update requests, extract the index information and hash fingerprint of the target profile in the blockchain, obtain the current profile content and version information stored outside the chain, and generate atomic update tasks; S2: By analyzing the dependency and execution order of the update tasks, the complexity of each task path and the stability of node execution are evaluated, and a set of temporary transition nodes with corresponding processing capabilities are matched; S3: Perform hash comparison on archive integrity through temporary transition node set, implement multi-level rule verification of user operation permissions, and generate verification logs containing data difference and permission status; S4: Combined with the verification log, a preset fault tolerance threshold is used to trigger the consensus mechanism, and an executable token carrying the data version number and transaction tracking code is generated through dynamic weight voting decisions; S5: Based on the executable token, the synchronization processing unit of the smart contract is activated. When the blockchain updates the archive metadata fingerprint, it sends a double hash matching update instruction to the off-chain storage to complete the cross-chain data consistency solidification.

2. According to the blockchain-based archival information analysis and management method of claim 1, it is characterized in that: Step S1 includes the following contents: First, the user's file update request is received, and the unique identifier of the target file is extracted based on the request content; then, the metadata of the corresponding file on the blockchain is queried and extracted through the blockchain smart contract; at the same time, the actual content and version information of the file are queried from the off-chain storage through the interface to ensure that the off-chain data is consistent with the on-chain index information; all the obtained file information is unified and constructed into atomic update tasks.

3. According to the blockchain-based archival information analysis and management method of claim 2, it is characterized in that: Step S2 includes the following contents: When selecting a temporary transition node set, it is necessary to first construct a task dependency graph. The task dependency graph is constructed by first decomposing the archive update request submitted by the user into multiple subtasks. Each subtask represents a verification or execution link. Each subtask will be a node in the task dependency graph. The dependency relationship between subtasks will be connected by directed edges to indicate the order of task execution. Based on the task dependency graph, the path complexity information and the node execution task stability information are calculated. The resonance degree of task and node pairing is obtained based on the combination of the path complexity information and the node execution task stability information. The task topology resonance degree is obtained by comprehensive calculation, and the temporary transition node set that is most suitable for executing the current task is screened out; the path complexity information includes the dependent path vortex density, and the node execution task stability information includes the node resonance frequency-amplitude ratio.

4. According to the blockchain-based archival information analysis and management method of claim 3, it is characterized by: Dependency path vortex density; First, each edge in the task dependency graph The weight is marked as ; Next, calculate the depth of each edge , whose depth is determined by the level of the edge; in addition, the global importance of each edge is determined by ranking Calculate the vortex contribution of each edge Calculated according to the following formula: ; Then, for each subtask, the vortex density of all its predecessor paths is calculated, which is the sum of the vortex contribution values ​​of all edges in the path.

5. According to the blockchain-based archival information analysis and management method of claim 4, it is characterized by: The node resonance frequency ratio evaluates the degree of spectrum alignment between the node execution efficiency and the theoretical optimal execution time through frequency domain analysis; first, for each node, its recent actual execution time series is collected , and calculate its difference with the theoretical optimal time series The difference between the actual execution time of the node and the ideal execution time is quantified by performing frequency domain analysis on the two time series through fast Fourier transform and extracting the energy difference of the first five frequency bands: ;in and The actual and theoretical execution time series are The amplitude of the frequency domain component; then, the spectral gradient norm of the actual execution time series of the node is calculated; finally, the node resonance frequency amplitude ratio It is calculated as the ratio of the difference energy and the gradient norm.

6. According to the blockchain-based archival information analysis and management method of claim 5, it is characterized by: For each task, first calculate the vortex density of its dependent path, and then combine the two to obtain the resonance degree of the corresponding task and node pairing based on the node resonance frequency-amplitude ratio of the node. Based on the calculation results, select the temporary transition node set that is most suitable for executing the current task; sort the candidate nodes according to the task topology resonance value, and select the top M nodes with the highest task topology resonance value. When the task topology resonance values ​​of multiple nodes are close to the standard, the node with the lowest consensus offset inertia factor value is selected to perform the task.

7. According to the blockchain-based archival information analysis and management method of claim 6, it is characterized in that: Step S3 includes the following contents: First, the temporary transition node set will compare the hash value in the archive index stored on the blockchain with the hash value of the actual archive data in the off-chain storage. The hash comparison calculates the hash value of the current archive content and matches it with the hash fingerprint recorded on the blockchain. If the two are inconsistent, the temporary transition node set immediately marks the data as incomplete or invalid; if the data comparison, signature verification and permission verification are all passed, the temporary transition node set will continue to retain the update record and generate a verification log containing the data difference and permission status.

8. The archival information analysis and management method based on blockchain according to claim 7 is characterized in that: Step S4 specifically includes the following contents: The temporary transition node set collectively votes on the verification results of each task according to the predetermined consensus algorithm to confirm whether the update conditions are met; the temporary transition node set analyzes the generated verification log and determines the acceptable range of the verification results based on the set fault tolerance threshold; when the temporary transition node set reaches a consensus through voting and all verification tasks pass the fault tolerance threshold test, the temporary transition node set will generate an executable token.

9. The archival information analysis and management method based on blockchain according to claim 8 is characterized in that: Step S5 includes the following contents: The smart contract starts the data synchronization process by receiving and verifying the executable token. First, the smart contract verifies the legitimacy of the token, including checking whether the signature of the token is valid and whether the data in the token is consistent. At the same time, the contract sends an update instruction to the off-chain storage system, which includes the data to be updated and the hash value of the new version. After receiving the instruction, the off-chain storage system will actually update the archive data and generate an updated archive hash value. This hash value is then returned to the smart contract, which will verify the hash value returned from the off-chain again. If the verification is successful, the contract will solidify the new archive index and data hash on the blockchain, completing the data synchronization and solidification process to ensure the integrity and consistency of the archive data. If data inconsistency or verification errors occur at any stage, the contract will roll back the update operation.

10. A blockchain-based archive information analysis and management system, used to implement a blockchain-based archive information analysis and management method according to any one of claims 1 to 9, characterized in that: include: Task processing module, dependency assessment module, data verification module, consensus decision module and synchronization solidification module; Task processing module: receives the file update request submitted by the user, extracts the index information and hash fingerprint of the target file in the blockchain, obtains the current file content and version information in the off-chain storage, generates atomic update tasks and passes them to the dependency evaluation module; Dependency evaluation module: By analyzing the dependency and execution order of the update tasks, evaluating the complexity of each task path and the stability of node execution, matching the temporary transition node set with corresponding processing capabilities, and sending the temporary task transition node set to the data verification module; Data verification module: Perform hash comparison on archive integrity through temporary transition node set, implement multi-level rule verification of user operation permissions, generate verification log containing data difference and permission status, and pass the verification log to consensus decision module; Consensus decision module: Combined with the verification log, the preset fault tolerance threshold is used to trigger the consensus mechanism. Through dynamic weight voting, an executable token carrying the data version number and transaction tracking code is generated, and the executable token is passed to the synchronization and curing module. Synchronization and curing module: A synchronization processing unit based on executable tokens that activates smart contracts. When the blockchain updates the archive metadata fingerprint, it sends a double hash matching update instruction to the off-chain storage to complete cross-chain data consistency curing.

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