Enterprise registration file sharing and shareholder right-of-knowledge guarantee system based on full-chain data tracing and auditing

Abstract

The invention discloses an enterprise registration archive sharing and shareholder right-of-knowledge guarantee system based on full-chain data tracing and auditing, and relates to the technical field of archive management. The system comprises an identity authentication module, an archive storage management module, an archive sharing module, a shareholder right of knowledge guarantee module, a comprehensive statistics module and a system security module. According to the system, full-chain tracing of archive data is realized by utilizing a block chain technology, all change operations are completely recorded, and the transparency of archive management is remarkably improved; the security and authenticity of the shared archive are ensured through electronic signature, watermark and two-dimensional code anti-counterfeiting technologies; shareholders can track stock equity states and change records of enterprises in real time and know enterprise change information in time through a dynamic notification mechanism, so that the right of knowing of the shareholders is comprehensively guaranteed; through dynamic encryption, authority management and abnormal behavior detection, the security of file storage and access is ensured, and data leakage and tampering are effectively prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of archive management, and in particular to an enterprise registration archive sharing and shareholder right-to-know protection system based on full-chain data tracing and auditing. Background Art

[0002] With the rapid development of informatization and digitalization, the electronicization and informatization of enterprise registration files have gradually become a trend. As important basic data of enterprises, enterprise registration files contain key information such as enterprise establishment, change, and equity structure. However, the existing enterprise registration file management and sharing model still has many problems in practical application, especially in terms of query efficiency, management transparency, security, and user rights protection, which need to be improved and enhanced.

[0003] At present, the query process of most enterprise registration files still relies on the traditional offline mode. Users need to submit paper applications at the industrial and commercial enterprise query window. The file query process is cumbersome and the query cycle is long. Especially for remote users, the cost of obtaining file information is high. The existing system is difficult to support fast online query, and users cannot obtain the latest registration information of enterprises in real time, which limits the timeliness and availability of information.

[0004] The management of enterprise registration files usually adopts a centralized model, which is uniformly stored and maintained by relevant institutions or departments. Since the registration files involve a large amount of data and frequent changes, it is difficult for the centralized management model to synchronize information in a timely manner, which may cause users to obtain outdated data. Under the traditional model, the storage and access records of file data cannot be traced, and it is difficult for users to verify whether the file information has been modified or leaked, which reduces the transparency of file management.

[0005] With the advancement of enterprise information management, the demand for sharing enterprise registration files has increased significantly. Different user groups (such as shareholders and the public) have different access requirements for files, but the existing system is difficult to flexibly adjust access rights according to user identity, which may lead to insufficient or excessive data sharing. In the process of file sharing, the data anti-counterfeiting and anti-tampering capabilities are insufficient, and files may be maliciously tampered with, forged or leaked.

[0006] Enterprise registration files play an important role in safeguarding the rights and interests of shareholders, but the current file system lacks effective means to verify whether the query user is a legitimate shareholder, resulting in the inability of legitimate shareholders to obtain the information they deserve. It is difficult for shareholders to grasp the changes in the company's equity in real time, and it is impossible to track the company's major changes in time, which limits the shareholders' ability to supervise the company. Summary of the invention

[0007] In response to the problems of insufficient efficiency in enterprise registration file management and insufficient data credibility, the present invention proposes an enterprise registration file sharing and shareholder right to know protection system based on full-chain data traceability and auditing, which can improve query efficiency, enhance information transparency, ensure data security and improve user rights protection mechanism to better meet the needs of enterprise informatization development.

[0008] In order to achieve the above object, the present invention designs the following technical solutions:

[0009] A system for sharing enterprise registration files and protecting shareholders' right to know based on full-chain data traceability and auditing, the system includes:

[0010] The identity authentication module is used to realize the user identity authentication of social public users, government agency users and contracted users through the distributed key management system combined with the CA authentication platform and the electronic business license platform, and to determine whether the inquirer is a shareholder through the interface of the natural person identity authentication platform;

[0011] The archive storage management module is used to realize the storage, management, tracing and auditing of enterprise registration archives based on blockchain technology and distributed storage technology;

[0012] The archive sharing module is used for multi-level directory index tree information display and quick query of enterprise registration archives, automatic retrieval of archive information, interactive multi-level index query, and quick location of archive images based on the case directory and the file directory within the volume. It also performs electronic signature, watermark and QR code anti-counterfeiting processing on archive images under authorized conditions, and shares enterprise registration archive information;

[0013] The shareholder right to know protection module is used to provide share ownership tracking through blockchain technology, check the company's share status and the update of the company's registration information, and ensure the shareholders' right to know;

[0014] Comprehensive statistics module, used to collect statistics on archive query and shared data according to the query person, enterprise directory, query content and query time, and generate various forms of reports;

[0015] The system security module is used to provide file classification control, user authority management and full-process security log recording functions to ensure the security of file query and sharing.

[0016] As a preferred solution of the present invention, the identity authentication module includes:

[0017] The identity credential verification unit is used to receive the identity credentials submitted by the user, including CA certificate, electronic business license or natural person identity information, verify the legitimacy of the CA certificate by connecting to the CA authentication platform, verify the validity of the business license by connecting to the electronic business license platform, and call the natural person identity authentication platform to verify whether the user is a legal corporate shareholder;

[0018] A permission allocation unit, used to dynamically generate user access permission information based on identity credential verification results, including user identity category, permission level, and user-accessible data range;

[0019] A distributed key generation unit, used to generate dynamic keys for user authentication and authority information based on a distributed key management system to support subsequent data encryption and decryption operations;

[0020] The verification result output unit is used to output the user identity verification result, including the user identity verification status, user role type, user authority level, identity verification timestamp, verification source, and whether it matches the enterprise information.

[0021] As a preferred solution of the present invention, the archive storage management module includes:

[0022] The data storage unit is used for the storage and management of enterprise registration files. It uses distributed storage technology to store the file image data, associated information and multi-level index data of the enterprise registration files, stores the metadata of the enterprise registration files on the blockchain, and uses a distributed key management system to dynamically encrypt the stored data;

[0023] The data tracing unit is used to trace the change records, user operation logs and data access records of enterprise registration archives based on the blockchain timestamp function. It supports the traceability of archive information at any time point, generates a change chain, displays all historical versions of archive data, and supports visual display of tracing results, including the time of archive data change, operation subject and specific change content;

[0024] The data audit unit is used to record and audit all archival data access operations in the system in real time through blockchain smart contracts, verify the permissions of archival operations, mark and record unauthorized operations or abnormal behaviors, and generate audit logs.

[0025] As a preferred solution of the present invention, the data tracing unit performs pattern matching and change chain analysis of archive change records based on an improved multiple sequence alignment algorithm, specifically including:

[0026] When formatting archival data, we use knowledge graph technology to build semantic associations of archival data, modeling enterprise registration information, shareholder structure, and change records as directed graphs of entities and relationships. Nodes represent fields, and edges represent semantic relationships between fields. The fields of each archival version are structured into a graph model.

[0027] The multi-version archive comparison process is expanded to a comparison based on graph embedding and graph neural network. The formatted archive field graph is input into the graph embedding model, and the graph nodes are mapped into feature vectors of fixed dimensions. The feature similarity of multi-version archive fields is learned through GNN, and the change similarity of each field is calculated. The comparison result matrix is ​​generated, and the matching, change or insertion status of the field is automatically marked.

[0028] Among them, the graph embedding model formula is: i =f(W·A·h i-1 + b);

[0029] In the formula, h i represents the embedding vector of node i, h i-1 represents the embedding vector of the previous layer node i-1; A is the adjacency matrix of the archive field graph; W represents the weight matrix; b is the bias term; f is the activation function;

[0030] The field change similarity formula is:

[0031] In the formula, h j represents the embedding vector of node j; Sim(i,j) represents the change similarity between node i and node j;

[0032] When Sim(i,j) is greater than the preset threshold, it is judged as a matching field, otherwise it is changed;

[0033] Use density clustering algorithms to cluster field states in change chains and automatically identify common change patterns;

[0034] Use dynamic network diagrams to represent the change relationship between fields. Changes to multiple fields by the same subject are represented by dynamic edges.

[0035] Use heatmaps to show how often fields are changed.

[0036] As a preferred solution of the present invention, the file sharing module includes:

[0037] A directory index generating unit, used to generate a multi-level directory index based on the structured data of the enterprise registration file, and organize and display it in a tree-like information form;

[0038] The archive image positioning unit is used to quickly locate and retrieve archive images based on the file directory and the file directory within the volume, combined with OCR technology and super-resolution generative adversarial network;

[0039] The archive anti-counterfeiting processing unit is used to perform comprehensive anti-counterfeiting processing on shared archive images using electronic signatures, watermarks, and dynamic QR codes to ensure the authenticity and tamper-proof nature of shared archives;

[0040] The quick query and authorization unit is used for interactive multi-level index query of enterprise registration archives, dynamically authorizes access to shared archives based on user permissions, and provides archive sharing function when the authorization conditions are met.

[0041] As a preferred solution of the present invention, the shareholder right to know protection module includes:

[0042] The share tracking unit is used to track the status and change records of corporate shares based on graph neural networks and blockchain technology to ensure real-time update and transparency of all share information;

[0043] The equity status display unit is used to generate shareholder shareholding ratios and share change history charts through dynamic data visualization technology to intuitively display the equity status to shareholders;

[0044] The real-time update notification unit is used to trigger the notification mechanism through the blockchain and send real-time update reminders to shareholders when there are changes in the company registration information or shareholder share status.

[0045] As a preferred solution of the present invention, the steps for implementing the share tracking unit are as follows:

[0046] Defining a dynamic graph: Using a dynamic graph G t =(V t ,E t ,W t ) represents the time series model of equity relationship, where V t is the node set at time t, E t is the edge set at time t, W t is the edge weight at time t;

[0047] Add a time attribute to each node and edge: in is the initial feature vector of node v; Init(v) represents the function of initializing the initial characteristics of node v and generating the feature vector related to the node; represents the feature concatenation operation; TimeEmbed(t) is the time embedding vector;

[0048] Incremental update of equity changes: When the blockchain records equity changes, it dynamically modifies G t, add new nodes or modify edge weights;

[0049] Aggregation formula optimization: When aggregating neighbor features, introduce time weight α and edge weight W e The joint weighting of: Where m v is the aggregation result of neighbor features of node v; N(v) is the set of neighbor nodes of node v; α t is the time weight; is the feature representation of neighbor node u in the lth layer of the graph neural network;

[0050] Node feature update: Use the optimized neighbor feature aggregation formula: in, is the feature vector of node v at layer l+1; is the feature vector of node v in layer l; Q is the weight matrix; k is the bias vector; σ is the activation function;

[0051] Hierarchical storage: Dynamic graphs are stored in layers according to time. Recently changed nodes and edges are stored in high-priority layers, and historical data is stored in low-priority layers. Distributed storage technology is used to manage data layers.

[0052] Index optimization: Create a time index for each node and edge to support quick search of equity relationships at a specified time point:

[0053] Index(v,t)={edges(v)|t start ≤t≤t end};

[0054] In the formula, Index(v,t) is the edge index set of node v at time t; edges(v) represents the edge set connected to node v; t start is the start time of the time range, t end The end time of the time range.

[0055] As a preferred solution of the present invention, the comprehensive statistics module includes:

[0056] Multidimensional data analysis unit, used to perform statistical analysis on archival data based on the query person, enterprise directory, query content and query time;

[0057] Report generation unit, used to generate various forms of reports based on statistical results, including charts and tables, and supports exporting to PDF or Excel files;

[0058] The dynamic statistics display unit is used to display statistical data in real time through a visual dashboard and supports dynamic adjustment of statistical conditions.

[0059] As a preferred solution of the present invention, the system security module includes:

[0060] The confidentiality control unit is used to dynamically control the user's query authority according to the confidentiality level of the archives, ensuring that users of different levels can only access the archive data within their authority range;

[0061] The security log management unit is used to record the user's login, query, sharing and other operation logs, and ensure the logs cannot be tampered with based on blockchain technology;

[0062] The abnormal behavior detection unit is used to analyze user behavior through machine learning algorithms, automatically detect and mark abnormal operations, and issue security alerts to administrators.

[0063] As a preferred solution of the present invention, the distributed key management system includes:

[0064] Key generation unit, used to generate dynamic keys based on distributed key management technology, supporting user identity authentication and encrypted storage of archival data;

[0065] Key distribution unit, used to securely distribute dynamic keys to different modules to support archive access, sharing and encrypted transmission;

[0066] The key update and recovery unit is used to dynamically update keys when user permissions change, and to safely recover keys that are no longer in use to avoid key leakage.

[0067] The beneficial effects of the present invention are: full-chain traceability of archive data is achieved by using blockchain technology, all change operations are fully recorded, the transparency of archive management is significantly improved, and the authenticity and credibility of data are guaranteed; multi-level directory indexing and interactive query functions make archive query fast and convenient, while electronic signatures, watermarks and QR code anti-counterfeiting technologies are used to ensure the security and authenticity of shared archives; shareholders can track the company's equity status and change records in real time, and timely understand the company's change information through a dynamic notification mechanism, fully protecting shareholders' right to know; through dynamic encryption, authority management and abnormal behavior detection, the security of archive storage and access is ensured, and data leakage and tampering are effectively prevented; support for multi-dimensional data analysis and report generation provides strong data support for enterprise management and decision-making; user permissions are dynamically allocated through an efficient identity authentication module to ensure that different users only access data within their authorized scope. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] 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 labor.

[0069] in:

[0070] Figure 1 It is a system modular structure diagram of the present invention;

[0071] Figure 2 This is a structural diagram of a comprehensive statistics module in an embodiment of the present invention;

[0072] Figure 3 4 is a structural diagram of a system security module in an embodiment of the present invention. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0074] like Figure 1-Figure 3 FIG. 1 is an embodiment of the present invention, which provides a system for sharing enterprise registration files and protecting shareholders' right to know based on full-chain data tracing and auditing, including:

[0075] (1) Identity Authentication Module

[0076] It is used to realize user identity authentication for social public users, government agency users and contracted users through the distributed key management system combined with the CA authentication platform and the electronic business license platform, and to determine whether the inquirer is a shareholder through the interface of the natural person identity authentication platform.

[0077] The distributed key management system includes:

[0078] Key generation unit, used to generate dynamic keys based on distributed key management technology, supporting user identity authentication and encrypted storage of archival data;

[0079] Key distribution unit, used to securely distribute dynamic keys to different modules to support archive access, sharing and encrypted transmission;

[0080] The key update and recovery unit is used to dynamically update keys when user permissions change, and to safely recover keys that are no longer in use to avoid key leakage.

[0081] In one embodiment, the identity authentication module includes:

[0082] The identity credential verification unit is used to receive the identity credentials submitted by the user, including CA certificate, electronic business license or natural person identity information, verify the legitimacy of the CA certificate by connecting to the CA authentication platform, verify the validity of the business license by connecting to the electronic business license platform, and call the natural person identity authentication platform to verify whether the user is a legal corporate shareholder;

[0083] A permission allocation unit, used to dynamically generate user access permission information based on identity credential verification results, including user identity category, permission level, and user-accessible data range;

[0084] A distributed key generation unit, used to generate dynamic keys for user authentication and authority information based on a distributed key management system to support subsequent data encryption and decryption operations;

[0085] The verification result output unit is used to output the user identity verification result, including the user identity verification status, user role type, user authority level, identity verification timestamp, verification source, and whether it matches the enterprise information.

[0086] User identity verification status: whether the verification has been passed, the output value is "success" or "failure", such as whether the CA certificate or electronic business license verification matches the legal user information in the system database; if the identity verification result is failure, the user is denied access to subsequent modules, and an error message is returned to the user terminal, and the failed identity verification log is recorded for tracking by the audit module;

[0087] User role type: This is to verify the identity category of the user, such as corporate shareholders, corporate legal representatives, government agency users (such as public security, procuratorial and judicial organs), and social public users. The role type determines the scope of data that the user can access;

[0088] User permission level: Verify the permission level of the user in the system, such as: ordinary user permission (can only access public corporate information), shareholder user permission (can view share information and corporate registration files), high-level permission user (such as corporate legal person, management personnel, can operate sensitive data), the output value is the permission identifier (such as level 1, level 2, level 3, etc., or a specific permission list);

[0089] Authentication timestamp: records the time of authentication for logging and subsequent tracking, example: 2024-12-2810:15:00;

[0090] Verification source: Indicate the method by which the verification is completed (such as CA certificate, electronic business license, liveness authentication, etc.), as well as the platform source of the verification (such as the national unified identity authentication platform);

[0091] Whether the enterprise information matches (for shareholder or legal representative verification): Check whether the user identity matches the enterprise information stored in the system, whether the user belongs to the target enterprise (such as shareholder, legal person, etc.), and whether the enterprise registration information in the integrated data platform is associated.

[0092] The authentication result is output as structured data, which is convenient for other modules to use. The following is an example output:

[0093] {

[0094] "status":"Success",

[0095] "user_role":"Enterprise Shareholder",

[0096] "user_permissions":["View share information","Query company registration files"],

[0097] "timestamp":"2024-12-28 10:15:00",

[0098] "verification_source":"CA certification platform",

[0099] "company_match":"Match successful",

[0100] "company_name":"ABC Technology Co., Ltd."

[0101] }

[0102] (2) Archives storage management module

[0103] Used to store, manage, trace and audit enterprise registration files based on blockchain technology and distributed storage technology.

[0104] Specifically, the archive storage management module includes:

[0105] The data storage unit is used for the storage and management of enterprise registration archives. It uses distributed storage technology to store the archive image data (such as scanned documents, PDF files), associated information and multi-level index data of the enterprise registration archives, and stores the metadata of the enterprise registration archives (such as index information, access logs and change records) on the blockchain to achieve the integrity and immutability of the archive data. The metadata storage content includes: timestamp, operation subject, operation type, data hash value, etc.; the stored data is dynamically encrypted using the distributed key management system (DMS) to ensure that only authorized users can access the archive data. The encryption strategy is dynamically adjusted based on user permissions to ensure the privacy and security of the stored data;

[0106] Related information can be understood as the correlation information between enterprise registration archive data and other relevant data. This information is usually used to support the system's retrieval, management and traceability functions, mainly including the logical correlation of archive content, the correlation between archives and external data, the correlation of archive management information, the upstream and downstream correlation of archive data, and the extended correlation of archive indexes.

[0107] The logical association of archive contents refers to the association information between different data within the enterprise registration archives, which is used to describe the logical structure and content relationship of enterprise archives:

[0108] Basic information of the enterprise: such as enterprise name, unified social credit code, registered address, date of establishment, etc.;

[0109] Shareholder information: shareholder name, shareholding ratio, shareholder change record, etc.;

[0110] Business information: business scope, business license information, permit information, etc.

[0111] Historical change information: change records and time of company name, registered address, legal representative, shareholder structure, and business scope;

[0112] Archival image index: The relationship between archival files and corresponding image data, such as the mapping of file directories to specific image files.

[0113] The association between archives and external data refers to the association information between enterprise registration archives and other systems or external data resources, which is used to achieve cross-system data sharing or expansion functions:

[0114] Industrial and commercial data: the correspondence between enterprise registration files and data in the industrial and commercial registration database;

[0115] Tax data: the association of corporate tax records or tax registration information with archival data;

[0116] Judicial records: litigation information related to the enterprise, court judgment records, etc.;

[0117] Regulatory information: inspection reports and penalty records shared with market regulatory authorities.

[0118] The association of archive management information refers to metadata related to archive storage and operation, which is used to support the process and traceability of archive management:

[0119] Archive version record: describes different versions of archive files (such as revised or changed versions) and their timestamps;

[0120] Access control information: the association between access rights to stored archive files and user identities, such as archive level (public, restricted, confidential);

[0121] Operation records: modification records of archive data, operation subjects, modification time, etc.;

[0122] Cryptographic information: Key distribution and encryption logic associated with a distributed key management system.

[0123] The upstream and downstream association of file data refers to the upstream and downstream data relationship of enterprise registration files in the business process:

[0124] Business data association: enterprise files and their associated business process data, such as enterprise qualification approval, administrative licensing process, etc.;

[0125] Shareholder association information: information on the association of each shareholder with other companies or individuals in the company registration file (such as other companies held shares, control relationship diagram, etc.);

[0126] Inter-enterprise relations: the relationship between an enterprise and its affiliated enterprises (such as parent companies, subsidiaries, branches, cooperative enterprises, etc.) The extended relationship of the archive index refers to the auxiliary information generated by the expansion of archive data in order to improve retrieval efficiency and sharing capabilities:

[0127] Full-text index information: the association between text content generated based on OCR technology and the corresponding archival images;

[0128] Multi-level directory structure: organization and index mapping of archives at different levels (such as enterprises, shareholders, subsidiaries, etc.);

[0129] Search keyword association: The correspondence between keywords and specific archive content established based on user search behavior.

[0130] The data tracing unit is used to record the change time, operation subject and specific change content of the archives based on the blockchain timestamp function, and to trace the change records, user operation logs and data access records of the enterprise registration archives. It supports the tracing of archive information at any time point, generates a change chain, and displays all historical versions of the archive data. It supports the display of tracing results in a visual form (such as a timeline, change chart or chain structure), including the time, operation subject and specific change content of the archive data change;

[0131] In one preferred embodiment, the data tracing unit performs pattern matching and change chain analysis of archive change records based on an improved multiple sequence alignment algorithm, specifically including:

[0132] Introduce archival data modeling based on knowledge graph: When formatting archival data, use knowledge graph technology to build semantic associations of archival data, model enterprise registration information, shareholder structure, and change records as directed graphs of entities and relationships, with nodes representing fields and edges representing semantic relationships between fields. Structure the fields of each archival version into a graph model rather than a linear sequence (combined with the MSA algorithm) to capture potential associations between fields, such as whether a change in a field will trigger synchronous changes in other fields. Knowledge graphs can enhance the ability to understand the semantic information of archival data, optimize the in-depth analysis of pattern matching, and provide more intuitive data structure support for subsequent change chain analysis.

[0133] Combining graph embedding and GNN (graph neural network) technology: Expand the multi-version archive comparison process from traditional dynamic programming to an efficient comparison method based on graph embedding (Graph Embedding) and graph neural network (Graph Neural Network, GNN), input the formatted archive field graph into the graph embedding model, map the graph nodes (fields) to feature vectors of fixed dimensions; learn the feature similarity of multi-version archive fields through GNN, calculate the change similarity of each field; generate a comparison result matrix, and automatically mark the matching, change or insertion status of the field. GNN can use the structured relationship between fields to improve the efficiency and accuracy of multiple sequence comparisons. The feature vector of the graph embedding model can be used as input for change pattern analysis, providing a global perspective across versions.

[0134] Among them, the graph embedding model formula is: i =f(W·A·h i-1 + b);

[0135] In the formula, h i represents the embedding vector of node i, h i-1represents the embedding vector of the previous layer node i-1; A is the adjacency matrix of the archive field graph; W represents the learnable weight matrix; b is the bias term; f is the activation function;

[0136] The field change similarity formula is:

[0137] In the formula, h j represents the embedding vector of node j; Sim(i,j) represents the change similarity between node i and node j;

[0138] When Sim(i,j) is greater than the preset threshold, it is judged as a matching field, otherwise it is changed.

[0139] Use clustering algorithms to identify change patterns: Use the density clustering algorithm (DBSCAN, Density-Based Spatial Clustering of Applications with Noise) to cluster the field status in the change chain and automatically identify common change patterns. For example, multiple changes to a field are clustered into the same type of pattern, and the clustering results are used to annotate abnormal patterns;

[0140] The specific steps are: Use a vector to represent the field status of the change chain:

[0141] x i =[change time, operation subject characteristics, change type];

[0142] The change status of each field is represented by a three-dimensional vector;

[0143] Compute the Euclidean distance between changed states:

[0144] Cluster change chains and output common patterns.

[0145] The clustering algorithm can automatically discover high-frequency change patterns, providing a basis for summarizing the rules of file changes. Combined with the abnormal annotation mechanism, it can identify abnormal change behaviors and improve the intelligence of the system.

[0146] Combine dynamic network diagrams and heat maps to display change chains: Use dynamic network diagrams to represent the change relationship between fields, for example, changes to multiple fields by the same subject are represented by dynamic edges. Use heat maps to display the frequency of field changes, for example, the depth of color represents the number of changes;

[0147] Tool support: Visualization tools such as D3.js or Cytoscape.js.

[0148] Dynamic network diagrams facilitate intuitive display of change relationships, and heat maps can quickly identify frequently changed fields.

[0149] The data audit unit is used to record and audit all archive data access operations in the system in real time (including user ID, operation time, operation type (such as query, change, export) and specific archive content accessed), perform permission verification on archive operation behaviors, and review whether the user's archive operation is in compliance with the scope of their authority. For example, shareholder users can only access information related to their own shares; unauthorized operations or abnormal behaviors (such as unauthorized operations) are marked and recorded, and audit logs are generated to provide the system's risk management module with complete user operation audit information (including access frequency, abnormal behavior statistics and operation log lists) for further security assessment and analysis.

[0150] (3) File Sharing Module

[0151] It is used for multi-level directory index tree information display and quick query of enterprise registration archives, automatic retrieval of archive information, interactive multi-level index query, and quick positioning of archive images based on case directory and file directory within the volume. It also performs anti-counterfeiting processing of archive images with electronic signatures, watermarks and QR codes under authorized conditions, and shares enterprise registration archive information.

[0152] In one embodiment, the file sharing module includes:

[0153] A directory index generating unit, used to generate a multi-level directory index based on the structured data of the enterprise registration file, and organize and display it in a tree-like information form;

[0154] The archive image positioning unit is used to quickly locate and retrieve archive images based on the file directory and the file directory within the volume, combined with OCR technology and super-resolution generative adversarial network;

[0155] Optionally, the implementation steps of the archive image positioning unit are as follows:

[0156] Standardize the format and resize the target archival images retrieved from the archival storage system to ensure the compatibility of the input images with the deep learning model. The standardized low-resolution images are used as input to enter the subsequent processing flow.

[0157] By introducing a super-resolution generative adversarial network (SRGAN), the quality of low-resolution archival images is enhanced and the details are reconstructed. The self-attention mechanism is combined in the generator network to improve the modeling ability of the global features of the image and enhance the restoration effect of complex textures and detail areas. The discriminator network optimizes the perceptual quality of the generated image through adversarial training, making the enhanced image closer to the real high-resolution image. The multi-scale fusion of perceptual loss, adversarial loss and pixel loss further improves the image restoration and ensures the clarity and realism of the archival image.

[0158] Combined with Fourier transform, frequency domain loss is introduced to optimize the high-frequency details of the image (such as character boundaries, image borders, etc.), and improve the image's ability to restore textures and fine areas. It is particularly suitable for processing old, blurred or low-quality archival images.

[0159] Through the multi-task learning architecture, the super-resolution generation task is combined with the OCR text recognition task. While reconstructing the image, the model enhances the restoration accuracy of the text area and improves the accuracy of subsequent OCR analysis.

[0160] After image enhancement, OCR technology is used to extract text information from archive images, and combined with the semantic understanding ability of the Transformer model, the fuzzy query conditions entered by the user are intelligently matched to quickly locate the target archive image; the extracted text information and query conditions are sorted through similarity calculations (such as cosine similarity or edit distance) to ensure the accuracy of the positioning results;

[0161] Introducing a dynamic weight allocation mechanism in super-resolution model training, adaptively adjusting the proportion of perceptual loss, pixel loss, and adversarial loss according to the characteristics of the input image, to ensure the performance stability and adaptability of the model in diverse scenarios (such as high-noise, low-resolution images);

[0162] Combining multi-task learning and semantic understanding technology, the unit can quickly locate the target archives of user queries and generate high-resolution images for display; it supports a variety of interactive functions, such as image zooming in, zooming out, rotating and area labeling, to facilitate further processing by users.

[0163] The archive anti-counterfeiting processing unit is used to perform comprehensive anti-counterfeiting processing on shared archive images using electronic signatures, watermarks, and dynamic QR codes to ensure the authenticity and tamper-proof nature of shared archives;

[0164] The quick query and authorization unit is used for interactive multi-level index query of enterprise registration archives, dynamically authorizes access to shared archives based on user permissions, and provides archive sharing function when the authorization conditions are met.

[0165] (4) Shareholders’ right to know protection module

[0166] It is used to provide share ownership tracking through blockchain technology, check the status of corporate shares and updates of corporate registration information, and ensure shareholders' right to know.

[0167] Specifically, the shareholder right to know protection module includes:

[0168] The share tracking unit is used to track the status and change records of corporate shares based on blockchain technology to ensure real-time update and transparency of all share information;

[0169] The equity status display unit is used to generate shareholder shareholding ratios and share change history charts through dynamic data visualization technology to intuitively display the equity status to shareholders;

[0170] The real-time update notification unit is used to trigger the notification mechanism through the blockchain and send real-time update reminders to shareholders when there are changes in the company registration information or shareholder share status.

[0171] Furthermore, the steps for implementing the share tracking unit are as follows:

[0172] 1) Dynamic graph modeling

[0173] Defining a dynamic graph: Using a dynamic graph G t =(V t ,E t ,W t ) represents the time series model of equity relationship, where V t is the node set at time t, E t is the edge set at time t, W t is the edge weight (shareholding ratio) at time t;

[0174] Add a time attribute to each node and edge: in is the initial feature vector of node v, which represents the feature representation of the node when the graph neural network is initialized, combining the characteristics of the node itself and time embedding; Init(v) represents the function of initializing the initial characteristics of node v, generating a feature vector related to the node. For example, for a shareholder node, the initial characteristics can be the type of shareholder (natural person or legal person), total shareholding ratio, etc.; for an enterprise node, the characteristics can be the enterprise market value, number of shareholders, etc.; It represents the feature concatenation operation, which combines the initial feature vector and the time embedding vector of the node to form a new feature representation; TimeEmbed(t) is the time embedding vector used to represent time information.

[0175] Incremental update of equity changes: When the blockchain records equity changes, it dynamically modifies G t , add new nodes (new shareholders or enterprises); modify edge weights (changes in shareholding ratio).

[0176] 2) Weighted aggregation strategy

[0177] Aggregation formula optimization: When aggregating neighbor features, introduce time weight α and edge weight W e The joint weighting of: Where m v is the neighbor feature aggregation result (message vector) of node v, which represents the information aggregated from all neighbor nodes of node v; N(v) is the set of neighbor nodes of node v, which represents other nodes connected to node v; αt is the time weight, which indicates the degree to which the importance of neighbor node features decays over time (the farther away from the current time, the lower the weight); is the feature representation of neighbor node u in the lth layer of the graph neural network.

[0178] Node feature update: Use the optimized neighbor feature aggregation formula:

[0179] In the formula, is the feature vector of node v at layer l+1, which combines its own features and the aggregation results of neighbor features; is the feature vector of node v in layer l; Q is a trainable weight matrix used for linear transformation to convert the concatenated feature vector into a new feature representation; k is a bias vector; σ is an activation function;

[0180] 3) Hierarchical storage and indexing mechanism

[0181] Hierarchical storage: Dynamic graphs are stored in layers by time. Recently changed nodes and edges are stored in a high-priority layer (memory), and historical data is stored in a low-priority layer (disk). Distributed storage technology (such as IPFS) is used to manage data layers.

[0182] Index optimization: Create a time index for each node and edge to support quick search of equity relationships at a specified time point:

[0183] Index(v,t)={edges(v)|t start ≤t≤t end};

[0184] In the formula, Index(v,t) is the edge index set of node v at time t, which means that the edge is connected to node v and in the time range [t start ,t end ]; edges(v) represents the set of edges connected to node v; t start is the start time of the time range, t end The end time of the time range.

[0185] Dynamic graph modeling is used to more accurately capture the time series characteristics of equity changes; the weighted aggregation strategy effectively balances the impact of time and equity weight; and the hierarchical storage and indexing mechanism significantly improves query efficiency.

[0186] (5) Comprehensive statistics module

[0187] Used to collect statistics on archive query and sharing data based on the query person, enterprise directory, query content and query time, and generate reports in various forms;

[0188] The comprehensive statistics module includes:

[0189] Multidimensional data analysis unit, used to perform statistical analysis on archival data based on the query person, enterprise directory, query content and query time;

[0190] Report generation unit, used to generate various forms of reports based on statistical results, including charts and tables, and supports exporting to PDF or Excel files;

[0191] The dynamic statistics display unit is used to display statistical data in real time through a visual dashboard and supports dynamic adjustment of statistical conditions.

[0192] (6) System security module

[0193] It is used to provide file classification control, user authority management and full-process security log recording functions to ensure the security of file query and sharing;

[0194] Specifically, the system security module includes:

[0195] The confidentiality control unit is used to dynamically control the user's query authority according to the confidentiality level of the archives, ensuring that users of different levels can only access the archive data within their authority range;

[0196] The security log management unit is used to record the user's login, query, sharing and other operation logs, and ensure the logs cannot be tampered with based on blockchain technology;

[0197] The abnormal behavior detection unit is used to analyze user behavior through machine learning algorithms, automatically detect and mark abnormal operations, and issue security alerts to administrators.

[0198] In summary, the present invention uses blockchain technology to achieve full-chain traceability of archival data, completely record all change operations, significantly improve the transparency of archival management, and ensure the authenticity and credibility of data; multi-level directory indexing and interactive query functions make archival queries fast and convenient, while electronic signatures, watermarks and QR code anti-counterfeiting technologies are used to ensure the security and authenticity of shared archives; shareholders can track the company's equity status and change records in real time, and timely understand the company's change information through a dynamic notification mechanism, fully protecting shareholders' right to know; through dynamic encryption, authority management and abnormal behavior detection, the security of archive storage and access is ensured, and data leakage and tampering are effectively prevented; support multi-dimensional data analysis and report generation, providing strong data support for enterprise management and decision-making; user permissions are dynamically allocated through an efficient identity authentication module to ensure that different users only access data within their authorized scope.

[0199] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The enterprise registration file sharing and shareholder right to know protection system based on full-chain data traceability and auditing is characterized by: The system comprises: The identity authentication module is used to realize the user identity authentication of social public users, government agency users and contracted users through the distributed key management system combined with the CA authentication platform and the electronic business license platform, and to determine whether the inquirer is a shareholder through the interface of the natural person identity authentication platform; The archive storage management module is used to realize the storage, management, tracing and auditing of enterprise registration archives based on blockchain technology and distributed storage technology; The archive sharing module is used for multi-level directory index tree information display and quick query of enterprise registration archives, automatic retrieval of archive information, interactive multi-level index query, and quick location of archive images based on the case directory and the file directory within the volume. It also performs electronic signature, watermark and QR code anti-counterfeiting processing on archive images under authorized conditions, and shares enterprise registration archive information; The shareholder right to know protection module is used to provide share ownership tracking through blockchain technology, check the company's share status and the update of the company's registration information, and ensure the shareholders' right to know; Comprehensive statistics module, used to collect statistics on archive query and shared data according to the query person, enterprise directory, query content and query time, and generate various forms of reports; The system security module is used to provide file classification control, user authority management and full-process security log recording functions to ensure the security of file query and sharing.

2. The enterprise registration file sharing and shareholder right to know protection system based on full-chain data tracing and auditing as claimed in claim 1 is characterized by: The identity authentication module comprises: The identity credential verification unit is used to receive the identity credentials submitted by the user, including CA certificate, electronic business license or natural person identity information, verify the legitimacy of the CA certificate by connecting to the CA authentication platform, verify the validity of the business license by connecting to the electronic business license platform, and call the natural person identity authentication platform to verify whether the user is a legal corporate shareholder; A permission allocation unit, used to dynamically generate user access permission information based on identity credential verification results, including user identity category, permission level, and user-accessible data range; A distributed key generation unit, used to generate dynamic keys for user authentication and authority information based on a distributed key management system to support subsequent data encryption and decryption operations; The verification result output unit is used to output the user identity verification result, including the user identity verification status, user role type, user authority level, identity verification timestamp, verification source, and whether it matches the enterprise information.

3. The enterprise registration file sharing and shareholder right to know protection system based on full-chain data tracing and auditing as claimed in claim 1 is characterized in that: The archive storage management module includes: The data storage unit is used for the storage and management of enterprise registration files. It uses distributed storage technology to store the file image data, associated information and multi-level index data of the enterprise registration files, stores the metadata of the enterprise registration files on the blockchain, and uses a distributed key management system to dynamically encrypt the stored data; The data tracing unit is used to trace the change records, user operation logs and data access records of enterprise registration archives based on the blockchain timestamp function. It supports the traceability of archive information at any time point, generates a change chain, displays all historical versions of archive data, and supports visual display of tracing results, including the time of archive data change, operation subject and specific change content; The data audit unit is used to record and audit all archival data access operations in the system in real time through blockchain smart contracts, verify the permissions of archival operations, mark and record unauthorized operations or abnormal behaviors, and generate audit logs.

4. The enterprise registration file sharing and shareholder right to know protection system based on full-chain data tracing and auditing as claimed in claim 3 is characterized by: The data tracing unit performs pattern matching and change chain analysis of archive change records based on an improved multiple sequence alignment algorithm, specifically including: When formatting archival data, we use knowledge graph technology to build semantic associations of archival data, modeling enterprise registration information, shareholder structure, and change records as directed graphs of entities and relationships. Nodes represent fields, and edges represent semantic relationships between fields. The fields of each archival version are structured into a graph model. The multi-version archive comparison process is expanded to a comparison based on graph embedding and graph neural network. The formatted archive field graph is input into the graph embedding model, and the graph nodes are mapped into feature vectors of fixed dimensions. The feature similarity of multi-version archive fields is learned through GNN, and the change similarity of each field is calculated. The comparison result matrix is ​​generated, and the matching, change or insertion status of the field is automatically marked. Among them, the graph embedding model formula is: i =f(W·A·h i-1 + b); In the formula, h i represents the embedding vector of node i, h i-1 represents the embedding vector of the previous layer node i-1; A is the adjacency matrix of the archive field graph; W represents the weight matrix; b is the bias term; f is the activation function; The field change similarity formula is: In the formula, h j represents the embedding vector of node j; Sim(i,j) represents the change similarity between node i and node j; When Sim(i,j) is greater than the preset threshold, it is judged as a matching field, otherwise it is changed; Use density clustering algorithms to cluster field states in change chains and automatically identify common change patterns; Use dynamic network diagrams to represent the change relationships between fields, use dynamic edges to represent changes to multiple fields by the same entity, and use heat maps to show the frequency of field changes.

5. The enterprise registration file sharing and shareholder right to know protection system based on full-chain data tracing and auditing as claimed in claim 1 is characterized in that: The file sharing module includes: A directory index generating unit, used to generate a multi-level directory index based on the structured data of the enterprise registration file, and organize and display it in a tree-like information form; The archive image positioning unit is used to quickly locate and retrieve archive images based on the file directory and the file directory within the volume, combined with OCR technology and super-resolution generative adversarial network; The archive anti-counterfeiting processing unit is used to perform comprehensive anti-counterfeiting processing on shared archive images using electronic signatures, watermarks, and dynamic QR codes to ensure the authenticity and tamper-proof nature of shared archives; The quick query and authorization unit is used for interactive multi-level index query of enterprise registration archives, dynamically authorizes access to shared archives based on user permissions, and provides archive sharing function when the authorization conditions are met.

6. The enterprise registration file sharing and shareholder right to know protection system based on full-chain data tracing and auditing as claimed in claim 1 is characterized by: The shareholder right to know protection module includes: The share tracking unit is used to track the status and change records of corporate shares based on graph neural networks and blockchain technology to ensure real-time update and transparency of all share information; The equity status display unit is used to generate shareholder shareholding ratios and share change history charts through dynamic data visualization technology to intuitively display the equity status to shareholders; The real-time update notification unit is used to trigger the notification mechanism through the blockchain and send real-time update reminders to shareholders when there are changes in the company registration information or shareholder share status.

7. The enterprise registration file sharing and shareholder right to know protection system based on full-chain data tracing and auditing as claimed in claim 6 is characterized by: The steps for implementing the share tracking unit are as follows: Defining a dynamic graph: Using a dynamic graph G t =(V t ,E t ,W t ) represents the time series model of equity relationship, where V t is the node set at time t, E t is the edge set at time t, W t is the edge weight at time t; Add a time attribute to each node and edge: in is the initial feature vector of node v; Init(v) represents the function of initializing the initial characteristics of node v and generating the feature vector related to the node; ⊕ represents the feature concatenation operation; TimeEmbed(t) is the time embedding vector; Incremental update of equity changes: When the blockchain records equity changes, it dynamically modifies G t , add new nodes or modify edge weights; aggregation formula optimization: when aggregating neighbor features, introduce time weight α and edge weight W e The joint weighting of: Where m v is the aggregation result of neighbor features of node v; N(v) is the set of neighbor nodes of node v; α t is the time weight; is the feature representation of neighbor node u in the lth layer of the graph neural network; Node feature update: Use the optimized neighbor feature aggregation formula: in, is the feature vector of node v at layer l+1; is the feature vector of node v in layer l; Q is the weight matrix; k is the bias vector; σ is the activation function; Hierarchical storage: Dynamic graphs are stored in layers according to time. Recently changed nodes and edges are stored in high-priority layers, and historical data is stored in low-priority layers. Distributed storage technology is used to manage data layers. Index optimization: Create a time index for each node and edge to support quick search of equity relationships at a specified time point: Index(v,t)={edges(v)∣t start ≤t≤t end }; In the formula, Index(v,t) is the edge index set of node v at time t; edges(v) represents the edge set connected to node v; t start is the start time of the time range, t end The end time of the time range.

8. The enterprise registration file sharing and shareholder right to know protection system based on full-chain data tracing and auditing as claimed in claim 1 is characterized in that: The comprehensive statistics module includes: Multidimensional data analysis unit, used to perform statistical analysis on archival data based on the query person, enterprise directory, query content and query time; Report generation unit, used to generate various forms of reports based on statistical results, including charts and tables, and supports exporting to PDF or Excel files; The dynamic statistics display unit is used to display statistical data in real time through a visual dashboard and supports dynamic adjustment of statistical conditions.

9. The enterprise registration file sharing and shareholder right to know protection system based on full-chain data tracing and auditing as claimed in claim 1 is characterized in that: The system security module includes: The confidentiality control unit is used to dynamically control the user's query authority according to the confidentiality level of the archives, ensuring that users of different levels can only access the archive data within their authority range; The security log management unit is used to record the user's login, query, sharing and other operation logs, and ensure the logs cannot be tampered with based on blockchain technology; The abnormal behavior detection unit is used to analyze user behavior through machine learning algorithms, automatically detect and mark abnormal operations, and issue security alerts to administrators.

10. The enterprise registration file sharing and shareholder right to know protection system based on full-chain data tracing and auditing as claimed in claim 1 is characterized in that: The distributed key management system comprises: Key generation unit, used to generate dynamic keys based on distributed key management technology, supporting user identity authentication and encrypted storage of archival data; Key distribution unit, used to securely distribute dynamic keys to different modules to support archive access, sharing and encrypted transmission; The key update and recovery unit is used to dynamically update keys when user permissions change, and to safely recover keys that are no longer in use to avoid key leakage.

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