Electronic archive management method and device based on knowledge graph, equipment and medium

Abstract

Embodiments of the present application provide a kind of based on knowledge graph's electronic archives management method, device, equipment and medium, it is related to data management technical field.The method comprises: in the archiving link of electronic archives, the key information corresponding to electronic archives is acquired;Key information includes archive identification, operation subject, operation time and business scenario;With key information as node, the knowledge graph data corresponding to electronic archives is constructed, and the association between each node is established;The credential data corresponding to electronic archives is generated;Wherein, credential data includes the credential verification information of electronic archives;Knowledge graph data and credential data are associatedly stored.The key information of the present application embodiment is acquired to establish node and association relationship to form knowledge graph data, and knowledge graph data and corresponding credential data are associatedly stored, so as to improve the management efficiency and reliability of electronic archives.

Description

Technical Field

[0001] This application relates to the field of data management technology, and more specifically, to a method, apparatus, equipment, and medium for managing electronic records based on knowledge graphs. Background Technology

[0002] Traditional methods for ensuring the evidentiary value of electronic records often rely on single technologies, such as digital signatures, hash verification, or system log recording. These methods achieve preliminary verification of authenticity and integrity by recording isolated information about the content or key operations of electronic records. However, during the archiving, management, storage, and utilization processes, the key information of electronic records is typically scattered across different systems or modules, lacking an effective mechanism for integration and correlation. The failure to establish effective structured connections between the various core elements of electronic records results in fragmented evidentiary information. Therefore, current electronic record management suffers from low efficiency and unreliability. Summary of the Invention

[0003] The purpose of this application is to provide a knowledge graph-based electronic record management method, apparatus, equipment, and medium to improve the efficiency and reliability of electronic record management.

[0004] In a first aspect, embodiments of this application provide a knowledge graph-based electronic record management method, including: In the archiving process of electronic records, key information corresponding to the electronic records is obtained; wherein, the key information includes record identifier, operating entity, operating time, and business scenario; Using the key information as nodes, construct the knowledge graph data corresponding to the electronic archives, and establish the relationship between each node; Generate voucher data corresponding to the electronic file; wherein, the voucher data includes the voucher verification information of the electronic file; The knowledge graph data and the credential data are stored together.

[0005] In this embodiment of the application, by acquiring key information of electronic archives, nodes and relationships are established to form knowledge graph data, and the knowledge graph data and corresponding voucher data are stored together. This enables the effective association and integration of key information and voucher information of electronic archives, thereby improving the management efficiency and reliability of electronic archives.

[0006] In some embodiments, the knowledge graph-based electronic record management method further includes: In each subsequent stage after the electronic archive completes the archiving stage, corresponding key information is obtained based on the operation events of the electronic archive as updated key information; wherein, the subsequent stages include management stage, storage stage, utilization stage or destruction stage; Using the updated key information as a new node, the knowledge graph data is updated, and a relationship between the new node and historical nodes is established from at least one association dimension; wherein, the association dimension includes at least one of time dimension, subject dimension and archive dimension.

[0007] In this embodiment of the application, by responding to operation events in subsequent stages to obtain updated key information and update knowledge graph data, new nodes can be dynamically added and connections can be established from multiple dimensions to form a chain-like credential structure that runs through the entire life cycle, thereby further improving the efficiency and reliability of electronic record management.

[0008] In some embodiments, the knowledge graph-based electronic record management method further includes: In response to a user's credential chain query request, the credential chain query request is parsed to obtain the user's query intent; Based on the query intent, matching target information is retrieved from the knowledge graph data; A chain-based credential traceability report is generated based on the target information.

[0009] In this embodiment of the application, by parsing the user's query intent and retrieving matching information from the knowledge graph data, it is possible to quickly query and obtain the file transfer records and generate a visual traceability report, thereby effectively improving the efficiency of credential verification.

[0010] In some embodiments, the knowledge graph-based electronic record management method further includes: Based on a preset risk identification model, risk identification is performed on the nodes and relationships in the knowledge graph data; wherein, the risk identification model is constructed based on preset normal rules for file circulation; Upon identification of an abnormal risk, pre-defined anomaly handling measures are triggered; wherein, the abnormal risk includes at least one of operational anomaly, integrity anomaly, or storage anomaly. The anomaly handling measures include at least one of the following: Generate an early warning report corresponding to the aforementioned abnormal risk and push it to the management personnel's terminal; The target risk nodes corresponding to abnormal risks in the knowledge graph data are marked.

[0011] In this embodiment of the application, by constructing a risk identification model based on the normal rules of archive circulation and monitoring knowledge graph data, risks such as operational anomalies and integrity anomalies can be proactively identified, thereby further improving the reliability of electronic archive management.

[0012] In some embodiments, the knowledge graph-based electronic record management method further includes: In the preset verification step of the electronic archive, at least one business verification is performed on the nodes and relationships in the knowledge graph data based on preset association rules; wherein, the preset verification step includes an archiving verification step and a pre-use verification step; the business verification includes at least one of element integrity verification and credential chain integrity verification; When the business verification fails, an alert is triggered and / or a correction prompt is pushed.

[0013] In this embodiment of the application, by performing business verification based on preset association rules in stages such as archiving verification and pre-use verification, the integrity of elements and the integrity of the credential chain can be automatically verified, thereby further improving the standardization of electronic record management.

[0014] In some embodiments, the knowledge graph-based electronic record management method further includes: The knowledge graph is optimized based on the obtained optimization information; The optimization information includes at least one of the following: regulatory and standard update information and business process change information obtained periodically through a preset optimization model. The optimization process includes at least one of the following: Optimize the association rules of the knowledge graph; Optimize the node definition of the knowledge graph; Optimize the risk identification model of knowledge graph; Clean up redundant data in the knowledge graph.

[0015] In this embodiment of the application, by regularly acquiring information on updates to regulations and standards or changes in business processes and optimizing the knowledge graph, the accuracy and effectiveness of electronic record management are further improved.

[0016] In some embodiments, the step of associating and storing the knowledge graph data and the credential data includes: The credential data is stored in a hierarchical storage architecture according to a preset hierarchical storage strategy.

[0017] In this embodiment of the application, by storing voucher data in a hierarchical storage architecture according to a preset hierarchical storage strategy, different voucher data can be stored in a differentiated manner, thereby further improving the flexibility and security of electronic record management.

[0018] In some embodiments, storing the credential data in a tiered storage architecture according to a preset tiered storage strategy includes: Voucher data with an access frequency exceeding a preset frequency threshold will be transferred and stored in the online storage layer of the hierarchical storage architecture. Voucher data with an access frequency not exceeding the preset frequency threshold will be transferred and stored in the near-line storage layer of the hierarchical storage architecture. The core voucher data corresponding to the electronic archive is permanently stored in the offline storage layer of the hierarchical storage architecture; The offline storage layer uses a read-only storage medium, and the core credential data includes at least one of the following: the final version verification code of the electronic file, key node data of the knowledge graph, and important verification reports.

[0019] In this embodiment, by storing high-frequency data in the online storage layer, low-frequency data in the near-line storage layer, and core voucher data in the read-only offline storage layer, differentiated storage can be achieved according to access frequency and data importance, thereby further improving the flexibility and security of electronic record management.

[0020] In some embodiments, the knowledge graph-based electronic record management method further includes: In response to a credential verification request, knowledge graph data and credential data stored in association are retrieved based on the credential verification request; Obtain the credential tracing path based on the knowledge graph data; According to the document tracing path, obtain the document data corresponding to each node and perform hash comparison and / or signature verification; A verification report is generated based on the results of hash comparison and / or signature verification.

[0021] In this embodiment of the application, by retrieving the knowledge graph data and credential data stored in association, the traceability path is obtained, and hash comparison or signature verification is performed based on the credential data according to the traceability path. This enables the linkage of credential data and the closed loop of chain authentication, thereby further improving the efficiency and reliability of electronic archive credential verification.

[0022] Secondly, embodiments of this application provide an electronic record management device based on a knowledge graph, comprising: The information acquisition module is used to acquire key information corresponding to the electronic archives during the archiving process; wherein, the key information includes archive identifier, operating entity, operating time, and business scenario; The graph construction module is used to construct the knowledge graph data corresponding to the electronic archive using the key information as nodes, and to establish the relationship between the nodes. A voucher generation module is used to generate voucher data corresponding to the electronic file; wherein, the voucher data includes the voucher verification information of the electronic file; The associated storage module is used to associate and store the knowledge graph data and the voucher data.

[0023] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, can implement the method described in any embodiment of the first aspect.

[0024] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the method described in any embodiment of the first aspect.

[0025] Fifthly, embodiments of this application provide a computer program product, the computer program product including a computer program, wherein when the computer program is executed by a processor, it can implement the method described in any embodiment of the first aspect. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A flowchart illustrating a knowledge graph-based electronic record management method provided in this application embodiment; Figure 2 One of the specific flowcharts of the knowledge graph-based electronic record management method provided in the embodiments of this application; Figure 3 The second detailed flowchart of the knowledge graph-based electronic record management method provided in this application embodiment; Figure 4 A schematic diagram of the structure of a knowledge graph-based electronic record management device provided in an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0028] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0029] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0030] It should be noted that traditional methods of ensuring the evidentiary value of electronic records often rely on single technologies, such as digital signatures, timestamps, and hash verification. These methods achieve preliminary verification of authenticity and integrity by recording the content or key operations of electronic records in isolation. In the archiving, management, storage, and utilization of electronic records, process control is mainly achieved through manual review or simple system log recording. However, the evidentiary information at each stage is stored in a scattered manner, lacking an effective mechanism for correlation and integration, resulting in low efficiency in process control. Furthermore, traditional storage methods often use single-media storage, failing to form a hierarchical storage and physically isolated anti-tampering system, making it difficult to cope with the risks of data corruption or illegal tampering during long-term storage.

[0031] The traditional electronic record management process is roughly as follows: after the electronic record is generated, a single credential information (such as hash value, digital signature) is generated through relevant technologies; in the subsequent circulation process, the operation logs of each link are recorded respectively; when storing, online or near-line storage media are mostly used, and there is a lack of dedicated offline solidified storage solutions; when it is necessary to verify the authenticity of the credential, the credential data scattered in each link needs to be retrieved manually and compared and verified one by one.

[0032] In the aforementioned traditional solutions, the management of voucher information is fragmented and lacks a unified framework. It is impossible to intuitively present the complete voucher chain from the generation to the destruction of electronic records. Furthermore, the storage security is insufficient, resulting in low efficiency of voucher verification and high risk of long-term preservation. These solutions are difficult to adapt to the rapid growth in the number of electronic records and the promotion of single-set management (a management model that archives and manages documents only in electronic form and does not generate paper versions).

[0033] Specifically, the traditional approach has the following drawbacks: 1. Lack of Voucher Information Linkage: Traditional technologies can only record voucher information in a single dimension, and cannot effectively link key information in multiple dimensions such as the generator of electronic files, operation behavior, business scenario, and time node. The resulting voucher chain is incomplete and not intuitive, making it difficult to support voucher traceability throughout the entire life cycle.

[0034] 2. Passive and delayed risk identification: Due to the lack of systematic integration and intelligent analysis of voucher information, traditional technologies cannot proactively identify abnormalities in the electronic record circulation process, such as mismatched operation permissions, contradictory timestamp logic, and missing node information. They can only be passively investigated after problems arise with the voucher, and cannot achieve early warning of risks.

[0035] 3. Low verification efficiency: When it is necessary to verify the authenticity of electronic records, it is necessary to manually retrieve scattered voucher data from multiple systems and multiple links, and compare and analyze them one by one. The operation is cumbersome and time-consuming, which is difficult to meet the management needs of the rapidly increasing number of electronic records.

[0036] 4. Insufficient storage security: Traditional storage solutions lack tiered storage and physical isolation design. Core credential data mostly rely on online storage media, which are susceptible to network attacks, equipment failures, and other factors. Furthermore, they do not employ dedicated offline hardening technology, posing a risk of data tampering, loss, or damage in long-term storage scenarios.

[0037] 5. Insufficient adaptability and scalability: Traditional technologies often have fixed rules for recording vouchers. When the business scenarios, management processes, and storage methods of electronic records change, it is difficult to quickly adjust the collection and association logic of voucher information, and it cannot adapt to diverse electronic record management needs.

[0038] 6. Lack of dynamic optimization capability: Once the credential protection mechanism of traditional technologies is determined, it cannot be self-optimized based on data such as verification results and anomaly handling in actual applications. The accuracy of credential information collection and the rationality of association rules are difficult to continuously improve.

[0039] To address at least one of the problems existing in the prior art, this application provides an electronic record management method based on knowledge graphs. By constructing a full lifecycle credential association system based on knowledge graphs and combining it with optical disc solidification technology, the method achieves secure storage of core credential data, thus solving at least one of the problems in traditional technologies, such as fragmented credential information, passive risk identification, low verification efficiency, and insufficient storage security.

[0040] like Figure 1 As shown in the figure, this application provides a knowledge graph-based electronic record management method, which may include the following steps: S1. In the archiving process of electronic records, obtain the key information corresponding to the electronic records; among which, the key information includes the record identifier, the operating entity, the operating time, and the business scenario.

[0041] For example, the method of this application embodiment can be executed by an electronic record management system, and the electronic record management system can integrate an AI knowledge graph engine (i.e., a system engine that integrates artificial intelligence technology based on knowledge graph data).

[0042] like Figure 2 As shown, when electronic records are generated and enter the archiving stage, the first step is to obtain the corresponding key information, which mainly includes the following: record identifier, operating entity, operation time, and business scenario. For example, using "time-record-operator-business scenario" as the four core elements, NLP (Natural Language Processing) technology is used to automatically extract key information such as the file creator ID (operating entity), operation timestamp (operation time), business process ID (business scenario), unique record identifier (record identifier, such as file number), and approval node, which serve as the data basis for constructing a knowledge graph.

[0043] It should be noted that in most cases, when processing electronic records, the key information captured is structured data. Therefore, keyword matching can be used to extract key information from electronic records. However, when key fields of structured data are missing, NLP technology can be used for auxiliary extraction. For example, entity extraction (based on the LSTM-CRF model) can be used to extract necessary key field information from the record description.

[0044] S2. Using key information as nodes, construct the knowledge graph data corresponding to the electronic archives and establish the relationship between each node.

[0045] Based on the key information extracted from the electronic archives, initial nodes and relationships of a structured knowledge graph can be constructed, forming the knowledge graph data corresponding to the electronic archive (the one formed during the archiving process can be called the initial knowledge graph). Each key information item serves as a node in the knowledge graph data, and relationships can be established between these nodes based on pre-defined association rules.

[0046] For example, if the "XX Project Acceptance Report" was created by Zhang San on March 24, 2020, it belongs to the project acceptance archiving scenario; then, the "archive identifier" is "XX Project Acceptance Report", the "operating subject" is "Zhang San", the "operation time" is "2020-3-24", and the "business scenario" is "project acceptance archiving".

[0047] Based on this, a knowledge graph data can be constructed for the electronic archive, including at least four nodes: “XX Project Acceptance Report”, “Zhang San”, “2020-3-24”, and “Project Acceptance Archive”. The relationship between node “Zhang San” and node “XX Project Acceptance Report” is “Create”, the relationship between node “XX Project Acceptance Report” and node “2020-3-24” is “Archived”, and so on.

[0048] It should be noted that the relationships in the initial knowledge graph can cover dimensions such as time (establishing a timeline of file flow based on the order of operations), subject (associating the identity of the operator, their department, permission level, and operational behavior), and file (associating the version evolution of files, metadata changes, business affiliation, etc.).

[0049] S3. Generate voucher data corresponding to the electronic archive; wherein, the voucher data includes the voucher verification information of the electronic archive.

[0050] After the initial knowledge graph is constructed, the system can generate credential data corresponding to the electronic archives. This credential data is used to ensure the authenticity and integrity of the electronic archive content and serves as the cryptographic basis for subsequent credential verification.

[0051] For example, the voucher data includes the voucher verification information of the electronic file (such as the voucher verification code). Specifically, the hash value calculated by the hash algorithm on the content of the electronic file, or the digital signature generated by the digital signature algorithm, can be used as the voucher verification information of the electronic file.

[0052] S4. Link and store the knowledge graph data and voucher data together.

[0053] Finally, the knowledge graph data of the electronic archives and its corresponding credential data are bound together and stored in association. It should be noted that, in the archiving stage, the credential data of the electronic archives can be considered as the trusted metadata of that electronic archive. For example, in the archiving stage, the system can perform multi-dimensional binding between the knowledge graph and the trusted metadata of the electronic archives (credential data in the archiving stage; the binding method for credential data in subsequent stages is similar): on the one hand, core fields of the trusted metadata (such as initial hash value, digital signature, timestamp, etc.) can be embedded in the archive nodes of the knowledge graph, establishing an explicit association between the archive nodes and the trusted metadata; on the other hand, the initial knowledge graph data and trusted metadata can be encapsulated using nested XML signature technology. Then, the associated credential data is stored.

[0054] It should be noted that this application embodiment breaks through the dilemma of low efficiency and low reliability in electronic file management caused by fragmented credential information in traditional solutions by constructing a chain-like credential system of "multi-dimensional information binding + multi-level encryption verification". For example, a file HASH (such as the SHA-256 algorithm) can be used to generate a unique digital fingerprint of the file content. Combined with XML signature technology, the credential data is encrypted and authenticated for the first layer. Then, the operation records and related information of multiple links are encapsulated by nested XML structure to form a complete credential chain of "content hash - node signature - chain encapsulation". The multi-level encryption mechanism ensures the immutability and traceability of credential information at each link, providing a solid data foundation for the whole process chain authentication.

[0055] Specifically, the association between knowledge graph data and voucher data can include the following three aspects: 1. XML can be associated in a nested manner of "file-step-operation": with the unique file number as the root, the information of each step and each operation, such as archiving and management, is hierarchically packed into it, and association tags are added to allow the operations of each step to be linked according to time and business logic, so as to prevent the disorganization. 2. Perform "encryption anchoring" and "attribute mapping" processing on XML and knowledge graph: Store the encrypted identifier and signature of XML in the root node of the knowledge graph archive as core attributes. At the same time, the steps and operation information in XML will correspond to the attributes of the relevant nodes in the knowledge graph, so that the knowledge graph nodes can directly correspond to the specific credential data in XML. 3. Mutual "retrieval entry points" enable bidirectional queries: The storage location and retrieval identifier of XML are added to the knowledge graph nodes. When querying the knowledge graph, the detailed records in the XML can be directly retrieved, and vice versa, querying the XML can also quickly find the corresponding nodes in the knowledge graph.

[0056] Based on this, by acquiring key information from electronic archives, establishing nodes and relationships to form knowledge graph data, and storing the knowledge graph data and corresponding voucher data together, the key information and voucher information of electronic archives can be effectively linked and integrated, thereby improving the management efficiency and reliability of electronic archives.

[0057] In some embodiments, the knowledge graph-based electronic record management method further includes: In each subsequent stage after the electronic archive is archived, key information is obtained based on the operation events of the electronic archive as the updated key information; these subsequent stages include management, storage, utilization, or destruction. The knowledge graph data is updated by using updated key information as new nodes, and the relationship between new nodes and historical nodes is established from at least one correlation dimension; wherein, the correlation dimension includes at least one of time dimension, subject dimension and archive dimension.

[0058] Continue as Figure 2As illustrated, in the subsequent stages of the entire lifecycle of electronic records management (such as sorting, transfer, migration, conversion, etc.), storage, utilization, and destruction, whenever a new operation or status change (i.e., an operation event for the electronic record) occurs, the system can capture the corresponding key information (such as operation type, operation subject, operation time, change content, approval basis, etc.) in the same way as step S1, as updated key information; then, these updated key information are added as new nodes to the initial knowledge graph (i.e., the knowledge graph data formed by the above steps S1-S2), and establish the association relationship between these new nodes and the corresponding historical nodes of the electronic record (including time dimension, subject dimension, and record dimension, etc.), finally forming a chain-like credential structure that runs through the entire lifecycle, realizing the dynamic iteration of the knowledge graph and ensuring the continuity of the credential chain.

[0059] It should be noted that each file-type node's electronic archive is uniquely identified by its file number. The archive itself is unique, ensuring that when other types of nodes (such as operating entities, storage devices, etc.) establish a relationship with a certain archive, the archive node they point to is unique.

[0060] Based on pre-defined multi-dimensional standardized relationships, such as each file having one and only one unique "stored in" relationship, when a new "stored in" relationship is established, the old "stored in" relationship will be changed to "previously stored in" and will acquire attributes such as "transferred" or "migrated".

[0061] In addition, each new relationship is subject to corresponding node verification. For example, when electronic files are transferred, the timestamp attribute of the newly generated "stored in" relationship needs to be later than the timestamp attribute of the old "stored in" relationship.

[0062] It should be noted that after updating the knowledge graph data of the electronic archives, supplementary voucher data for the corresponding stages can be generated based on the newly added nodes and new relationships, and the voucher data stored in the association can be updated.

[0063] Based on this, by responding to operational events in subsequent stages to obtain updated key information and update knowledge graph data, new nodes can be dynamically added and connections can be established from multiple dimensions to form a chain-like credential structure that runs through the entire lifecycle, thereby further improving the efficiency and reliability of electronic record management.

[0064] In some embodiments, the knowledge graph-based electronic record management method further includes: In response to a user's credential chain query request, the credential chain query request is parsed to obtain the user's query intent; Based on the query intent, retrieve matching target information from the knowledge graph data; Generate a chain of credential traceability reports based on target information.

[0065] For example, when a user needs to query the credential chain of an electronic file, they can enter the query content and initiate a credential chain query request.

[0066] like Figure 3 As shown, for example, the system can support natural language query functionality. For instance, a user can input "Query the file transfer records of XX project archived by XX department in March 2021" and initiate a credential chain query request. The system can parse this credential chain query request to obtain the user's query intent. For example, the query intent parsing process can employ large model technology, using natural language processing to identify key elements in the query request, such as time range, operating entity, file identifier, and business scenario.

[0067] Based on the query intent obtained from the parsing, the system can retrieve matching target information from the knowledge graph data. Specifically, the system traverses the nodes and relationships in the knowledge graph according to the elements in the query intent, and locates the target nodes and related paths that meet the conditions. For example, it locates time nodes based on time range, operator nodes based on operation subject, and file nodes based on file identifier, and then extracts the complete flow path along the relationships in each dimension.

[0068] Finally, the system can generate a chain-linked credential traceability report based on the retrieved target information. This report presents all operations, subjects, time, and status changes of the document from its creation to the current stage in a visual manner. In this way, users can intuitively understand the entire lifecycle of electronic documents without manually retrieving scattered data from multiple systems, significantly improving the efficiency of credential verification.

[0069] Based on this, by parsing the user's query intent and retrieving matching information from the knowledge graph data, it is possible to quickly retrieve the archive transfer records and generate a visual traceability report, thereby effectively improving the efficiency of credential verification.

[0070] In some embodiments, the knowledge graph-based electronic record management method further includes: Based on a pre-defined risk identification model, risks are identified in the nodes and relationships within the knowledge graph data; the risk identification model is constructed based on pre-defined normal rules for document circulation. Upon identification of an anomaly risk, pre-defined anomaly handling measures are triggered; the anomaly risk includes at least one of operational anomalies, integrity anomalies, or storage anomalies. Anomaly handling measures include at least one of the following: Generate early warning reports corresponding to abnormal risks and push them to the management personnel's terminals; Mark the target risk nodes in the knowledge graph data that correspond to abnormal risks.

[0071] like Figure 3 As illustrated, the system, based on a pre-defined risk identification model, can perform real-time risk identification of nodes and relationships in knowledge graph data. This risk identification model can be constructed according to normal document flow rules, which may include permission-operation matching relationships, timestamp continuity standards, and business process node integrity rules. For example, permission-operation matching rules stipulate that operators with specific permission levels can only execute operation types matching their permissions; timestamp continuity standards stipulate that operation timestamps for the same document should conform to a pre-defined logical order and should not exhibit logical problems such as time reversal; and business process node integrity rules can stipulate that a complete sequence of operation nodes should be included in a specific business scenario.

[0072] For example, the preset risk identification model can be a rule-based judgment model, such as converting the normal rules of file circulation into corresponding rule strategies according to a preset format. When it is found that the nodes and relationships in the knowledge graph data do not conform to the rule strategies, it is determined that an abnormal risk has been identified.

[0073] For example, the risk identification model can also be constructed using a graph neural network. First, the nodes and relationships in the knowledge graph are transformed into graph-structured data that the model can process. The model is then trained using historical archive flow data (i.e., datasets manually labeled as "normal") to learn normal flow graph structure patterns, ultimately resulting in a trained risk identification model. In real-time monitoring, the model encodes the nodes and relationships to be detected and calculates their deviation from the learned normal patterns. When the deviation exceeds a preset threshold, an abnormal risk is identified.

[0074] The system can monitor nodes and relationships in the knowledge graph in real time and compare real-time data with normal rules for document flow. When anomalies or risks are identified, the system can trigger preset anomaly handling measures.

[0075] The types of abnormal risks include at least one of the following: operational anomalies, integrity anomalies, or storage anomalies. Operational anomalies refer to the identification of abnormal situations such as unauthorized entity operations or unauthorized transfers across business scenarios; integrity anomalies refer to the identification of situations such as missing nodes or broken associations; storage anomalies refer to the identification of high-value files (determined based on preset identifiers) not being deployed according to a tiered storage strategy.

[0076] Anomaly handling measures may include at least one of the following: 1. Generating an early warning report corresponding to the anomaly risk and pushing it to the management personnel terminal. The early warning report may include the risk type, the relevant files, the associated nodes, and the handling suggestions; 2. Marking the target risk nodes in the knowledge graph data that correspond to the anomaly risk to facilitate subsequent tracing and analysis.

[0077] For example, different anomaly handling measures can be configured according to different needs for different anomaly risks. For instance, the corresponding anomaly handling measures can be determined based on factors such as the type, severity, and scope of impact of the anomaly risk. Specifically, when integrity anomalies such as missing nodes, broken links, or incomplete elements are identified, anomaly handling measures such as marking the relevant nodes can be triggered for subsequent traceability and correction. When operational anomalies such as unauthorized entity operations, unauthorized cross-business scenario transfers, or insufficient permission levels are identified, the operation needs to be blocked immediately and an early warning report needs to be generated and pushed out so that managers can be aware of the risk situation and intervene in a timely manner.

[0078] It's worth noting that, leveraging the deep learning and real-time analysis capabilities of the AI ​​knowledge graph engine, a leapfrog upgrade from passive screening to proactive early warning has been achieved. The system learns the normal rules of electronic record circulation (such as permission-operation matching relationships, timestamp continuity standards, and business process specifications) to build a multi-dimensional risk identification model. It monitors node data and relationships during record circulation in real time, automatically identifying abnormal risks such as unauthorized entity operations, cross-scenario unauthorized circulation, missing nodes, and timestamp logical contradictions. Simultaneously, combining the correlation analysis capabilities of the AI ​​knowledge graph engine, it traces the source and scope of risk, generating early warning reports that include the risk type, involved records, related nodes, and handling suggestions. These reports are pushed to management personnel immediately, ensuring effective control of risks at the nascent stage and guaranteeing the continuity and security of the chain of credentials.

[0079] Based on this, by constructing a risk identification model based on the normal rules of archive circulation and monitoring knowledge graph data, risks such as operational anomalies and integrity anomalies can be proactively identified, thereby further improving the reliability of electronic archive management.

[0080] In some embodiments, the knowledge graph-based electronic record management method further includes: In the pre-verification stage of electronic archives, at least one business verification is performed on the nodes and relationships in the knowledge graph data based on pre-defined association rules; the pre-verification stage includes an archiving verification stage and a pre-use verification stage; the business verification includes at least one of element integrity verification and voucher chain integrity verification. When business verification fails, trigger an alert and / or push a correction prompt message.

[0081] like Figure 3As shown, it should be noted that in the pre-defined verification stages of electronic archives (such as the archiving verification stage and the pre-use verification stage), the system can perform one or more business verifications on the nodes and relationships in the knowledge graph data based on pre-defined association rules. Specifically, the archiving verification stage is performed when the archive is completed, mainly to ensure the integrity of the credential information of the archived nodes; the pre-use verification stage is performed before the archive is viewed or downloaded, mainly to ensure that the credential chain of the archive is complete before use.

[0082] For example, business verification may include at least one of element integrity verification and credential chain integrity verification. Element integrity verification mainly checks whether nodes in the knowledge graph contain necessary attribute information, such as whether file nodes contain initial hash values, whether operator associations are missing, and whether timestamps are continuous. Credential chain integrity verification mainly checks whether the association relationships are continuous, for example, tracing back from the current node along the timeline to the initial node to check for missing nodes or broken associations.

[0083] For example, when any business verification fails, the system immediately triggers an alert and / or pushes a correction prompt. For instance, if an operation node is found to be missing a timestamp association, the system pushes a correction prompt message for "missing operation time" to the administrator; if a link in the voucher chain is found to be broken, the system generates an alert report and marks the broken node.

[0084] Based on this, by conducting business verification based on preset association rules in stages such as archiving verification and pre-use verification, the integrity of elements and the integrity of the voucher chain can be automatically verified, thereby further improving the standardization of electronic record management.

[0085] In some embodiments, the knowledge graph-based electronic record management method further includes: The knowledge graph is optimized based on the obtained optimization information; The optimization information includes at least one of the following: regulatory and standard updates and business process changes obtained periodically through a preset optimization model. The optimization process includes at least one of the following: Optimize the association rules of the knowledge graph; Optimize the node definition of the knowledge graph; Optimize the risk identification model of knowledge graph; Clean up redundant data in the knowledge graph.

[0086] It should be noted that the system can optimize the knowledge graph based on the acquired optimization information to achieve the self-optimization function of the knowledge graph. This optimization information may include at least one of the following: updated regulatory standards information and business process change information obtained periodically through a preset optimization model.

[0087] For example, the preset optimization model can use large model technology to periodically read relevant laws and standards documents, business process requirement documents, etc., for electronic record management, and extract changes from them as input for learning and optimization.

[0088] For example, the preset optimization model can also adopt an optimization engine based on rule definition configuration. When new rule information is manually entered (such as adding new node types or association rules), the optimization model can periodically scan these rules and automatically trigger the expansion of node types and the updating of association rules in the knowledge graph.

[0089] For example, the preset optimization model can also employ machine learning models, such as traditional NLP techniques like text classification, named entity recognition, and relation extraction, to extract structured information from regulatory documents and business process documents, and then transform this structured information into optimization instructions for the knowledge graph, such as adding new node types or association rules.

[0090] For example, the optimization processing performed by the system based on the optimization information may specifically include at least one of the following: 1. Optimizing the association rules of the knowledge graph, such as adjusting the association permission rules between operators and files according to the latest permission management requirements; 2. Optimizing the node definitions of the knowledge graph, including adding and deleting node types, such as adding node types corresponding to new storage media or new business scenarios; 3. Optimizing the risk identification model of the knowledge graph, such as adjusting the rules for identifying abnormal risks according to new abnormal types; 4. Cleaning up redundant data in the knowledge graph, automatically identifying and removing duplicate and invalid nodes and associations based on the optimization information to automatically clean up redundant data in the knowledge graph.

[0091] Cleaning up redundant data in knowledge graphs can be divided into two types: duplicate data identification and outdated data identification. Among these, duplicate data mainly includes the following two situations: 1. Node duplication, including: under the same electronic file, nodes with completely identical operation subjects, operation types, timestamps, and changed content; or, due to occasional problems, the system repeatedly captures and generates multiple identical nodes for the same operation.

[0092] 2. Redundancy of attributes, including: duplicate attribute fields in a node (such as the same node recording the same operator ID or approval basis multiple times), or redundant data in the knowledge graph that is not related to the core of the node (such as duplicate log records or invalid remarks). These are judged as redundant data.

[0093] For effective data, there are mainly three situations: 1. Operation failure: The generated node corresponds to an operation that was not executed successfully or has been undone (such as canceling after initiating file utilization or rejecting after submitting for archiving). Since the operation did not actually cause a change in the file status, the corresponding node is determined to be invalid.

[0094] 2. Broken Relationship: The node cannot establish a legitimate relationship with the historical nodes or subsequent nodes of the archive (e.g., no matching timestamp, no compliant operation permissions), becomes an isolated node, cannot be integrated into the chain of credentials, and is judged as invalid.

[0095] 3. Data expired / obsolete: The business basis, regulations and standards corresponding to the node have become obsolete, or the temporary verification data and transitional metadata stored by the node have completed their mission (such as temporary path information after file migration) and no longer need to be called, and are judged as invalid data.

[0096] Based on this, by regularly obtaining information on updates to regulations and standards or changes in business processes and optimizing the knowledge graph, the accuracy and effectiveness of electronic record management have been further improved.

[0097] In some embodiments, knowledge graph data and credential data are stored together, including: The credential data is stored in the hierarchical storage architecture according to the preset hierarchical storage strategy.

[0098] It should be noted that when storing knowledge graph data and credential data together, the credential data can be stored in a hierarchical storage architecture according to a preset hierarchical storage strategy. For example, the hierarchical storage strategy can allocate data to different storage levels based on characteristics such as data access frequency and security level, in order to balance access efficiency and storage cost.

[0099] For example, the tiered storage strategy defines the storage hierarchy for different types of voucher data. For instance, frequently accessed recent voucher data (such as the latest operation records or data to be verified) can be stored in the online storage tier (such as an online magnetic storage system); voucher data that has entered a stable retention period and is accessed less frequently is automatically migrated to the near-line storage tier to balance storage costs and access efficiency; and core voucher data that needs to be stored for a long time is permanently stored in the offline storage tier.

[0100] Specific hierarchical storage strategies can be set according to requirements, and the embodiments of this application are not limited thereto. Based on this, by storing voucher data in a hierarchical storage architecture according to a preset hierarchical storage strategy, different voucher data can be stored differently, thereby further improving the flexibility and security of electronic record management.

[0101] In some embodiments, storing credential data in a tiered storage architecture according to a preset tiered storage strategy includes: Voucher data with access frequency exceeding a preset frequency threshold will be transferred and stored in the online storage layer of a tiered storage architecture. Voucher data with an access frequency not exceeding a preset frequency threshold will be transferred and stored in the near-line storage layer of a tiered storage architecture. The core voucher data corresponding to the electronic records is permanently stored in the offline storage layer of a hierarchical storage architecture; The offline storage layer uses read-only storage media, and the core credential data includes at least one of the following: the final version verification code of the electronic file, key node data of the knowledge graph, and important verification reports.

[0102] Specifically, in the first aspect, the system can transfer and store voucher data whose access frequency exceeds a preset frequency threshold (frequency threshold or number of accesses threshold) to the online storage layer of the hierarchical storage architecture. The online storage layer can employ a magnetic storage system, such as a solid-state drive (SSD) or a hard disk drive (HDD) array, to provide high throughput and low latency access performance, ensuring convenient access. For example, voucher data generated or pending verification within the past week is retained in the online storage layer; similarly, voucher data accessed more than 10 times within the past week is transferred to the online storage layer.

[0103] Secondly, the system can transfer and store voucher data with an access frequency not exceeding the preset frequency threshold to the near-line storage layer of the tiered storage architecture. The near-line storage layer can use lower-cost storage media, such as high-capacity hard disk drives or tape libraries, offering moderate access performance while significantly reducing storage costs, thus balancing storage cost and efficiency. For example, voucher data that has entered a stable storage period and has a low access frequency can be automatically migrated to the near-line storage layer.

[0104] Thirdly, the core voucher data corresponding to the electronic archives is permanently stored in the offline storage layer of the hierarchical storage architecture. The offline storage layer uses read-only storage media, preferably a read-only Blu-ray disc library.

[0105] For example, core credential data may include at least one of the following: the final version verification code of the electronic file, key node data of the knowledge graph, and important verification reports. Specifically, this includes file hash values, XML signature files, complete chained credentials, and key verification reports. This core data is permanently stored offline by periodically burning it to a read-only Blu-ray disc library. The irreversible write capability of the read-only disc ensures physical data isolation, fundamentally eliminating the risk of unauthorized tampering. For example, the storage environment of the read-only disc can be set to a standard storage environment with a temperature of 18-22℃ and humidity of 40%-60%, ensuring no strong magnetic field interference. This ensures the integrity and security of the data during long-term storage (design life ≥ 50 years), effectively resisting various risks such as network attacks and equipment failures.

[0106] Based on this, by storing high-frequency data in the online storage layer, low-frequency data in the near-line storage layer, and core voucher data in the read-only offline storage layer, differentiated storage can be achieved according to access frequency and data importance, thereby further improving the flexibility and security of electronic record management.

[0107] In some embodiments, the knowledge graph-based electronic record management method further includes: In response to a credential verification request, retrieve the associated knowledge graph data and credential data based on the credential verification request; Obtaining credential traceability paths based on knowledge graph data; According to the voucher tracing path, obtain the voucher data corresponding to each node and perform hash comparison and / or signature verification; A verification report is generated based on the results of hash comparison and / or signature verification.

[0108] For example, when a user needs to verify an electronic record, they can initiate a verification request. Upon receiving a verification request for an electronic record, the system first retrieves the associated knowledge graph data and credential data based on the verification request. For instance, the system can locate and read the corresponding knowledge graph data (including nodes and relationships) and the associated credential data (e.g., hash values, digital signatures, etc.) from the storage medium based on the record identifier in the verification request.

[0109] Then, the system can obtain the credential traceability path based on knowledge graph data. For example, the system can start from the current archive node, traverse backwards along the timeline relationship, and extract the complete node sequence from the initial node to the current node to form the credential traceability path. This path can include all operation type nodes, operation subject nodes, time nodes, and business scenario nodes that the archive has undergone from archiving to the current state.

[0110] Subsequently, the system can retrieve the credential data corresponding to each node according to the credential tracing path and perform hash comparison and / or signature verification. Specifically, for the document content node, the system can calculate the hash value of the current original document and compare it with the initial hash value in the solidified credential data; if they match, it proves that the document content has not been tampered with. For the operation node, the system can obtain the digital signature corresponding to that operation node and verify it using the public key; if the verification passes, it proves that the operation record is authentic, complete, and trustworthy. And so on, the system sequentially completes the verification of all nodes according to each tracing path.

[0111] Finally, the system can generate a verification report based on the results of hash comparison and / or signature verification. This verification report may include the file identifier, verification time, traceability path, verification results at each node, and a comprehensive judgment conclusion. Optionally, the verification report supports PDF or OFD format and embeds an electronic signature compliant with the Electronic Signature Law to ensure the legal validity of the verification report itself.

[0112] In this way, the proposed solution forms a chain-like authentication closed loop through the entire process design of "initialization-update-verification-storage-authentication", ensuring that the electronic record credentials are traceable, verifiable, and trustworthy throughout their entire lifecycle.

[0113] It should be noted that the nodes and relationships in the knowledge graph are strongly associated with the credential verification information of the electronic archives (including the file's own code and management information metadata). The system's credential layer can uniformly manage knowledge graph data, verification code data, and verification reports.

[0114] For example, to achieve a strong association between knowledge graph data and credential verification information, two core attributes, "credential verification code" and "historical credential verification code," can be set for each node and its relationship. When a verification code is generated or changed, the system will promptly update the "credential verification code" attribute and migrate and store the old verification code along with its timestamp to the "historical credential verification code." In this way, the attributes of each node and its relationship not only contain the current credential verification information but also record historically generated credential verification information, thus achieving a strong association between knowledge graph data and credential verification information.

[0115] Based on this, by retrieving knowledge graph data and voucher data from associated storage to obtain traceability paths, and by performing hash comparisons or signature verifications based on voucher data according to the traceability paths, it is possible to achieve voucher data linkage and chain authentication closed loop, thereby further improving the efficiency and reliability of electronic archive voucher verification.

[0116] Compared with the prior art, the embodiments of this application have the following beneficial effects: 1. Significantly improved integrity and intuitiveness of chained vouchers: By constructing a chained voucher structure that spans the entire lifecycle through a knowledge graph, multi-dimensional key information of electronic archives is integrated, clearly presenting the archive flow trajectory, solving the problem of fragmented voucher information in traditional technology, and providing comprehensive data support for chained authentication.

[0117] 2. Enhanced proactive risk identification and improved authentication accuracy: Leveraging the intelligent analysis capabilities of knowledge graphs, it can proactively and in real-time identify abnormal risks in the electronic record circulation process, enabling early risk warnings and preventing damage to credentials. At the same time, through automated business verification, it ensures the compliance and accuracy of each node in the chain authentication.

[0118] 3. Significantly improved efficiency in credential verification: Supports natural language query and generation of visual traceability reports, eliminating the need for manual retrieval of scattered credential data, simplifying the verification process, shortening verification time, and enabling rapid response to credential verification needs in multiple scenarios, adapting to the current management situation of rapidly increasing electronic records.

[0119] 4. Significantly enhanced storage security: Through the synergistic application of hierarchical storage and read-only Blu-ray disc hardening technology, physical isolation of core voucher data is achieved, providing strong anti-tampering capabilities and effectively resisting risks such as network attacks, equipment failures, and illegal tampering, ensuring the integrity and security of core electronic archive voucher data during long-term preservation.

[0120] 5. Enhanced System Adaptability and Scalability: The system has self-optimization capabilities and can automatically adjust the node definitions and association rules of the knowledge graph according to updates to regulations and standards, changes in business processes, etc. At the same time, the three-level storage architecture can flexibly adapt to data storage needs with different access frequencies and security levels, thereby fully adapting to diverse electronic archive business scenarios and management needs.

[0121] Please refer to Figure 4 , Figure 4 The diagram illustrates a block diagram of a knowledge graph-based electronic records management device provided in some embodiments of this application. It should be understood that this knowledge graph-based electronic records management device is similar to the one described above. Figure 1 Corresponding to the method embodiments, it is able to perform each step involved in the above method embodiments. The specific functions of the knowledge graph-based electronic record management device can be found in the description above. To avoid repetition, detailed descriptions are appropriately omitted here.

[0122] Figure 4 The knowledge graph-based electronic record management device includes at least one software functional module that can be stored in a memory or embedded in the knowledge graph-based electronic record management device in the form of software or firmware. The knowledge graph-based electronic record management device includes: The information acquisition module 410 is used to acquire key information corresponding to electronic archives during the archiving process; the key information includes archive identifier, operating entity, operating time, and business scenario. The graph construction module 420 is used to construct knowledge graph data corresponding to electronic archives with key information as nodes, and to establish the relationship between each node; The voucher generation module 430 is used to generate voucher data corresponding to the electronic archive; wherein, the voucher data includes the voucher verification information of the electronic archive; The associated storage module 440 is used to associate and store knowledge graph data and voucher data.

[0123] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention. The knowledge graph-based electronic record management device provided by the embodiments of the present invention can realize the knowledge graph-based electronic record management method provided by any one of the method embodiments of the present invention.

[0124] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the aforementioned method, and will not be elaborated further here.

[0125] like Figure 5 As shown, some embodiments of this application provide an electronic device 500, which includes: a memory 510, a processor 520, and a computer program stored in the memory 510 and executable on the processor 520. When the processor 520 reads the program from the memory 510 via a bus 530 and executes the program, it can implement any of the methods included in the above-described knowledge graph-based electronic record management method.

[0126] Processor 520 can process digital signals and can include various computing architectures. For example, it can be a complex instruction set computer architecture, a reduced instruction set computer architecture, or an architecture that implements multiple instruction set combinations. In some examples, processor 520 can be a microprocessor.

[0127] The memory 510 can be used to store instructions executed by the processor 520 or data related to the execution of instructions. These instructions and / or data may include code for implementing some or all of the functions of one or more modules described in the embodiments of this application. The processor 520 of this disclosure embodiment can be used to execute the instructions in the memory 510 to implement the methods shown above. The memory 510 includes dynamic random access memory, static random access memory, flash memory, optical memory, or other memories well known to those skilled in the art.

[0128] Some embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, describes the method described in the method embodiments.

[0129] Some embodiments of this application also provide a computer program product that, when run on a computer, causes the computer to perform the methods described in the method embodiments.

[0130] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For apparatus embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0131] It should be understood, in the several embodiments provided in this application, that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0132] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0133] If the aforementioned functions are implemented as software functional modules 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 this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0134] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0135] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0136] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A knowledge graph-based electronic record management method, characterized in that, include: In the archiving process of electronic records, key information corresponding to the electronic records is obtained; wherein, the key information includes record identifier, operating entity, operating time, and business scenario; Using the key information as nodes, construct the knowledge graph data corresponding to the electronic archives, and establish the relationship between each node; Generate voucher data corresponding to the electronic file; wherein, the voucher data includes the voucher verification information of the electronic file; The knowledge graph data and the credential data are stored together.

2. The knowledge graph-based electronic records management method according to claim 1, characterized in that, Also includes: In each subsequent stage after the electronic archive completes the archiving stage, corresponding key information is obtained based on the operation events of the electronic archive as updated key information; wherein, the subsequent stages include management stage, storage stage, utilization stage or destruction stage; Using the updated key information as a new node, the knowledge graph data is updated, and a relationship between the new node and historical nodes is established from at least one association dimension; wherein, the association dimension includes at least one of time dimension, subject dimension and archive dimension.

3. The knowledge graph-based electronic records management method according to claim 1, characterized in that, Also includes: In response to a user's credential chain query request, the credential chain query request is parsed to obtain the user's query intent; Based on the query intent, matching target information is retrieved from the knowledge graph data; A chain-based credential traceability report is generated based on the target information.

4. The knowledge graph-based electronic records management method according to claim 1, characterized in that, Also includes: Based on a preset risk identification model, risk identification is performed on the nodes and relationships in the knowledge graph data; wherein, the risk identification model is constructed based on preset normal rules for file circulation; Upon identification of an abnormal risk, pre-defined anomaly handling measures are triggered; wherein, the abnormal risk includes at least one of operational anomaly, integrity anomaly, or storage anomaly. The anomaly handling measures include at least one of the following: Generate an early warning report corresponding to the aforementioned abnormal risk and push it to the management personnel's terminal; The target risk nodes corresponding to abnormal risks in the knowledge graph data are marked.

5. The knowledge graph-based electronic records management method according to claim 1, characterized in that, Also includes: In the preset verification step of the electronic archive, at least one business verification is performed on the nodes and relationships in the knowledge graph data based on preset association rules; wherein, the preset verification step includes an archiving verification step and a pre-use verification step; the business verification includes at least one of element integrity verification and credential chain integrity verification; When the business verification fails, an alert is triggered and / or a correction prompt is pushed.

6. The knowledge graph-based electronic records management method according to claim 1, characterized in that, Also includes: The knowledge graph is optimized based on the obtained optimization information; The optimization information includes at least one of the following: regulatory and standard update information and business process change information obtained periodically through a preset optimization model. The optimization process includes at least one of the following: Optimize the association rules of the knowledge graph; Optimize the node definition of the knowledge graph; Optimize the risk identification model of knowledge graph; Clean up redundant data in the knowledge graph.

7. The knowledge graph-based electronic records management method according to claim 1, characterized in that, The step of associating and storing the knowledge graph data and the credential data includes: The credential data is stored in a hierarchical storage architecture according to a preset hierarchical storage strategy.

8. The knowledge graph-based electronic records management method according to claim 7, characterized in that, The step of storing the credential data in a hierarchical storage architecture according to a preset hierarchical storage strategy includes: Voucher data with an access frequency exceeding a preset frequency threshold will be transferred and stored in the online storage layer of the hierarchical storage architecture. Voucher data with an access frequency not exceeding the preset frequency threshold will be transferred and stored in the near-line storage layer of the hierarchical storage architecture. The core voucher data corresponding to the electronic archive is permanently stored in the offline storage layer of the hierarchical storage architecture; The offline storage layer uses a read-only storage medium, and the core credential data includes at least one of the following: the final version verification code of the electronic file, key node data of the knowledge graph, and important verification reports.

9. The knowledge graph-based electronic records management method according to claim 1, characterized in that, Also includes: In response to a credential verification request, knowledge graph data and credential data stored in association are retrieved based on the credential verification request; Obtain the credential tracing path based on the knowledge graph data; According to the document tracing path, obtain the document data corresponding to each node and perform hash comparison and / or signature verification; A verification report is generated based on the results of hash comparison and / or signature verification.

10. A knowledge graph-based electronic record management device, characterized in that, include: The information acquisition module is used to acquire key information corresponding to the electronic archives during the archiving process; wherein, the key information includes archive identifier, operating entity, operating time, and business scenario; The graph construction module is used to construct the knowledge graph data corresponding to the electronic archive using the key information as nodes, and to establish the relationship between the nodes. A voucher generation module is used to generate voucher data corresponding to the electronic file; wherein, the voucher data includes the voucher verification information of the electronic file; The associated storage module is used to associate and store the knowledge graph data and the voucher data.

11. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it can implement the knowledge graph-based electronic record management method according to any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, performs the knowledge graph-based electronic record management method as described in any one of claims 1-9.

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