Blockchain-based cultural relic data storage and access authorization management method

Abstract

The application discloses a cultural relic data storage and access authorization management method based on a block chain, relates to the technical field of database storage, and comprises the following steps: based on a cultural relic block alliance chain member table, evaluating access permissions of each member of the cultural relic block alliance chain, constructing an authorized link network of the cultural relic block alliance chain, analyzing relative target cultural relic access actions of each alliance chain member in a historical access log, obtaining a cultural relic node access behavior knowledge graph in the authorized link network, training a cultural relic access path reasonable behavior model, obtaining a cultural relic block alliance chain authorized link network compliance access path vector, constructing a cultural relic access path abnormal behavior model, marking an authorized link network abnormal access path in a real-time access log, and updating the access permissions of each member of the cultural relic block alliance chain.The cultural relic data access security and operation and maintenance efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of database storage technology, specifically to a blockchain-based method for storing and managing the access authorization of cultural relics data. Background Technology

[0002] Existing blockchain-based data storage and access authorization for cultural relics relies on subjective manual review and static role allocation for permission assessment. This leads to a lack of comparability of permissions among heterogeneous institutions and an inability to dynamically adjust permissions based on member behavior, resulting in a rigid permission system. Access control treats each record as an isolated on-chain event, ignoring the contextual relationships and continuous patterns between access behaviors, and fails to identify the essential differences between legitimate research paths and malicious probing behaviors. Anomaly detection uses static rule sets and hard thresholds, lacking data support and a tiered handling mechanism. It can only achieve post-event auditing, and the determination of critical points is significantly unfair. Minor misoperations and malicious attacks are treated with the same strategy, seriously affecting user experience and system security. Summary of the Invention

[0003] To address the aforementioned technical issues, this technical solution provides a blockchain-based method for storing and authorizing access to cultural relics data.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: Blockchain-based methods for storing and authorizing access to cultural relics include: S1. Based on the member table of the cultural relics blockchain consortium chain, obtain the structured data of the contract attributes of each consortium chain member, evaluate the access permissions of each member of the cultural relics blockchain consortium chain, and construct the authorization link network of the cultural relics blockchain consortium chain. S2. Based on the historical access logs of each consortium chain member, analyze the access actions of each consortium chain member relative to the target cultural relic in the historical access logs, obtain the knowledge graph of the access behavior of each cultural relic node in the authorized link network, train the reasonable behavior model of cultural relic access path, and obtain the compliant access path vector of the authorized link network of the cultural relic block consortium chain. S3. Obtain the real-time access logs of each consortium blockchain member, construct an abnormal behavior model of cultural relic access path according to the authorized link network compliant access path vector of the cultural relic blockchain consortium blockchain, mark the abnormal access path of the authorized link network in the real-time access logs, and update the access permissions of each member of the cultural relic blockchain consortium blockchain.

[0005] Preferably, step S1 specifically includes: Obtain structured data of contract attributes of each consortium blockchain member, standardize numerical and textual data according to Min-Max normalization and one-hot encoding, and build a global information pool of contract attributes of consortium blockchain members. Based on the global information pool of contract attributes of the consortium blockchain members, the proportion of the individual contract attributes of each off-chain member in the global information pool of contract attributes is traversed, and the entropy of the structured data information of the contract attributes of each member of the chain is calculated. Based on the entropy of the structured data of the contract attributes of each member of the chain, the contribution value of the individual contract attributes of each member of the off-chain under the global information pool of contract attributes is traversed and standardized to obtain the structured data weight of the contract attributes of each member of the chain. By weighting the structured data of the contract attributes of each off-chain member with the structured data of the contract attributes of each off-chain member, the dynamic permission weight of each off-chain member is obtained. Based on fuzzy logic-based permission hierarchy, several permission levels are preset. The dynamic permission weights of each member in the chain are used as input. The members are filtered according to the membership function. Through defuzzification, the dynamic permission weight of the highest membership level is selected to obtain the access permissions of each member of the cultural relics blockchain alliance chain.

[0006] Preferably, step S1 further includes: Based on the digital cultural relics database, a list of access rules for several known cultural relics is obtained and standardized to obtain structured data of access rules for several known cultural relics. Based on the Analytic Hierarchy Process (AHP), a quantitative function for access requirements of digital cultural relics is established, with the known access requirements of several cultural relics as the target layer, the known access rule list of several cultural relics as the quasi-test layer, and the known cultural relics as the option layer. The quantification function for access requirements of digital cultural relics includes: Based on a known list of access rules for several cultural relics, the importance of each access rule relative to other rules is recursively compared, and weight ratios are assigned to the known lists of access rules for several cultural relics to construct a weight ratio discrimination matrix for access rule lists. Based on the access rule list weight ratio discrimination matrix, the weight ratio of each access rule list is normalized by dividing it by the sum of the columns, and the normalized values ​​of each row are averaged to obtain the access rule weights of several known cultural relics. Then, a weighted formula for access requirements of cultural relics is established with the access rule list of several known cultural relics to quantify the access rule permission requirement vector of several known cultural relics. Based on the member table of the cultural relics blockchain consortium chain and the access rule list of several known cultural relics, the members of the cultural relics blockchain consortium chain are used as access member nodes, the access permissions of each member of the cultural relics blockchain consortium chain are used as access member node attributes, the accessed cultural relic nodes of several known cultural relics are used, and the access rule permission requirement vector of several known cultural relics is used as accessed cultural relic node attributes. According to fuzzy logic, the access member node attributes are used as access conditions, and the access member node attributes are used as access conditions to establish the initial authorization link network of the cultural relics blockchain consortium chain.

[0007] Preferably, step S2 specifically includes: Based on the historical access logs of each consortium blockchain member, using the access action timestamp as the anchor point and the unit time as the observation window, the historical access logs are used as the observation object to extract the historical access action time series of each consortium blockchain member. Seasonal differences are used to preprocess the historical access action time series of each consortium blockchain member, and an ARIMA autoregressive moving average model is trained. The order of the autoregressive term and the order of the moving average term are determined by the autocorrelation function ACF and the partial autocorrelation function PACF to conduct an adaptability test. The historical access action is used as input and the predicted future access action per unit time is used as output. Based on the Bayesian network and the authorization link network of the initial cultural relic block consortium chain, an edge path is established between the accessing member node and the accessed cultural relic node to predict the prior distribution of future access actions per unit time for each accessed cultural relic node, and the posterior probability of each accessing member node relative to the accessed cultural relic node is given under the historical access action time series; the knowledge graph of the access behavior of each cultural relic node in the authorization link network is determined.

[0008] Preferably, step S2 further includes: A rational behavior model for cultural relic access paths is constructed using a GNN-LSTM hybrid network. Based on the reasonable behavior model of cultural relic access paths, the features of each accessing member node and the accessed cultural relic node in the knowledge graph of access behavior of each cultural relic node in the authorized link network are used as input to the GNN graph neural network. Through the graph convolution layer, the features of each node in the graph are updated by graph convolution operation to obtain the access relationship between each accessing member node and the accessed cultural relic node. According to the information transmission mechanism, the features of the accessing member node are exchanged to obtain the context information of each accessing member node and the accessed cultural relic node in the knowledge graph of access behavior of each cultural relic node in the authorized link network per unit time. Based on the reasonable behavior model of cultural relic access paths, and using the LSTM long short-term memory network, we analyze the time-varying patterns of access actions of each member of each chain in the knowledge graph of access behavior of cultural relic nodes in the authorized link network. Through memory units and gating mechanisms, we capture the dependencies of access behavior as it increases over time, and predict the future access behavior of each consortium chain member in the knowledge graph of access behavior of cultural relic nodes in the authorized link network per unit time. Based on the reasonable behavior model of cultural relic access paths, and according to the fusion layer, the context information of each accessing member node and the accessed cultural relic node in the knowledge graph of access behavior of each cultural relic node in the authorized link network under a unit time, and the future access behavior of each consortium chain member under a unit time are weighted and averaged to generate the access path vector between each accessing member node and the accessed cultural relic node under a unit time, thus obtaining the compliant access path vector of the authorized link network of the cultural relic block consortium chain.

[0009] Preferably, step S3 specifically includes: Based on the authorized link network compliant access path vector of the cultural relics blockchain consortium chain, the access action timestamps of the access member nodes and the accessed cultural relics nodes are marked, and the structured feature data of the access behavior of the access member nodes and the structured tag data of the accessed cultural relics nodes are extracted per unit time. Based on the real-time access logs of each consortium blockchain member, data standardization processing is performed to obtain the standard structured data of the real-time access logs of each consortium blockchain member, the structured feature data of the access behavior of accessing member nodes per unit time, and the structured label data of the accessed cultural relic nodes, thus constructing the original real-time access dataset of each consortium blockchain member. Based on Random Forest, a decision tree for the access behavior of each member in the cultural relic blockchain is established. The structured feature data of the access behavior of member nodes per unit time is used as the access decision branch node, and the structured label data of the accessed cultural relic node is used as the leaf node of the access path to build an abnormal behavior model of cultural relic access path. The model of abnormal behavior of cultural relic access paths uses the real-time access raw dataset of each consortium chain member as input and the real-time access path vector of the authorized link network of the cultural relic block consortium chain per unit time as output.

[0010] Preferably, step S3 further includes: Based on the authorized link network compliant access path vector of the cultural relics blockchain consortium chain, the 95th percentile of the authorized link network compliant access path vector of each off-chain member is calculated to determine the initial threshold of the authorized link network compliant access path vector. Based on the Euclidean distance formula, the spatial distance between the real-time access path vector of the authorized link network of the cultural relics blockchain alliance chain and the compliant access path vector of the authorized link network of the cultural relics blockchain alliance chain under a unit time is calculated, and the spatial distance is normalized to obtain the deviation value of the real-time access path vector of the authorized link network of the cultural relics blockchain alliance chain relative to the compliant access path vector of the authorized link network under a unit time. Based on a nonlinear piecewise function, and through square root transformation, the nonlinear risk value between the deviation of the real-time access path vector of the authorized link network of the cultural relics blockchain from the compliant access path vector of the authorized link network per unit time and the initial threshold of the compliant access path vector of the authorized link network is calculated. A piecewise exponential decay function is then established to update the access permissions of each member of the cultural relics blockchain, specifically: , in, Let t represent the access permissions of each member of the cultural relics blockchain consortium at time t. Initial access permissions for each member of the cultural relics blockchain consortium. Let be the nonlinear risk value between the deviation of the real-time access path vector of the authorized link network of the cultural relics blockchain relative to the compliant access path vector of the authorized link network at the t-th unit time and the initial threshold of the compliant access path vector of the authorized link network. Let be the deviation of the real-time access path vector of the authorized link network of the cultural relics blockchain relative to the compliant access path vector of the authorized link network at the t-th unit time. Let be the initial threshold for the authorized link network compliant access path vector in the t-th unit of time. for Weight, It is a natural constant.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes a blockchain-based method for managing the storage and access authorization of cultural relics. Based on an information entropy and fuzzy logic-based member permission evaluation system and an AHP (Automatic Guided Access) mechanism for quantifying cultural relic needs, it establishes an objective and flexible human-object matching authorization link network. Through an access behavior prediction and spatiotemporal feature fusion model, it generates quantitatively expressed compliant access path vectors. Combining real-time abnormal access path identification using random forests and a nonlinear exponential decay permission control mechanism, it achieves autonomous identification, accurate quantification, and dynamic feedback of abnormal behavior. This solves the problems of subjective rigidity, lack of spatiotemporal correlation of access behavior, and delayed abnormal response in traditional permission management, significantly improving the security and operational efficiency of cultural relic data access. Attached Figure Description

[0012] Figure 1 This is a flowchart of a blockchain-based method for storing and authorizing access to cultural relics. Detailed Implementation

[0013] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0014] Reference Figure 1 As shown, the blockchain-based method for storing and authorizing access to cultural relics includes: S1. Based on the member table of the cultural relics blockchain consortium chain, obtain the structured data of the contract attributes of each consortium chain member, evaluate the access permissions of each member of the cultural relics blockchain consortium chain, and construct the authorization link network of the cultural relics blockchain consortium chain. Step S1 specifically includes: Obtain structured data of contract attributes of each consortium blockchain member, standardize numerical and textual data according to Min-Max normalization and one-hot encoding, and build a global information pool of contract attributes of consortium blockchain members. Based on the global information pool of contract attributes of the consortium blockchain members, the proportion of the individual contract attributes of each off-chain member in the global information pool of contract attributes is traversed, and the entropy of the structured data information of the contract attributes of each member of the chain is calculated. Based on the entropy of the structured data of the contract attributes of each member of the chain, the contribution value of the individual contract attributes of each member of the off-chain under the global information pool of contract attributes is traversed and standardized to obtain the structured data weight of the contract attributes of each member of the chain. By weighting the structured data of the contract attributes of each off-chain member with the structured data of the contract attributes of each off-chain member, the dynamic permission weight of each off-chain member is obtained. Based on fuzzy logic-based permission hierarchy, several permission levels are preset. The dynamic permission weights of each member in the chain are used as input. The members are filtered according to the membership function. Through defuzzification, the dynamic permission weight of the highest membership level is selected to obtain the access permissions of each member of the cultural relics blockchain alliance chain.

[0015] As a further explanation, the membership function hierarchical filtering, through the specific process of defuzzification, includes: Step 1: Preset several permission levels: ; Step 2: Define the level boundary using triangular membership functions: , , , in, Let be the dynamic permission weight of the i-th member off-chain. , , The membership functions are in the order of high, medium, and low levels; Step 3: Deblurring operation:

[0016] in, This represents the access permissions of the i-th member in the chain.

[0017] Step S1 also includes: Based on the digital cultural relics database, a list of access rules for several known cultural relics is obtained and standardized to obtain structured data of access rules for several known cultural relics. Based on the Analytic Hierarchy Process (AHP), a quantitative function for access requirements of digital cultural relics is established, with the known access requirements of several cultural relics as the target layer, the known access rule list of several cultural relics as the quasi-test layer, and the known cultural relics as the option layer. The quantification function for access requirements of digital cultural relics includes: Based on a known list of access rules for several cultural relics, the importance of each access rule relative to other rules is recursively compared, and weight ratios are assigned to the known lists of access rules for several cultural relics to construct a weight ratio discrimination matrix for access rule lists. As a further explanation, the importance of each access rule relative to other rules is determined by a recursive comparison of the opinions of experts from various cultural heritage conservation centers. The importance of each access rule relative to other rules is also determined by the opinions of at least three experts to ensure that the implementation of the access rules can reasonably reflect the protection needs of cultural relics.

[0018] Based on the access rule list weight ratio discrimination matrix, the weight ratio of each access rule list is normalized by dividing it by the sum of the columns, and the normalized values ​​of each row are averaged to obtain the access rule weights of several known cultural relics. Then, a weighted formula for access requirements of cultural relics is established with the access rule list of several known cultural relics to quantify the access rule permission requirement vector of several known cultural relics. Based on the member table of the cultural relics blockchain consortium chain and the access rule list of several known cultural relics, the members of the cultural relics blockchain consortium chain are used as access member nodes, the access permissions of each member of the cultural relics blockchain consortium chain are used as access member node attributes, the accessed cultural relic nodes of several known cultural relics are used, and the access rule permission requirement vector of several known cultural relics is used as accessed cultural relic node attributes. According to fuzzy logic, the access member node attributes are used as access conditions, and the access member node attributes are used as access conditions to establish the initial authorization link network of the cultural relics blockchain consortium chain.

[0019] When using it, please refer to the steps outlined above: As a further development, this scheme utilizes information entropy to achieve objective and dynamic calibration of attribute weights, avoiding the subjectivity of subjective weight assignment in traditional methods. Simultaneously, it introduces a triangular membership function based on fuzzy logic for flexible permission grading, bridging the gap between precise numerical values ​​and fuzzy concepts. Furthermore, it combines AHP (Analytic Hierarchy Process) to quantify the experience of cultural relic protection experts into access requirements for cultural relics, ultimately establishing an initial authorization link network for "person-object" matching. Compared to existing schemes, the dynamic attribute weight self-calibration mechanism based on information entropy enables permission assessment to adaptively adjust to changes in the composition of the consortium blockchain; and the flexible permission grading based on the triangular membership function replaces the traditional hard threshold division, significantly improving the robustness of permission boundaries and the rationality of decision-making.

[0020] S2. Based on the historical access logs of each consortium chain member, analyze the access actions of each consortium chain member relative to the target cultural relic in the historical access logs, obtain the knowledge graph of the access behavior of each cultural relic node in the authorized link network, train the reasonable behavior model of cultural relic access path, and obtain the compliant access path vector of the authorized link network of the cultural relic block consortium chain. Step S2 specifically includes: Based on the historical access logs of each consortium blockchain member, using the access action timestamp as the anchor point and the unit time as the observation window, the historical access logs are used as the observation object to extract the historical access action time series of each consortium blockchain member. Seasonal differences are used to preprocess the historical access action time series of each consortium blockchain member, and an ARIMA autoregressive moving average model is trained. The order of the autoregressive term and the order of the moving average term are determined by the autocorrelation function ACF and the partial autocorrelation function PACF to conduct an adaptability test. The historical access action is used as input and the predicted future access action per unit time is used as output. Based on the Bayesian network and the authorization link network of the initial cultural relic block consortium chain, an edge path is established between the accessing member node and the accessed cultural relic node to predict the prior distribution of future access actions per unit time for each accessed cultural relic node, and the posterior probability of each accessing member node relative to the accessed cultural relic node is given under the historical access action time series; the knowledge graph of the access behavior of each cultural relic node in the authorization link network is determined.

[0021] Step S2 also includes: A rational behavior model for cultural relic access paths is constructed using a GNN-LSTM hybrid network. Based on the reasonable behavior model of cultural relic access paths, the features of each accessing member node and the accessed cultural relic node in the knowledge graph of access behavior of each cultural relic node in the authorized link network are used as input to the GNN graph neural network. Through the graph convolution layer, the features of each node in the graph are updated by graph convolution operation to obtain the access relationship between each accessing member node and the accessed cultural relic node. According to the information transmission mechanism, the features of the accessing member node are exchanged to obtain the context information of each accessing member node and the accessed cultural relic node in the knowledge graph of access behavior of each cultural relic node in the authorized link network per unit time. Based on the reasonable behavior model of cultural relic access paths, and using the LSTM long short-term memory network, we analyze the time-varying patterns of access actions of each member of each chain in the knowledge graph of access behavior of cultural relic nodes in the authorized link network. Through memory units and gating mechanisms, we capture the dependencies of access behavior as it increases over time, and predict the future access behavior of each consortium chain member in the knowledge graph of access behavior of cultural relic nodes in the authorized link network per unit time. Based on the reasonable behavior model of cultural relic access paths, and according to the fusion layer, the context information of each accessing member node and the accessed cultural relic node in the knowledge graph of access behavior of each cultural relic node in the authorized link network under a unit time, and the future access behavior of each consortium chain member under a unit time are weighted and averaged to generate the access path vector between each accessing member node and the accessed cultural relic node under a unit time, thus obtaining the compliant access path vector of the authorized link network of the cultural relic block consortium chain.

[0022] When using it, please refer to the steps outlined above: As a further step, existing access control only records whether a single access event occurs, failing to capture the inherent connections between visits to different cultural relics. Step S2 constructs an access behavior knowledge graph, transforming scattered log records into a graph structure of nodes and edges, revealing potential association patterns between members and cultural relics, thus elevating access behavior analysis from isolated events to the network topology level. The ARIMA model extracts the periodic and trend features of time series data, and a Bayesian network is used to calculate posterior probabilities, transforming historical access patterns into probabilistic predictions of future access behaviors. This enables the system to anticipate members' access intentions rather than passively responding to every request, achieving predictable modeling of access behavior. The GNN layer extracts the spatial dependencies between members and cultural relics in the authorization link network, while the LSTM layer captures the long-term dependency patterns of access behavior evolving over time. Integrating spatial dependencies and long-term dependency patterns allows the model to simultaneously understand "who is associated with whom" and "how the behavior evolves," forming a complete spatiotemporal cognitive access path vector for access behavior. This vector serves as a benchmark template for subsequent anomaly detection, enabling the system to quantitatively assess the degree of deviation between real-time access behavior and compliance patterns.

[0023] S3. Obtain the real-time access logs of each consortium blockchain member, construct an abnormal behavior model of cultural relic access path according to the authorized link network compliant access path vector of the cultural relic blockchain consortium blockchain, mark the abnormal access path of the authorized link network in the real-time access logs, and update the access permissions of each member of the cultural relic blockchain consortium blockchain. Step S3 specifically includes: Based on the authorized link network compliant access path vector of the cultural relics blockchain consortium chain, the access action timestamps of the access member nodes and the accessed cultural relics nodes are marked, and the structured feature data of the access behavior of the access member nodes and the structured tag data of the accessed cultural relics nodes are extracted per unit time. Based on the real-time access logs of each consortium blockchain member, data standardization processing is performed to obtain the standard structured data of the real-time access logs of each consortium blockchain member, the structured feature data of the access behavior of accessing member nodes per unit time, and the structured label data of the accessed cultural relic nodes, thus constructing the original real-time access dataset of each consortium blockchain member. Based on Random Forest, a decision tree for the access behavior of each member in the cultural relic blockchain is established. The structured feature data of the access behavior of member nodes per unit time is used as the access decision branch node, and the structured label data of the accessed cultural relic node is used as the leaf node of the access path to build an abnormal behavior model of cultural relic access path. The model of abnormal behavior of cultural relic access paths uses the real-time access raw dataset of each consortium chain member as input and the real-time access path vector of the authorized link network of the cultural relic block consortium chain per unit time as output.

[0024] Step S3 also includes: Based on the authorized link network compliant access path vector of the cultural relics blockchain consortium chain, the 95th percentile of the authorized link network compliant access path vector of each off-chain member is calculated to determine the initial threshold of the authorized link network compliant access path vector. Based on the Euclidean distance formula, the spatial distance between the real-time access path vector of the authorized link network of the cultural relics blockchain alliance chain and the compliant access path vector of the authorized link network of the cultural relics blockchain alliance chain under a unit time is calculated, and the spatial distance is normalized to obtain the deviation value of the real-time access path vector of the authorized link network of the cultural relics blockchain alliance chain relative to the compliant access path vector of the authorized link network under a unit time. Based on a nonlinear piecewise function, and through square root transformation, the nonlinear risk value between the deviation of the real-time access path vector of the authorized link network of the cultural relics blockchain from the compliant access path vector of the authorized link network per unit time and the initial threshold of the compliant access path vector of the authorized link network is calculated. A piecewise exponential decay function is then established to update the access permissions of each member of the cultural relics blockchain, specifically: , in, Let t represent the access permissions of each member of the cultural relics blockchain consortium at time t. Initial access permissions for each member of the cultural relics blockchain consortium. Let be the nonlinear risk value between the deviation of the real-time access path vector of the authorized link network of the cultural relics blockchain relative to the compliant access path vector of the authorized link network at the t-th unit time and the initial threshold of the compliant access path vector of the authorized link network. Let be the deviation of the real-time access path vector of the authorized link network of the cultural relics blockchain relative to the compliant access path vector of the authorized link network at the t-th unit time. Let be the initial threshold for the authorized link network compliant access path vector in the t-th unit of time. for Weight, It is a natural constant.

[0025] As a further explanation, The initial threshold for the compliant access path vector of the authorized link network per unit time is to recalculate the historical data quantiles every three months through an A / B test, in order to adapt to changes in the access behavior patterns of off-chain members relative to cultural relics.

[0026] When using it, please refer to the steps outlined above: As a further development, an abnormal behavior recognition model is constructed based on random forest ensemble learning. A decision tree ensemble mechanism maps real-time access features to real-time access path vectors. Euclidean distance is used to calculate the spatial deviation between the real-time path and the compliant path, and a 95th percentile statistical threshold is combined to quantitatively assess the degree of abnormality. A nonlinear risk control mechanism is established using square root transformation and a piecewise exponential decay function, converting the deviation value into dynamic permission update weights and feeding them back to the initial authorization network. This achieves millisecond-level autonomous identification of abnormal access, establishes a differentiated quantitative system ranging from minor transgressions to serious violations, balances user experience and system security through nonlinear permission control, forms a closed-loop adaptive evolution capability, significantly reduces manual auditing costs and false alarm rates, and provides defensive protection for digital cultural relics assets.

[0027] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A blockchain-based method for storing and authorizing access to cultural relics data, characterized in that: include: S1. Based on the member table of the cultural relics blockchain consortium chain, obtain the structured data of the contract attributes of each consortium chain member, evaluate the access permissions of each member of the cultural relics blockchain consortium chain, and construct the authorization link network of the cultural relics blockchain consortium chain. S2. Based on the historical access logs of each consortium chain member, analyze the access actions of each consortium chain member relative to the target cultural relic in the historical access logs, obtain the knowledge graph of the access behavior of each cultural relic node in the authorized link network, train the reasonable behavior model of cultural relic access path, and obtain the compliant access path vector of the authorized link network of the cultural relic block consortium chain. S3. Obtain the real-time access logs of each consortium blockchain member, construct an abnormal behavior model of cultural relic access path according to the authorized link network compliant access path vector of the cultural relic blockchain consortium blockchain, mark the abnormal access path of the authorized link network in the real-time access logs, and update the access permissions of each member of the cultural relic blockchain consortium blockchain.

2. The method for storing and authorizing access to cultural relics based on blockchain according to claim 1, characterized in that, Step S1 specifically includes: Obtain structured data of contract attributes of each consortium blockchain member, standardize numerical and textual data according to Min-Max normalization and one-hot encoding, and build a global information pool of contract attributes of consortium blockchain members. Based on the global information pool of contract attributes of the consortium blockchain members, the proportion of the individual contract attributes of each off-chain member in the global information pool of contract attributes is traversed, and the entropy of the structured data information of the contract attributes of each member of the chain is calculated. Based on the entropy of the structured data of the contract attributes of each member of the chain, the contribution value of the individual contract attributes of each member of the off-chain under the global information pool of contract attributes is traversed and standardized to obtain the structured data weight of the contract attributes of each member of the chain. By weighting the structured data of the contract attributes of each off-chain member with the structured data of the contract attributes of each off-chain member, the dynamic permission weight of each off-chain member is obtained. Based on fuzzy logic-based permission hierarchy, several permission levels are preset. The dynamic permission weights of each member in the chain are used as input. The members are filtered according to the membership function. Through defuzzification, the dynamic permission weight of the highest membership level is selected to obtain the access permissions of each member of the cultural relics blockchain alliance chain.

3. The method for storing and authorizing access to cultural relics based on blockchain according to claim 2, characterized in that, Step S1 also includes: Based on the digital cultural relics database, a list of access rules for several known cultural relics is obtained and standardized to obtain structured data of access rules for several known cultural relics. Based on the Analytic Hierarchy Process (AHP), a quantitative function for access requirements of digital cultural relics is established, with the known access requirements of several cultural relics as the target layer, the known access rule list of several cultural relics as the quasi-test layer, and the known cultural relics as the option layer. The quantification function for access requirements of digital cultural relics includes: Based on a known list of access rules for several cultural relics, the importance of each access rule relative to other rules is recursively compared, and weight ratios are assigned to the known lists of access rules for several cultural relics to construct a weight ratio discrimination matrix for access rule lists. Based on the access rule list weight ratio discrimination matrix, the weight ratio of each access rule list is normalized by dividing it by the sum of the columns, and the normalized values ​​of each row are averaged to obtain the access rule weights of several known cultural relics. Then, a weighted formula for access requirements of cultural relics is established with the access rule list of several known cultural relics to quantify the access rule permission requirement vector of several known cultural relics. Based on the member table of the cultural relics blockchain consortium chain and the access rule list of several known cultural relics, the members of the cultural relics blockchain consortium chain are used as access member nodes, the access permissions of each member of the cultural relics blockchain consortium chain are used as access member node attributes, the accessed cultural relic nodes of several known cultural relics are used, and the access rule permission requirement vector of several known cultural relics is used as accessed cultural relic node attributes. According to fuzzy logic, the access member node attributes are used as access conditions, and the access member node attributes are used as access conditions to establish the initial authorization link network of the cultural relics blockchain consortium chain.

4. The method for storing and authorizing access to cultural relics based on blockchain according to claim 3, characterized in that, Step S2 specifically includes: Based on the historical access logs of each consortium blockchain member, using the access action timestamp as the anchor point and the unit time as the observation window, the historical access logs are used as the observation object to extract the historical access action time series of each consortium blockchain member. Seasonal differences are used to preprocess the historical access action time series of each consortium blockchain member, and an ARIMA autoregressive moving average model is trained. The order of the autoregressive term and the order of the moving average term are determined by the autocorrelation function ACF and the partial autocorrelation function PACF to conduct an adaptability test. The historical access action is used as input and the predicted future access action per unit time is used as output. Based on the Bayesian network and the authorization link network of the initial cultural relic block consortium chain, an edge path is established between the accessing member node and the accessed cultural relic node to predict the prior distribution of future access actions per unit time for each accessed cultural relic node, and the posterior probability of each accessing member node relative to the accessed cultural relic node is given under the historical access action time series; the knowledge graph of the access behavior of each cultural relic node in the authorization link network is determined.

5. The method for storing and authorizing access to cultural relics based on blockchain according to claim 4, characterized in that, Step S2 also includes: A rational behavior model for cultural relic access paths is constructed using a GNN-LSTM hybrid network. Based on the reasonable behavior model of cultural relic access paths, the features of each accessing member node and the accessed cultural relic node in the knowledge graph of access behavior of each cultural relic node in the authorized link network are used as input to the GNN graph neural network. Through the graph convolution layer, the features of each node in the graph are updated by graph convolution operation to obtain the access relationship between each accessing member node and the accessed cultural relic node. According to the information transmission mechanism, the features of the accessing member node are exchanged to obtain the context information of each accessing member node and the accessed cultural relic node in the knowledge graph of access behavior of each cultural relic node in the authorized link network per unit time. Based on the reasonable behavior model of cultural relic access paths, and using the LSTM long short-term memory network, we analyze the time-varying patterns of access actions of each member of each chain in the knowledge graph of access behavior of cultural relic nodes in the authorized link network. Through memory units and gating mechanisms, we capture the dependencies of access behavior as it increases over time, and predict the future access behavior of each consortium chain member in the knowledge graph of access behavior of cultural relic nodes in the authorized link network per unit time. Based on the reasonable behavior model of cultural relic access paths, and according to the fusion layer, the context information of each accessing member node and the accessed cultural relic node in the knowledge graph of access behavior of each cultural relic node in the authorized link network under a unit time, and the future access behavior of each consortium chain member under a unit time are weighted and averaged to generate the access path vector between each accessing member node and the accessed cultural relic node under a unit time, thus obtaining the compliant access path vector of the authorized link network of the cultural relic block consortium chain.

6. The method for storing and authorizing access to cultural relics based on blockchain according to claim 5, characterized in that, Step S3 specifically includes: Based on the authorized link network compliant access path vector of the cultural relics blockchain consortium chain, the access action timestamps of the access member nodes and the accessed cultural relics nodes are marked, and the structured feature data of the access behavior of the access member nodes and the structured tag data of the accessed cultural relics nodes are extracted per unit time. Based on the real-time access logs of each consortium blockchain member, data standardization processing is performed to obtain the standard structured data of the real-time access logs of each consortium blockchain member, the structured feature data of the access behavior of accessing member nodes per unit time, and the structured label data of the accessed cultural relic nodes, thus constructing the original real-time access dataset of each consortium blockchain member. Based on Random Forest, a decision tree for the access behavior of each member in the cultural relic blockchain is established. The structured feature data of the access behavior of member nodes per unit time is used as the access decision branch node, and the structured label data of the accessed cultural relic node is used as the leaf node of the access path to build an abnormal behavior model of cultural relic access path. The model of abnormal behavior of cultural relic access paths uses the real-time access raw dataset of each consortium chain member as input and the real-time access path vector of the authorized link network of the cultural relic block consortium chain per unit time as output.

7. The method for storing and authorizing access to cultural relics based on blockchain according to claim 6, characterized in that, Step S3 also includes: Based on the authorized link network compliant access path vector of the cultural relics blockchain consortium chain, the 95th percentile of the authorized link network compliant access path vector of each off-chain member is calculated to determine the initial threshold of the authorized link network compliant access path vector. Based on the Euclidean distance formula, the spatial distance between the real-time access path vector of the authorized link network of the cultural relics blockchain alliance chain and the compliant access path vector of the authorized link network of the cultural relics blockchain alliance chain under a unit time is calculated, and the spatial distance is normalized to obtain the deviation value of the real-time access path vector of the authorized link network of the cultural relics blockchain alliance chain relative to the compliant access path vector of the authorized link network under a unit time. Based on a nonlinear piecewise function, and through square root transformation, the nonlinear risk value between the deviation of the real-time access path vector of the authorized link network of the cultural relics blockchain from the compliant access path vector of the authorized link network per unit time and the initial threshold of the compliant access path vector of the authorized link network is calculated. A piecewise exponential decay function is then established to update the access permissions of each member of the cultural relics blockchain, specifically: in, Let t represent the access permissions of each member of the cultural relics blockchain consortium at time t. Initial access permissions for each member of the cultural relics blockchain consortium. Let be the nonlinear risk value between the deviation of the real-time access path vector of the authorized link network of the cultural relics blockchain relative to the compliant access path vector of the authorized link network at the t-th unit time and the initial threshold of the compliant access path vector of the authorized link network. Let be the deviation of the real-time access path vector of the authorized link network of the cultural relics blockchain relative to the compliant access path vector of the authorized link network at the t-th unit time. Let be the initial threshold for the authorized link network compliant access path vector in the t-th unit of time. for Weight, It is a natural constant.

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