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

Abstract

The present invention discloses a blockchain-based file information analysis and management system and method, which specifically relates to the field of file management and is used to solve the data consistency problem during task update. Through task dependency analysis, node execution evaluation, and smart contract synchronization mechanism, it can efficiently and stably execute file update tasks, ensure the optimization of the order of task execution and resource allocation, and thus avoid 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, it can effectively reduce the occurrence of bottleneck paths and ensure the efficient processing of key tasks; at the same time, with the help of the consensus mechanism and double hash matching, it guarantees the consistency and integrity of on-chain and off-chain data, ultimately improving the overall execution efficiency, data synchronization accuracy, and operation security of the system, and effectively reducing potential execution risks and bottlenecks.

Description

Technical Field

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

[0002] In traditional file management systems, as the amount of file data continues to increase, the synchronization and update of data both on-chain and off-chain become particularly complex. Especially in a distributed storage environment, on-chain data (such as metadata and indexes) and off-chain data (such as file contents) often need to be synchronized and updated 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 operation of the two is vulnerable to network latency, node failures, or execution order issues, resulting in data inconsistency or update failures. 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] To overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a blockchain-based file information analysis and management system and method. Through task dependency analysis, node execution evaluation, and smart contract synchronization mechanism, it can efficiently and stably execute file update tasks, ensure the optimization of the execution order and resource allocation of tasks, and thus avoid delays and errors caused by the complexity of task dependency paths or unstable node executions; by dynamically selecting the most suitable temporary transition nodes, combining path complexity and node stability, it can effectively reduce the occurrence of bottleneck paths and ensure efficient processing of critical tasks; at the same time, with the help of consensus mechanisms and double-hash matching, it ensures the consistency and integrity of on-chain and off-chain data, ultimately improving the overall execution efficiency, data synchronization accuracy, and operation security of the system, effectively reducing potential execution risks and bottlenecks, so as to solve the problems raised in the above background art.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A blockchain-based file information analysis and management method, including the steps of:

[0006] S1: Receive a user file update request, extract the index information and hash fingerprint of the target file in the blockchain, and obtain the current file content and version information stored off-chain, and generate an atomic update task;

[0007] S2: By analyzing the dependency relationships and execution order of the update tasks, evaluate the complexity of each task path and the stability of node executions, and match a set of temporary transition nodes with corresponding processing capabilities;

[0008] 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;

[0009] 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;

[0010] 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.

[0011] In a preferred embodiment, step S1 includes the following contents:

[0012] 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.

[0013] In a preferred embodiment, step S2 includes the following contents:

[0014] 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.

[0015] 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, calculate the vortex density of all its predecessor paths, that is, the sum of the vortex contribution values of all edges in the path.

[0016] In a preferred embodiment, the node resonance frequency amplitude ratio evaluates the spectral alignment degree between the node execution efficiency and the theoretical optimal execution time through frequency domain analysis; First, for each node, collect its recent actual execution time series , and calculate its difference from the theoretical optimal time series ; Perform frequency domain analysis on these two time series through fast Fourier transform, and extract the energy differences of the first 5 frequency bands to quantify the deviation between the actual execution time and the ideal execution time of the node: ; Where and are the amplitudes of the th frequency domain components of the actual and theoretical execution time series respectively; Then, calculate the spectral gradient norm of the node actual execution time series; Finally, the node resonance frequency amplitude ratio is calculated by the ratio of the difference energy to the gradient norm.

[0017] In a preferred embodiment, for each task, first calculate the vortex density of its dependency path, and based on the node resonance frequency amplitude ratio value of the node, combine the two to obtain the resonance degree of the corresponding task-node pairing, and screen out the set of temporary transition nodes most suitable for executing the current task according to the calculation results; Sort the candidate nodes according to the task topology resonance degree value, and select the top M nodes with the highest task topology resonance degree value. When the task topology resonance degree values of multiple nodes are similar and meet the standard, preferentially select the node with the lowest consensus offset inertia factor value to execute the task.

[0018] In a preferred embodiment, step S3 includes the following content:

[0019] First, the set of temporary transition nodes will compare the hash value in the archive index stored on the blockchain with the hash value of the actual archive data stored outside the chain. The hash comparison is performed by calculating the hash value of the current archive content and matching it with the hash fingerprint recorded on the blockchain. If the two are inconsistent, the set of temporary transition nodes will immediately mark the data as incomplete or invalid; If the data comparison, signature verification, and permission verification all pass, the set of temporary transition nodes will continue to retain this update record and generate a verification log containing the data difference degree and permission status.

[0020] In a preferred embodiment, step S4 specifically includes the following content:

[0021] The temporary transition node set makes a collective voting decision on the verification results of each task according to a 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 inspection of the fault tolerance threshold, the temporary transition node set will generate an executable token.

[0022] In a preferred embodiment, step S5 includes the following:

[0023] The smart contract starts the data synchronization process by receiving and verifying the executable token; first, the smart contract verifies the legality 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, and the instruction content 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 the hash value of the updated archive; subsequently, this hash value will be returned to the smart contract, and the contract 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 to complete the data synchronization and solidification process, ensuring the integrity and consistency of the archive data; if data inconsistency or verification error occurs at any stage, the contract will roll back this update operation.

[0024] A blockchain-based archive information analysis and management system includes: a task processing module, a dependency evaluation module, a data verification module, a consensus decision module, and a synchronization and solidification module;

[0025] The task processing module: receives the archive update request submitted by the user, extracts the index information and hash fingerprint of the target archive in the blockchain, and obtains the current archive content and version information in the off-chain storage, and generates atomized update tasks and passes them to the dependency evaluation module;

[0026] The dependency evaluation module: evaluates the complexity of each task path and the stability of node execution by analyzing the dependency relationship and execution order of the update tasks, matches the temporary transition node set with the corresponding processing ability, and sends the temporary task transition node set to the data verification module;

[0027] The data verification module: performs hash comparison on the archive integrity through the temporary transition node set, implements multi-level rule verification of user operation permissions, generates a verification log containing the data difference degree and permission status, and passes the verification log to the consensus decision module;

[0028] 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.

[0029] 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.

[0030] The technical effects and advantages of the blockchain-based archive information analysis and management system and method of the present invention are as follows:

[0031] 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

[0032] Figure 1 This is a flowchart of a blockchain-based archival information analysis and management method of the present invention;

[0033] 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

[0034] 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.

[0035] Embodiment 1:Figure 1 A method for analyzing and managing archive information based on blockchain according to the present invention is provided, including:

[0036] S1: Receive a user archive update request, extract the index information and hash fingerprint of the target archive in the blockchain, and obtain the current archive content and version information stored outside the chain, and generate an atomic update task;

[0037] S2: By analyzing the dependency relationship and execution order of the update tasks, evaluate the complexity of each task path and the stability of node execution, and match a set of temporary transition nodes with corresponding processing capabilities;

[0038] S3: Perform a hash comparison on the archive integrity through the set of temporary transition nodes, implement multi-level rule verification of user operation permissions, and generate a verification log including the data difference degree and permission status;

[0039] S4: Combine the verification log and trigger a consensus mechanism using a preset fault tolerance threshold, and generate an executable token carrying a data version number and a transaction tracking code through dynamic weight voting decision-making;

[0040] S5: Activate the synchronization processing unit of the smart contract based on the executable token, and send a double-hash matching update instruction to the off-chain storage while updating the archive metadata fingerprint in the blockchain to complete the cross-chain data consistency solidification.

[0041] In archive 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 to avoid inconsistencies or errors, it is necessary to comprehensively collect and preprocess update requests at the initial stage of the update operation. Step S1 mainly lays a solid foundation for subsequent verification, decision-making, and execution steps by parsing the user update request and combining the archive information in the blockchain and off-chain storage.

[0042] Step S1 includes the following:

[0043] First, receive the user's file update request, and extract the unique identifier of the target file (such as file number or ID) according to the request content. Then, through the blockchain smart contract, query and extract the metadata of the file on the blockchain, including the index information and hash fingerprint of the file, which are used for subsequent version verification and consistency check. At the same time, the system queries and obtains the actual content and its version information of the file from off-chain storage through the interface to ensure the consistency between off-chain data and on-chain index information. All the obtained file information, including the index on the blockchain, hash fingerprint, content and version information in off-chain storage, will be unified and aggregated to construct an atomic update task, which contains the unique identifier of the task, data version, update requirements and related operation types. This atomic update task will be used as the input for subsequent steps to ensure the coherence and consistency of data updates.

[0044] Step S2 includes the following:

[0045] When selecting the temporary transition node set, it is necessary to first construct a task dependency graph because the task dependency graph can clearly identify the execution order and dependency relationships among various subtasks, thus providing an accurate basis for subsequent node selection. Through the dependency graph, the system can identify which tasks are on the critical path, which tasks have complex dependencies, and which paths may become bottlenecks. Only by understanding the topological structure and dependency relationships of the tasks in advance can the system reasonably allocate resources according to the complexity and importance of the tasks and select the most suitable temporary transition nodes to execute different tasks. If the task dependency graph is not constructed first, it is impossible to accurately identify the priority order of task execution and resource requirements, which may lead to improper task execution, path blockage, or unstable nodes being assigned to critical tasks, thus 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.

[0046] The construction process of the task dependency graph first decomposes the file update request submitted by the user into multiple subtasks. Each subtask represents a verification or execution link, such as data integrity verification, permission verification, hash matching, etc. Each subtask will serve as a node in the task dependency graph, and the dependency relationships between subtasks will be connected by directed edges, indicating the task execution order. Next, weights are assigned to each edge. The weight of each edge is used to represent the urgency of the task dependency and the importance of the task to the final update result. The weight is calculated through weighted calculation 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 subsequent task execution order and node selection, ensuring that complex and critical path tasks are executed first and avoiding potential bottleneck risks. At the same time, the system performs a topological sort on the task dependency graph to ensure that each subtask is executed only after its dependent predecessor tasks are completed, thereby ensuring the orderliness and coherence of the update operation.

[0047] Based on the task dependency graph, path complexity information and node task execution stability information are calculated. Based on the path complexity information and the node task execution stability information, the resonance degree of the task-node pairing is obtained by combination, and the task topological resonance degree is comprehensively calculated to screen out the set of temporary transition nodes most suitable for executing the current task.

[0048] Among them, the path complexity information includes the vortex density of the dependency path, and the node task execution stability information includes the node resonance amplitude ratio.

[0049] Vortex density of the dependency path It is used to evaluate the complexity of the critical path and the potential bottleneck effect in the task dependency graph. First, the weight of each edge in the task dependency graph is marked as . Then, the depth of each edge is calculated. Its depth is determined by the level where the edge is located. 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 calculated through ranking . The edges with higher rankings will be amplified by the hyperbolic cosine function in subsequent calculations to highlight the clustering effect of the critical path. The vortex contribution value of each edge is calculated according to the following formula: ;

[0050] This formula indicates that the edges with greater path depth and higher edge weights contribute more 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 vortex density of the dependency path usually indicate the existence of more dependency overlaps or the aggregation effect of the core task path, which may form a bottleneck. Therefore, nodes with higher stability need to be allocated to execute these paths to avoid execution blocking.

[0051] The node resonance frequency amplitude ratio evaluates the spectral alignment degree between the node execution efficiency and the theoretical optimal execution time through frequency domain analysis. First, for each node, collect its recent actual execution time series , and calculate its difference from the theoretical optimal time series . Perform frequency domain analysis on these two time series through fast Fourier transform, and extract the energy difference of the first 5 frequency bands to quantify the deviation between the actual execution time consumption and the ideal execution time consumption of the node: ; where and are the amplitudes of the th frequency domain components of the actual and theoretical execution time series respectively. Then, calculate the spectral gradient norm of the node's actual execution time series to suppress the influence of high-frequency noise on the execution effect: ;

[0052] Finally, the node resonance frequency amplitude ratio is calculated by the ratio of the difference energy to the gradient norm: ; Nodes with a lower node resonance frequency amplitude ratio indicate that the distribution of their actual execution time is close to the theoretical optimal time, with better execution stability and low-frequency noise, and are suitable for executing tasks with high timing requirements.

[0053] The task topology resonance degree 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 degree takes into account the vortex density of the path and the resonance frequency amplitude ratio of the node. For each task, first calculate the vortex density of its dependency path, and combine the two according to the node resonance frequency amplitude ratio of the node to obtain the resonance degree of the pairing of this task and the node:

[0054] ;

[0055] where is the smoothing factor, usually taking the mean value of the vortex density of all task paths to prevent numerical overflow caused by too small denominator. The higher the task topology resonance degree of the node and path combination, the higher the stability and lower the execution delay of the node in the execution of this task path. The system will preferentially select these combinations for execution. This calculation process ensures that the path complexity and the node execution stability are effectively balanced in node selection, avoiding the interference of bottleneck paths and unstable nodes.

[0056] After the calculation of the task topology resonance degree is completed, the most suitable set of temporary transition nodes for executing the current task is selected according to the calculation results. First, hard condition filtering is performed to exclude candidate nodes with insufficient resources or low reputation. Then, the candidate nodes are sorted according to the task topology resonance degree value, and the top M nodes with the highest task topology resonance degree values are selected. These nodes are considered to be the most suitable for executing the current task and have high path processing capabilities and execution stability. When the task topology resonance degree values of multiple nodes are similar and meet the standard, dynamic tuning is performed according to the consensus deviation inertia factor, and the node with the lowest consensus deviation inertia factor value is preferentially selected to avoid lags or errors during system execution. Through this screening process, the system can accurately select temporary transition nodes according to the complexity of the task and the stability of the nodes, maximizing the task execution efficiency and reducing potential execution delays and risks.

[0057] The process of obtaining the consensus deviation inertia factor is to calculate the average deviation between the execution time of a node in historical tasks and the synchronization time of other nodes, and combine it with the failure rate of the node to obtain the deviation inertia of the node relative to the ideal execution state, reflecting its execution stability and potential lag errors.

[0058] Secondly, during dynamic tuning, when the task topology resonance degree values of multiple nodes are similar and meet the standard, the node with the lowest consensus deviation inertia factor value is preferentially selected to execute the task, ensuring the synchronization and stability during task execution and reducing potential lags and errors.

[0059] By screening to obtain the set of temporary transition nodes, it can ensure that the system selects nodes with stable execution and small consensus synchronization deviations when executing tasks, thereby improving the accuracy and efficiency of the task execution process. Nodes with a low consensus deviation inertia factor have small execution deviations and low delay risks, which helps to reduce lags and errors during task execution. Especially when dealing with complex paths or critical tasks, it can avoid performance bottlenecks caused by unstable node execution. This screening mechanism not only optimizes the use of system resources by preferentially allocating high-stability nodes, but also ensures the smoothness of the task execution process and improves the overall task processing ability and reliability of the system.

[0060] Step S3 includes the following content:

[0061] The temporary transition node set conducts integrity verification, legality confirmation, and feasibility identification on the collected archival data. First, the temporary transition node set will compare the hash value in the archival index stored on the blockchain with the hash value of the actual archival data stored outside the chain to ensure that the off-chain data has not been tampered with or lost. The hash comparison is performed by calculating the hash value of the current archival content and matching it with the hash fingerprint recorded on the blockchain. If the two do not match, the temporary transition node set immediately marks the data as incomplete or invalid and stops the subsequent process. Secondly, the digital signature attached to the archival update request will be verified to check whether the signature matches the identity of the requester and confirm the legality of the request. If the signature is invalid or does not match, the temporary transition node set will mark this operation as rejected. Then, multi-level rule verification of the user's access rights is carried out to check whether the operation permissions in the update request comply with the preset rules, such as whether there is the right to update a specific archival type, whether the updated fields match the archival type, and whether it conforms to the role permissions, etc. If the permissions are insufficient, the operation will also be rejected. If the data comparison, signature verification, and permission verification all pass, the temporary transition node set will continue to retain this update record and generate a verification log containing the data difference degree and permission status. The log content includes the verification result of data hash consistency, signature verification status, and permission verification result. Finally, these verification results will be written into the verifiable log maintained by the temporary transition node set for reference in the subsequent decision consensus stage. If all verifications pass, the temporary transition node set will pass the verification results to the subsequent steps for further decision-making.

[0062] Step S4 specifically includes the following content:

[0063] The temporary transition node set makes a collective voting decision 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. This threshold is used to determine the maximum error or inconsistency allowed during the verification process, ensuring that the system can continue to execute even 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 inspection of the fault tolerance threshold, the temporary transition node set will generate an executable token. This token includes the following key information: a unique transaction identifier used to distinguish each update operation; a data hash used to ensure the data integrity and consistency of the update operation; operation permission information used to confirm the legitimacy of executing the update operation; and security verification information used to ensure the authenticity of the token's identity and the legitimacy of the operation. The generated executable token serves as a voucher for subsequent update operations, ensuring agreement on the same operation among multiple nodes and enabling secure execution. If there are disagreements during the consensus process or the verification results do not pass the fault tolerance threshold, the token will not be generated. At this time, the temporary transition node set records the specific reasons for the failure, including the subtasks with verification failures and the specific items that do not pass the fault tolerance threshold, and passes them to the relevant management module through the log system for analysis. In this way, the generation of the token ensures the security and effectiveness of the entire update process, avoiding potential risks caused by consensus disagreements or verification failures.

[0064] Step S5 includes the following:

[0065] The smart contract starts the data synchronization process by receiving and validating executable tokens. First, the smart contract validates the legality of the tokens, including checking whether the signature of the tokens is valid and whether the data in the tokens is consistent. For example, the contract will verify whether the data hash in the tokens matches the hash value stored on the blockchain to confirm that the update conditions of the task are met. After passing the validation, the contract updates the archival metadata on the blockchain according to the instructions in the tokens, including key information such as the storage location and version number of the archives. At the same time, the contract sends an update instruction to the off-chain storage system, and the instruction content 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 archival data and generate the hash value of the updated archives. The off-chain storage system then returns this hash value to the smart contract, and the contract will verify the hash value returned off-chain again to ensure the consistency between the off-chain data and the on-chain data. The verification process includes comparing whether the off-chain hash value is consistent 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 archival index and data hash on the blockchain to complete the data synchronization and solidification process, ensuring the integrity and consistency of the archival data. If data inconsistency or verification errors occur at any stage, the contract will roll back the current update operation to ensure the atomicity of the entire process and prevent incorrect data from being permanently recorded. Through this step, the on-chain and off-chain data are synchronized and updated to ensure the consistency and integrity of the archival data in the blockchain and the off-chain storage system.

[0066] Embodiment 2: Figure 2 A blockchain-based archival information analysis and management system of the present invention is provided, including: a task processing module, a dependency evaluation module, a data verification module, a consensus decision module, and a synchronization and solidification module;

[0067] The task processing module: receives the archival update request submitted by the user, extracts the index information and hash fingerprint of the target archive in the blockchain, and obtains the current archival content and version information in the off-chain storage, and generates an atomic update task to be passed to the dependency evaluation module;

[0068] The dependency evaluation module: by analyzing the dependency relationship and execution order of the update tasks, evaluates the complexity of each task path and the stability of node execution, matches a set of temporary transition nodes with corresponding processing capabilities, and sends the set of temporary task transition nodes to the data verification module;

[0069] The data verification module: performs hash comparison on the archival integrity through the set of temporary transition nodes, implements multi-level rule verification of user operation permissions, generates a verification log containing the data difference degree and permission status, and passes the verification log to the consensus decision module;

[0070] Consensus decision-making module: Trigger the consensus mechanism by combining the verification log with a preset fault tolerance threshold, and generate an executable token carrying the data version number and transaction tracking code through dynamic weight voting decision-making, and pass the executable token to the synchronization and solidification module;

[0071] Synchronization and solidification module: Activate the synchronization processing unit of the smart contract based on the executable token, and send a double-hash matching update instruction to off-chain storage while updating the metadata fingerprint of the blockchain archive to complete cross-chain data consistency solidification.

[0072] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0073] Only some exemplary embodiments of the present invention have been described by way of illustration above. Undoubtedly, for those of ordinary skill in the art, without departing from the spirit and scope of the present invention, the described embodiments can be modified in various different ways. 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.

[0074] It should be noted that in this article, if there are relational terms such as first and second, 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 "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0075] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to 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; 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, 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; 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 contributions of all edges in the path; 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 by the ratio of the difference energy to the gradient norm; 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 according to the node resonance frequency-amplitude ratio of the node, and select the temporary transition node set that is most suitable for executing the current task according to the calculation results; sort the candidate nodes according to the task topology resonance value, and select the first 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; 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 1, 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.

4. According to the blockchain-based archival information analysis and management method of claim 3, it 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.

5. According to the blockchain-based archival information analysis and management method of claim 4, it is characterized in that: Step S5 Includes the following: 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.

6. A blockchain-based archive information analysis and management system, used to implement a blockchain-based archive information analysis and management method as described in any one of claims 1 to 5, 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 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.

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