Intelligent responsibility identification and automatic distribution method for power grid data management
By constructing a power grid data value chain model and a dynamic responsibility allocation model, and combining graph databases and blockchain technology, the problems of unclear responsibility identification and low allocation efficiency in power grid data governance have been solved, achieving accurate identification and efficient allocation of power grid data responsibilities.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- STATE GRID INFO TELECOM GREAT POWER SCI & TECH
- Filing Date
- 2026-02-02
- Publication Date
- 2026-04-28
AI Technical Summary
Existing power grid data governance suffers from problems such as data redundancy, low quality, insufficient accuracy in responsibility identification, low allocation efficiency, and lack of closed-loop management. In particular, it is difficult to achieve accurate responsibility identification and automated allocation in a multi-source data environment.
By acquiring power grid data through distributed acquisition technology, constructing a power grid data value chain model, using graph databases and machine learning algorithms to identify candidate sets of responsible entities, and combining a dynamic responsibility allocation model and blockchain technology, the automatic allocation and dynamic optimization of responsibilities can be achieved.
It has enabled accurate identification and efficient allocation of responsibilities for power grid data, reduced the time cost of responsibilities assessment, avoided the problem of unclear responsibilities, and improved the automation and adaptability of data governance.
Smart Images
Figure CN121935486A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power grid data governance technology, and in particular to a method for intelligent identification and automated allocation of responsibility for power grid data governance. Background Technology
[0002] As the State Grid Corporation of China deepens its digital transformation, the demand for data fusion and sharing among source-end business systems continues to grow, and the requirements for data quality and sharing efficiency are constantly increasing. However, the current power grid data governance process has the following shortcomings: 1. During the governance process, data redundancy and low quality are caused by multi-source storage, multi-source maintenance, and multi-source use; 2. The existing data responsibility system lacks accuracy, making it difficult to accurately match responsibilities to specific data and responsible persons. At the same time, the relevant responsibility list is not updated in a timely manner and is lagging behind in optimization, making it difficult to make timely adjustments to data changes and business operations. There are also problems such as overlapping personnel and unclear responsibilities. 3. The allocation of responsibilities relies on manual operation, which is inefficient and difficult to adapt to dynamic changes in data status.
[0003] While existing technologies have been researched on data governance both domestically and internationally, there is a lack of specialized intelligent responsibility identification and automated allocation solutions tailored to the unique characteristics of the power grid industry (complex business scenarios, large data volumes, strong cross-disciplinary collaboration needs, and high compliance requirements). Current methods primarily focus on building general data governance frameworks, failing to fully integrate the business attributes and lifecycle characteristics of power grid data. This results in insufficient dynamism, accuracy, and automation in responsibility allocation, failing to effectively address core issues in power grid data governance such as ambiguous responsibility identification, low allocation efficiency, and lack of closed-loop management.
[0004] Therefore, designing a method for intelligent identification and automated allocation of responsibility for power grid data governance that can solve the above-mentioned technical problems is a technical issue that needs to be addressed. Summary of the Invention
[0005] To address the aforementioned issues, the present invention aims to provide a method for intelligent identification and automated allocation of responsibilities for power grid data governance, which enables accurate identification, efficient allocation, and dynamic optimization of power grid data responsibilities.
[0006] To achieve the above objectives, the present invention adopts the following technical solution, comprising the following steps: Step S1: Acquire basic data from various source-end business systems of the power grid through distributed acquisition technology and preprocess the multi-source heterogeneous data; Step S2: Construct a power grid data value chain model, establish a relationship graph between data objects and business objects, and use graph databases for analysis while employing machine learning algorithms to automatically identify the candidate set of responsible entities for each life cycle node of the power grid data value chain model; Step S3: Based on data importance, update frequency, business relevance, and some responsibility boundaries, construct a dynamic responsibility allocation model, integrate rule engine and optimization algorithm, select the optimal responsible subject from the candidate set of responsible subjects, and complete the automatic allocation of responsibilities; Step S4: Implement monitoring of data status changes, establish a responsibility adjustment trigger mechanism, and when the data status reaches the preset conditions, recalculate the optimal responsible party based on the dynamic responsibility allocation model, automatically adjust the responsibility attribution and synchronize it to the responsible party; Step S5: Construct a compliance verification rule base to verify the compliance of the initial allocation results in Step 3 and the adjustment results in Step 4. At the same time, use blockchain technology to record the responsibility allocation log, adjustment records, and data flow paths.
[0007] Furthermore, in step S1, data preprocessing is performed, and the specific process is as follows: Step S11: After identifying missing values through rule matching, perform incomplete checks, then use the IsolationForest algorithm to detect outliers, combine with business standards to remove invalid data, and finally unify the field format; Step S12: Based on the data encoding standard and attribute mapping rules, convert heterogeneous data from different systems into a unified standard format, and achieve data attribute alignment through field mapping and unit conversion; Step S13 establishes deduplication rules based on the combination of primary key and key fields, identifies duplicate records through hash comparison, and performs data deduplication.
[0008] Furthermore, in step S3, the dynamic responsibility allocation model also includes business constraints, efficiency constraints, load constraints, and quality constraints; The business constraints are used to match the scope of business responsibilities of the responsible entity with the business domain of the data, so that responsibilities and duties are matched. The efficiency constraint is used to improve the speed of governance response by prioritizing the allocation of governance responsibilities to responsible entities whose historical processing efficiency is higher than a preset threshold. The load constraint is used to distribute the load evenly based on the number of unfulfilled responsibilities of the responsible parties, thereby avoiding the concentration of responsibilities. The quality constraints are used to prioritize the responsible parties.
[0009] Furthermore, in step S4, the preset conditions for the responsibility adjustment triggering mechanism include data update triggering, exception triggering, process triggering, and feedback triggering.
[0010] Furthermore, in step S2, the business objects of the association graph include business departments at all levels, data management positions, and grassroots responsible persons.
[0011] The present invention has the following beneficial effects: 1-This invention generates a candidate set of responsible entities by constructing a power grid data value chain model, and automatically allocates responsibilities by selecting the optimal responsible entity from the candidate set based on a dynamic responsibility allocation model. This replaces the traditional manual screening and allocation mode, significantly reducing the time cost of responsibility identification. At the same time, relying on business mapping relationships and historical data, combined with exclusive responsibility allocation rules, it effectively avoids the problem of unclear responsibility identification. Attached Figure Description
[0012] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation
[0013] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: See Figure 1 As shown, the solution includes the following steps: Step S1: Acquire basic data from various source-end business systems of the power grid through distributed acquisition technology and preprocess the multi-source heterogeneous data to provide data support for subsequent identification and allocation of responsible entities; Step S2: Construct a power grid data value chain model, establish a relationship graph between data objects and business objects, and use a graph database for analysis while employing machine learning algorithms to automatically identify the candidate set of responsible entities for each life cycle node of the power grid data value chain model; specifically, use the Autoencoder algorithm to extract the core features of data objects, including data type, business domain, update frequency, importance level, etc.
[0014] Step S3: Based on data importance, update frequency, business relevance, and partial responsibility boundaries, a dynamic responsibility allocation model is constructed. This model integrates a rule engine and optimization algorithm to select the optimal responsible entity from the candidate set and automatically allocate responsibilities. The lifecycle nodes include generation, transmission, storage, use, and destruction. Specifically, the optimization algorithm uses graph theory-based network analysis to construct a responsibility allocation network, achieving globally optimal allocation.
[0015] Step S4: Implement monitoring of data status changes and establish a responsibility adjustment trigger mechanism. When the data status reaches the preset conditions, recalculate the optimal responsible subject based on the dynamic responsibility allocation model, automatically adjust the responsibility attribution and synchronize it to the responsible subject; based on real-time data stream processing technologies such as Apache Flink and Kafka Streams, monitor changes in data attributes, storage location, business relationships, and data quality anomalies.
[0016] Step S5: Construct a compliance verification rule base to verify the compliance of the initial allocation results in Step 3 and the adjustment results in Step 4. At the same time, use blockchain technology to record the responsibility allocation log, adjustment records, and data flow paths.
[0017] Furthermore, in step S1, data preprocessing is performed, and the specific process is as follows: Step S11: After identifying missing values through rule matching, perform incomplete checks, then use the IsolationForest algorithm to detect outliers, combine with business standards to remove invalid data, and finally unify the field format; Step S12: Based on the data encoding standard and attribute mapping rules, convert heterogeneous data from different systems into a unified standard format, and achieve data attribute alignment through field mapping and unit conversion; Step S13 establishes deduplication rules based on the combination of primary key and key fields, identifies duplicate records through hash comparison, and performs data deduplication.
[0018] Furthermore, in step S3, the dynamic responsibility allocation model also includes business constraints, efficiency constraints, load constraints, and quality constraints; The business constraints are used to match the scope of business responsibilities of the responsible entity with the business domain of the data, so that responsibilities and duties are matched. The efficiency constraint is used to improve the speed of governance response by prioritizing the allocation of governance responsibilities to responsible entities whose historical processing efficiency is higher than a preset threshold. The load constraint is used to distribute the load evenly based on the number of unfulfilled responsibilities of the responsible parties, thereby avoiding the concentration of responsibilities. The quality constraints are used to prioritize the responsible parties.
[0019] Furthermore, in step S4, the preset conditions for the responsibility adjustment triggering mechanism include data update triggering, anomaly triggering, process triggering, and feedback triggering. Specifically, data update triggering occurs when data attributes, storage locations, or business relationships change; anomaly triggering occurs when a data quality anomaly is detected by the real-time monitoring module (including accuracy, completeness, and consistency not meeting preset standards); process triggering occurs when business process optimization or departmental responsibility adjustments lead to a change in the responsible entity; and feedback triggering occurs when the responsible entity raises an objection, which is then approved.
[0020] Furthermore, in step S2, the business objects of the association graph include business departments at all levels, data management positions, and grassroots responsible persons.
[0021] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0022] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0023] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0024] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0025] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.
Claims
1. A method for intelligent identification and automated allocation of responsibility for power grid data governance, characterized in that, Includes the following steps: Step S1: Acquire basic data from various source-end business systems of the power grid through distributed acquisition technology and preprocess the multi-source heterogeneous data; Step S2: Construct a power grid data value chain model, establish a relationship graph between data objects and business objects, and use graph databases for analysis while employing machine learning algorithms to automatically identify the candidate set of responsible entities for each life cycle node of the power grid data value chain model; Step S3: Based on data importance, update frequency, business relevance, and some responsibility boundaries, construct a dynamic responsibility allocation model, integrate rule engine and optimization algorithm, select the optimal responsible subject from the candidate set of responsible subjects, and complete the automatic allocation of responsibilities; Step S4: Implement monitoring of data status changes, establish a responsibility adjustment trigger mechanism, and when the data status reaches the preset conditions, recalculate the optimal responsible party based on the dynamic responsibility allocation model, automatically adjust the responsibility attribution and synchronize it to the responsible party; Step S5: Construct a compliance verification rule base to verify the compliance of the initial allocation results in Step 3 and the adjustment results in Step 4. At the same time, use blockchain technology to record the responsibility allocation log, adjustment records, and data flow paths.
2. In the method for intelligent identification and automated allocation of responsibility for power grid data governance according to claim 1, in step S1, the data preprocessing is carried out as follows: Step S11: After identifying missing values through rule matching, perform incomplete checks, then use the IsolationForest algorithm to detect outliers, combine with business standards to remove invalid data, and finally unify the field format; Step S12: Based on the data encoding standard and attribute mapping rules, convert heterogeneous data from different systems into a unified standard format, and achieve data attribute alignment through field mapping and unit conversion; Step S13 establishes deduplication rules based on the combination of primary key and key fields, identifies duplicate records through hash comparison, and performs data deduplication.
3. The method for intelligent identification and automated allocation of responsibility for power grid data governance according to claim 1, wherein in step S3, the dynamic responsibility allocation model further includes business constraints, efficiency constraints, load constraints, and quality constraints; The business constraints are used to match the scope of business responsibilities of the responsible entity with the business domain of the data, so that responsibilities and duties are matched. The efficiency constraint is used to improve the speed of governance response by prioritizing the allocation of governance responsibilities to responsible entities whose historical processing efficiency is higher than a preset threshold. The load constraint is used to distribute the load evenly based on the number of unfulfilled responsibilities of the responsible parties, thereby avoiding the concentration of responsibilities. The quality constraints are used to prioritize the responsible parties.
4. In the method for intelligent identification and automated allocation of responsibility for power grid data governance according to claim 1, in step S4, the preset conditions of the responsibility adjustment triggering mechanism include data update triggering, anomaly triggering, process triggering, and feedback triggering.
5. In the method for intelligent identification and automated allocation of responsibility for power grid data governance according to claim 1, in step S2, the business objects of the association graph include business departments at all levels, data management positions, and grassroots responsible persons.