User information management method and system based on application information

Abstract

The invention discloses a user information management method and system based on application information, and relates to the technical field of user information management, and the method comprises the steps: obtaining user information through an application program, generating a unique distributed identity label, and carrying out the multi-stage verification of the user identity through an edge calculation node and a distributed node network; user behaviors are analyzed in real time through a fuzzy logic algorithm, the priority of user data is adjusted according to an analysis result, and the data access permission is dynamically allocated. According to the method, the unique distributed identity label is generated, multi-level verification is carried out in combination with the edge computing node and the distributed node network, the identity verification efficiency and safety are remarkably improved, user behaviors are analyzed in real time through the fuzzy logic algorithm, the data priority is dynamically adjusted, the access authority is allocated, and the user experience is improved. The efficient management of resources is realized, and the flexibility of authority management and the user experience are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of user information management, and in particular to a user information management method and system based on application information. Background Art

[0002] With the rapid development of the Internet and mobile applications, user information management technology has gradually become one of the important research directions in the field of information technology. Traditional user information management systems usually rely on centralized servers for identity authentication and data management. This method played an important role in early Internet applications. However, with the sharp increase in data volume and the increasing rampantness of network attacks, the security and reliability of centralized identity management and data storage have gradually exposed their shortcomings. In recent years, the rise of distributed computing and blockchain technology has provided new ideas and methods for user information management. Distributed identity authentication systems (DID) and edge computing have gradually become research hotspots. Through these emerging technologies, the security and scalability of the system can be effectively improved, and the single point failure risk of centralized systems can be reduced. However, the existing distributed identity management system still faces many challenges in practical applications, and there is still room for improvement in the real-time processing capabilities of user data, the accuracy of identity authentication, and the flexibility of permission management. Summary of the invention

[0003] In view of the problems existing in the above-mentioned existing user information management method and system based on application information, the present invention is proposed.

[0004] Therefore, the problem to be solved by the present invention is that, however, the existing distributed identity management system still faces many challenges in practical applications, and still needs to be improved in terms of the real-time processing capability of user data, the accuracy of identity authentication, and the flexibility of authority management.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: a user information management method based on application information, which includes the following steps: obtaining user information through an application and generating a unique distributed identity, performing multi-level verification of the user identity through edge computing nodes and distributed node networks; performing real-time analysis of user behavior through a fuzzy logic algorithm, adjusting the priority of user data and dynamically allocating data access rights based on the analysis results; classifying and storing user data according to data priority and permission settings, and verifying user permissions when the user issues a data access request.

[0006] As a preferred solution of the user information management method based on application information described in the present invention, wherein: obtaining user information through an application and generating a unique distributed identity identifier refers to collecting user-related data, preprocessing the collected data, using an elliptic curve encryption algorithm to generate the user's public key and private key, hashing the generated public key and the user's identity information through a secure hash algorithm to generate a unique distributed identity identifier, and storing the generated distributed identity identifier together with the user's public key and identity information in an edge computing node.

[0007] As a preferred solution of the user information management method based on application information of the present invention, wherein: the multi-level verification of user identity information through edge computing nodes and distributed node networks refers to mapping distributed identity identifiers with user information through edge computing nodes to form a complete user data set;

[0008] Perform cryptographic hashing on each data field to generate a hash value and combine the generated hash values ​​through chain hashing technology to generate the final chain hash value;

[0009] Generate a smart contract and deploy it to each node in the distributed node network, and send the generated chain hash value and the user's distributed identity to multiple nodes in the distributed node network;

[0010] After receiving the verification request, the node triggers the smart contract to verify the user's distributed identity and chain hash value, and generates a local verification result after completing the verification;

[0011] Connect the device registration information library of the application, send a query request to the device registration information library based on the user identity information, retrieve the history and verification data related to the user, use TF-IDF to extract data features from the user identity information and the retrieved related data and convert text and structured data into feature vectors, and use the cosine similarity method to evaluate the similarity between feature vectors;

[0012] Set a judgment threshold Q. If the similarity score is lower than or equal to the judgment threshold Q, the user identity information is marked as abnormal and cannot pass the preliminary identity information verification. If the similarity score is higher than the judgment threshold Q, the user identity information is normal and passes the preliminary identity information verification.

[0013] Integrate the user identity information obtained through matching the device registration information database with the user behavior data to form a time series data set, and standardize the integrated data;

[0014] Use the LSTM model to predict user behavior patterns, obtain future user behavior patterns, extract time series features from them, and generate prediction sequence B;

[0015] Compare the current user behavior data with the future user behavior pattern, extract the time series features from the current user behavior data, and form time series A;

[0016] Create a two-dimensional DTW matrix of size n*m, where n is the length of sequence A and m is the length of sequence B;

[0017] Initialize the starting point of the matrix, the first row of the matrix, and the first column of the matrix;

[0018] For each element DTW[i][j] in the matrix, calculate the distance d(A[i],B[j]) between the time series A[i] and B[j], and use the dynamic programming formula to calculate the minimum cumulative distance for each element in the matrix;

[0019] Fill the entire DTW matrix in sequence, and use the value DTW[n][m] in the lower right corner of the final DTW matrix as the final DTW distance;

[0020] The smaller the calculated DTW distance is, the more similar the current behavior sequence is to the predicted future behavior pattern. The larger the calculated DTW distance is, the greater the difference between the two.

[0021] Set the judgment threshold U and compare it with the DTW distance to determine the abnormality of user behavior:

[0022] If the DTW distance value is greater than U, it means that the behavior data is abnormal, and the user account is marked and locked, waiting for re-authentication;

[0023] If the DTW distance value is less than or equal to U, it means that the behavior data is normal and passes the identity authentication;

[0024] The user's identity information is mapped to the semantic network through knowledge graph technology. The core node of the knowledge graph is initialized by aligning the results of the device registration information database. The anomaly detection tags of time series analysis are added to the knowledge graph and connected to the core nodes as auxiliary nodes to form a complete graph structure. The relationship edges are constructed according to the relevance of user information and the semantic relationship between nodes is defined.

[0025] The reasoning engine of the knowledge graph performs semantic reasoning on the user's identity information, traverses the nodes and relationship edges in the knowledge graph, and analyzes the semantic consistency between the information. After the reasoning is completed, a verification structure for the semantic consistency is generated. If an abnormality in the semantic relationship is found during the reasoning process, the abnormality is marked and a warning signal is issued. The user needs to authenticate his identity again. If no abnormality is found, the user has successfully passed the identity authentication.

[0026] As a preferred embodiment of the user information management method based on application information according to the present invention, wherein: the real-time analysis of user behavior through the fuzzy logic algorithm and the adjustment of the priority of user data and the dynamic allocation of data access rights according to the analysis results mean that after the user passes the authentication, the operation behavior of the user in the application is monitored and collected in real time, and the collected behavior data is preprocessed;

[0027] The preprocessed behavior data is fuzzified, converting continuous variables into membership degrees in the fuzzy set. The main input variables include the application usage frequency, user activity, and data importance, and the output variable is the data priority;

[0028] Define fuzzy logic rules, use the Mamdani inference method to map the input variables into membership degrees, calculate the membership degrees of fuzzy priorities for each rule, and aggregate the results of multiple rules to form the final fuzzy output;

[0029] After the fuzzy inference is completed, use the centroid method to convert the fuzzy priority result into a priority value;

[0030] According to the calculated priority value P, dynamically adjust the user's access rights to the data.

[0031] As a preferred embodiment of the user information management method based on application information according to the present invention, wherein: the dynamically adjusting the user's access rights to the data according to the calculated priority value P means defining the access rights as the highest right, high right, medium right, and lowest right;

[0032] Set the judgment thresholds P1, P2, and P3 for different priorities, compare them with the priority value P, and judge the user's access rights:

[0033] When P≥P1, the highest right is assigned;

[0034] When P2≤P<P1, the high right is assigned;

[0035] When P3≤P<P2, the medium right is assigned;

[0036] When P<P3, the lowest right is assigned.

[0037] As a preferred embodiment of the user information management method based on application information according to the present invention, wherein: the classifying and storing user data according to the priority and permission settings of the data means classifying all user data according to the calculated priority value, and assigning corresponding permission levels according to the priority of the data and the user role, including full access rights, limited access rights, and restricted access rights;

[0038] High-priority data is stored on the blockchain and encrypted with AES. The hash value of the data is generated using the SHA-256 algorithm. The encrypted data and hash value are written into the blockchain as transaction records.

[0039] Store medium-priority data in a distributed storage network, encrypt the data and store the encrypted data in the IPFS network to generate a unique CID;

[0040] The low-priority data is stored in the cloud storage, and its metadata and hash value are recorded in the distributed hash table, the data is encrypted, and the SHA-256 hash value of the data is generated and recorded.

[0041] As a preferred solution of the user information management method based on application information described in the present invention, wherein: the verification of user permissions when a user issues a data access request means that when the user requests to access data, the basic information of the request is recorded, the identity and permission level of the user are verified, and the operation type requested by the user is matched with the queried permission level. If the user's requested operation matches his or her permission level, the system allows the operation to be performed and records the operation. If the user's requested operation does not match his or her permission level, the system rejects the request, returns the reason for rejection, and records the rejection operation.

[0042] Another object of the present invention is to provide a user information management system based on application information, which comprises:

[0043] User identity management module, which is used to collect user data through applications, generate the user's public key and private key using elliptic curve encryption algorithm, generate the user's unique distributed identity using secure hash algorithm, map the user's distributed identity and information, and form a complete user data set;

[0044] The identity verification module is used to generate chain hash values ​​and smart contracts and deploy them to the distributed node network, automatically trigger smart contracts in the node network, and verify the user's DID and chain hash value;

[0045] The identity matching verification module is used to query the device registration information database based on the user identity information, determine the matching degree of the user identity information, and integrate the matching results of the device registration information database with the user behavior data to form a time series data set;

[0046] The behavior verification module is used to predict user behavior patterns using the LSTM model, compare current user behavior with future behavior patterns using the dynamic time warping algorithm, calculate the cumulative minimum distance of the optimal alignment path, determine abnormal user behavior, mark and lock abnormal user accounts, and trigger further identity authentication;

[0047] The semantic verification module is used to use the knowledge graph technology to semantically map the user identity information with the matching results of the device registration information library, and to build a knowledge graph to analyze the semantic consistency of the user identity information;

[0048] The permission management module is used to obfuscate behavioral data, calculate data priority, and dynamically adjust the user's data access rights;

[0049] The request verification module is used to classify and store user data according to priority values, assign corresponding permission levels, verify the user's identity and permission level when the user requests access to data, and match the operation type with the permission level.

[0050] A computer device comprises: a memory and a processor; the memory stores a computer program, and the processor implements the steps of a user information management method based on application information when executing the computer program.

[0051] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a user information management method based on application information.

[0052] The beneficial effects of the present invention are as follows: the present invention significantly improves the efficiency and security of identity authentication by generating a unique distributed identity identifier and combining edge computing nodes and distributed node networks for multi-level verification. It performs real-time analysis of user behavior through fuzzy logic algorithms, dynamically adjusts data priorities and allocates access rights, thereby achieving efficient resource management and improving the flexibility of permission management and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0054] Figure 1 The figure is a flowchart of a user information management method based on application information.

[0055] Figure 2 It is a structural diagram of a user information management system based on application information. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0059] Example 1, reference Figure 1 , which is the first embodiment of the present invention, and which provides a user information management method based on application information. The user information management method based on application information includes the following steps:

[0060] S1. Obtain user information through the application and generate a unique distributed identity, and perform multi-level verification of the user identity through edge computing nodes and distributed node networks;

[0061] Specifically, obtaining user information through an application and generating a unique distributed identity means collecting the identity information submitted by the user when using the application and the user's behavior data, including login time, device information, and geographic location, pre-processing the collected data, using an elliptic curve encryption algorithm to generate the user's public key and private key, hashing the generated public key and the user's identity information through a secure hash algorithm to generate a unique distributed identity, and storing the generated distributed identity together with the user's public key and identity information in an edge computing node.

[0062] Distributed identity can effectively reduce the risk of single point failure and improve the security of the system. At the same time, by combining identity information with behavioral data, a more dynamic and accurate identity can be generated, further enhancing the privacy protection of users. This distributed identity also provides users with better cross-platform consistency and user experience, without the need for repeated registration or verification in different applications. Compared with the traditional RSA algorithm, the ECC algorithm can significantly reduce the key length and computational complexity while providing the same level of security, ensuring efficient encryption and decryption operations, thereby improving the overall performance of the system. In addition, the ECC algorithm provides strong security protection in the process of user identity generation and authentication, effectively preventing the risk of identity theft and data tampering. The identity generated by the hash algorithm has high anti-collision properties, and can ensure the uniqueness and data integrity of different identity even in the face of large-scale data. The introduction of edge computing significantly reduces the delay of data transmission and improves the response speed of the system, especially in the identity authentication scenario, which can quickly verify the user end and improve the user experience.

[0063] Further, multi-level verification of user identity information through edge computing nodes and distributed node networks refers to mapping distributed identity identifiers with user information through edge computing nodes to form a complete user data set;

[0064] After preprocessing the user data set, the format and integrity of the data set are verified;

[0065] Use a secure hash algorithm (such as SHA-256) to encrypt and hash each data field that passes the verification to generate a hash value and combine the generated hash values ​​through chain hashing technology to generate a final chain hash value;

[0066] Generate smart contracts based on identity authentication rules and processes, including clarifying the specific requirements for user identity authentication, including the type of user identity information, such as distributed identity identifiers (DIDs), public keys, behavioral data, etc., and specific rules for the verification process, including DID legitimacy checks, data consistency verification, and calls to external data sources. Based on the requirements for security and performance, a detailed requirements document is formed to provide clear guidance for the writing of smart contracts;

[0067] Design the structure of the smart contract according to the requirements document, including defining the data structure for storing user identity information, such as DID, public key, chain hash value, etc., writing core functions to verify DID, public key and chain hash value, defining functions to obtain data from external data sources (such as third-party identity verification platforms or social networks), defining how the contract updates the status based on the verification results (for example, recording information about successful or failed verification), and designing mechanisms to trigger events, such as triggering logging or notifying other parts of the system when verification is completed;

[0068] According to the design document, use Solidity or other smart contract programming languages ​​to write code and deploy it to each node in the distributed node network, and send the generated chain hash value and the user's distributed identity to multiple nodes in the distributed node network;

[0069] After receiving the verification request, the node automatically triggers the smart contract deployed on each node to verify the user's distributed identity and chain hash value. After the smart contract of each node completes the verification, a local verification result is generated and recorded in the distributed ledger;

[0070] Connect the device registration information library of the application, send a query request to the device registration information library based on the user identity information, retrieve the history and verification data related to the user, use TF-IDF to extract data features from the user identity information and the retrieved related data and convert text and structured data into feature vectors, and use the cosine similarity method to evaluate the similarity between feature vectors;

[0071] Set a judgment threshold Q, Q = μ1-2σ1, μ1 is the average value of the similarity score, σ1 is the standard deviation of the similarity score. If the similarity score is lower than or equal to the judgment threshold Q, the user identity information is marked as abnormal and cannot pass the preliminary identity information verification. If the similarity score is higher than the judgment threshold Q, the user identity information is normal and passes the preliminary identity information verification.

[0072] Integrate the user identity information obtained through matching the device registration information database with the user behavior data to form a time series data set, and standardize the integrated data;

[0073] Use the LSTM model to predict user behavior patterns. Use user historical behavior data as the training set to input the LSTM model for iterative training. Define the loss function and Adam optimizer to iteratively optimize the model parameters. When the loss of the LSTM model no longer decreases significantly during continuous iterations, stop iterating and output the model parameters to update the LSTM model.

[0074] Input the time series data set into the LSTM model to obtain the future user behavior pattern and extract the time series features from it to generate the prediction sequence B;

[0075] Compare the current user behavior data with the future user behavior pattern, extract the time series features from the current user behavior data, and form time series A;

[0076] Create a two-dimensional matrix DTW of size n*m, where n is the length of sequence A, m is the length of sequence B, and the matrix element DTW[i][j] represents the cumulative minimum distance between the i-th time point in time series A and the j-th time point in sequence B;

[0077] The starting point of the initialization matrix is ​​DTW[0][0], and the first line of the initialization matrix is: DTW[0][j] = ∞, (for i>0) indicating that the cumulative cost from the first time point to other time points is infinite;

[0078] The first column of the initialization matrix is: DTW[i][0] = ∞, (for j>0), which means that the cumulative cost from the first time point to other time points is infinite;

[0079] For each element DTW[i][j] in the matrix, calculate the distance d(A[i],B[j]) between time series A[i] and B[j]:

[0080]

[0081] Use the dynamic programming formula to calculate the minimum cumulative distance for each element in the matrix:

[0082] DTW[i,j]=d(A[i],B[j])+min(DTW[i-1,j],DTW[i,j-1],DTW[i-1,j-1])

[0083] Where DTW[i-1,j] represents the cumulative distance from the point above to the current point, DTW[i,j-1] represents the cumulative distance from the point on the left to the current point, and DTW[i-1,j-1] represents the cumulative distance from the point on the upper left to the current point.

[0084] Fill the entire DTW matrix in sequence, ensuring that the value of each element represents the optimal alignment distance between the current behavior pattern and the future behavior pattern at the corresponding time point. The value DTW[n][m] in the lower right corner of the final DTW matrix is ​​used as the final DTW distance, which represents the cumulative minimum distance of the optimal alignment path between the time series of the current user behavior and the time series of the predicted future user pattern.

[0085] The smaller the calculated DTW distance is, the more similar the current behavior sequence is to the predicted future behavior pattern, and the larger the distance is, the greater the difference between the two.

[0086] Set the judgment threshold U, U = μ2 + k*σ2, and compare it with the DTW distance to determine the abnormality of user behavior:

[0087] If the DTW distance value is greater than U, it means that the behavior data is abnormal, and the user account is marked and locked, waiting for re-authentication;

[0088] If the DTW distance value is less than or equal to U, it means that the behavior data is normal and passes the identity authentication;

[0089] μ2 is the average DTW distance of normal behavior, σ2 is the standard deviation of the DTW distance of normal behavior, and the value of the adjustment coefficient k is based on the statistical characteristics of the normal distribution. It is usually selected between 1 and 3. The specific value depends on the system's anomaly detection requirements and tolerance for false alarms. In practical applications, combining historical data and system goals, selecting an appropriate k value can achieve a balance between anomaly detection accuracy and false alarm rate;

[0090] The user's identity information is mapped to the semantic network through the knowledge graph technology, and the matching results with the device registration information library and the user's identity information are integrated as the basic data for building the knowledge graph, including the user's basic identity information, historical behavior data, device information and matching identity data. The core node of the knowledge graph is initialized by aligning the results of the device registration information library, and the anomaly detection mark of the time series analysis is added to the knowledge graph. It is connected with the core node as an auxiliary node to form a complete graph structure. The anomaly mark node of the time series analysis can represent the user's behavior pattern and possible risk points in different time periods. This information will help the system identify potential anomalies during semantic reasoning. Relationship edges are constructed based on the correlation of user information, and the semantic relationship between nodes is defined (for example, the user's device ID and geographic location may be connected through a "location association" relationship, and the login time and operation habits may be connected through a "time association" relationship. The construction of relationship edges ensures that the system can identify the semantic connection between different nodes during reasoning. These connections can help the system determine whether there is unreasonable change in information during the verification process);

[0091] The inference engine of the knowledge graph performs semantic inference on the user identity information, traverses the nodes and relationship edges in the knowledge graph, and analyzes the semantic consistency between the information; (During the inference process, the system will focus on the abnormal nodes marked by the time series analysis, and check whether these nodes maintain a reasonable semantic relationship with other nodes. For example, if the user's geographical location changes abnormally in a short period of time, the system will check whether the change is consistent with other information in the device registration information library (such as device information or login time)).

[0092] After the reasoning is completed, a semantic consistency verification structure is generated. If an abnormality in the semantic relationship is found during the reasoning process, the abnormality is marked and a warning signal is issued. The user needs to authenticate the identity again. If no abnormality is found, the user has successfully passed the identity authentication.

[0093] The user identity information is verified at multiple levels through edge computing nodes and distributed node networks, and the user data fields are encrypted in combination with chain hash technology to ensure the security and integrity of user data during transmission and verification. The traditional single-level verification method usually has a high security risk, while the present invention significantly improves the security of the system and reduces the risk of data tampering through a multi-level verification mechanism. In addition, the application of chain hash technology enables the association between each data field to be retained, ensuring the integrity of the data in the entire chain. Through the design and deployment of smart contracts, the automation and decentralization of identity authentication are achieved. Smart contracts clarify the rules and processes for user identity authentication and can automatically perform verification tasks. Not only does it improve the execution efficiency of the system, it also reduces the possibility of human intervention and ensures the fairness and transparency of the verification process. Compared with the traditional manual verification method, the use of smart contracts greatly reduces the operating cost, while improving the speed and reliability of verification. The user behavior is analyzed in real time through fuzzy logic algorithms, and the priority of user data is dynamically adjusted and access rights are allocated according to the analysis results. This dynamic adjustment mechanism ensures the reasonable allocation of system resources, and can respond to changes in user behavior in real time, avoiding the problems of resource waste and abuse of authority. Compared with the traditional static permission management method, dynamic permission allocation is more flexible and adaptable, and can better meet the actual needs of users. The LSTM model can accurately predict the user's future behavior pattern by training the user's historical behavior data. This prediction ability not only helps the system to identify potential risk behaviors in advance, but also adjusts the system settings in advance according to the user's behavior changes. Compared with the traditional linear prediction model, the LSTM model has stronger memory and the ability to process complex sequence data, so it performs well in user behavior prediction. By integrating user identity information with behavior data through knowledge graph technology, the system can identify abnormal user behavior in a complex semantic network. The application of knowledge graph enhances the system's semantic understanding ability, can effectively identify the correlation between user information and potential risk points, and ensure that the identity authentication process is more comprehensive and accurate. Compared with the traditional rule matching method, the knowledge graph can better handle complex relational data and improve the system's reasoning and decision-making capabilities.

[0094] S2, real-time analysis of user behavior through fuzzy logic algorithm, adjusting the priority of user data and dynamically allocating data access rights based on the analysis results;

[0095] Specifically, the user behavior is analyzed in real time through the fuzzy logic algorithm, the priority of the user data is adjusted according to the analysis results, and the data access rights are dynamically allocated. This means that after the user passes the identity authentication, the user's operation behavior in the application is monitored and collected in real time, and the collected behavior data is pre-processed;

[0096] The pre-processed behavior data is fuzzy processed to convert continuous variables into membership in fuzzy sets. The main input variables include application usage frequency, user activity and data importance, and the output variable is data priority.

[0097] Define fuzzy logic rules, including the correspondence between fuzzy sets of input variables and output priorities:

[0098] Rule 1: If F is "High", and A is "High", and I is "High", then the data priority (P) is "Very High";

[0099] Rule 2: If F is "low", and A is "medium", and I is "low", then the data priority (P) is "low";

[0100] Each rule defines the correspondence between the fuzzy set of input variables and the output priority;

[0101] The input variables are mapped to membership using the Mamdani inference method, the membership of the fuzzy priority is calculated for each rule, and the results of multiple rules are aggregated to form the final fuzzy output;

[0102] After the fuzzy inference is completed, the fuzzy priority results are converted into priority values ​​using the centroid method:

[0103]

[0104] Where P is the final priority score, z is the value range of the priority score, and μ P (z) is the membership function corresponding to the output variable z, which represents the membership of different priority values, and is preferably a gradient function in this embodiment;

[0105] According to the calculated priority value P, dynamically adjust the user's access rights to data, including:

[0106] Define corresponding fuzzy sets for each input and output variable, including defining application usage frequency as high, medium, and low, and defining data priority as high, medium, and low;

[0107] Fuzzy rules are formulated based on business logic and actual needs. Each rule is usually expressed in the form of "if-then", such as "if the application is used frequently, the user activity is high, and the data is important, then the data priority is extremely high."

[0108] The user behavior data is fuzzy processed by fuzzy logic algorithm, and the continuous variables are converted into membership in fuzzy sets. This solves the problem of too high accuracy requirements for user behavior data in traditional data processing methods. User operation behaviors are often complex and difficult to express with precise numerical values. Fuzzy processing methods can handle these uncertain data more flexibly and improve the system's adaptability to different user behavior patterns. For example, the user's application usage frequency and activity are not always linearly related. Through fuzzification, these uncertain features can be more appropriately expressed, thereby avoiding oversimplification or neglect of user behavior, defining fuzzy logic rules, and the corresponding relationship between fuzzy sets of input variables and output priorities. This process reflects the system's flexible response to different user behavior patterns. For example, through rules 1 and 2, the system can automatically identify the priority of high-frequency, high-activity, and high-importance data, and give them extremely high priority, thereby ensuring that important data is given priority when accessed. Compared with traditional static rule settings, fuzzy logic rules can reflect changes in user behavior more dynamically and finely, and improve the resource allocation efficiency of the system. The Mamdani reasoning method obtains the fuzzy output of data priority through comprehensive analysis of multiple input variables, and then converts the fuzzy output into a specific priority score (P) through the centroid method. It solves the problem that it is difficult to handle complex relationships between multiple variables in the existing technology. The Mamdani reasoning method can handle the nonlinear relationship between multiple input variables, making the priority calculation more scientific and reasonable. The centroid method provides a balanced output result by calculating the center of mass, avoiding the occurrence of extreme situations and improving the stability and robustness of the system. Dynamic adjustment can respond quickly according to the real-time behavior changes of users, ensuring that high-priority data is processed in a timely manner, reducing delays and potential risks. Secondly, by real-time monitoring and adjusting permissions, the system can improve the flexibility and efficiency of resource allocation while maintaining security.

[0109] Furthermore, according to the calculated priority value P, dynamically adjusting the user's access rights to the data refers to defining the access rights as the highest rights: full access rights, allowing reading, modifying, and deleting data;

[0110] Advanced permissions: allow reading and modifying data, but not deleting;

[0111] Intermediate permissions: only allow reading of data, not modification or deletion;

[0112] Least privilege: limited access, only allowing viewing of some data, or requiring further verification for access;

[0113] Statistically analyze the user behavior data, calculate the mean and standard deviation of the user behavior data, and set thresholds with different priorities according to the mean and standard deviation: P1 = μ + σ, P2 = μ, P3 = μ - σ. Compare with the priority value P to determine the user's access rights:

[0114] When P ≥ P1, the highest privilege is assigned;

[0115] When P2 ≤ P < P1, the high privilege is assigned;

[0116] When P3 ≤ P < P2, the medium privilege is assigned;

[0117] When P < P3, the lowest privilege is assigned;

[0118] According to the change of the priority value P, the user's access rights are adjusted in real time. If P increases above P1, the user's privilege is automatically upgraded to the highest privilege, allowing the user to perform comprehensive operations on the data. If P decreases below P3, the user's privilege is downgraded to the lowest privilege, restricting the user's access to the data.

[0119] According to the real-time changes of the user behavior data, dynamically adjust the user's access rights to the data to ensure the reasonable allocation of system resources and data security. Compared with the traditional static privilege management mode, this dynamic adjustment method has higher flexibility and adaptability, can respond to the changes of user behavior in a timely manner, thus improving the security of the system and the user experience. Calculating the mean and standard deviation of the user behavior data can provide a scientific basis for setting priorities. The mean reflects the user's regular behavior pattern, while the standard deviation reveals the fluctuation range of the user's behavior. Through these statistics, the system can reasonably set the priority thresholds, thus ensuring security while avoiding excessive restrictions on user privileges. Adjusting the privileges in real time and automatically upgrading or downgrading the privileges according to the change of the priority value P significantly improves the response ability of the system. This dynamic adjustment mechanism can not only prevent the problem of privilege lag, but also adjust the privileges in a timely manner when the user behavior changes, avoiding unnecessary security risks.

[0120] S3. Classify and store the user data according to the priority and privilege settings of the data, and verify the user's privileges when the user sends a data access request;

[0121] Specifically, classifying and storing user data according to data priority and permission settings means classifying all user data according to the calculated priority value, and assigning corresponding permission levels according to the data priority and user role, including full access permission high priority data assigned to administrators or specific users, allowing reading, modification, and deletion operations, limited access permission medium priority data assigned to ordinary users, allowing reading and partial modification operations but not allowing deletion of data, and limited access permission low priority data only allowing users to read operations but not modify or delete;

[0122] High-priority data is stored on the blockchain and encrypted with AES. The hash value of the data is generated using the SHA-256 algorithm. The encrypted data and hash value are written into the blockchain as transaction records.

[0123] Store medium-priority data in a distributed storage network (such as IPFS), and only record its metadata (such as CID and data summary) on the blockchain. Use AES encryption technology to encrypt the data, store the encrypted data in the IPFS network, generate a unique CID, and record the CID, data summary and timestamp on the blockchain to ensure the traceability and integrity of the data.

[0124] Store low-priority data in cloud storage and record its metadata and hash values ​​in a distributed hash table (DHT). Encrypt the data using a lighter encryption algorithm (such as XOR encryption) to generate a SHA-256 hash value for the data to ensure data integrity. Record the hash value and metadata in the DHT for subsequent search and access.

[0125] By classifying data by priority and assigning corresponding permission levels according to user roles, high-priority data is ensured to be fully protected, while optimizing the resource allocation of the system. This design avoids the waste of resources and security risks caused by the unified processing of all data in traditional systems. By introducing a priority classification mechanism, the flexibility of permission management is solved, ensuring that data of different priorities are properly protected and have access rights. It not only improves the security of the system, but also improves the access efficiency. Especially when facing a large amount of data, it can significantly reduce the resources occupied by low-priority data. High-priority data is stored on the blockchain, combined with AES encryption and SHA-256 hash algorithm to ensure that the data has a high degree of security and cannot be tampered with during storage and transmission. At the same time, the confidentiality and integrity of the data are further improved by AES encryption and SHA-256 hash algorithm. By storing medium-priority data in a distributed storage network (such as IPFS) and recording its metadata (CID and data summary) on the blockchain, distributed storage and management of data is achieved. This not only ensures the traceability of data, but also improves the availability and disaster recovery of data through distributed storage. Low-priority data is stored in cloud storage using lighter-weight encryption algorithms (such as XOR encryption) and SHA-256 hashing algorithms to ensure data integrity, and metadata and hash values ​​are managed through a distributed hash table (DHT).

[0126] Furthermore, when a user issues a data access request, verifying user permissions means recording basic information of the request, including request time, requesting user ID, requested data ID, and operation type, verifying the user's identity and permission level, and matching the operation type requested by the user with the queried permission level. If the user's requested operation matches his or her permission level, the system allows the operation to be performed and records the operation. If the user's requested operation does not match his or her permission level, the system rejects the request, returns the reason for rejection, and records the rejection operation.

[0127] Recording the basic information of the request helps the system track the user's operation throughout the process. By recording the request time, user ID, data ID and operation type in detail, the system can establish a comprehensive operation log and provide a basis for post-audit. Not only can it trace the user's historical operations, but it can also provide an important basis for subsequent permission verification, anomaly detection and security analysis. By verifying the user's identity and permission level at each data access request, the system can monitor the user's operation behavior in real time to ensure that only authorized users can perform the corresponding operations. Compared with the traditional permission management mode, the real-time verification mechanism of the present invention can significantly improve the security of the system and prevent unauthorized operations from occurring. By matching and verifying the user's request operation type with its permission level, it can ensure that the user's operation permissions comply with the system settings and avoid high-privilege operations being performed by low-privilege users. By recording the reasons for rejection, system administrators can more quickly discover loopholes or potential security issues in permission settings and make timely adjustments.

[0128] Example 2, reference Figure 2 , which is a second embodiment of the present invention, and which is different from the previous embodiment, provides a user information management system based on application information, which includes:

[0129] User identity management module, which is used to collect user identity information and behavior data through applications, generate user public and private keys using elliptic curve encryption algorithm, generate user's unique distributed identity using secure hash algorithm, map user's distributed identity and information, and form a complete user data set;

[0130] The identity verification module is used to encrypt the data field using a secure hash algorithm, generate a chain hash value, generate a smart contract and deploy it to a distributed node network, automatically trigger the smart contract in the node network, and verify the user's DID and chain hash value;

[0131] The identity matching and verification module is used to query the device registration information database based on the user identity information, retrieve relevant historical records and verification data, determine the matching degree of the user identity information, and integrate the matching results of the device registration information database with the user behavior data to form a time series data set;

[0132] The behavior verification module is used to predict user behavior patterns using the LSTM model, compare current user behavior with future behavior patterns using the dynamic time warping algorithm, calculate the cumulative minimum distance of the optimal alignment path, determine abnormal user behavior, mark and lock abnormal user accounts, and trigger further identity authentication;

[0133] The semantic verification module is used to use the knowledge graph technology to semantically map the user identity information with the matching results of the device registration information library, build a knowledge graph, and use the reasoning engine of the knowledge graph to analyze the semantic consistency of the user identity information;

[0134] The permission management module is used to obfuscate behavioral data, calculate data priority, and dynamically adjust the user's data access rights;

[0135] The request verification module is used to classify and store user data according to priority values, assign corresponding permission levels, verify the user's identity and permission level when the user requests access to data, and match the operation type with the permission level.

[0136] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0137] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0138] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0139] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

Claims

1. A user information management method based on application information, characterized in that: The following steps are included: Obtain user information through the application and generate a unique distributed identity, and perform multi-level verification of user identity through edge computing nodes and distributed node networks; Analyze user behavior in real time through fuzzy logic algorithms, adjust the priority of user data and dynamically allocate data access rights based on the analysis results; User data is stored in categories based on data priority and permission settings, and user permissions are verified when users make data access requests.

2. The user information management method based on application information according to claim 1, characterized in that: Obtaining user information through an application and generating a unique distributed identity identifier refers to collecting user-related data, preprocessing the collected data, using an elliptic curve encryption algorithm to generate the user's public key and private key, hashing the generated public key and the user's identity information through a secure hash algorithm to generate a unique distributed identity identifier, and storing the generated distributed identity identifier together with the user's public key and identity information in an edge computing node.

3. The user information management method based on application information as claimed in claim 2, characterized in that: The multi-level verification of user identity information through edge computing nodes and distributed node networks refers to mapping distributed identity identifiers with user information through edge computing nodes to form a complete user data set; Perform cryptographic hashing on each data field to generate a hash value and combine the generated hash values ​​through chain hashing technology to generate the final chain hash value; Generate a smart contract and deploy it to each node in the distributed node network, and send the generated chain hash value and the user's distributed identity to multiple nodes in the distributed node network; After receiving the verification request, the node triggers the smart contract to verify the user's distributed identity and chain hash value, and generates a local verification result after completing the verification; Connect the device registration information library of the application, send a query request to the device registration information library based on the user identity information, retrieve the history and verification data related to the user, use TF-IDF to extract data features from the user identity information and the retrieved related data and convert text and structured data into feature vectors, and use the cosine similarity method to evaluate the similarity between feature vectors; Set a judgment threshold Q. If the similarity score is lower than or equal to the judgment threshold Q, the user identity information is marked as abnormal and cannot pass the preliminary identity information verification. If the similarity score is higher than the judgment threshold Q, the user identity information is normal and passes the preliminary identity information verification. Integrate the user identity information obtained through matching the device registration information database with the user behavior data to form a time series data set, and standardize the integrated data; Use the LSTM model to predict user behavior patterns, obtain future user behavior patterns, extract time series features from them, and generate prediction sequence B; Compare the current user behavior data with the future user behavior pattern, extract the time series features from the current user behavior data, and form time series A; Create a two-dimensional DTW matrix of size n*m, where n is the length of sequence A and m is the length of sequence B; Initialize the starting point of the matrix, the first row of the matrix, and the first column of the matrix; For each element DTW[i][j] in the matrix, calculate the distance d(A[i], B[j]) between time series A[i] and B[j], and use the dynamic programming formula to calculate the minimum cumulative distance of each element in the matrix; Fill the entire DTW matrix in sequence, and take the value DTW[n][m] in the lower right corner of the final DTW matrix as the final DTW distance; The smaller the calculated DTW distance, the more similar the current behavior sequence is to the predicted future behavior pattern. The larger the calculated DTW distance, the greater the difference between the two; Set a judgment threshold U and compare it with the DTW distance to judge the abnormal situation of user behavior: If the DTW distance value is greater than U, it means that the behavior data is abnormal, mark and lock the user account, and wait for re-initiation of identity verification; If the DTW distance value is less than or equal to U, it means that the behavior data is normal and the identity verification is passed; Map the user's identity information to the semantic network through knowledge graph technology. Initialize the core nodes of the knowledge graph through the results of aligning the device registration information repository. Add the anomaly detection marks of time series analysis to the knowledge graph as auxiliary nodes connected to the core nodes to form a complete graph structure. Construct relationship edges according to the relevance of user information and define the semantic relationships between nodes; Perform semantic reasoning on the user identity information through the inference engine of the knowledge graph, traverse the nodes and relationship edges in the knowledge graph, analyze the semantic consistency between information. After the reasoning is completed, generate a verification structure of semantic consistency. If semantic relationship anomalies are found during the reasoning process, mark the anomalies and send warning signals, and the user needs to perform identity verification again. If no anomalies are found, the user successfully passes the identity verification.

4. The user information management method based on application information as claimed in claim 3, characterized in that: The real-time analysis of user behavior through the fuzzy logic algorithm and the adjustment of the priority of user data and dynamic allocation of data access rights according to the analysis results refer to that after the user passes the identity verification, real-time monitor and collect the operation behavior of the user in the application, and preprocess the collected behavior data; Perform fuzzy processing on the preprocessed behavior data, convert continuous variables into membership degrees in the fuzzy set. The main input variables include application usage frequency, user activity, and data importance, and the output variable is data priority; Define fuzzy logic rules, use the Mamdani reasoning method to map the input variables to membership degrees, calculate the membership degrees of fuzzy priorities for each rule, and aggregate the results of multiple rules to form the final fuzzy output; After the fuzzy reasoning is completed, use the centroid method to convert the fuzzy priority results into priority values; Dynamically adjust the user's access rights to data according to the calculated priority value P.

5. The user information management method based on application information as claimed in claim 4, characterized in that: The dynamic adjustment of the user's access rights to data according to the calculated priority value P refers to defining the access rights as the highest right, high right, medium right, and lowest right; Set judgment thresholds P1, P2, and P3 for different priorities, compare them with the priority value P, and judge the user's access rights: When P≥P1, assign the highest right; When P2≤P<P1, assign the high right; When P3≤P<P2, assign the medium right; When P < P3, the lowest permission is assigned.

6. The user information management method based on application information as claimed in claim 5, characterized in that: The classification storage of user data according to the priority and permission settings of the data means classifying all user data according to the calculated priority values, and assigning corresponding permission levels according to the priority of the data and the user role, including full access permission, limited access permission, and restricted access permission; Store high-priority data on the blockchain for AES encryption and generate a hash value of the data through the SHA-256 algorithm. Write the encrypted data and the hash value as a transaction record into the blockchain; Store medium-priority data in a distributed storage network, encrypt the data, and store the encrypted data in the IPFS network to generate a unique CID; Store low-priority data in cloud storage, record its metadata and hash value in a distributed hash table, encrypt the data, generate the SHA-256 hash value of the data and record it.

7. The user information management method based on application information according to claim 6, characterized in that: The verification of the user's permission when the user issues a data access request means that when the user requests to access data, record the basic information of the request, verify the user's identity and permission level, and match the operation type requested by the user with the queried permission level. If the user's requested operation matches their permission level, the system allows the operation to be executed and records the operation. If the user's requested operation does not match their permission level, the system rejects the request, returns the reason for rejection, and records this rejection operation at the same time.

8. A user information management system based on application information based on the user information management method based on application information according to any one of claims 1 to 7, characterized in that: Including, A user identity management module for collecting user data through an application, generating the user's public key and private key using the elliptic curve encryption algorithm, generating the user's unique distributed identity identifier using the secure hash algorithm, and mapping the user's distributed identity identifier and information to form a complete user data set; An identity verification module for generating a chained hash value and a smart contract and deploying them to a distributed node network, automatically triggering the smart contract in the node network to verify the user's DID and the chained hash value; An identity matching verification module for querying the device registration information library according to the user identity information, judging the matching degree of the user identity information, and integrating the matching result of the device registration information library with the user behavior data to form a time series data set; A behavior verification module for predicting the user behavior pattern using the LSTM model, comparing the current user behavior with the future behavior pattern using the dynamic time warping algorithm, calculating the cumulative minimum distance of the optimal alignment path, judging the abnormal situation of the user behavior, marking and locking the abnormal user account, and triggering further identity verification; A semantic verification module for semantically mapping the matching result of the user identity information and the device registration information library using knowledge graph technology, and constructing a knowledge graph to analyze the semantic consistency of the user identity information; A permission management module for fuzzily processing the behavior data, calculating the data priority, and dynamically adjusting the user's data access permission; A request verification module for classifying and storing user data according to the priority value, assigning corresponding permission levels, verifying the user's identity and permission level when the user requests to access data, and matching the operation type with the permission level.

9. A computer device comprising: A memory and a processor; The memory stores a computer program, wherein the processor implements the steps of the user information management method based on application information according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the user information management method based on application information described in any one of claims 1 to 7 are implemented.

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