Data storage and verification system based on block chain technology

Abstract

The invention discloses a block chain technology-based data storage and verification system, which relates to the technical field of block chains and comprises a data management module, a data storage module, an identity authentication module and an access control module. According to the invention, the data storage module generates the timestamp for the feature data and stores the timestamp on the block chain, and because the block chain has a non-tampering characteristic, the stored data cannot be easily modified once being written, so that the integrity and authenticity of the data are ensured; and the access control module further manages the authority of the query operation, so that the authority requirement is met when the user checks the data, the unauthorized access is effectively prevented, and the security of the data is improved.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a data storage and verification system based on blockchain technology. Background Art

[0002] Blockchain is a decentralized chain data structure, whose core features are the immutability, scalability and full synchronization of data. Through the immutability of on-chain data, once the data is added to the blockchain, it will be difficult to be changed or deleted, ensuring the security and credibility of the data. At the same time, the scalability of the blockchain structure allows new data to be continuously added to the existing chain, thereby accommodating a large amount of transaction information. Whenever a new transaction or data is added to the block, the information is automatically propagated to the entire network, ensuring that all nodes obtain the latest data in real time, so that all network participants have a consistent view of all transaction data on the chain. This structure not only enhances the fault tolerance of the system, but also removes the reliance on centralization, thereby reducing the risk of single point failure. The data verification mechanism of the blockchain enables the system to operate in a trustless environment, which is suitable for data storage and verification in multiple scenarios such as finance, logistics, and supply chain.

[0003] In the prior art, it is not easy to generate timestamps for feature data and store them on the blockchain, it is not easy to ensure the integrity and authenticity of the data, and it is not easy to authenticate the data verifier, so that it cannot be ensured that only authorized users can access the stored data. It cannot be guaranteed that users meet the authority requirements when checking data, which reduces the security of the data.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In response to the problems in the related technology, the present invention proposes a data storage and verification system based on blockchain technology to overcome the above-mentioned technical problems existing in the existing related technology.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] A data storage and verification system based on blockchain technology, the data storage and verification system comprising:

[0008] The data management module is used to interact with the blockchain, obtain the original data that needs to be stored, and pre-process the original data that needs to be stored to obtain the characteristic data of the original data;

[0009] A data storage module, used to generate a timestamp for the obtained feature data, and store the generated timestamp and the obtained feature data on the blockchain;

[0010] The identity authentication module is used to authenticate and record the identity of the data checker to ensure that the access user is authorized;

[0011] The access control module is responsible for the permission management and access control of query operations to ensure that data verification is consistent with the authorization management of the blockchain.

[0012] Furthermore, the data management module includes:

[0013] The data acquisition module is used to interact with the blockchain in advance and obtain the original data that needs to be stored;

[0014] The data processing module is used to extract vital signs from the original data to be stored to obtain characteristic data.

[0015] Furthermore, the raw data to be stored is subjected to physical sign extraction, and the characteristic data obtained include:

[0016] Perform multi-scale decomposition on the original data to be stored to generate feature data layers of different scales;

[0017] In the feature data hierarchy of each scale, local extreme points are identified by comparing adjacent data points and marked as preliminary feature points;

[0018] For each preliminary feature point, calculate the gradient and direction of its adjacent data points, generate a feature point direction distribution map, and determine the main direction of the feature point through the peak value of the direction distribution map;

[0019] Taking each feature point as the center, construct a feature point neighborhood, and calculate the gradient direction and amplitude within the feature point neighborhood;

[0020] The calculated gradient direction and amplitude are combined into a feature vector as a descriptor of the feature point to perform feature recognition and obtain the feature data of the original data.

[0021] Furthermore, the data storage module includes:

[0022] A timestamp generation module, used to create an unalterable timestamp according to the time when the feature data is generated, and associate the timestamp with the feature data;

[0023] The blockchain storage module is used to record the feature data and the generated timestamp as blocks of blockchain records and send them to the blockchain network for storage.

[0024] Furthermore, creating an unalterable timestamp based on the time when the feature data is generated, and associating the timestamp with the feature data includes:

[0025] When feature data is generated, the current time is recorded and used as the timestamp basis of the feature data;

[0026] Use the timestamp mechanism of the blockchain to convert the current time recorded into a preset timestamp format;

[0027] The generated timestamp is used as the meta-information of the feature data and is appended to the feature data to form a complete feature data record including the timestamp.

[0028] Furthermore, the identity authentication module includes:

[0029] An identity verification module, used to receive the identity information of the data verifier and confirm the identity of the verifier through biometric recognition technology;

[0030] The authorization filing module is used to perform authorization checks on verified persons based on anomaly detection algorithms and record access behaviors for future inspection.

[0031] Furthermore, based on the behavior anomaly detection algorithm, the identity of the verifier is checked for authorization, and the access behavior is recorded for inspection, including:

[0032] Based on the behavioral data set of the checker, a behavioral feature matrix is ​​constructed, and the behavioral feature matrix is ​​used to capture the key behavioral attributes of the checker's behavior;

[0033] Based on the behavior feature matrix, the behavior feature vector corresponding to the zero eigenvalue is identified;

[0034] Based on each behavior vector of the behavior data set of the checker, the behavior weight vector of the checker is calculated, and the key behavior feature vector is extracted from the calculated behavior weight vector;

[0035] Construct a behavior transformation matrix based on the key behavior feature vector, analyze the changes of the inspector in different behavior dimensions, and extract the corresponding key behavior feature values ​​through the behavior transformation matrix;

[0036] Based on the key feature vector, a behavior feature space is constructed, and the behavior feature vector of the checker is projected into the behavior feature space, and a preset classification algorithm is used to determine the category to which each checker belongs in the behavior feature space;

[0037] According to the inspector's performance and category in the behavioral feature space, the behavioral analysis results of each inspector are output. Based on the inspector's behavioral analysis results, the inspector whose identity has been confirmed is authorized and the access behavior is recorded for inspection.

[0038] Furthermore, the preset classification algorithm is used to determine the category of each checker in the behavior feature space, including:

[0039] Initialize the parameters of the classification model, each parameter represents a candidate classification model solution;

[0040] Evaluate the parameters of each classification model, calculate its classification accuracy in the behavioral feature space, and select the classification model with the highest classification accuracy as the central classification model;

[0041] Adjust the parameter set of the non-central classification model and determine the number of new parameters to be generated based on the respective classification errors;

[0042] Based on the current classification model parameters, a new classification model parameter set is generated, and boundary detection is performed on the new classification model parameter set;

[0043] Mutate the selected classification model parameter set using Gaussian mutation to generate an additional parameter set;

[0044] From all current classification model parameters, select the classification model parameters with the lowest classification accuracy to continue iteration, and record the classification model with the highest classification accuracy;

[0045] Check whether the preset number of iterations has been reached. If so, stop the iteration and record the classification model with the highest classification accuracy as the final classification result. Output the classification result and mark the category of each inspector.

[0046] Furthermore, the formula for boundary detection of the new classification model parameter set is:

[0047]

[0048] In the formula, Represents the value of the parameter in the a-th new classification model parameter set after boundary detection in the d-th dimension;

[0049] M represents the parameters in the new classification model parameter set;

[0050] M LB，d Represents the lower boundary of the parameters in the new classification model parameter set in the dth dimension;

[0051] M UB，d Represents the upper boundary of the parameters in the new classification model parameter set in the dth dimension.

[0052] Furthermore, the access control module includes:

[0053] The permission verification module is used to verify the authority of the verifier when performing the query operation and check the identity of the verifier and its corresponding permission level;

[0054] The access control policy module is used to ensure that data verification is consistent with the authorization management of the blockchain by interacting with blockchain data and dynamically updating access rights based on the identity of the verifier and its corresponding permission level.

[0055] The beneficial effects of the present invention are:

[0056] 1. The present invention generates a timestamp for feature data through a data storage module and stores it on a blockchain. Since the blockchain has the characteristic of being tamper-proof, the stored data cannot be easily modified once written, which ensures the integrity and authenticity of the data. It is suitable for data storage scenarios that require high security. The data verifier is authenticated through the identity authentication module to ensure that only authorized users can access the stored data. The access control module further manages the permissions for query operations to ensure that users meet permission requirements when checking data. This multi-level access control mechanism effectively prevents unauthorized access and improves data security. The distributed structure of the blockchain allows data to be stored on multiple nodes, avoiding the risk of single point failure. At the same time, the data synchronization mechanism between nodes ensures the high reliability and availability of the system.

[0057] 2. The present invention uses biometric recognition technology to verify the identity of the verifier, which can effectively prevent the occurrence of identity fraud. Compared with traditional password or token authentication, biometric recognition can more accurately confirm the user's identity and reduce the risk of identity fraud. By introducing a behavioral anomaly detection algorithm, the verifier's behavior is monitored in real time through the analysis of the user's behavior feature matrix. This monitoring mechanism can capture the verifier's key behavioral attributes and conduct in-depth analysis of its behavior patterns to identify abnormal behavior. If it is detected that the behavior deviates from expectations, the system can block access or strengthen authorization verification in real time, thereby greatly improving the security of the system and preventing potential malicious behavior.

[0058] 3. The present invention initializes multiple classification models and continuously iterates and optimizes classification parameters. The system can adaptively select the most appropriate classification model to achieve the highest classification accuracy. It not only improves the accuracy of classifying the behavioral characteristics of the inspector, but also dynamically adapts to the behavioral characteristics of different inspectors, which helps to accurately identify legitimate users and abnormal actors. It introduces multiple candidate classification models, selects the optimal classification center model by comparing the classification accuracy of each model, and adjusts the non-central model parameters. It can effectively respond to changes in behavioral characteristics and potential security threats, generate additional parameter sets through Gaussian mutation, and the system explores a wider set of solutions in the parameter space, which helps to find a more adaptive classification model. The performance of the classification model is further optimized, so that the judgment of the inspector's behavior category is more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1 It is a principle block diagram of a data storage and verification system based on blockchain technology according to an embodiment of the present invention.

[0061] In the figure:

[0062] 1. Data management module; 2. Data storage module; 3. Identity authentication module; 4. Access control module. DETAILED DESCRIPTION

[0063] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0064] According to an embodiment of the present invention, a data storage and verification system based on blockchain technology is provided.

[0065] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to a data storage and verification system based on blockchain technology according to an embodiment of the present invention, the data storage and verification system includes:

[0066] Data management module 1 is used to interact with the blockchain, obtain the original data to be stored, and pre-process the original data to be stored to obtain the characteristic data of the original data;

[0067] Specifically, the ways to interact with the blockchain include:

[0068] Smart Contract: The data management module can store and access data by calling smart contracts on the blockchain. Smart contracts can define the data storage format, permission control, and data processing logic. The module writes data to the blockchain or reads data from the blockchain by calling the relevant methods of the contract.

[0069] Blockchain node API: Most blockchains provide standardized API interfaces, such as JSON-RPC or RESTAPI. Data management modules can use these interfaces to interact with blockchain nodes to complete data uploading and query.

[0070] Event monitoring mechanism: By monitoring specific events on the blockchain, the data management module can obtain relevant data when specified conditions (such as new transactions, block generation, etc.) are triggered, thereby updating or synchronizing data in real time.

[0071] Specifically, the content of the raw data depends on the specific application scenario, including:

[0072] User behavior data: such as user operation records, access time, device information, etc.

[0073] Transaction information: such as transaction records and payment information in the financial system.

[0074] Document or file data: such as contracts, vouchers, electronic invoices, and other data that need to be verified for authenticity.

[0075] Sensor or device data: In the IoT scenario, raw data may come from real-time data collected by sensors.

[0076] Specifically, the characteristic data of the original data include:

[0077] Data hash value: The original data is hashed to obtain a unique fingerprint, which can be stored on the blockchain and used for subsequent verification.

[0078] Timestamp: records the time point when data is generated or stored, used to track the order and source of data.

[0079] Identifier: such as user ID and device ID, used to associate the source of original data.

[0080] Statistical features: Statistical information such as the mean and variance of behavioral data can reflect the characteristics of the data without storing all the detailed information.

[0081] Classification or grading information: Label information extracted after classifying the raw data, such as classifying transaction data into categories such as "high risk" and "low risk".

[0082] Data storage module 2, used to generate a timestamp for the obtained feature data, and store the generated timestamp and the obtained feature data on the blockchain;

[0083] Identity authentication module 3, used to authenticate and record the identity of the data checker to ensure that the access user is authorized;

[0084] Access control module 4 is responsible for the authority management and access control of query operations to ensure that data verification is consistent with the authorization management of the blockchain.

[0085] Preferably, the data management module 1 includes:

[0086] The data acquisition module is used to interact with the blockchain in advance and obtain the original data that needs to be stored;

[0087] The data processing module is used to extract vital signs from the original data to be stored to obtain characteristic data.

[0088] Preferably, the raw data to be stored is subjected to vital sign extraction to obtain characteristic data including:

[0089] Perform multi-scale decomposition (i.e., Gaussian pyramid decomposition) on the original data to be stored to generate feature data hierarchies of different scales;

[0090] Specifically, multi-scale decomposition refers to decomposing the original data at different scales to obtain feature representations of the data at different levels of detail.

[0091] In the feature data hierarchy of each scale, local extreme points are identified by comparing adjacent data points and marked as preliminary feature points;

[0092] For each preliminary feature point, calculate the gradient and direction of its adjacent data points, generate a feature point direction distribution map, and determine the main direction of the feature point through the peak value of the direction distribution map;

[0093] Taking each feature point as the center, construct a feature point neighborhood, and calculate the gradient direction and amplitude within the feature point neighborhood;

[0094] The calculated gradient direction and amplitude are combined into a feature vector as a descriptor of the feature point to perform feature recognition and obtain the feature data of the original data.

[0095] Preferably, the data storage module 2 comprises:

[0096] A timestamp generation module, used to create an unalterable timestamp according to the time when the feature data is generated, and associate the timestamp with the feature data;

[0097] The blockchain storage module is used to record the feature data and the generated timestamp as blocks of blockchain records and send them to the blockchain network for storage.

[0098] Preferably, creating an unalterable timestamp according to the time when the characteristic data is generated, and associating the timestamp with the characteristic data comprises:

[0099] When feature data is generated, the current time is recorded and used as the timestamp basis of the feature data;

[0100] Use the timestamp mechanism of the blockchain to convert the current time recorded into a preset timestamp format;

[0101] The generated timestamp is used as the meta-information of the feature data and is appended to the feature data to form a complete feature data record including the timestamp.

[0102] Specifically, a timestamp is a mark of a specific time point, which is used to record the exact time when the feature data is generated or stored. The purpose of the timestamp is to provide an unalterable time record for the feature data, so that in any subsequent inspection or verification process, the timestamp can be used to trace the generation time of the data and its storage history.

[0103] Specifically, the timestamp mechanism refers to converting the current time of the record into a standardized timestamp format and ensuring that the generated timestamp cannot be tampered with. In the blockchain, the settings of the timestamp mechanism include:

[0104] Time synchronization: Nodes in the blockchain network need to maintain time synchronization to ensure that all participants have consistent records of the same point in time. Time synchronization can be achieved through methods such as the Network Time Protocol (NTP).

[0105] Block generation timestamp: In the blockchain, each block usually carries a timestamp, which indicates the time when the block was generated or confirmed. The blockchain uses this mechanism to record the exact time when the data was written, ensuring the order and time reliability of the data.

[0106] Immutability: Due to the immutable nature of blockchain, once data is recorded in the blockchain and timestamped, it is difficult to change. This makes the timestamp a guarantee of the immutable history of the data, ensuring that the data has a highly credible time record throughout its life cycle.

[0107] Standardization of timestamp format: Timestamp mechanisms usually specify a specific timestamp format (such as ISO 8601) to ensure that all nodes store and parse timestamps consistently. This standardization ensures cross-platform use of data on the blockchain and facilitates subsequent verification and inspection.

[0108] Preferably, the identity authentication module 3 includes:

[0109] An identity verification module, used to receive the identity information of the data verifier and confirm the identity of the verifier through biometric recognition technology;

[0110] Specifically, biometric identification technology refers to the technology that uses the unique biological characteristics of the human body to confirm the identity of an individual. Biometric identification technology verifies the identity of the person by collecting, analyzing and comparing the biometric data of the person being checked, including:

[0111] Fingerprint recognition: Use the unique texture of the fingerprint to confirm identity. Everyone's fingerprint is unique, even twins' fingerprints are not exactly the same. It is widely used and suitable for many occasions with high security requirements, such as mobile phone unlocking, attendance system, etc.

[0112] Face recognition: Identity verification is performed by capturing and analyzing facial features (such as eye distance, nose shape, chin contour, etc.). This technology has been widely used in security systems, mobile phone unlocking, public place monitoring and other scenarios.

[0113] Iris recognition: uses the unique pattern of the iris to confirm identity. The complex structure and invariance of the iris make it highly accurate and secure. Iris recognition technology is often used in high-security situations such as banks and the military.

[0114] Retinal scanning: uses the blood vessel pattern of the eye's retina to identify individuals. This type of identification is extremely difficult to forge and is very secure. Due to the high cost of the equipment, it is mostly used for high-level security authentication.

[0115] Voiceprint recognition: Confirm identity by analyzing voice characteristics (such as frequency, pitch, intonation, etc.). Everyone's voice is unique, and voiceprint recognition can distinguish subtle differences. It is suitable for remote identity authentication and is often used in application scenarios such as telephone banking and voice assistants.

[0116] Palm print and palm vein recognition: Palm print recognition confirms identity through the lines on the palm, while palm vein recognition recognizes the vein pattern inside the palm. Palm vein recognition requires special equipment and has higher security. It is often used in places with high security requirements such as access control systems and bank self-service equipment.

[0117] Gait recognition: Uses the individual's walking style and posture characteristics to identify the individual. Each person's gait is unique and can be effectively identified even at a long distance. Gait recognition has potential applications in monitoring, security and criminal investigation.

[0118] The authorization filing module is used to perform authorization checks on verified persons based on anomaly detection algorithms and record access behaviors for future inspection.

[0119] Preferably, based on the behavior anomaly detection algorithm, authorization check is performed on the verified identity checker, and the access behavior is recorded for inspection, including:

[0120] Based on the behavioral data set of the checker, a behavioral feature matrix is ​​constructed, and the behavioral feature matrix is ​​used to capture the key behavioral attributes of the checker's behavior;

[0121] Based on the behavior feature matrix, the behavior feature vector corresponding to the zero eigenvalue is identified;

[0122] Based on each behavior vector of the behavior data set of the checker, the behavior weight vector of the checker is calculated, and the key behavior feature vector is extracted from the calculated behavior weight vector;

[0123] Construct a behavior transformation matrix based on the key behavior feature vector, analyze the changes of the inspector in different behavior dimensions, and extract the corresponding key behavior feature values ​​through the behavior transformation matrix;

[0124] Based on the key feature vector, a behavior feature space is constructed, and the behavior feature vector of the checker is projected into the behavior feature space, and a preset classification algorithm is used to determine the category to which each checker belongs in the behavior feature space;

[0125] According to the inspector's performance and category in the behavioral feature space, the behavioral analysis results of each inspector are output. Based on the inspector's behavioral analysis results, the inspector whose identity has been confirmed is authorized and the access behavior is recorded for inspection.

[0126] Specifically, the behavior anomaly detection algorithm is an improved linear discriminant subspace pattern recognition algorithm (I-VLDNS algorithm), which is an improvement on the traditional linear discriminant analysis (LDA). Aiming at the subspace optimization and "small sample" problems commonly found in existing pattern discriminant analysis methods, the physical meaning of the null space of the three scatter matrices of the overall, intra-class and inter-class distribution matrices is first analyzed, the core principles of the effective null space and the effective linear discriminant null space are deeply explained, and the relevant theoretical propositions are proved; secondly, the correlation between the inter-class scatter matrices in different subspaces is studied; finally, based on the theory of effective linear discriminant null space, an improved linear discriminant subspace pattern recognition algorithm (I-VLDNS algorithm) is obtained, which conducts in-depth structural and pattern analysis on the behavior data of the inspector, and improves the accuracy of identifying and classifying abnormal behaviors by understanding the key features and change patterns of the behavior data. This improved algorithm can capture the behavioral characteristics of the inspector more accurately, has a higher degree of distinction in identifying normal and abnormal behaviors, ensures the effectiveness of the authorization inspection, and provides comprehensive behavior analysis records for subsequent inspections.

[0127] Preferably, using a preset classification algorithm to determine the category of each checker in the behavior feature space includes:

[0128] Initialize the parameters of the classification model, each parameter represents a candidate classification model solution;

[0129] Evaluate the parameters of each classification model, calculate its classification accuracy in the behavioral feature space, and select the classification model with the highest classification accuracy as the central classification model;

[0130] Adjust the parameter set of the non-central classification model and determine the number of new parameters to be generated based on the respective classification errors;

[0131] Based on the current classification model parameters, a new classification model parameter set is generated, and boundary detection is performed on the new classification model parameter set;

[0132] Mutate the selected classification model parameter set using Gaussian mutation to generate an additional parameter set;

[0133] From all current classification model parameters, select the classification model parameters with the lowest classification accuracy to continue iteration, and record the classification model with the highest classification accuracy;

[0134] Check whether the preset number of iterations has been reached. If so, stop the iteration and record the classification model with the highest classification accuracy as the final classification result. Output the classification result and mark the category of each inspector.

[0135] Specifically, the preset classification algorithm is the improved fireworks algorithm. In the basic fireworks algorithm, the number of explosion sparks and the size of the explosion radius generated by an individual are completely determined by the difference in fitness values ​​between the individual and other individuals. In the later stage of algorithm optimization, when the population is close to the optimal value, the numerator of the explosion operator is close to 0, that is, the explosion radius tends to 0, then the subsequent optimization process is basically invalid, and its local optimization ability has certain limitations. For this reason, some improvements have been made to the basic fireworks algorithm, that is, the generation of the optimal individual explosion radius in the fireworks population is dynamically changed, not just based on the fitness value change. Compared with the basic fireworks algorithm, the improved fireworks algorithm has greater local exploration ability. Because its optimization radius is dynamically changed and gradually reduced, the improved algorithm does not have the problem of optimization invalidation, and its local optimization accuracy is greatly improved, thereby realizing the accurate classification of the inspector in the behavior feature space and improving the classification accuracy.

[0136] Preferably, the formula for performing boundary detection on the new classification model parameter set is:

[0137]

[0138] In the formula, Represents the value of the parameter in the a-th new classification model parameter set after boundary detection in the d-th dimension;

[0139] M represents the parameters in the new classification model parameter set;

[0140] M LB，dRepresents the lower boundary of the parameters in the new classification model parameter set in the dth dimension;

[0141] M UB，d Represents the upper boundary of the parameters in the new classification model parameter set in the dth dimension.

[0142] Preferably, the access control module 4 includes:

[0143] The permission verification module is used to verify the authority of the verifier when performing the query operation and check the identity of the verifier and its corresponding permission level;

[0144] Specifically, the permission levels include:

[0145] Access level: defines the scope of data that the verifier can access. For example, it may be divided into different levels such as "public data", "restricted data", "sensitive data", etc. Only verifiers who reach the corresponding level can access specific categories of data.

[0146] Operation level: determines the type of operations that the inspector can perform. For example, there may be different operation permission levels such as "read only", "read and write", "manage", etc. Higher operation levels allow inspectors to not only view data, but also modify or manage data.

[0147] Role level: permissions are assigned based on the role of the verifier, such as "user", "administrator", "auditor", etc. Different roles correspond to different permission levels, ensuring that permission allocation is consistent with the role responsibilities of the verifier.

[0148] The access control policy module is used to ensure that data verification is consistent with the authorization management of the blockchain by interacting with blockchain data and dynamically updating access rights based on the identity of the verifier and its corresponding permission level.

[0149] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A data storage and verification system based on blockchain technology, characterized in that: The data storage and verification system includes: The data management module is used to interact with the blockchain, obtain the original data that needs to be stored, and pre-process the original data that needs to be stored to obtain the characteristic data of the original data; A data storage module, used to generate a timestamp for the obtained feature data, and store the generated timestamp and the obtained feature data on the blockchain; The identity authentication module is used to authenticate and record the identity of the data checker to ensure that the access user is authorized; The access control module is responsible for the permission management and access control of query operations to ensure that data verification is consistent with the authorization management of the blockchain.

2. According to the data storage and verification system based on blockchain technology in claim 1, it is characterized in that: The data management module comprises: The data acquisition module is used to interact with the blockchain in advance and obtain the original data that needs to be stored; The data processing module is used to extract vital signs from the original data to be stored to obtain characteristic data.

3. According to claim 2, a data storage and verification system based on blockchain technology is characterized in that: The raw data to be stored is subjected to physical sign extraction to obtain characteristic data including: Perform multi-scale decomposition on the original data to be stored to generate feature data layers of different scales; In the feature data hierarchy of each scale, local extreme points are identified by comparing adjacent data points and marked as preliminary feature points; For each preliminary feature point, calculate the gradient and direction of its adjacent data points, generate a feature point direction distribution map, and determine the main direction of the feature point through the peak value of the direction distribution map; Taking each feature point as the center, construct a feature point neighborhood, and calculate the gradient direction and amplitude within the feature point neighborhood; The calculated gradient direction and amplitude are combined into a feature vector as a descriptor of the feature point to perform feature recognition and obtain the feature data of the original data.

4. According to the data storage and verification system based on blockchain technology in claim 1, it is characterized in that: The data storage module comprises: A timestamp generation module, used to create an unalterable timestamp according to the time when the feature data is generated, and associate the timestamp with the feature data; The blockchain storage module is used to record the feature data and the generated timestamp as blocks of blockchain records and send them to the blockchain network for storage.

5. According to claim 4, a data storage and verification system based on blockchain technology is characterized in that: The step of creating an unalterable timestamp according to the time when the feature data is generated, and associating the timestamp with the feature data comprises: When feature data is generated, the current time is recorded and used as the timestamp basis of the feature data; Use the timestamp mechanism of the blockchain to convert the current time recorded into a preset timestamp format; The generated timestamp is used as the meta-information of the feature data and is appended to the feature data to form a complete feature data record including the timestamp.

6. According to the data storage and verification system based on blockchain technology in claim 1, it is characterized in that: The identity authentication module comprises: An identity verification module, used to receive the identity information of the data verifier and confirm the identity of the verifier through biometric recognition technology; The authorization filing module is used to perform authorization checks on verified persons based on anomaly detection algorithms and record access behaviors for future inspection.

7. According to claim 6, a data storage and verification system based on blockchain technology is characterized in that: The behavior anomaly detection algorithm is used to conduct authorization checks on the verified identity and record the access behavior for inspection, including: Based on the behavioral data set of the checker, a behavioral feature matrix is ​​constructed, and the behavioral feature matrix is ​​used to capture the key behavioral attributes of the checker's behavior; Based on the behavior feature matrix, the behavior feature vector corresponding to the zero eigenvalue is identified; Based on each behavior vector of the behavior data set of the checker, the behavior weight vector of the checker is calculated, and the key behavior feature vector is extracted from the calculated behavior weight vector; Construct a behavior transformation matrix based on the key behavior feature vector, analyze the changes of the inspector in different behavior dimensions, and extract the corresponding key behavior feature values ​​through the behavior transformation matrix; Based on the key feature vector, a behavior feature space is constructed, and the behavior feature vector of the checker is projected into the behavior feature space, and a preset classification algorithm is used to determine the category to which each checker belongs in the behavior feature space; According to the inspector's performance and category in the behavioral feature space, the behavioral analysis results of each inspector are output. Based on the inspector's behavioral analysis results, the inspector whose identity has been confirmed is authorized and the access behavior is recorded for inspection.

8. According to claim 7, a data storage and verification system based on blockchain technology is characterized in that: The method of using a preset classification algorithm to determine the category of each checker in the behavior feature space includes: Initialize the parameters of the classification model, each parameter represents a candidate classification model solution; Evaluate the parameters of each classification model, calculate its classification accuracy in the behavioral feature space, and select the classification model with the highest classification accuracy as the central classification model; Adjust the parameter set of the non-central classification model and determine the number of new parameters to be generated based on the respective classification errors; Based on the current classification model parameters, a new classification model parameter set is generated, and boundary detection is performed on the new classification model parameter set; Mutate the selected classification model parameter set using Gaussian mutation to generate an additional parameter set; From all current classification model parameters, select the classification model parameters with the lowest classification accuracy to continue iteration, and record the classification model with the highest classification accuracy; Check whether the preset number of iterations has been reached. If so, stop the iteration and record the classification model with the highest classification accuracy as the final classification result. Output the classification result and mark the category of each inspector.

9. A data storage and verification system based on blockchain technology according to claim 8, characterized in that: The formula for performing boundary detection on the new classification model parameter set is: In the formula, Represents the value of the parameter in the a-th new classification model parameter set after boundary detection in the d-th dimension; M represents the parameters in the new classification model parameter set; M LB，d Represents the lower boundary of the parameters in the new classification model parameter set in the dth dimension; M UB，d Represents the upper boundary of the parameters in the new classification model parameter set in the dth dimension.

10. According to the data storage and verification system based on blockchain technology in claim 1, it is characterized in that: The access control module comprises: The permission verification module is used to verify the authority of the verifier when performing the query operation and check the identity of the verifier and its corresponding permission level; The access control policy module is used to ensure that data verification is consistent with the authorization management of the blockchain by interacting with blockchain data and dynamically updating access rights based on the identity of the verifier and its corresponding permission level.

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