A power grid construction engineering equipment whole life cycle electronic archives organic management method

Abstract

The application discloses a kind of organic management methods of power grid construction engineering equipment full life cycle electronic archives, it is related to power grid engineering archives management technical field, the method constructs organic management system, runs through equipment planning design, procurement manufacturing, construction installation, operation maintenance, five stages such as decommissioning recycling, realizes multi-source data automatic acquisition by thing connection sensing terminal, based on block chain storage guarantee cross-subject trusted cooperation, with the aid of digital twin construction dynamic mapping, rely on data middle station to complete data management and value mining, form full-process management.The application makes electronic archives change from static record sheet to dynamic digital twin and decision knowledge base, improves the credibility of archives data, collaborative efficiency and value utilization rate, provides solid support for fine and intelligent management of power grid equipment, and is suitable for full life cycle archives management and control of various voltage grade power grid construction engineering equipment.

Description

Technical Field

[0001] This invention relates to the field of power grid engineering archive management technology, and in particular to an organic management method for electronic archives of power grid construction equipment throughout its entire life cycle. This method is applicable to the full-process management of electronic archives of various power grid construction equipment, such as transformers, switchgear, transmission line towers, and communication modules, from planning and design to decommissioning and recycling. Background Technology

[0002] With the accelerated construction of new power systems, the scale of power grid construction projects continues to expand, with a wide variety and large quantity of equipment. Their lifecycles are long and involve many stages, generating massive amounts of heterogeneous archival data. Electronic archives, as the core carrier of equipment archive management, directly impact the safety and efficiency of power grid construction and operation. However, existing management methods suffer from four major pain points:

[0003] First, there are data silos and inconsistent formats. Equipment files are scattered across multiple independent platforms such as design drawing systems, material procurement systems, engineering management systems, and production management systems. Data standards are not unified, interfaces are not interoperable, and there is a lack of organic integration mechanisms, making it impossible to achieve continuous tracking of electronic files throughout the entire life cycle of equipment.

[0004] Second, static records are disconnected from dynamic ones. Traditional record management mainly relies on static attribute records, which cannot integrate dynamic data during the equipment operation phase in real time, making it difficult to reflect the current real status of the equipment and its future evolution trend.

[0005] Third, there is a lack of prediction and passive recording. Most archival systems are merely passive recording tools for business processes, lacking proactive analysis and early warning capabilities, and failing to use historical archival data in depth for predictive maintenance and optimization decisions.

[0006] Therefore, this invention provides a method for organic management of electronic archives of equipment throughout the entire life cycle of power grid construction projects. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method for the organic management of electronic archives throughout the entire lifecycle of equipment in power grid construction projects, thus solving the problems mentioned in the background.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for organically managing the electronic archives of equipment throughout the entire lifecycle of power grid construction projects, comprising the following steps:

[0009] S1: Construct an electronic record management system for the entire life cycle of equipment in power grid construction projects, identify the data collection entities, clarify the five major stages of record management coverage: planning and design, procurement and manufacturing, construction and installation, operation and maintenance, and decommissioning and recycling, clarify the division of responsibilities among the entities and establish a collaborative mechanism, and build a three-dimensional management framework;

[0010] S2: Develop standardized rules for collecting archives, define archive collection fields based on the type of power grid equipment, including basic equipment attribute fields, process record fields, environmental association fields and association relationship fields, and use multi-source collection methods to obtain electronic archive data at each stage;

[0011] S3: Build a blockchain evidence storage platform with a consortium blockchain architecture. Each collecting entity accesses the platform through identity authentication. Based on smart contracts, it defines rules for uploading archive data, cross-entity collaboration rules, and data access permissions. After generating hash values, the collected electronic archive data is uploaded to the blockchain evidence storage platform to achieve tamper-proof evidence storage of archive data and automatic cross-entity collaboration.

[0012] S4: Construct a digital twin model of power grid equipment in BIM and GIS modes, and combine laser scanning and oblique photography technologies to link archive data at each stage with the digital twin model to form a dynamic mapping relationship;

[0013] S5: Establish a data middle platform with a front-end and back-end separation architecture to perform data cleaning, standardization transformation, association mapping and structured storage on the archival data in the blockchain evidence storage platform, forming a standardized archival data asset library;

[0014] S6: Based on the data platform, the archive data asset library enables equipment operation and maintenance decision support, engineering settlement review assistance, equipment risk warning, decision-making on the reuse of retired equipment and cost analysis. At the same time, it collects feedback data from various application scenarios, iterates and optimizes the archive collection rules, data governance standards, value mining models and smart contract rules, and completes the management process.

[0015] As a further technical solution of the present invention, the data collection entities in step S1 include power grid construction units, design units, construction units, operation and maintenance units, regulatory units, equipment manufacturers and construction units, and the data collection standard adopts the current archive management standard of the power industry, and is optimized and adjusted in combination with equipment type.

[0016] As a further technical solution of the present invention, the multi-source acquisition method in step S2 includes IoT sensing acquisition, system docking acquisition, and manual supplementary acquisition.

[0017] The smart terminals deployed for IoT sensing and data collection include smart turnover trays, image recognition units, RFID readers, smart safety helmets, construction video recorders, online monitoring sensor networks, and dedicated terminals for decommissioning assessment, which are used to automatically collect structured and unstructured data.

[0018] As a further technical solution of the present invention, the standardized archive collection rules in step S2 specifically include:

[0019] S21: The basic attribute fields of the equipment include the equipment model, manufacturer, supplier, rated parameters, installation location, service life and unique physical ID. The unique physical ID corresponds to the QR code / RFID on the physical nameplate of the equipment and adopts encryption encoding technology.

[0020] S22: The process record fields cover five major stages, specifically including feasibility study reports, design drawings, procurement contracts, test records, installation and acceptance reports, inspection records, fault records, scrap assessment reports, etc.

[0021] S23: Environmental association fields include remote sensing images of the installation area, electronic topographic maps, geological survey data and meteorological data, and access to data from local meteorological departments, natural resources departments and archives;

[0022] S24: The association fields include equipment topology association, engineering section affiliation association, management entity responsibility association, and component matching association, which are generated through the equipment spatial relationship module.

[0023] As a further technical solution of the present invention, in step S3, the blockchain evidence storage platform is equipped with a visual blockchain monitoring module, and the smart contract execution process includes:

[0024] S31: When each node uploads archive data, it attaches a digital signature and a timestamp. The smart contract automatically verifies the data format and integrity. After verification, a hash value is generated and written to the blockchain.

[0025] S32: When a node updates its archive data, the smart contract automatically sends a data synchronization notification to the associated node and records the update log.

[0026] S33: Access control is implemented based on a role-permission-data binding mechanism, access operations are written to the blockchain in real time, and dynamic adjustment of permissions is supported;

[0027] S34: Realize smart contract operation status monitoring and security auditing through a visual blockchain monitoring module.

[0028] As a further technical solution of the present invention, the construction and association process of the digital twin model in step S4 includes:

[0029] S41: During the planning and design phase, an initial three-dimensional model is established based on the physical form of the equipment, design drawings, and technical parameters, and relevant archival data is embedded to form a basic digital twin model;

[0030] S42: During the construction and installation phase, link installation records, test data and other archives, and achieve initial alignment with the physical equipment by adjusting model parameters;

[0031] S43: During the operation and maintenance phase, access IoT sensing data to achieve real-time synchronization between model status and equipment operating status, with an update frequency of no less than once per hour. When equipment malfunctions, the abnormal location is automatically marked and associated with the maintenance file.

[0032] S44: During the decommissioning and recycling phase, data is recorded in the scrap assessment report, disposal records, and other archives to form a closed-loop full lifecycle model.

[0033] As a further technical solution of the present invention, in step S5, the data platform is equipped with a multi-dimensional retrieval engine and a value mining module. The data governance process of the data platform includes:

[0034] S51: Data cleaning uses a combination of automated cleaning and manual review to remove duplicate data, correct erroneous data, fill in missing fields, and mark abnormal data;

[0035] S52: Standardization conversion converts archive data of different formats into a unified standard format and standardizes the field content;

[0036] S53: The association mapping is based on the unique physical ID of the device to establish a data chain for different stages and types of files, and associate the digital twin model ID with the blockchain hash value;

[0037] S54: Structured storage is classified according to the major category of equipment, then to the minor category of equipment, and then to the entire life cycle stage. It adopts distributed storage technology and establishes a multi-dimensional index directory.

[0038] As a further technical solution of the present invention, the value mining module includes an intelligent quality inspection model for archives, an equipment health assessment model, a remaining life prediction model, and an evaluation model for the reuse of decommissioned equipment.

[0039] As a further technical solution of the present invention, the decision-making process for reusing decommissioned equipment in step S6 includes providing corresponding suggestions based on the comprehensive life cycle archives and on-site testing data, and matching potential reuse demand scenarios.

[0040] This invention provides a method for the organic management of electronic archives throughout the entire lifecycle of equipment in power grid construction projects, which has the following advantages compared with existing technologies:

[0041] 1. This design proposes an organic management method for electronic archives of power grid construction equipment throughout its entire life cycle. By using a three-dimensional management framework and a blockchain collaboration mechanism, it breaks down data silos, achieves reliable collaboration across entities and domains, ensures data synchronization, immutability, and traceability, and constructs a mapping relationship between archives, models, and entities through a digital twin model. This links the entire life cycle data with the equipment entity, maps the operating status in real time, and breaks through the limitations of traditional static records.

[0042] 2. This design proposes an organic management method for electronic archives of power grid construction equipment throughout its entire lifecycle. By integrating multi-source data and applying specialized models, it achieves a leap from data recording to decision support, uncovering the value of archives in operation and maintenance optimization, reuse decisions, and cost control. Furthermore, through application feedback and iterative optimization, it continuously improves management adaptability, promoting the transformation of archive management from passive management to proactive optimization. This method is applicable to various voltage levels and various types of power grid equipment, with flexible data collection methods, convenient operation, and easy large-scale promotion, providing support for the refined and intelligent management of power grid equipment. Attached Figure Description

[0043] Figure 1 This is a flowchart of the present invention;

[0044] Figure 2 This is a flowchart illustrating the standardized data collection rules in this invention.

[0045] Figure 3 This is a flowchart illustrating the blockchain-based evidence storage and collaboration process in this invention.

[0046] Figure 4 This is a flowchart illustrating the full lifecycle application and closed-loop iteration in this invention. Detailed Implementation

[0047] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0048] Please see Figure 1-4 This invention provides a technical solution for the organic management of electronic archives throughout the entire lifecycle of equipment in power grid construction projects: A method for organically managing electronic archives throughout the entire lifecycle of equipment in power grid construction projects includes the following steps:

[0049] S1: Building a Full Lifecycle Organic Management System

[0050] By clearly defining the scope of archive management to cover five major stages—planning and design, procurement and manufacturing, construction and installation, operation and maintenance, and decommissioning and recycling—the system identifies the main entities responsible for data collection, including power grid construction units, design units, construction units, operation and maintenance units, regulatory units, equipment manufacturers, and construction units. Each entity has a clear division of responsibilities and a collaborative mechanism has been established.

[0051] Then, a three-dimensional management framework of subject → stage → data is built. The data standard adopts the current archive management standards of State Grid and China Southern Power Grid, and is optimized and adjusted in combination with equipment type (such as branch and node) to ensure that the data format is unified and the semantics are consistent across different stages and subjects.

[0052] S2: Automated Acquisition of Multi-Source Heterogeneous Data

[0053] Standardized data collection rules were established, and collection fields were defined based on the type of power grid equipment (such as branch type and node type), including basic equipment attribute fields, process record fields, environmental association fields, and association relationship fields. A multi-source data collection method was adopted, including IoT sensing data collection, system docking data collection, and manual supplementary data collection.

[0054] Among them, IoT sensing and data collection includes: deploying intelligent turnover pallets, image recognition units, RFID readers, smart safety helmets, construction video recorders, online monitoring sensor networks, and dedicated terminals for decommissioning assessment, etc., to automatically collect structured and unstructured data such as equipment inventory data, installation data, operating status data, and decommissioning test data;

[0055] System integration and data acquisition: Enables automatic data synchronization with power grid planning and design systems, construction management systems, operation and maintenance monitoring systems, and equipment production systems, reducing manual data entry;

[0056] Manual supplementary data collection: This is used to collect non-standard archival data (such as on-site acceptance photos, scanned copies of handwritten records, etc.). When uploading, the digital signature and timestamp of the data collector are attached to ensure data traceability.

[0057] Furthermore, the standardized data collection rules are further refined, specifically including the following:

[0058] ① Equipment basic attribute fields: including equipment model, manufacturer, supplier, rated parameters, installation location, service life, unique physical ID (corresponding to the physical nameplate QR code / RFID, using encrypted encoding), supporting face recognition and QR code scanning to retrieve files;

[0059] ② Process record fields: Planning and design stage includes feasibility study report, design drawings, 3D model, and approval documents; procurement and manufacturing stage includes procurement contract, type test report, and key component serial number; construction and installation stage includes installation record, test record, acceptance report, and progress record; operation and maintenance stage includes inspection record, maintenance report, condition monitoring data, and fault record; decommissioning and recycling stage includes scrap assessment report, disposal record, and residual value recovery record;

[0060] ③ Environmental related fields: including remote sensing images of the installation area, electronic topographic maps, geological survey data, and meteorological data, and access to data from local meteorological departments, natural resources departments, and archives to achieve government-enterprise collaboration;

[0061] ④ Association fields: These include equipment topology association, engineering section affiliation association, management entity responsibility association, and component matching association, which are generated through the equipment spatial relationship module.

[0062] S3: Blockchain-based Trusted Evidence Storage and Cross-Entity Collaboration

[0063] By building a blockchain evidence storage platform with a consortium blockchain architecture, only authorized data collection entities can access the platform through identity authentication (which can be achieved using a combination of digital certificates and dynamic passwords). Based on smart contracts, the platform defines rules for uploading archive data, cross-entity collaboration, and access permissions. After generating hash values, the collected electronic archive data is uploaded to the blockchain evidence storage platform, achieving tamper-proof evidence storage of archive data and automatic cross-entity collaboration. The smart contract execution process includes:

[0064] Data storage: When each entity uploads archive data, it attaches a digital signature and a timestamp. The smart contract automatically verifies the data format and integrity. After the verification is passed, a hash value is generated and written to the blockchain to achieve tamper-proof storage.

[0065] Collaborative synchronization: When a subject updates the archive data, the smart contract automatically sends a synchronization notification to related subjects and records the update log to ensure data consistency;

[0066] Access control: Based on the role → permission → data binding mechanism, different roles can only access data within their access scope, and access operations are written to the blockchain in real time, supporting dynamic adjustment of permissions;

[0067] In addition, the blockchain evidence storage platform is equipped with a visual blockchain monitoring module, which enables monitoring of the smart contract's operational status and security auditing.

[0068] S4: Digital Twin Model Construction and Dynamic Association

[0069] A digital twin model of power grid equipment using a combination of BIM and GIS is constructed. By combining laser scanning and oblique photogrammetry technologies, a high-precision 3D model of the equipment at the centimeter level and component-level granularity is formed, enabling dynamic mapping from archival data to model status and then to the physical equipment. The construction and association process of the digital twin model includes the following steps:

[0070] S41: Planning and Design Phase: Based on the physical form of the equipment, design drawings and technical parameters, an initial three-dimensional model is established, and archival data such as design drawings, rated parameters and approval documents are embedded to form a basic digital twin model. It can use the "air-ground combination" acquisition method to obtain laser point cloud and oblique photography data to ensure the accuracy of the initial model.

[0071] S42: Construction and Installation Phase: Link installation records, test data, acceptance reports and other archives, calibrate the model installation position and component assembly status, record construction changes synchronously, achieve initial alignment with the equipment entity through model parameter adjustment, and record various changes during the construction process;

[0072] S43: Operation and maintenance phase: Access IoT sensing data to achieve real-time synchronization between model status and equipment operating status (update frequency not less than once per hour). When equipment malfunctions, the abnormal location is automatically marked and associated with the maintenance file. Combine Beidou positioning technology to achieve high-precision positioning of the equipment and file association. Furthermore, integrate the existing sensors and online monitoring devices of the equipment to synchronize temperature, vibration, partial discharge and other status data in real time.

[0073] S44: Retirement and recycling stage: Write the scrap assessment report, disposal records and other archives to form a closed loop of the whole life cycle model, which is used for analysis and optimization of similar equipment. In addition, the digital twin archive is archived to the historical knowledge base for quality backtracking and selection optimization of similar equipment.

[0074] S5: Data Platform Governance and Value Mining

[0075] A data platform with a front-end and back-end separation architecture is established to conduct end-to-end governance of blockchain-based evidence storage data. This includes data cleaning, standardization, association mapping, and structured storage of archival data within the blockchain evidence storage platform, thereby forming a standardized archival data asset library. The archival data governance process of the data platform includes the following steps:

[0076] S51: Data Cleaning: Use a combination of automated cleaning and manual review to remove duplicate data, correct erroneous data, fill in missing fields, and mark abnormal data;

[0077] S52: Standardization Conversion: Convert data of different formats, such as CAD drawings, PDF reports, and Excel record sheets, into a unified standard format (such as PDF / A, JSON), and standardize the field content;

[0078] S53: Association Mapping: Based on the unique physical ID of the device (such as QR code or RFID), establish a data chain for different stages and types of archives, associate the digital twin model ID with the blockchain hash value, and realize three-way linkage;

[0079] S54: Structured storage: a classification storage method based on major device category → minor device category → full lifecycle stage, using distributed storage technology to establish multi-dimensional indexes of device, time, subject, and keywords;

[0080] S55: Value Mining: The data platform is equipped with a multi-dimensional search engine and a value mining module. The search engine supports multi-dimensional combined searches, and the document search time is no more than 5 minutes. The value mining module combines machine learning algorithms to achieve in-depth data analysis. The value mining module includes an intelligent document quality inspection model, an equipment health assessment model, a remaining life prediction model, and a retired equipment reuse assessment model.

[0081] S6: Full Lifecycle Application and Closed-Loop Iteration

[0082] Based on the data platform's archival data asset repository, it enables multi-scenario applications and continuously optimizes by collecting feedback data, specifically including:

[0083] S61. Equipment Operation and Maintenance Decision Support: Analyze historical data and twin model status trends of equipment, predict remaining lifespan and potential failures, generate refined operation and maintenance suggestions, and link parts files to achieve precise allocation of spare parts.

[0084] S62. Project Settlement Audit Assistance: Automatically compares the consistency of settlement data with contractual agreements and acceptance standards, identifies deviations and marks the reasons;

[0085] S63. Equipment Risk Warning: Establish a multi-dimensional warning model based on fault records, status thresholds, and environmental data to trigger graded warnings and push historical handling plans.

[0086] S64. Reuse decision-making for retired equipment: Based on the full life cycle archives and on-site testing data, suggestions are given through the reuse assessment model, including direct reuse, reuse after repair, dismantling and utilization, or scrapping and recycling.

[0087] S65. Cost Analysis: Integrate equipment lifecycle cost data to generate a cost analysis report, providing a reference for equipment selection and cost budgeting;

[0088] S66. Closed-loop iteration: Regularly collect application feedback, optimize collection rules, data governance standards, value mining models, and smart contract rules to form a management closed loop.

[0089] Furthermore, the value mining model can be further refined, specifically including the following:

[0090] ① Intelligent quality inspection of archives: Built-in knowledge graph of standard compliance of archives at each stage, automatically verifies the integrity, rationality and logical consistency of data, and automatically alerts and triggers the supplementary data entry process when problems are found;

[0091] ② Health assessment: Apply a multi-feature fusion scoring model to dynamically calculate the Equipment Health Index (EHI);

[0092] ③ Remaining useful life prediction: Based on machine learning algorithms such as LSTM time series network, combined with equipment degradation process modeling, the remaining useful life is predicted;

[0093] ④ Reuse Value Assessment: Based on the comprehensive performance history, health status, testing data, and cost model of the equipment, we provide accurate reuse suggestions and match them with potential demand scenarios.

[0094] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A method for organically managing electronic archives of equipment throughout the entire lifecycle of power grid construction projects, characterized in that, Includes the following steps: S1: Construct an electronic record management system for the entire life cycle of equipment in power grid construction projects, identify the data collection entities, clarify the five major stages of record management coverage: planning and design, procurement and manufacturing, construction and installation, operation and maintenance, and decommissioning and recycling, clarify the division of responsibilities among the entities and establish a collaborative mechanism, and build a three-dimensional management framework; S2: Develop standardized rules for collecting archives, define archive collection fields based on the type of power grid equipment, including basic equipment attribute fields, process record fields, environmental association fields and association relationship fields, and use multi-source collection methods to obtain electronic archive data at each stage; S3: Build a blockchain evidence storage platform with a consortium blockchain architecture. Each collecting entity accesses the platform through identity authentication. Based on smart contracts, it defines rules for uploading archive data, cross-entity collaboration rules, and data access permissions. After generating hash values, the collected electronic archive data is uploaded to the blockchain evidence storage platform to achieve tamper-proof evidence storage of archive data and automatic cross-entity collaboration. S4: Construct a digital twin model of power grid equipment in BIM and GIS modes, and combine laser scanning and oblique photography technologies to link archive data at each stage with the digital twin model to form a dynamic mapping relationship; S5: Establish a data middle platform with a front-end and back-end separation architecture to perform data cleaning, standardization transformation, association mapping and structured storage on the archival data in the blockchain evidence storage platform, forming a standardized archival data asset library; S6: Based on the data platform, the archive data asset library enables equipment operation and maintenance decision support, engineering settlement review assistance, equipment risk warning, decision-making on the reuse of retired equipment and cost analysis. At the same time, it collects feedback data from various application scenarios, iterates and optimizes the archive collection rules, data governance standards, value mining models and smart contract rules, and completes the management process.

2. The method for organic management of electronic archives throughout the entire life cycle of equipment in power grid construction projects according to claim 1, characterized in that, The data collection entities in step S1 include power grid construction units, design units, construction units, operation and maintenance units, regulatory units, equipment manufacturers, and construction units. The data acquisition standard adopts the current archive management standard in the power industry, and is optimized and adjusted according to the equipment type.

3. The method for organic management of electronic archives throughout the entire life cycle of equipment in power grid construction projects according to claim 1, characterized in that, The multi-source acquisition methods mentioned in step S2 include IoT sensing acquisition, system docking acquisition, and manual supplementary acquisition; The intelligent terminals deployed for IoT sensing and data collection include intelligent turnover trays, image recognition units, RFID readers, intelligent safety helmets, construction video recorders, online monitoring sensor networks, and dedicated terminals for decommissioning assessment, which are used to automatically collect structured and unstructured data.

4. The method for organic management of electronic archives for the entire life cycle of equipment in power grid construction projects according to claim 1, characterized in that, The standardized document collection rules mentioned in step S2 specifically include: S21: The basic attribute fields of the equipment include the equipment model, manufacturer, supplier, rated parameters, installation location, service life and unique physical ID. The unique physical ID corresponds to the QR code / RFID on the physical nameplate of the equipment and adopts encryption encoding technology. S22: The process record fields cover five major stages, specifically including feasibility study reports, design drawings, procurement contracts, test records, installation and acceptance reports, inspection records, fault records, scrap assessment reports, etc. S23: Environmental association fields include remote sensing images of the installation area, electronic topographic maps, geological survey data and meteorological data, and access to data from local meteorological departments, natural resources departments and archives; S24: The association fields include equipment topology association, engineering section affiliation association, management entity responsibility association, and component matching association, which are generated through the equipment spatial relationship module.

5. The method for organic management of electronic archives throughout the entire life cycle of equipment in power grid construction projects according to claim 1, characterized in that, The blockchain evidence storage platform described in step S3 is equipped with a visual blockchain monitoring module, and the smart contract execution process includes: S31: When each node uploads archive data, it attaches a digital signature and a timestamp. The smart contract automatically verifies the data format and integrity. After verification, a hash value is generated and written to the blockchain. S32: When a node updates its archive data, the smart contract automatically sends a data synchronization notification to the associated node and records the update log. S33: Access control is implemented based on a role-permission-data binding mechanism, access operations are written to the blockchain in real time, and dynamic adjustment of permissions is supported; S34: Realize smart contract operation status monitoring and security auditing through a visual blockchain monitoring module.

6. The method for organic management of electronic archives throughout the entire life cycle of equipment in power grid construction projects according to claim 1, characterized in that, The construction and association process of the digital twin model in step S4 includes: S41: During the planning and design phase, an initial three-dimensional model is established based on the physical form of the equipment, design drawings, and technical parameters, and relevant archival data is embedded to form a basic digital twin model; S42: During the construction and installation phase, link installation records, test data and other archives, and achieve initial alignment with the physical equipment by adjusting model parameters; S43: During the operation and maintenance phase, access IoT sensing data to achieve real-time synchronization between model status and equipment operating status, with an update frequency of no less than once per hour. When equipment malfunctions, the abnormal location is automatically marked and associated with the maintenance file. S44: During the decommissioning and recycling phase, data is recorded in the scrap assessment report, disposal records, and other archives to form a closed-loop full lifecycle model.

7. The method for organic management of electronic archives throughout the entire life cycle of equipment in power grid construction projects according to claim 1, characterized in that, Step S5 describes the data platform's setup of a multi-dimensional search engine and a value mining module. The data governance process of the data platform includes: S51: Data cleaning uses a combination of automated cleaning and manual review to remove duplicate data, correct erroneous data, fill in missing fields, and mark abnormal data; S52: Standardization conversion converts archive data of different formats into a unified standard format and standardizes the field content; S53: The association mapping is based on the unique physical ID of the device to establish a data chain for different stages and types of files, and associate the digital twin model ID with the blockchain hash value; S54: Structured storage is classified according to the major category of equipment, then to the minor category of equipment, and then to the entire life cycle stage. It adopts distributed storage technology and establishes a multi-dimensional index directory.

8. The method for organic management of electronic archives for the entire life cycle of equipment in power grid construction projects according to claim 1, characterized in that, The value mining module includes an intelligent quality inspection model for archives, an equipment health assessment model, a remaining life prediction model, and an evaluation model for the reuse of decommissioned equipment.

9. The method for organic management of electronic archives for the entire life cycle of equipment in power grid construction projects according to claim 1, characterized in that, The decision-making process for reusing decommissioned equipment described in step S6 includes providing corresponding suggestions based on a comprehensive full lifecycle record and on-site testing data, and matching potential reuse scenarios.

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