Power archive management method and system based on human-computer interaction

Abstract

The present application relates to the technical field of electric power file management, in particular to an electric power file management method and system based on human-computer interaction. It comprises real-time calculation of risk scores of adjacent nodes in each scene; according to the risk control level matching encryption mode. The present application extracts the scene adjacent nodes in the corresponding scene change route simulation graph and calculates the risk scores of the corresponding nodes, combines the grade division of each storage interval in the file database and the interval encryption algorithm matching, calculates the risk scores of the electric power file data in real time according to the application scene change of the electric power file data, as the basis for adjusting the storage path of the electric power file data, and carries out adaptive encryption processing on the electric power file data after adjusting the storage position according to the encryption algorithm of the corresponding storage interval, to adapt to different scene change requirements, both to ensure the free circulation of electric power file data and to provide real-time emergency protection.

Description

Technical Field

[0001] This invention relates to the field of power record management technology, and more specifically, to a power record management method and system based on human-computer interaction. Background Technology

[0002] In the digital transformation of the power industry, record management is evolving from traditional physical storage to a data-driven intelligent service model. As original evidence and information carriers recording the entire process of power grid planning, construction, operation, and management, power records possess irreplaceable strategic value in ensuring the safe operation of the power grid and supporting corporate decision-making. In recent years, technologies such as the Internet of Things, robotic process automation, and natural language processing have been introduced into record management, initially realizing automatic inventory, location tracking, and content-based intelligent retrieval of physical records, significantly improving management efficiency.

[0003] However, with the deep application of archival data in complex business scenarios, the security vulnerabilities exposed during its transfer process, especially the contradiction between scenario-specific risks and the rudimentary nature of encryption protection, have become a key technological bottleneck restricting the development of intelligent archival management. Specifically, existing management paradigms have the following significant shortcomings in dealing with dynamic and ever-changing transfer risks:

[0004] First, the complexity and risk heterogeneity of archival application scenarios are overlooked;

[0005] The circulation of power records is not a simple, static storage activity, but a dynamic process that spans the entire lifecycle from "in-store storage to internal borrowing, on-site operations, and inter-unit transfer." The risk profiles differ drastically across different scenarios:

[0006] Routine management within the warehouse: The main risks lie in unauthorized access and abnormal movement.

[0007] On-site access: When archives are removed from a controlled physical environment, they face risks such as equipment loss, unauthorized copying, and accidental data leakage in complex electromagnetic environments.

[0008] Cross-unit transfer: This involves the transfer of data sovereignty and carries uncontrollable risks such as secondary distribution and use beyond the scope by the receiving party.

[0009] While existing systems can record file trajectories, they generally lack the ability to accurately define and automatically identify the transfer scenarios, as well as the ability to dynamically assess the risks specific to those scenarios, resulting in a disconnect between security strategies and real risks.

[0010] Second, the "one-size-fits-all" centralized encryption model has fallen into a dilemma between "security" and "usability".

[0011] To address the potential risk of data leakage, the current mainstream protection method is to centrally encrypt power sector archive data. However, this approach has resulted in two extreme and undesirable outcomes in practice:

[0012] Path 1: Employ strong encryption and unified key management. All archives, regardless of their security level or usage scenario, are subject to high-strength encryption. While this approach provides basic security, frequent internal circulation and cross-departmental collaboration require frequent manual intervention or complex permission approvals for encryption and decryption operations. This severely slows down business response speed, damages the circulation value of archive data, and runs counter to the original intention of digitization to improve efficiency.

[0013] Path Two: Employing simple or symbolic encryption. To ensure ease of circulation, files are simply packaged or protected with weak passwords. This renders files containing highly sensitive information such as core technical parameters, power grid geographic information, and important user data virtually unprotected against targeted attacks or internal violations, posing an extremely high risk of leakage.

[0014] To address the aforementioned issues, there is an urgent need for a human-computer interaction-based power record management method capable of scene-route adaptive encryption processing. Summary of the Invention

[0015] The purpose of this invention is to provide a power file management method and system based on human-computer interaction to solve the problems mentioned in the background art.

[0016] To achieve the above objectives, one of the objectives of this invention is to provide a power file management method based on human-computer interaction, comprising the following steps:

[0017] S1. Construct a human-computer interactive power archive management platform, including the execution layer. Platform layer and decision-making level ;

[0018] decision-making level Used to identify the status of power archive data, construct a mapping relationship between power archive data status and processing scheme, and match processing schemes according to data status;

[0019] Execution layer The response and processing plan is implemented, and the power archive data is processed simultaneously.

[0020] Platform layer Used to build an archive database and store the processed power archive data;

[0021] S2. Construct a power archive risk management and control platform to monitor power archive data in real time and obtain monitoring status results;

[0022] S3. Based on the monitoring status results, verify the single scenario mode and generate a simulation diagram of the power archive data scenario change route;

[0023] S4. Extract adjacent nodes of the scene in the scene change route simulation map. Real-time calculation of adjacent nodes in each scene Risk score ;

[0024] S5. Construct a power archive emergency management system, based on adjacent nodes in the scenario. Risk score Risk management level of power archive data updated in real time after changes in scenarios According to the risk control level Match encryption method.

[0025] As a further improvement to this technical solution, the method for constructing a human-computer interactive archive management platform for power archives in step S1 includes the following steps:

[0026] S1.1, Utilizing the decision-making level Identify the status of power archive data and generate a processing plan based on the data status;

[0027] S1.2 Extract multi-format original archive data through the constructed multimodal recognition model;

[0028] S1.3. The current original archive data format is identified through format recognition routing. Its multimodal recognition model integrates a text parsing engine, an image parsing engine, and a structured data parsing engine.

[0029] The text parsing engine extracts key entities, performs topic classification and clustering, and enables automatic archiving; it also establishes a sensitive word database for power archives, which identifies sensitive words in the text portion of the original archive data.

[0030] The image analysis engine identifies visual elements in an image, distinguishes between text and image parts, extracts text from the image through OCR (Optical Character Recognition), converts labels, annotations, and explanatory texts in scanned drawings or photographs into labelable and searchable text, and trains CV models to recognize specific legend symbols in drawings.

[0031] The structured data parsing engine understands the semantics of the fields in the text portion to obtain the expressive content of the text and image data;

[0032] S1.4. Using the recognition and processing results of the text parsing engine, image parsing engine, and structured data parsing engine, obtain the current status of the power archive data and construct the mapping relationship between the power archive data status and the processing scheme.

[0033] S1.5, Platform Layer An archive database is established to store the processed power archive data. The archive database is divided into thematic storage areas, and the classified information is divided into intervals based on the classified information of the thematic storage areas. Each thematic storage area is further divided into intervals according to the classified information of the power archive data. Each interval stores power archive data of the same theme but different classified information levels.

[0034] S1.6, Execution Layer The system responds to the processing plan and processes the power archive data simultaneously, matching the subject storage area with the corresponding storage interval, constructing the power archive data storage route, and obtaining its storage area and storage interval markers.

[0035] As a further improvement to this technical solution, the monitoring status obtained in S2 includes the changed scene item, duration, and the identity level of the person handling the change.

[0036] As a further improvement to this technical solution, the method for generating the power archive data scenario change route simulation diagram in S3 includes the following steps:

[0037] S3.1 Extract the storage route of the current power archive data in the archive database and obtain the corresponding storage area and storage interval markers;

[0038] S3.2 Bind the storage route, change scenario items, duration, and processing personnel identity level of the current power archive data;

[0039] S3.3 Obtain the monitoring status result of a single scene, mark it as a scene status point, and generate a simulation diagram of the scene change route of the power archive data according to the historical scene status points of the current power archive data in chronological order.

[0040] As a further improvement to this technical solution, the risk scores of adjacent nodes in S4 are... The calculation method is as follows:

[0041] S4.1 Obtain the monitoring status data of the previous scene status point of the current adjacent node;

[0042] S4.2 Assign initial risk scores to each change scenario item, and establish a relationship between change scenario items and initial risk scores. The mapping set between;

[0043] S4.3, Verification Processing Personnel Identity Level Dataset ,in - To differentiate personnel based on their status level, according to their rank... , where n represents the level quantity, and is the value for processing the personnel identity level dataset. Each level is assigned a level constant value according to its level, from highest to lowest. The range of values ​​is ;

[0044] S4.4, and construct a time-varying set group. ,in - Sets at different points in time, and Assign time constants to sets of different time points. time constant The range of values ​​is ;

[0045] S4.5. Collect the current change scenario items and compare them with the initial risk score. By comparing the mapping sets between them, the initial risk score of the project in the current change scenario is extracted. ;

[0046] S4.6 Verify the personnel identity level in the current application scenario, based on the processed personnel identity level dataset. Extract the level constant of the corresponding level ;

[0047] S4.7, Set group based on time changes Match the corresponding set of time points and select the time constant corresponding to the matched set of time points. ;

[0048] S4.8 Calculate the final risk score ;

[0049] S4.9 Obtain the risk score of the current change scenario project. Calculate the risk scores between each change scenario item before adjacent nodes using the steps described above. The sum is used as the final risk score for the current neighboring nodes.

[0050] As a further improvement to this technical solution, the level constant in S4.3 It is inversely proportional to the level of personnel status.

[0051] As a further improvement to this technical solution, the time constant in S4.4 It is directly proportional to the length of time.

[0052] As a further improvement to this technical solution, in step S5, the risk control level is adjusted accordingly. The method for matching encryption methods includes the following steps:

[0053] S5.1 Construct risk scores among adjacent nodes Risk control level Mapping relationship, and risk score Partition the numerical set;

[0054] S5.2, Obtain each adjacent node Real-time risk score Match the corresponding set of values ​​to determine the risk control level. Mapping selection;

[0055] S5.3. Distribute the encryption method according to the interval distribution in the storage area, and classify the confidentiality level of each interval;

[0056] S5.4 Match the risk score range set according to the interval after classifying the confidentiality level. Different intervals of different confidentiality levels correspond to different risk score range sets.

[0057] S5.5, Real-time risk score of the current application scenario. The data is matched with the corresponding risk score range set, and the current power file data is retrieved to the corresponding level range. The current power file data is then encrypted using the encryption algorithm of the storage range.

[0058] As a further improvement to this technical solution, the risk control level in S5.1 The number of partitions is consistent with the number of interval partitions in each storage area of ​​the archive database.

[0059] The second objective of this invention is to provide a system for implementing a human-computer interaction-based power archive management method, including a human-computer interaction power archive management platform and a power archive risk control platform;

[0060] The power archive human-computer interaction archive management platform is used for storing power archive data and managing application scenarios, specifically including a decision-making layer, an execution layer, and a platform layer;

[0061] The decision-making layer is used to identify the status of power archive data, construct a mapping relationship between power archive data status and processing schemes, and match processing schemes according to data status.

[0062] The execution layer response processing scheme is described, and the power archive data is processed simultaneously.

[0063] The platform layer is used to build an archive database, store the processed power archive data, construct archive database interval storage rules, divide storage areas and storage intervals, classify each storage interval into confidentiality levels, and match the corresponding encryption method according to the confidentiality level.

[0064] The power archive risk management platform includes a data acquisition terminal, a route simulation module, a data calculation module, and a data storage and adjustment module.

[0065] The data acquisition terminal is used to monitor the power archive data in real time, obtain the monitoring status results, and feed the monitoring status results back to the route simulation module. The route simulation module, in conjunction with the current power archive data, generates a route simulation map of power archive data scene changes in chronological order based on the historical scene status points of the current power archive data.

[0066] The data calculation module calculates the risk score of each adjacent node in the scenario change route simulation diagram in real time based on the monitoring status results, and updates the risk control level of the power archive data after the scenario change in real time based on the risk scores of the adjacent nodes in the scenario. The number of risk control levels is consistent with the number of intervals in each storage area of ​​the archive database.

[0067] The data storage adjustment module is used to construct a mapping relationship between risk control levels and storage intervals. It matches the corresponding storage intervals with the risk control levels of the current power archive data, and works with the execution layer to divide the power archive data into storage intervals in real time. The current power archive data is then encrypted using the encryption method corresponding to the divided storage intervals.

[0068] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0069] This human-computer interaction-based power archive management method and system extracts adjacent nodes from the scenario change route simulation diagram and calculates the risk scores of the corresponding nodes. Combined with the hierarchical classification of each storage interval in the archive database and interval encryption algorithm matching, the risk scores of the power archive data are calculated in real time according to the application scenario changes. This serves as the basis for adjusting the power archive data storage path. Combined with the encryption algorithm of the corresponding storage interval, the power archive data after the storage location is adjusted is adaptively encrypted to adapt to the needs of different scenario changes. This ensures the free flow of power archive data and provides it with real-time emergency protection. Attached Figure Description

[0070] Figure 1 This is a diagram illustrating the overall method steps of the present invention;

[0071] Figure 2 This is a flowchart illustrating the steps of constructing a human-computer interactive archive management platform for power archives according to the present invention.

[0072] Figure 3 This is a simulation diagram of the scene change route in this invention;

[0073] Figure 4This is a flowchart illustrating the method steps for generating a scenario change route simulation diagram for power archive data according to the present invention.

[0074] Figure 5 Risk score of adjacent nodes in this invention Calculation method steps diagram;

[0075] Figure 6 According to the risk control level of the present invention A diagram illustrating the steps involved in matching encryption methods;

[0076] Figure 7 This is a block diagram of the overall system structure of the present invention. Detailed Implementation

[0077] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0078] Please see Figure 1 As shown, one of the objectives of this invention is to provide a power data management method based on human-computer interaction, comprising the following steps:

[0079] S1. Construct a human-computer interactive power archive management platform, including the execution layer. Platform layer and decision-making level ;

[0080] decision-making level Used to identify the status of power archive data, construct a mapping relationship between power archive data status and processing scheme, and match processing schemes according to data status;

[0081] Execution layer The response and processing plan is implemented, and the power archive data is processed simultaneously.

[0082] Platform layer Used to build an archive database and store the processed power archive data;

[0083] S2. Construct a power archive risk management and control platform to monitor power archive data in real time and obtain monitoring status results;

[0084] S3. Based on the monitoring status results, verify the single scenario mode and generate a simulation diagram of the power archive data scenario change route;

[0085] S4. Extract adjacent nodes of the scene in the scene change route simulation map. Real-time calculation of adjacent nodes in each scene Risk score ;

[0086] S5. Construct a power archive emergency management system, based on adjacent nodes in the scenario. Risk score Risk management level of power archive data updated in real time after changes in scenarios According to the risk control level Match encryption method.

[0087] The details are as follows:

[0088] Firstly, in the process of managing power records, in order to ensure that each piece of power record data can be managed in an orderly and systematic manner, this solution constructs an execution layer... Platform layer and decision-making level The power archive human-computer interactive archive management platform utilizes the decision-making level Identify the status of power archive data and generate a processing plan based on the data status, such as... Figure 2 As shown, the status of its power archive data includes data source, data content, data format, data type (text, image, video, etc.) and data size. A multimodal recognition model is built to extract original archive data in multiple formats (reports, drawings, logs, etc.). The format recognition is used to route the current original archive data format. Its multimodal recognition model integrates a text parsing engine, an image parsing engine, and a structured data parsing engine.

[0089] The text parsing engine extracts key entities, such as data source equipment, location, and management information. It performs keyword recognition on the original archive data, identifying key entities like "equipment name," "geographical location," and "time" in the text. Using the LDA algorithm, it automatically determines the document topic, including fault analysis, completion acceptance, and operation inspection, and performs topic classification and clustering to achieve automatic archiving. Furthermore, it establishes a power archive sensitive word database to identify sensitive words in the text of the original archive data.

[0090] The image analysis engine identifies visual elements in an image, distinguishes between text and image parts, extracts text from the image using OCR (Optical Character Recognition), converts labels, annotations, and explanatory text in scanned drawings or photographs into labelable and searchable text, and trains a CV (Computer Vision) model to recognize specific legend symbols in drawings, such as circuit breaker and transformer icons, as well as red line annotations and seals, thus enabling image annotation and analysis.

[0091] Finally, a structured data parsing engine is used to understand the semantics of the text fields and obtain the expressive content of the text and image data.

[0092] By using the recognition and processing results of text parsing engine, image parsing engine, and structured data parsing engine, the current status of power archive data is obtained, and a mapping relationship between power archive data status and processing scheme is constructed. Different power archive data statuses correspond to different processing schemes. The processing schemes include the power archive data storage type (based on the source of the power archive data, equipment model, and submitting employee, etc.) and the confidentiality level (based on the data classification results and the topics involved, etc.). The corresponding processing scheme is matched according to the current power archive data status.

[0093] In the specific storage process of power archive data, in order to adapt to the storage work of different types of power archive data and subsequent application scenarios, a platform layer is used. An archival database is established to store the processed power archive data. The database is divided into thematic storage areas, with each area storing power archive data corresponding to a specific theme. These thematic storage areas are further divided into security classification zones, and each zone is further subdivided according to the security classification of the power archive data. Each zone stores power archive data of the same theme but at different security classification levels. When the power archive data passes through the decision-making level… After processing, the corresponding processing solution is obtained and executed through the execution layer. The system responds to the processing plan and processes the power archive data simultaneously, matching the subject storage area with the corresponding storage interval, constructing the power archive data storage route, and obtaining its storage area and storage interval markers.

[0094] After completing the collection and storage of power archive data, the data frequently needs to be changed in different scenarios. These changes include employee requests for access and borrowing, engineers bringing equipment files to the site, and transferring data to other departments. Because the people involved, the content of the scenario, and the duration of these changes vary, the corresponding level of confidentiality risk also differs. To ensure the confidentiality protection of different power archive data, this solution constructs a power archive risk management platform to monitor the data in real time and obtain monitoring status results, including changes to the scenario items. (Employee secondment, inter-departmental borrowing, and data transfer), duration, and the identity level of the personnel handling the data are all monitored. During the monitoring process, an RFID-based check-in mechanism is used to track power archive data. This means that a corresponding check-in marker is assigned based on different change scenarios. For example, when an employee applies for power archive data, they submit a borrowing request in the system. This request includes the purpose of borrowing (i.e., the change scenario), the employee's current level (i.e., the personnel handling the data's identity level), and the required borrowing time (i.e., the duration). After approval, the system generates an electronic transfer slip with a unique QR code and authorizes the employee's borrowing rights. Figure 4 As shown, the storage route of the current power archive data in the archive database is extracted, and the corresponding storage area and storage interval markers are obtained. The storage route, change scenario items, duration, and processing personnel identity level of the current power archive data are bound together. The monitoring status result of a single scenario is obtained and marked as a scenario status point. Based on the historical scenario status points of the current power archive data, a simulation diagram of the power archive data scenario change route is generated in chronological order, as shown. Figure 3 The diagram shown is a simulation of the scene change route for the same power data archive. It includes scene status points ①, ②, ③, ④, and ⑤, which together form the scene change route for the current power data archive according to their chronological order.

[0095] Furthermore, since the risk of leakage of power archive data is related to the application scenario—that is, after being used in multiple scenarios and handled by multiple personnel and equipment—the risk of leakage gradually increases. To avoid the above problems, it is necessary to perform real-time encryption and modification processing based on the scenario change route of the power archive data. In this solution, adjacent nodes of the scenarios are extracted from the scenario change route simulation diagram. Real-time calculation of adjacent nodes in each scene Risk score Adjacent nodes in a scene are deployed at two adjacent scene state points, such as... Figure 3As shown, scene state point ① and scene state point ② are connected by adjacent node I, scene state point ② and scene state point ③ are connected by adjacent node II, and scene state points ④ and ⑤ are scene changes that occurred at the same time, with adjacent node III connected to scene state point ③. Since scene state points are divided according to time sequence, the corresponding adjacent nodes are also generated according to time sequence. This solution assigns risk scores to adjacent nodes. This serves as the basis for changing the encryption method when applying the next scenario state point, such as Figure 5 As shown, the risk scores of adjacent nodes are calculated in detail. The method is as follows:

[0096] First, obtain the monitoring status data of the scene status point of the current adjacent node, namely the changed scene item, duration, and the identity level of the person handling the change, and then assign an initial risk score to each changed scene item. Divide and establish change scenario projects and initial risk scores. The mapping set between them, that is, different initial risk scores corresponding to different change scenarios for projects. Verification and processing personnel identity level dataset ,in - To differentiate personnel based on their status level, according to their rank... , where n represents the level quantity, and is the value for processing the personnel identity level dataset. Each level is assigned a level constant value according to its level, from highest to lowest. The range of values ​​is That is, personnel status level Corresponding level constant Personnel status level Corresponding level constant Personnel status level Corresponding level constant And the grade constant The risk of information leakage is inversely proportional to the personnel's status level; the higher the personnel's status level, the lower the risk of leakage. The corresponding level constant is... The smaller the value, the more time-varying sets are constructed. ,in - Sets at different points in time, and Assign time constants to sets of different time points. time constant The range of values ​​is And time constant The time constant is directly proportional to the duration of the current application scenario; that is, the longer the duration of the current application scenario, the longer the corresponding time constant. The larger the risk score, the better when considering adjacent nodes. The first step in the calculation process is to collect the current change scenario items and compare them with the initial risk score. By comparing the mapping sets between them, the initial risk score of the project in the current change scenario is extracted. The second step is to verify the personnel identity level in the current application scenario, based on the personnel identity level dataset. Extract the level constant of the corresponding level The third step is to obtain the duration of the current change scenario project and group it according to the time change. Match the corresponding set of time points and select the time constant corresponding to the matched set of time points. Calculate the final risk score ,Right now: ;

[0097] Obtain the risk score of the project in the current change scenario. Calculate the risk scores between each change scenario item before adjacent nodes using the steps described above. The sum is used as the final risk score for the current neighboring nodes.

[0098] Furthermore, such as Figure 6 As shown, this solution constructs an emergency management system for power records, based on adjacent nodes in the scenario. Risk score Risk management level of power archive data updated in real time after changes in scenarios According to the risk control level Matching encryption methods and determining risk control levels During the update process, firstly, risk scores are constructed among adjacent nodes. Risk control level Mapping relationship, where risk scores Divide the numerical set into risk scores. Different sets of numerical values ​​correspond to different risk control levels. For example, risk control level Divided into three levels: general audience, mass audience, mass market ... Control level and risk level Risk score It will be divided into three sets of values, namely , as well as They correspond to the mass market. Control level and risk level It is worth noting that the risk control level The number of partitions is consistent with the number of interval partitions in each storage area of ​​the archive database, and each adjacent node is obtained. Real-time risk score Match the corresponding set of values ​​to determine the risk control level. Mapping selection.

[0099] Finally, encryption methods are distributed according to the range distribution within the storage area. Each range is classified into different security levels, with different encryption technologies corresponding to different security levels. For example, for general-level ranges, the default encryption function provided by the cloud service provider is used; for control-level ranges, the AES-128 encryption algorithm is used to encrypt the stored power archive data; and for risk-level ranges, the AES-256 encryption algorithm is used. Furthermore, risk score range sets are matched based on the classified security levels, with different security levels corresponding to different risk score range sets. This constructs the archive database range storage rules, enabling real-time risk scoring for the current application scenario. After the calculation, the data is matched with the corresponding risk score range set, and the current power archive data is retrieved to the corresponding level range. The current power archive data is then encrypted using the encryption algorithm of the storage range to adapt to the power archive data management work after the corresponding change scenario.

[0100] This solution acquires real-time monitoring status results of power archive data through a constructed power archive risk management platform, generates a simulation map of power archive data scenario change routes, extracts adjacent nodes in the scenario change route simulation map and calculates the risk scores of the corresponding nodes, combines the hierarchical classification of each storage area in the archive database with the matching of interval encryption algorithms, and calculates the risk scores of power archive data in real time according to the application scenario changes of power archive data. This serves as the basis for adjusting the power archive data storage path, and adaptive encryption processing of the power archive data after the storage location is adjusted is performed in combination with the encryption algorithm of the corresponding storage area to adapt to the needs of different scenario changes. This ensures the free flow of power archive data and provides it with real-time emergency protection.

[0101] Please see Figure 7 As shown, a second objective of this invention is to provide a system for implementing a human-computer interaction-based power archive management method, including a power archive human-computer interaction archive management platform and a power archive risk control platform;

[0102] The power archive human-computer interaction archive management platform is used for storing power archive data and managing application scenarios. It specifically includes a decision-making layer, an execution layer, and a platform layer.

[0103] The decision-making layer is used to identify the status of power archive data, construct a mapping relationship between power archive data status and processing schemes, match processing schemes according to data status, issue execution commands to the execution layer, and execute the storage and scenario application of power archive data in the archive database through the execution layer.

[0104] The execution layer responds to the processing scheme and processes the power archive data simultaneously;

[0105] The platform layer is used to build the archive database, store the processed power archive data, construct the archive database's interval storage rules, divide storage areas and intervals, classify each interval into confidentiality levels, and match corresponding encryption methods according to the confidentiality level, such as... Figure 7 As shown, the platform layer is divided into two regions (there are actually more regions), namely Region 1 and Region 2. Different regions store power archive data on different themes. Region 2 is divided into three intervals, namely Interval 1, Interval 2 and Interval 3. The confidentiality level of different intervals is different, and the corresponding encryption algorithms are different.

[0106] The power archive risk management platform includes a data acquisition terminal, a route simulation module, a data calculation module, and a data storage and adjustment module.

[0107] The data acquisition terminal is used to monitor power archive data in real time, obtain monitoring status results, including the changed scenario items, duration, and the identity level of the personnel handling the changes, and feed the monitoring status results back to the route simulation module. The route simulation module, in conjunction with the current power archive data, generates a power archive data scenario change route simulation diagram in chronological order based on the historical scenario status points of the current power archive data.

[0108] The data calculation module calculates the risk score of each adjacent node in the scenario change route simulation diagram in real time based on the monitoring status results. It updates the risk control level of the power archive data after the scenario change in real time based on the risk scores of the adjacent nodes in the scenario. The number of risk control levels is consistent with the number of intervals in each storage area of ​​the archive database.

[0109] The data storage adjustment module is used to construct a mapping relationship between risk control levels and storage intervals. It matches the corresponding storage intervals based on the risk control level of the current power archive data, and works with the execution layer to divide the power archive data into storage intervals in real time. The current power archive data is then encrypted using the encryption method corresponding to the divided storage intervals.

[0110] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A power record management method based on human-computer interaction, characterized in that: Includes the following steps: S1. Construct a human-computer interactive power archive management platform, including the execution layer. Platform layer and decision-making level ; decision-making level Used to identify the status of power archive data, construct a mapping relationship between power archive data status and processing scheme, and match processing schemes according to data status; Execution layer The response and processing plan is implemented, and the power archive data is processed simultaneously. Platform layer Used to build an archive database and store the processed power archive data; S2. Construct a power archive risk management and control platform to monitor power archive data in real time and obtain monitoring status results; S3. Based on the monitoring status results, verify the single scenario mode and generate a simulation diagram of the power archive data scenario change route; The method for generating a simulation diagram of the power archive data scenario change route in S3 includes the following steps: S3.1 Extract the storage route of the current power archive data in the archive database and obtain the corresponding storage area and storage interval markers; S3.2 Bind the storage route, change scenario items, duration, and processing personnel identity level of the current power archive data; S3.3 Obtain the monitoring status result of a single scene, mark it as a scene status point, and generate a simulation diagram of the scene change route of the power archive data according to the historical scene status points of the current power archive data in chronological order; S4. Extract adjacent nodes of the scene in the scene change route simulation map. Real-time calculation of adjacent nodes in each scene Risk score ; Risk scores of adjacent nodes in S4 The calculation method is as follows: S4.1 Obtain the monitoring status data of the previous scene status point of the current adjacent node; S4.2 Assign initial risk scores to each change scenario item, and establish a relationship between change scenario items and initial risk scores. The mapping set between; S4.3, Verification Processing Personnel Identity Level Dataset ,in - To differentiate personnel based on their status level, according to their rank... , where n represents the level quantity, and is the value for processing the personnel identity level dataset. Each level is assigned a level constant value according to its level, from highest to lowest. The range of values ​​is ; S4.4, and construct a time-varying set group. ,in - Sets at different points in time, and Assign time constants to sets of different time points. time constant The range of values ​​is ; S4.

5. Collect the current change scenario items and compare them with the initial risk score. By comparing the mapping sets between them, the initial risk score of the project in the current change scenario is extracted. ; S4.6 Verify the personnel identity level in the current application scenario, based on the processed personnel identity level dataset. Extract the level constant of the corresponding level ; S4.7, Set group based on time changes Match the corresponding set of time points and select the time constant corresponding to the matched set of time points. ; S4.8 Calculate the final risk score ; S4.9 Obtain the risk score of the current change scenario project. Calculate the risk scores between each change scenario item before adjacent nodes using the steps described above. The sum is used as the final risk score for the current neighboring nodes; S5. Construct a power archive emergency management system, based on adjacent nodes in the scenario. Risk score Risk management level of power archive data updated in real time after changes in scenarios According to the risk control level Match encryption method.

2. The power record management method based on human-computer interaction according to claim 1, characterized in that: The method for constructing a human-computer interactive power archive management platform in S1 includes the following steps: S1.1, Utilizing the decision-making level Identify the status of power archive data and generate a processing plan based on the data status; S1.2 Extract multi-format original archive data through the constructed multimodal recognition model; S1.

3. The current original archive data format is identified through format recognition routing. Its multimodal recognition model integrates a text parsing engine, an image parsing engine, and a structured data parsing engine. The text parsing engine extracts key entities, performs topic classification and clustering, and enables automatic archiving; A database of sensitive words in power archives will be established to identify sensitive words in the text portion of the original archive data. The image analysis engine identifies visual elements in an image, distinguishes between text and image parts, extracts text from the image through OCR (Optical Character Recognition), converts labels, annotations, and explanatory texts in scanned drawings or photographs into labelable and searchable text, and trains CV models to recognize specific legend symbols in drawings. The structured data parsing engine understands the semantics of the fields in the text portion to obtain the expressive content of the text and image data; S1.

4. Using the recognition and processing results of the text parsing engine, image parsing engine, and structured data parsing engine, obtain the current status of the power archive data and construct the mapping relationship between the power archive data status and the processing scheme. S1.5, Platform Layer An archive database is established to store the processed power archive data. The archive database is divided into thematic storage areas, and the classified information is divided into intervals based on the classified information of the thematic storage areas. Each thematic storage area is further divided into intervals according to the classified information of the power archive data. Each interval stores power archive data of the same theme but different classified information levels. S1.6, Execution Layer The system responds to the processing plan and processes the power archive data simultaneously, matching the subject storage area with the corresponding storage interval, constructing the power archive data storage route, and obtaining its storage area and storage interval markers.

3. The power record management method based on human-computer interaction according to claim 1, characterized in that: The monitoring status obtained in S2 includes the changed scene item, duration, and the identity level of the person handling the change.

4. The power record management method based on human-computer interaction according to claim 1, characterized in that: The level constant in S4.3 It is inversely proportional to the level of personnel status.

5. The power record management method based on human-computer interaction according to claim 1, characterized in that: The time constant in S4.4 It is directly proportional to the length of time.

6. The power record management method based on human-computer interaction according to claim 1, characterized in that: S5 is based on risk control level The method for matching encryption methods includes the following steps: S5.1 Construct risk scores among adjacent nodes Risk management level Mapping relationship, and risk score Partition the numerical set; S5.2, Obtain each adjacent node Real-time risk score Match the corresponding set of values ​​to determine the risk control level. Mapping selection; S5.

3. Distribute the encryption method according to the interval distribution in the storage area, and classify the confidentiality level of each interval; S5.4 Match the risk score range set according to the interval after classifying the confidentiality level. Different intervals of different confidentiality levels correspond to different risk score range sets. S5.5, Real-time risk score of the current application scenario. The data is matched with the corresponding risk score range set, and the current power file data is retrieved to the corresponding level range. The current power file data is then encrypted using the encryption algorithm of the storage range.

7. The power record management method based on human-computer interaction according to claim 6, characterized in that: Risk control levels in S5.1 The number of partitions is consistent with the number of interval partitions in each storage area of ​​the archive database.

8. A system for implementing the power file management method based on human-computer interaction as described in claim 1, characterized in that: This includes a human-computer interactive power archive management platform and a power archive risk control platform; The power archive human-computer interaction archive management platform is used for storing power archive data and managing application scenarios, specifically including a decision-making layer, an execution layer, and a platform layer; The decision-making layer is used to identify the status of power archive data, construct a mapping relationship between power archive data status and processing schemes, and match processing schemes according to data status. The execution layer response processing scheme is described, and the power archive data is processed simultaneously. The platform layer is used to build an archive database, store the processed power archive data, construct archive database interval storage rules, divide storage areas and storage intervals, classify each storage interval into confidentiality levels, and match the corresponding encryption method according to the confidentiality level. The power archive risk management platform includes a data acquisition terminal, a route simulation module, a data calculation module, and a data storage and adjustment module. The data acquisition terminal is used to monitor the power archive data in real time, obtain the monitoring status results, and feed the monitoring status results back to the route simulation module. The route simulation module, in conjunction with the current power archive data, generates a route simulation map of power archive data scene changes in chronological order based on the historical scene status points of the current power archive data. The data calculation module calculates the risk score of each adjacent node in the scenario change route simulation diagram in real time based on the monitoring status results, and updates the risk control level of the power archive data after the scenario change in real time based on the risk scores of the adjacent nodes in the scenario. The number of risk control levels is consistent with the number of intervals in each storage area of ​​the archive database. The data storage adjustment module is used to construct a mapping relationship between risk control levels and storage intervals. It matches the corresponding storage intervals with the risk control levels of the current power archive data, and works with the execution layer to divide the power archive data into storage intervals in real time. The current power archive data is then encrypted using the encryption method corresponding to the divided storage intervals.

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