Data security sharing method and device and medium

Abstract

The invention provides a data security sharing method and device and a medium, and relates to the technical field of data, and the method comprises the steps: obtaining real-time dynamic biological characteristics and real-time continuous environment factors of a user, the block chain data and at least one of real-time behavior parameters, real-time equipment information, real-time network parameters, real-time geographic positions and real-time time of the block chain data accessed by the user are used for forming a user real-time access feature vector; performing clustering analysis on the user real-time access feature vector and a user historical access feature vector pre-stored on a block chain to obtain a user access control strategy of a class where the user real-time access feature vector is located; and acquiring data on the block chain according to the user access control strategy, and sharing the acquired data to the user in real time. According to the method, the block chain technology is combined with biological feature recognition and user behavior pattern recognition, so that the security of data sharing is improved.

Description

Technical Field

[0001] The present disclosure at least relates to the field of data technology, and in particular to a data security sharing method, a data security sharing device, and a computer-readable storage medium. Background Art

[0002] In the digital age, data has become a key resource driving social progress. Whether it is individuals or enterprises, the demand for data sharing is growing. The popularization of the Internet and the development of cloud computing technology have made data collection, storage and processing more convenient, promoting the rapid development of big data and artificial intelligence.

[0003] With the popularization of data sharing, data security issues have become increasingly prominent, especially the protection of personal privacy and corporate trade secrets, which has become the focus of social attention. How to improve the security of data access has become a technical problem that needs to be urgently solved in this field. Summary of the invention

[0004] The technical problem to be solved by the present disclosure is to provide a data security sharing method, a data security sharing device and a computer-readable storage medium to address the above-mentioned deficiencies, so as to solve the problem of how to improve the security of data access.

[0005] In a first aspect, the present disclosure provides a method for secure data sharing, the method comprising, in response to a user accessing blockchain data:

[0006] Acquire the user's real-time dynamic biometrics and real-time continuous environmental factors, as well as at least one of the user's real-time behavior parameters, real-time device information, real-time network parameters, real-time geographic location, and real-time time for accessing blockchain data, to form a real-time access feature vector for the user;

[0007] Perform cluster analysis on the user's real-time access feature vector and the user's historical access feature vector pre-stored on the blockchain to obtain the user access control policy of the class to which the user's real-time access feature vector belongs;

[0008] The data on the blockchain is obtained according to the user access control policy, and the obtained data is shared with the user in real time.

[0009] Further, wherein:

[0010] The real-time dynamic biometric feature includes at least one of the user's dynamic fingerprint, iris, face, and voiceprint collected in real time and continuously;

[0011] The real-time continuous environmental factor includes at least one of the illumination, noise, and scenery of the user's environment collected in real time and continuously;

[0012] The real-time behavior parameter includes at least one of the data resource, frequency, and duration currently accessed by the user;

[0013] Real-time device information includes the unique identifier of the current user's device;

[0014] The real-time network parameters include at least one of the network environment and network performance to which the user equipment is currently connected;

[0015] The real-time geographic location includes the geographic name or longitude and latitude information currently obtained by the user device;

[0016] The real time is obtained through the current timestamp of the user's device.

[0017] Furthermore, a user real-time access feature vector is formed, specifically including:

[0018] Use convolutional neural networks to process real-time dynamic biometrics to obtain real-time biometric data of users;

[0019] Based on real-time continuous environmental factors, identify whether the user is currently stationary or moving, and is in an indoor or outdoor environment;

[0020] According to real-time behavior parameters, the user's current access behavior is identified as high-frequency trading or low-frequency trading;

[0021] Based on real-time network parameters, identify whether the user is currently in a public network or a private network, and whether the user's current network performance is good or poor;

[0022] According to the real time, identifying the current time period characteristics and / or the current date characteristics;

[0023] digitize the stationary state or mobile state, indoor environment or outdoor environment, high-frequency transaction or low-frequency transaction, public network or private network, good network performance or poor network performance, current time period characteristics and / or current date characteristics to obtain real-time dynamic adjustment data of users;

[0024] The user's real-time biometric data and the user's real-time dynamic adjustment data are combined to obtain the user's real-time access feature vector.

[0025] Furthermore, a convolutional neural network is used to process real-time dynamic biometric features to obtain real-time biometric data of users, including:

[0026] Performing differential processing and dimensionality reduction processing on the real-time dynamic biometrics to obtain real-time dynamic biometric data after dimensionality reduction;

[0027] Define the parameters of each layer of the convolutional neural network's convolutional layer, activation layer, pooling layer, and fully connected layer, including the size of the convolution kernel, stride, padding, and number of filters;

[0028] The convolution layer is used to extract features from the real-time dynamic biometric data after dimensionality reduction, the activation layer is used to learn the features, the pooling layer is used to implement maximum pooling or average pooling of the features, the fully connected layer is used to output feature classification labels, and the feature classification labels are digitized to obtain the user's real-time biometric data.

[0029] Further, wherein:

[0030] The differential processing includes comparing the real-time dynamic biometric features collected successively before and after to extract the real-time dynamic biometric feature data whose difference is equal to the first threshold value;

[0031] The dimension reduction process includes obtaining the optimal projection vector obtained by the linear discriminant analysis, and using the optimal projection vector to project the real-time dynamic biometric data to obtain the real-time dynamic biometric data after dimension reduction.

[0032] Furthermore, cluster analysis is performed on the user's real-time access feature vector and the user's historical access feature vector pre-stored on the blockchain to obtain the user access control policy of the class to which the user's real-time access feature vector belongs, specifically including:

[0033] Obtain each first cluster center stored in the blockchain, each first cluster center is a result obtained by clustering the user's historical access feature vector using the K-means clustering algorithm in advance;

[0034] Obtain the second cluster center that is closest to the user's real-time access feature vector from each first cluster center, and obtain the second-category user historical access feature vector and the second-category user role authority corresponding to the second cluster center;

[0035] Add the user's real-time access feature vector to the second category of user's historical access feature vector, and calculate and obtain the new third cluster center of this category;

[0036] Determine whether the distance between the third cluster center and the second cluster center is less than the second threshold. If so, the user access control policy is to allow the user to access the data corresponding to the second type of user role permissions. If not, the user access control policy is to not allow the user to access any data.

[0037] Further, wherein:

[0038] The distance calculation formula to the cluster center is: x is the data point, c i is the i-th cluster center, d is the dimension of the data, x j and c ij are x and c respectively i The value in the jth dimension;

[0039] The calculation formula of cluster center is: s iis the set of data points of the ith cluster, |s i | is the number of data points in the set.

[0040] Furthermore, obtaining data on the blockchain according to the user access control policy specifically includes:

[0041] In response to the user access control policy allowing the user to access data including cross-blockchain data, a data exchange standard is obtained through a cross-blockchain smart contract, and the cross-blockchain data is obtained according to the data exchange standard.

[0042] In a second aspect, the present disclosure provides a data security sharing device, the device is used to control user access to blockchain data, including:

[0043] A real-time data module, used to obtain the user's real-time dynamic biometrics and real-time continuous environmental factors, as well as at least one of the user's real-time behavior parameters, real-time device information, real-time network parameters, real-time geographic location and real-time time for accessing blockchain data, to form a user's real-time access feature vector;

[0044] A control strategy module is connected to the real-time data module and is used to perform cluster analysis on the user's real-time access feature vector and the user's historical access feature vector pre-stored on the blockchain to obtain the user access control strategy of the class to which the user's real-time access feature vector belongs;

[0045] The data sharing module is connected to the control policy module and is used to obtain data on the blockchain according to the user access control policy and share the obtained data with the user in real time.

[0046] In a third aspect, the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the data security sharing method as described above is implemented.

[0047] The present disclosure provides a data security sharing method, a data security sharing device and a computer-readable storage medium, which utilize blockchain technology in combination with biometric identification and user behavior pattern identification to improve the security of data sharing; including clustering user access behaviors by integrating user dynamic biometrics and environmental factors and user data access behavior characteristics, comparing real-time data with historical data, obtaining access data of corresponding categories, and improving data sharing security. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flow chart of a data security sharing method according to an embodiment of the present disclosure;

[0049] Figure 2is a flow chart of another data security sharing method according to an embodiment of the present disclosure;

[0050] Figure 3 is a flow chart of another data security sharing method according to an embodiment of the present disclosure;

[0051] Figure 4 It is a structural diagram of a data security sharing device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings.

[0053] It should be understood that the specific embodiments and drawings described herein are only used to explain the present disclosure rather than to limit the present disclosure.

[0054] It can be understood that, in the absence of conflict, the various embodiments of the present disclosure and the various features in the embodiments can be combined with each other.

[0055] It can be understood that, for the convenience of description, the drawings of the present disclosure only show parts related to the present disclosure, while parts irrelevant to the present disclosure are not shown in the drawings.

[0056] It can be understood that each module or unit involved in the embodiments of the present disclosure may correspond to only one entity structure, or may be composed of multiple entity structures, or multiple modules or units may be integrated into one entity structure.

[0057] It will be understood that, in the absence of conflict, the functions and steps marked in the flowcharts and block diagrams of the present disclosure may occur in an order different from that marked in the drawings.

[0058] It is understood that the flowcharts and block diagrams of the present disclosure illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to the various embodiments of the present disclosure. Each box in the flowchart or block diagram may represent a module, unit, program segment, or code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented by a hardware-based system that implements the specified functions, or may be implemented by a combination of hardware and computer instructions.

[0059] It can be understood that the modules and units involved in the embodiments of the present disclosure can be implemented by software or hardware, for example, the modules and units can be located in a processor.

[0060] In order to facilitate the understanding of the present disclosure, the main inventive concept and beneficial effects of the present disclosure are first described.

[0061] Although existing data encryption technologies can protect the confidentiality of data to a certain extent, they usually have some inherent defects. For example, although symmetric encryption algorithms have high computational efficiency, the key distribution and management process is complicated. Once the key is leaked, the security of the entire encryption system will be threatened; and although asymmetric encryption algorithms solve the problem of key distribution, their computational efficiency is low and they are not suitable for encryption processing of large-scale data; traditional password-based authentication methods are vulnerable to phishing attacks, password guessing and social engineering attacks. With the advancement of attack technology, the security of these methods is difficult to guarantee. Therefore, the existing technologies have obvious deficiencies in the security, convenience and scalability of data sharing.

[0062] Biometric recognition technology, as a unique means of identity authentication, has gradually become a research hotspot in the field of identity authentication due to its inherent uniqueness and difficulty in forging. Biometric features, such as fingerprints, irises, and faces, can effectively prevent identity fraud. However, biometric recognition still faces obvious drawbacks, such as insufficient adaptability in dealing with natural changes in fingerprint blur and facial changes, and no perfect solution has been given in terms of balancing convenience and user privacy rights. In addition, existing verification methods are not compatible enough in cross-platform and cross-device data sharing, which limits the widespread application of biometric recognition technology. These drawbacks highlight the need for a secure and efficient blockchain communication data security sharing verification method that uses biometrics.

[0063] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide a method for secure sharing and verification of communication data based on blockchain. By combining biometric recognition technology, it can provide a more secure and reliable identity authentication mechanism. The encrypted storage of biometric data and the security verification on the blockchain further ensure the integrity and traceability of the data, thereby significantly improving the security of the data.

[0064] The verification method includes: real-time collection of user biometric information, including but not limited to fingerprints, irises, faces, and voiceprints, and dynamic adjustment of sampling frequency and quality according to environmental conditions and user status; using blockchain technology to achieve secure data sharing and access control, and combining biometric recognition technology to achieve more flexible and efficient data management; through smart contracts, automatically executing verification of data sharing requests and changes in user permissions according to preset conditions, thereby reducing manual intervention and potential errors; applying biometric recognition technology to make dynamic verification of user identity possible, combining user behavior analysis and environmental factors to adjust access policies in real time to achieve more personalized and dynamic access control.

[0065] Compared with the existing technology, this blockchain-based communication data security sharing verification method has at least the following beneficial effects:

[0066] 1. Blockchain technology is used to achieve secure data sharing and access control. Combined with biometric recognition technology, more flexible and efficient data management is achieved. Through smart contracts, the verification of data sharing requests and changes in user permissions are automatically executed according to preset conditions, thereby reducing manual intervention and potential errors. The application of biometric recognition technology makes dynamic verification of user identity possible. Combined with user behavior analysis and environmental factors, access policies can be adjusted in real time to achieve more personalized and dynamic access control.

[0067] Second, by collecting multiple biometric information in real time and combining it with advanced algorithms for processing and verification, it can accurately identify user identities, eliminate misjudgments, and learn and adapt to the natural changes of user biometrics in real time, thereby improving the accuracy and stability of identity authentication and ensuring that the accuracy of verification always remains at a high level, thereby providing a solid and reliable line of defense for the secure sharing of communication data.

[0068] Embodiment 1:

[0069] like Figure 1 As shown, the present disclosure provides a data security sharing method, the method comprising, in response to a user accessing blockchain data:

[0070] S1. Acquire the user's real-time dynamic biometrics and real-time continuous environmental factors, as well as at least one of the user's real-time behavior parameters, real-time device information, real-time network parameters, real-time geographic location, and real-time time for accessing blockchain data, to form a real-time access feature vector for the user;

[0071] S2. Perform cluster analysis on the user's real-time access feature vector and the user's historical access feature vector pre-stored on the blockchain to obtain the user access control policy of the class to which the user's real-time access feature vector belongs;

[0072] S3. Obtain data on the blockchain according to the user access control policy, and share the acquired data with the user in real time.

[0073] In this embodiment, the method utilizes blockchain technology in combination with biometric identification and user behavior pattern recognition to improve the security of data sharing; including clustering user access behaviors by integrating user dynamic biometrics and environmental factors and user behavior characteristics of accessing data, comparing real-time data with historical data, obtaining access data of corresponding categories, and improving data sharing security.

[0074] More specifically, this embodiment provides Figure 2 A communication data security sharing verification method based on blockchain is shown, and the verification method includes the following steps:

[0075] S100, biometrics collection: responsible for real-time collection of users’ biometric information, including but not limited to fingerprints, irises, faces, and voiceprints, and dynamically adjusts the sampling frequency and quality according to environmental conditions and user status;

[0076] S200, dynamic biometric verification: preliminary processing of the collected raw biometric data, the ability to learn the changes of user biometrics in real time, continuous optimization of the verification model through deep learning algorithms, adapting to the natural changes of user biometrics, and dynamically adjusting the verification strategy in combination with user behavior analysis, environmental factors and historical verification data;

[0077] S300, blockchain data storage: uses a layered blockchain architecture to accurately compare biometrics with user registration biometrics pre-stored in the blockchain, separates high-frequency transactions from low-frequency transactions, and uses role-based access control to ensure that only authorized users can access specific data;

[0078] S400, Smart Contract Execution: Combines multiple dynamic factors, including the current timestamp, device unique identifier, network environment parameters, and automatically triggers according to preset conditions, i.e., verification of data sharing request, user permission change;

[0079] S500, security audit: using big data analysis technology to identify patterns in user access behaviors, detect abnormal behaviors in a timely manner, monitor data access and sharing behaviors in real time, and immediately trigger an alarm mechanism once suspicious operations are found;

[0080] S600, cross-platform and cross-chain interoperability: supports interoperability with other blockchain networks, provides a unified API (Application Programming Interface) gateway, enables different platforms and applications to access and realize data sharing and service calls.

[0081] It can be understood that the above steps are an example of a specific implementation method of the present disclosure. The core inventive idea is to combine multiple dynamic factors (including dynamic biometrics and real-time changing user environment and behavior parameters) to achieve dynamic control of user access rights to blockchain data. This dynamic control can be achieved through dynamic verification strategies, access rules, alarm mechanisms, etc.

[0082] In one embodiment, wherein:

[0083] The real-time dynamic biometric feature includes at least one of the user's dynamic fingerprint, iris, face, and voiceprint collected in real time and continuously;

[0084] The real-time continuous environmental factor includes at least one of the illumination, noise, and scenery of the user's environment collected in real time and continuously;

[0085] The real-time behavior parameter includes at least one of the data resource, frequency, and duration currently accessed by the user;

[0086] Real-time device information includes the unique identifier of the current user's device;

[0087] The real-time network parameters include at least one of the network environment and network performance to which the user equipment is currently connected;

[0088] The real-time geographic location includes the geographic name or longitude and latitude information currently obtained by the user device;

[0089] The real time is obtained through the current timestamp of the user's device.

[0090] In this embodiment, if Figure 2 The specific steps of S100 are as follows:

[0091] S101, Environmental Perception and Adjustment: Automatically adjust parameters including brightness, contrast, sampling rate, and noise reduction filtering based on environmental factors such as current lighting, noise level, and user location;

[0092] S102, real-time collection: continuously collect the user's biometric information, and use the inter-frame difference method in real-time processing technology to detect movement to ensure that the collected biometric information is a living person rather than a static template;

[0093] S103, quality assessment and feedback: the quality of biometric data is assessed through image clarity, texture richness and noise level indicators. If the quality is below the threshold, the user is prompted to reposition and change posture through visual and sound;

[0094] S104, dynamically adjust sampling: dynamically adjust the sampling frequency and duration according to the long-term stillness and frequent movement of user behavior and environmental changes to balance data quality and power consumption.

[0095] In S500, pattern recognition is performed on user access behaviors, including classification according to the characteristics of user access behaviors, namely, access time, access frequency, and accessed resource types, to identify typical access patterns.

[0096] Environmental factors such as user behavior patterns, device information, geographic location and timestamp are also used to dynamically adjust verification strategies and make verification decisions based on dynamic models and environmental analysis.

[0097] Biometrics and related metadata, namely user ID (Identity), collection time, device information, etc. are stored on the blockchain and stored separately using a layered blockchain architecture.

[0098] In one embodiment, forming a user real-time access feature vector specifically includes:

[0099] Use convolutional neural networks to process real-time dynamic biometrics to obtain real-time biometric data of users;

[0100] Based on real-time continuous environmental factors, identify whether the user is currently stationary or moving, and is in an indoor or outdoor environment;

[0101] According to real-time behavior parameters, the user's current access behavior is identified as high-frequency trading or low-frequency trading;

[0102] Based on real-time network parameters, identify whether the user is currently in a public network or a private network, and whether the user's current network performance is good or poor;

[0103] According to the real time, identifying the current time period characteristics and / or the current date characteristics;

[0104] digitize the stationary state or mobile state, indoor environment or outdoor environment, high-frequency transaction or low-frequency transaction, public network or private network, good network performance or poor network performance, current time period characteristics and / or current date characteristics to obtain real-time dynamic adjustment data of users;

[0105] The user's real-time biometric data and the user's real-time dynamic adjustment data are combined to obtain the user's real-time access feature vector.

[0106] In this embodiment, the first is biometric collection, which is responsible for real-time collection of user biometric information, including but not limited to fingerprints, irises, faces, and voiceprints, and dynamically adjusts the sampling frequency and quality according to environmental conditions and user status; then, blockchain technology is used to achieve secure data sharing and access control, and combined with biometric recognition technology, more flexible and efficient data management is achieved; data sharing request verification and user rights changes are automatically performed according to preset conditions, thereby reducing manual intervention and potential errors. The application of biometric recognition technology makes it possible to dynamically verify user identities, and combined with user behavior analysis and environmental factors, access policies are adjusted in real time to achieve more personalized and dynamic access control.

[0107] In order to achieve the above purpose, it is necessary to formulate calculation data for real-time adjustment of access policies, namely, user real-time access feature vector and user historical access feature vector. These two vectors have the same data structure and are both collected when users access data. The real-time dynamic biometric features of users and corresponding environmental factors, behavior patterns and other data are combined. According to the behavioral baseline of the user's historical access data, the data range that the user is allowed to access currently is determined.

[0108] In one embodiment, a convolutional neural network is used to process real-time dynamic biometric features to obtain real-time biometric feature data of a user, specifically including:

[0109] Performing differential processing and dimensionality reduction processing on the real-time dynamic biometrics to obtain real-time dynamic biometric data after dimensionality reduction;

[0110] Define the parameters of each layer of the convolutional neural network's convolutional layer, activation layer, pooling layer, and fully connected layer, including the size of the convolution kernel, stride, padding, and number of filters;

[0111] The convolution layer is used to extract features from the real-time dynamic biometric data after dimensionality reduction, the activation layer is used to learn the features, the pooling layer is used to implement maximum pooling or average pooling of the features, the fully connected layer is used to output feature classification labels, and the feature classification labels are digitized to obtain the user's real-time biometric data.

[0112] In this embodiment, if Figure 2 S200 shown in FIG. 1 pre-processes the collected biometric data, extracts key features from the pre-processed data, and uses convolutional neural networks (CNN) to train a verification model based on the user's historical biometric data and current environmental information to adapt to the natural changes in the biometrics. The model learns and updates the model in real time to reflect the latest status of the user's biometrics. The verification strategy is dynamically adjusted based on environmental factors such as the user's behavior pattern, device information, geographic location, and timestamp. A verification decision is made based on the dynamic model and environmental analysis. If the verification is passed, the user will be granted access rights. Otherwise, access will be denied.

[0113] Furthermore, the S200 uses a convolutional neural network (CNN) in a deep learning algorithm to process and recognize image and audio signals, i.e., facial recognition and voiceprint recognition. CNN can automatically learn spatial hierarchical features from input data. The specific steps of using CNN are as follows:

[0114] Data preprocessing: convert biometric data into CNN input format and perform inter-frame difference processing on voiceprint signals;

[0115] Build a verification model: Design a CNN architecture to build a verification model, including convolutional layers, activation layers, pooling layers, and fully connected layers. The parameters of each layer (i.e., convolution kernel size, step size, padding, number of filters) are configured according to the task;

[0116] Feature extraction: Use the convolution layer of CNN to extract features from biometric data. The convolution kernel slides on the input data and performs inner product operation with the local area to generate a feature map, that is, I is the input image, (m,n) is the pixel coordinate in the image, K is the convolution kernel, and (I*K)(s,t) is the value of the convolution result at position (s,t);

[0117] Non-linear transformation: The activation function is used to increase the non-linear ability of the network, helping the network learn complex features, that is, f(x) = max(0, x), and compare the input value x with 0, setting all negative values ​​to 0, while keeping the positive values ​​unchanged, and taking the maximum value as the output;

[0118] Downsampling (pooling): Use pooling layers (such as max pooling or average pooling) to reduce the dimensionality of the feature map while keeping the most important information, which helps reduce the amount of computation and prevent overfitting, i.e. h and w are the height and width of the pooling window, respectively. i and j are index variables used to traverse the input data. i represents the row index and j represents the column index. By moving within the range of i and j, the area in the input data is selected for the maximum pooling operation.

[0119] Classification and recognition: The fully connected layer converts the extracted features into classification labels, including fingerprint, iris, face and voiceprint labels. Fingerprint labels are used to distinguish the texture characteristics and detail point distribution of different individuals, face labels are used to distinguish facial features, iris labels are used to distinguish the texture and color characteristics of the iris, and voiceprint labels are used for different languages ​​and accent characteristics. The output is expressed as Y = WX + B, where W is the weight matrix used to map features to output categories, X is the feature vector, a vector composed of features extracted by the convolution and pooling layers, B is the bias vector used to adjust the output and help the model fit the data, and Y is the output vector, that is, the predicted category.

[0120] Training and optimization: Minimize the loss function to adjust the network parameters, i.e. N is the number of samples, y i is the true label of the i-th sample, p i is the probability of the prediction model for the category to which the i-th sample biometric belongs;

[0121] Real-time verification: During the biometric verification phase, the biometric data collected in real time is input into the trained CNN verification model, and the model output results are used to verify the user's identity;

[0122] Dynamically adjust strategies: Combine user behavior analysis, environmental factors, and historical verification data to comprehensively evaluate and dynamically adjust verification strategies.

[0123] In one embodiment, wherein:

[0124] The differential processing includes comparing the real-time dynamic biometric features collected successively before and after to extract the real-time dynamic biometric feature data whose difference is equal to the first threshold value;

[0125] The dimension reduction process includes obtaining the optimal projection vector obtained by the linear discriminant analysis, and using the optimal projection vector to project the real-time dynamic biometric data to obtain the real-time dynamic biometric data after dimension reduction.

[0126] In this embodiment, the S102 uses the inter-frame difference method to detect the movement of dynamic biological features of the face and iris, and identifies the moving area in the image by comparing the differences between consecutive video frames. The operation process is as follows:

[0127] Continuous frame capture: capture continuous two-frame and multi-frame images in the video stream;

[0128] Inter-frame comparison: For each pair of consecutive frames, calculate the pixel-level difference between them, that is, D = |F n+1 (x,y)-F n (x,y)|, x, y are two variables representing the pixel coordinates in the image, F n and F n+1 For two frames of images;

[0129] Motion region recognition: Identify the changed regions, indicating that motion has occurred;

[0130] Threshold processing: Apply a threshold to distinguish real motion from noise. Set the threshold T. If the difference D is greater than T, it is considered that the pixel M(x, y) has undergone significant changes caused by motion, that is,

[0131] Application of results: The detected motion information is used for dynamic collection of biometrics, triggering biometric collection to collect key areas and adjust sampling strategies.

[0132] The S200 performs preprocessing of biometric data by linear discriminant analysis (LDA), standardizes the biometric data to eliminate the dimensionality effect between features, and calculates the intra-class divergence based on the data of each biometric category. and the inter-class divergence e is the number of categories, n is k is the number of samples in the kth class, S k is the covariance matrix of the k-th class sample, μ k is the mean vector of the k-th class sample, and μ is the mean vector of all samples, which respectively describe the data changes within the class and the data differences between the classes. By maximizing the ratio of the inter-class divergence to the intra-class divergence, the optimal projection vector w is solved, that is, The projection vector w that maximizes the ratio is obtained, and the obtained eigenvector is used to project the data to obtain the biometric data after dimensionality reduction.

[0133] In one embodiment, cluster analysis is performed on the user's real-time access feature vector and the user's historical access feature vector pre-stored on the blockchain to obtain the user access control policy of the class to which the user's real-time access feature vector belongs, specifically including:

[0134] Obtain each first cluster center stored in the blockchain, each first cluster center is a result obtained by clustering the user's historical access feature vector using the K-means clustering algorithm in advance;

[0135] Obtain the second cluster center that is closest to the user's real-time access feature vector from each first cluster center, and obtain the second-category user historical access feature vector and the second-category user role authority corresponding to the second cluster center;

[0136] Add the user's real-time access feature vector to the second category of user's historical access feature vector, and calculate and obtain the new third cluster center of this category;

[0137] Determine whether the distance between the third cluster center and the second cluster center is less than the second threshold. If so, the user access control policy is to allow the user to access the data corresponding to the second type of user role permissions. If not, the user access control policy is to not allow the user to access any data.

[0138] In this embodiment, if Figure 2 The S300 shown encrypts the biometric data before uploading it to the blockchain to protect data privacy and security, uses the encrypted biometric data as a unique identifier for rapid retrieval and verification, and stores the biometrics and related metadata, namely user ID, collection time, and device information, on the blockchain. A layered blockchain architecture is adopted to allow separation of high-frequency transactions and low-frequency transactions, and role-based access control is implemented on the blockchain to ensure that only authorized users can access specific data. Even if the data is illegally obtained, unauthorized users cannot decrypt and understand the actual content of the data. The tamper-proof nature of the blockchain is used to regularly perform integrity checks on the stored biometric data to ensure that the data has not been tampered with.

[0139] Furthermore, the role-based access control of S300 is used to ensure that only authorized users can access and retrieve sensitive data stored on the blockchain, and its operation process is as follows:

[0140] Role definition: define different roles and assign corresponding permissions to each role, including administrator, ordinary user, and auditor;

[0141] Permission allocation: Assign access rights to blockchain data based on roles, including the rights to read, write, modify, and delete data;

[0142] User role mapping: associate users with roles. Each user has one or more roles and obtains corresponding permissions through roles.

[0143] Access request verification: When a user requests to access data on the blockchain, the user’s role and corresponding permissions are checked;

[0144] Smart contract execution: Use smart contracts to automatically execute access control rules. Smart contracts run in a distributed manner on the blockchain to ensure that all access requests comply with the preset role permissions;

[0145] Permission change management: When a user’s role and permissions need to be changed, this is achieved by updating the role definition and user role mapping, and recording the changes on the blockchain.

[0146] like Figure 2 The S500 shown performs cluster analysis on user access behaviors through K-means clustering algorithm, identifies typical access patterns, and combines role definition, permission allocation, user role mapping, and access request verification to ensure that only authorized users can access specific data. It compares the current user access behavior with the established normal pattern and detects abnormal behaviors that deviate from the normal pattern. Once suspicious operations are found, an alarm mechanism is immediately triggered, and access control rules are automatically implemented through smart contracts.

[0147] In one embodiment, wherein:

[0148] The distance calculation formula to the cluster center is: x is the data point, c i is the i-th cluster center, d is the dimension of the data, x j and c ij are x and c respectively i The value in the jth dimension;

[0149] The calculation formula of cluster center is: s i is the set of data points of the ith cluster, |s i | is the number of data points in the set.

[0150] In this embodiment, the S500 performs cluster analysis on user access behaviors through the K-means clustering algorithm, divides the user access behaviors according to their characteristics, i.e., access time, access frequency, and accessed resource type, identifies typical access patterns, and regards each access as a data point. This data point contains information of multiple dimensions, including access time, access frequency, accessed resource type, accessed resource ID, access duration, etc., extracts feature vectors of user access from the log, and randomly selects K data points as initial cluster centers c1, c2, ..., c K, assign each data point to the nearest cluster center, calculate the distance between each data point and the K cluster centers, and assign it to the cluster corresponding to the nearest cluster center, that is, x is the data point, c i is the i-th cluster center, d is the dimension of the data, x j and c ij are x and c respectively i The value on the jth dimension, for each cluster, calculate the feature mean of all objects it contains, and get the new cluster center, that is, s i is the set of data points of the ith cluster, |s i | is the number of data points in the set. Check the changes in the cluster center before and after the current iteration. If the change is less than the preset threshold, the algorithm ends. Compare the current user access behavior with the established normal pattern to detect abnormal behavior that deviates from the normal pattern. Once suspicious operations are found, the alarm mechanism is immediately triggered to notify relevant personnel for further investigation and processing.

[0151] In one embodiment, obtaining data on the blockchain according to the user access control policy specifically includes:

[0152] In response to the user access control policy allowing the user to access data including cross-blockchain data, a data exchange standard is obtained through a cross-blockchain smart contract, and the cross-blockchain data is obtained according to the data exchange standard.

[0153] In this embodiment, if Figure 2 The S600 shown clarifies the interface specifications of the unified API gateway, including the data format, request and response structure, encapsulates the core functions of the blockchain, namely data storage, smart contract execution, and identity authentication, as services and exposes them to external systems through APIs, implements cross-chain communication protocols, allows data and asset exchanges between different blockchain networks, develops smart contracts to handle cross-chain transactions and data sharing, ensures interoperability between different blockchains, formulates data exchange standards, enables seamless data exchange between different platforms and blockchain networks, and implements identity and permission mapping mechanisms between different systems, and implements additional security measures, including encrypted communications, identity authentication, and permission control, to protect data security in cross-platform and cross-chain interactions.

[0154] Combine the following Figure 3 , further providing two exemplary implementations of the present application:

[0155] Example 1:

[0156] A communication data security sharing and verification method based on blockchain, comprising the following steps: biometric collection, biometric dynamic verification, blockchain data storage, smart contract execution, security audit, cross-platform and cross-chain interoperability.

[0157] After verification begins, the biometric collection phase begins. Biometric data including fingerprints, irises, faces and voiceprints are collected. The inter-frame difference method is used to detect motion to ensure that the collected biometrics are living rather than static templates. The quality of the biometric data is evaluated through indicators such as image clarity, texture richness and noise level. If the quality is below the threshold, the user is prompted to reposition or change posture through visual or sound. The collection frequency and duration are dynamically adjusted according to user behavior (such as long periods of stillness and frequent movement) and environmental changes (such as sudden dimming of light) to balance data quality and power consumption. The collected raw data is preprocessed to facilitate subsequent feature extraction and matching.

[0158] Then, we enter the biometric dynamic verification stage, extract key features from the preprocessed data, and use the convolutional neural network in the deep learning algorithm to train the verification model based on the user's historical biometric data and current environmental information to adapt to the natural changes of the biometrics. We analyze the user's new biometric data in real time, learn and update the model to reflect the latest status of the user's biometrics, and dynamically adjust the verification strategy based on environmental factors such as the user's behavior pattern, device information, geographic location, and timestamp. For example, in a high-risk environment, stricter verification standards will be required. Verification decisions are made based on dynamic models and environmental analysis. If the verification passes, the user will be granted access; otherwise, access will be denied.

[0159] Next, we enter the blockchain data storage stage, where the preprocessed biometric data is uploaded to the blockchain and encrypted to protect data privacy and security. The encrypted biometric data is used as the unique identifier of the data for rapid retrieval and verification. The biometrics and related metadata (such as user ID, collection time, and device information) are stored on the blockchain. A layered blockchain architecture is used to allow the separation of high-frequency and low-frequency transactions. Role-based access control is implemented on the blockchain to ensure that only authorized users can access specific data. Even if the data is illegally obtained, unauthorized users cannot decrypt and understand the actual content of the data. Integrity checks are performed regularly on the stored biometric data to ensure that the data has not been tampered with.

[0160] Subsequently, the smart contract execution phase begins, which combines a variety of dynamic factors, including the current timestamp, the device’s unique identifier, and network environment parameters, and is automatically triggered based on preset conditions, i.e., verification of the data sharing request and changes in user permissions.

[0161] Next, we enter the security audit phase, continuously monitoring the user's access records to the data on the blockchain, including access time, visitor ID, access type (read, write, modify), and access results (success or failure). We use cluster analysis algorithms to analyze and identify user behavior patterns, establish a baseline for normal behavior based on historical access data, and identify access behaviors that deviate from normal patterns. Once potential abnormal access behavior is detected, the alarm mechanism is immediately triggered to notify the administrator to investigate. When abnormal behavior is detected, an alarm message is automatically sent to the designated manager. All access behaviors and alarm events are recorded in detail through email, text messages, and application notifications, and a security audit report is generated for subsequent analysis and compliance review.

[0162] Finally, we will enter the cross-platform and cross-chain interoperability stage, design a unified API specification, ensure that different platforms and applications use a consistent way to call and interact, encapsulate the core functions of the blockchain, including data storage, smart contract execution, and identity authentication, as services, expose them to external systems through APIs, implement cross-chain communication protocols, allow data and asset exchange between different blockchain networks, develop interoperability protocols to promote the interoperability of different blockchain networks, develop adapters for different platforms to connect to a unified API gateway for seamless integration, and optimize network communication and data processing processes to address high latency issues across platforms and chains, ensure efficient data sharing, and implement additional security measures such as encrypted communications, identity authentication, and permission control to protect data security in cross-platform and cross-chain interactions.

[0163] Through the implementation of the above steps, a more secure and reliable identity authentication mechanism is provided. The uniqueness and difficulty of duplication of biometrics make the identity authentication process more accurate, greatly reducing the risk of identity theft and unauthorized access. The encrypted storage of biometric data and the secure verification on the blockchain further ensure the integrity and traceability of the data.

[0164] Example 2:

[0165] This example aims to describe in detail the specific steps and conditions of a blockchain-based communication data secure sharing verification method. By collecting multiple biometric information in real time and combining it with advanced algorithms for processing and verification, it can accurately identify user identities and implement personalized data sharing services based on the needs and scenarios of different users, greatly improving the efficiency and value of data sharing.

[0166] First, biometric information is collected in real time, and motion is detected using the inter-frame difference method, that is, two or more consecutive frames are captured in the video stream, and for each pair of consecutive frames, the pixel-level difference between them is calculated, that is, D = |F n+1 (x,y)-F n(x, y)|, identify the changed area, indicating that motion has occurred, apply a threshold to distinguish true motion from noise, set the threshold T, and if the difference D is greater than T, it is considered that the pixel M(x, y) has undergone a significant change caused by motion, that is, The detected motion information is used for dynamic collection of biometric features, triggering biometric collection to collect key areas and adjust sampling strategies.

[0167] Then, after feature extraction of the collected biometric data, the convolutional neural network CNN is used to train the verification model based on the historical biometric data and current environmental information, and the biometric data is converted into the form of CNN input. The voiceprint signal is subjected to frame difference processing, and the CNN architecture is designed to build the verification model, including convolution layer, activation layer, pooling layer and fully connected layer. The parameters of each layer are configured according to the task, and the newly collected biometric data is analyzed in real time. The convolution layer of CNN is used to extract the features in the biometric data. The convolution kernel slides on the input data and performs inner product operation with the local area to generate a feature map, that is, The activation function increases the nonlinear ability of the network and helps the network learn complex features, that is, f(x) = max(0, x). Pooling layers (such as maximum pooling or average pooling) are used to reduce the dimension of the feature map while keeping the most important information, which helps to reduce the amount of calculation and prevent overfitting. The fully connected layer converts the extracted features into classification labels for identifying specific biometric patterns, i.e., Y = WX + B, and minimizes the loss function to adjust the parameters, i.e. During the biometric verification stage, the biometric data collected in real time will be input into the trained CNN verification model. The output of the model will be used to verify the user's identity, and the verification strategy will be dynamically adjusted based on environmental factors such as the user's behavior patterns and device information.

[0168] Linear discriminant analysis (LDA) is used for preprocessing and feature extraction of biometric data. The biometric data is standardized to eliminate the dimensionality effect between features. Based on the data of each category, the intra-class divergence is calculated. and the inter-class divergence They describe the data changes within the category and the data differences between categories respectively. By maximizing the ratio of the inter-class divergence to the intra-class divergence, the optimal projection vector w is solved, that is, The obtained feature vector is used to project the data to obtain the biometric data after dimensionality reduction.

[0169] A layered blockchain architecture is used to separate high-frequency transactions from low-frequency transactions, and role-based access control is implemented. Role definition, permission allocation, user role mapping, and access request verification work together to ensure that only authorized users can access specific data. The execution of smart contracts automatically implements access control rules, and permission change management allows dynamic adjustment of user roles and permissions. The K-means clustering algorithm is used to perform cluster analysis on user access behavior and identify typical access patterns. Each access is regarded as a data point, and K data points are randomly selected as the initial clustering centers c1, c2, ..., c K , assign each data point to the nearest cluster center, calculate the distance between each data point and the K cluster centers, and assign it to the cluster corresponding to the nearest cluster center, that is, For each cluster, calculate the feature mean of all objects it contains and get the new cluster center, that is, Check the changes in the cluster center before and after the current iteration. If the change is less than the predetermined threshold and reaches the maximum number of iterations, the algorithm ends and compares the current user access behavior with the established normal pattern to detect abnormal behavior that deviates from the normal pattern. Once suspicious operations are found, the alarm mechanism is immediately triggered to notify relevant personnel for further investigation and processing.

[0170] Finally, access and data sharing of different platforms and applications are achieved through a unified API gateway, allowing data and asset exchange between different blockchain networks, developing smart contracts to handle cross-chain transactions and data sharing, ensuring interoperability between different blockchains, and implementing additional security measures such as encrypted communication, identity authentication, and permission control to protect data security in cross-platform and cross-chain interactions.

[0171] In summary, by implementing the above steps, blockchain technology is used to achieve secure data sharing and access control. At the same time, combined with biometric recognition technology, more flexible and efficient data management is achieved. The decentralized nature of blockchain and the tamper-proof records maintain the consistency and integrity of the data, while the use of smart contracts automates the access control process. In addition, the application of biometric recognition technology makes dynamic verification of user identity possible. Combined with user behavior analysis and environmental factors, access policies can be adjusted in real time to achieve more personalized and dynamic access control.

[0172] The method of this embodiment proposes a more personalized and dynamic access control for data sharing security verification, which greatly improves the security, accuracy and stability in the field of identity authentication. This technical solution has the characteristics of simple implementation and no special requirements for wireless environment.

[0173] Embodiment 2:

[0174] like Figure 4 As shown, the present disclosure provides a data security sharing device, which is used to control user access to blockchain data, including:

[0175] Real-time data module 1, used to obtain the user's real-time dynamic biometrics and real-time continuous environmental factors, as well as at least one of the user's real-time behavior parameters, real-time device information, real-time network parameters, real-time geographic location and real-time time for accessing blockchain data, to form a user's real-time access feature vector;

[0176] The control strategy module 2 is connected to the real-time data module 1 and is used to perform cluster analysis on the user's real-time access feature vector and the user's historical access feature vector pre-stored on the blockchain to obtain the user access control strategy of the class to which the user's real-time access feature vector belongs;

[0177] The data sharing module 3 is connected to the control policy module 2 and is used to obtain data on the blockchain according to the user access control policy and share the obtained data with the user in real time.

[0178] In one embodiment, wherein:

[0179] The real-time dynamic biometric feature includes at least one of the user's dynamic fingerprint, iris, face, and voiceprint collected in real time and continuously;

[0180] The real-time continuous environmental factor includes at least one of the illumination, noise, and scenery of the user's environment collected in real time and continuously;

[0181] The real-time behavior parameter includes at least one of the data resource, frequency, and duration currently accessed by the user;

[0182] Real-time device information includes the unique identifier of the current user's device;

[0183] The real-time network parameters include at least one of the network environment and network performance to which the user equipment is currently connected;

[0184] The real-time geographic location includes the geographic name or longitude and latitude information currently obtained by the user device;

[0185] The real time is obtained through the current timestamp of the user's device.

[0186] In one embodiment, the real-time data module 1 specifically includes:

[0187] A biometric data unit, used to process real-time dynamic biometrics using a convolutional neural network to obtain real-time biometric data of the user;

[0188] Dynamically adjust data units for:

[0189] Based on real-time continuous environmental factors, identify whether the user is currently stationary or moving, and is in an indoor or outdoor environment;

[0190] According to real-time behavior parameters, the user's current access behavior is identified as high-frequency trading or low-frequency trading;

[0191] Based on real-time network parameters, identify whether the user is currently in a public network or a private network, and whether the user's current network performance is good or poor;

[0192] According to the real time, identifying the current time period characteristics and / or the current date characteristics;

[0193] digitize the stationary state or mobile state, indoor environment or outdoor environment, high-frequency transaction or low-frequency transaction, public network or private network, good network performance or poor network performance, current time period characteristics and / or current date characteristics to obtain real-time dynamic adjustment data of users;

[0194] The data combining unit is connected to the biometric data unit and the dynamic adjustment data unit, and is used to combine the user's real-time biometric data with the user's real-time dynamic adjustment data to obtain the user's real-time access feature vector.

[0195] In one embodiment, the biometric data unit specifically includes:

[0196] A preprocessing unit, used for performing differential processing and dimensionality reduction processing on the real-time dynamic biometric feature to obtain the real-time dynamic biometric feature data after dimensionality reduction;

[0197] Predefined units are used to define the parameters of each layer of the convolutional layer, activation layer, pooling layer and fully connected layer of the convolutional neural network, including the size of the convolution kernel, step size, padding, and number of filters;

[0198] The neural network processing unit is connected to the preprocessing unit and the predefined unit, and is used to extract features from the real-time dynamic biometric data after dimension reduction using the convolution layer, learn features using the activation layer, implement maximum pooling or average pooling of features using the pooling layer, output feature classification labels using the fully connected layer, and digitize the feature classification labels to obtain the user's real-time biometric data.

[0199] In one embodiment, wherein:

[0200] The differential processing includes comparing the real-time dynamic biometric features collected successively before and after to extract the real-time dynamic biometric feature data whose difference is equal to the first threshold value;

[0201] The dimension reduction process includes obtaining the optimal projection vector obtained by the linear discriminant analysis, and using the optimal projection vector to project the real-time dynamic biometric data to obtain the real-time dynamic biometric data after dimension reduction.

[0202] In one embodiment, the control strategy module 2 specifically includes:

[0203] A first center acquisition unit, used to acquire each first cluster center stored on the blockchain, each first cluster center is a result obtained by clustering the user's historical access feature vector using a K-means clustering algorithm in advance;

[0204] A second center acquisition unit is connected to the first center acquisition unit and is used to acquire the second cluster center that is closest to the user's real-time access feature vector among the first cluster centers, and acquire the second type of user historical access feature vector and the second type of user role authority corresponding to the second cluster center;

[0205] A third center acquisition unit, connected to the second center acquisition unit, is used to add the user's real-time access feature vector to the second category of user's historical access feature vector, and calculate and obtain a new third clustering center of this category;

[0206] The control strategy selection unit is connected to the third center acquisition unit and is used to determine whether the distance between the third cluster center and the second cluster center is less than a second threshold. If so, the user access control policy is to allow the user to access the data corresponding to the second type of user role permissions. If not, the user access control policy is to not allow the user to access any data.

[0207] In one embodiment, wherein:

[0208] The distance calculation formula to the cluster center is: x is the data point, c i is the i-th cluster center, d is the dimension of the data, x j and c ij are x and c respectively i The value in the jth dimension;

[0209] The calculation formula of cluster center is: s i is the set of data points of the ith cluster, |s i | is the number of data points in the set.

[0210] In one implementation, the data sharing module 3 specifically includes:

[0211] A cross-chain access unit is used to allow the user to access data including cross-blockchain data in response to the user access control policy, obtain a data exchange standard through a cross-blockchain smart contract, and obtain the cross-blockchain data according to the data exchange standard.

[0212] Embodiment 3:

[0213] Embodiment 3 of the present disclosure provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the data security sharing method as described in Embodiment 1 or the data security sharing device as described in Embodiment 2 is implemented.

[0214] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program elements or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable read only memory), flash memory or other memory technology, CD-ROM (Compact Disc Read-Only Memory), digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0215] In addition, the present disclosure may also provide a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes the data security sharing method as described in Example 1. The computer device may be the data security sharing device as described in Example 2.

[0216] The memory is connected to the processor, the memory may be a flash memory or a read-only memory or other memory, and the processor may be a central processing unit or a single-chip microcomputer.

[0217] Embodiments 1-3 of the present disclosure provide a data security sharing method, a data security sharing device, and a computer-readable storage medium, which utilize blockchain technology in combination with biometric identification and user behavior pattern recognition to improve the security of data sharing; including clustering user access behaviors by integrating user dynamic biometrics and environmental factors and user data access behavior characteristics, comparing real-time data with historical data, obtaining access data of corresponding categories, and improving data sharing security.

[0218] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present disclosure, but the present disclosure is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and substance of the present disclosure, and these modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A data security sharing method, characterized in that: The method includes, in response to a user accessing blockchain data: Acquire the user's real-time dynamic biometrics and real-time continuous environmental factors, as well as at least one of the user's real-time behavior parameters, real-time device information, real-time network parameters, real-time geographic location, and real-time time for accessing blockchain data, to form a real-time access feature vector for the user; Perform cluster analysis on the user's real-time access feature vector and the user's historical access feature vector pre-stored on the blockchain to obtain the user access control policy of the class to which the user's real-time access feature vector belongs; The data on the blockchain is obtained according to the user access control policy, and the obtained data is shared with the user in real time.

2. The method according to claim 1, characterized in that in: The real-time dynamic biometric feature includes at least one of the user's dynamic fingerprint, iris, face, and voiceprint collected in real time and continuously; The real-time continuous environmental factor includes at least one of the illumination, noise, and scenery of the user's environment collected in real time and continuously; The real-time behavior parameter includes at least one of the data resource, frequency, and duration currently accessed by the user; Real-time device information includes the unique identifier of the current user's device; The real-time network parameters include at least one of the network environment and network performance to which the user equipment is currently connected; The real-time geographic location includes the geographic name or longitude and latitude information currently obtained by the user device; The real time is obtained through the current timestamp of the user's device.

3. The method according to claim 2, characterized in that The user real-time access feature vector is composed of: Use convolutional neural networks to process real-time dynamic biometrics to obtain real-time biometric data of users; Based on real-time continuous environmental factors, identify whether the user is currently stationary or moving, and is in an indoor or outdoor environment; According to real-time behavior parameters, the user's current access behavior is identified as high-frequency trading or low-frequency trading; Based on real-time network parameters, identify whether the user is currently in a public network or a private network, and whether the user's current network performance is good or poor; According to the real time, identifying the current time period characteristics and / or the current date characteristics; digitize the stationary state or mobile state, indoor environment or outdoor environment, high-frequency transaction or low-frequency transaction, public network or private network, good network performance or poor network performance, current time period characteristics and / or current date characteristics to obtain real-time dynamic adjustment data of users; The user's real-time biometric data and the user's real-time dynamic adjustment data are combined to obtain the user's real-time access feature vector.

4. The method according to claim 3, characterized in that: Use convolutional neural networks to process real-time dynamic biometrics to obtain real-time biometric data of users, including: Performing differential processing and dimensionality reduction processing on the real-time dynamic biometrics to obtain real-time dynamic biometric data after dimensionality reduction; Define the parameters of each layer of the convolutional neural network's convolutional layer, activation layer, pooling layer, and fully connected layer, including the size of the convolution kernel, stride, padding, and number of filters; The convolution layer is used to extract features from the real-time dynamic biometric data after dimensionality reduction, the activation layer is used to learn the features, the pooling layer is used to implement maximum pooling or average pooling of the features, the fully connected layer is used to output the feature classification labels, and the feature classification labels are digitized to obtain the user's real-time biometric data.

5. The method according to claim 4, characterized in that in: The differential processing includes comparing the real-time dynamic biometric features collected successively before and after to extract the real-time dynamic biometric feature data whose difference is equal to the first threshold value; The dimension reduction process includes obtaining the optimal projection vector obtained by the linear discriminant analysis, and using the optimal projection vector to project the real-time dynamic biometric data to obtain the real-time dynamic biometric data after dimension reduction.

6. The method according to any one of claims 1 to 5, characterized in that: Perform cluster analysis on the user's real-time access feature vector and the user's historical access feature vector pre-stored on the blockchain to obtain the user access control policy of the class to which the user's real-time access feature vector belongs, including: Obtain each first cluster center stored in the blockchain, each first cluster center is a result obtained by clustering the user's historical access feature vector using the K-means clustering algorithm in advance; Obtain the second cluster center that is closest to the user's real-time access feature vector from each first cluster center, and obtain the second-category user historical access feature vector and the second-category user role authority corresponding to the second cluster center; Add the user's real-time access feature vector to the second category of user's historical access feature vector, and calculate and obtain the new third cluster center of this category; Determine whether the distance between the third cluster center and the second cluster center is less than the second threshold. If so, the user access control policy is to allow the user to access the data corresponding to the second user role authority. If not, the user access control policy is to not allow the user to access any data.

7. The method according to claim 6, characterized in that in: The distance calculation formula to the cluster center is: x is the data point, c i is the i-th cluster center, d is the dimension of the data, x j and c ij are x and c respectively i The value in the jth dimension; The calculation formula of cluster center is: s i is the set of data points of the ith cluster, |s i | is the number of data points in the set.

8. The method according to claim 6, characterized in that Acquiring data on the blockchain according to the user access control policy specifically includes: In response to the user access control policy allowing the user to access data including cross-blockchain data, a data exchange standard is obtained through a cross-blockchain smart contract, and the cross-blockchain data is obtained according to the data exchange standard.

9. A data security sharing device, characterized in that: The device is used to control user access to blockchain data, including: A real-time data module, used to obtain the user's real-time dynamic biometrics and real-time continuous environmental factors, as well as at least one of the user's real-time behavior parameters, real-time device information, real-time network parameters, real-time geographic location and real-time time for accessing blockchain data, to form a user's real-time access feature vector; A control strategy module is connected to the real-time data module and is used to perform cluster analysis on the user's real-time access feature vector and the user's historical access feature vector pre-stored on the blockchain to obtain the user access control strategy of the class to which the user's real-time access feature vector belongs; The data sharing module is connected to the control policy module and is used to obtain data on the blockchain according to the user access control policy and share the obtained data with the user in real time.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the data security sharing method according to any one of claims 1 to 8 is implemented.

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