Rule engine and low-code dual-drive power grid digital graph fusion treatment platform and method

Abstract

The invention discloses a rule engine and low-code dual-drive power grid digital graph fusion treatment platform and method. The platform comprises an infrastructure layer, a data layer, an algorithm engine layer and a business application layer. And the data layer is used for accessing and managing multi-source power grid data. And the algorithm engine layer comprises a rule engine module for configuring and executing data verification, digital graph association and exception handling rules, a data quality diagnosis algorithm module for performing data quality diagnosis to identify exceptions, and a digital graph fusion driving engine module for realizing bidirectional linkage between data and graphs. And the business application layer comprises a low-code arrangement platform for customizing a governance scene page and a work order process, a digital graph integrated display module for displaying multi-source power grid data on the same screen, and a closed-loop work order management module for automatically generating and distributing governance work orders. The method constructs a depth digital graph fusion system, improves the anomaly positioning and treatment efficiency, achieves automatic management and control and whole-course tracing, does not need a large amount of code development, and shortens the response period of a new business scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems, in particular to a power grid number map fusion management platform driven by a rule engine and low code, and also relates to a power grid number map fusion management method driven by a rule engine and low code. BACKGROUND

[0002] Power grid data management is the core link to ensure the construction and stable operation of digital twin power grids. The current mainstream power grid data management scheme mainly relies on traditional data management tools and graphical display systems. Specifically, in terms of data management, the database management system, ETL (Extraction, Transformation, Loading) tool and data quality checking software are usually taken as the core, and the data is entered, cleaned, checked and integrated through a tabular interface. In terms of graphical display, an independent geographic information system (GIS) or dispatching diagram system is used to present the spatial position, topological connection relationship and operating state of power grid equipment. These structured data and graphical data are stored in different systems, and only a loose association is established between them through key fields such as equipment ID. The whole management process relies on manual or simple work order system for promotion, and there is a lack of automatic connection and global traceability between the links from abnormality discovery, notification and rectification to review and archiving.

[0003] However, the above-mentioned prior art scheme has significant defects. First, data and graphics are stored and displayed separately, resulting in a shallow "number map fusion" degree. Users need to frequently switch between data management tools and graphical systems, manually associate information, and cannot obtain an integrated view, making abnormality positioning (such as topological connection error) time-consuming and prone to error, and the management interaction efficiency is low. Second, the management process does not form an effective closed loop, and the links from abnormality discovery to rectification and review rely on manual connection. The rectification progress cannot be monitored in real time, the results lack automatic checking, and the data and graphical changes before and after rectification cannot be associated, resulting in weak traceability in the whole process. Third, the system has poor business adaptability and high extension cost. The addition of new management scenarios (such as the connection of new energy stations) requires a large amount of customized code development for underlying checking rules, graphical interfaces and work order logic, with a long response period, which makes it difficult to meet the rapid change needs of power grid business. Finally, the security control and management process are seriously disconnected. For sensitive data, a static desensitization method is often used, which cannot be flexibly adjusted according to roles and scenarios, and over-desensitization or insufficient protection may occur. At the same time, the audit log cannot be associated with the management details, making it difficult to meet the compliance requirements of data traceability in the whole life cycle. SUMMARY

[0004] To solve the technical problems of number map separation and poor adaptability of existing power grid data management platforms and methods, the present application provides a power grid number map fusion management platform and method driven by a rule engine and low code.

[0005] The application adopts the following technical scheme to realize the power grid number map fusion management platform driven by a rule engine and a low code, which comprises: An infrastructure layer comprising a micro-service cluster and a containerized deployment module, and providing hybrid distributed storage and cache; A data layer for accessing and managing multi-source power grid data including power grid GIS topological maps; An algorithm engine layer comprising: A rule engine module for configuring and executing data checking, number map association and exception handling rules through a visual interface; A data quality diagnosis algorithm module for diagnosing data quality based on rules to identify data exceptions; A number map fusion driving engine module for realizing two-way linkage between data and graphics, which comprises automatic graphic annotation of data exceptions and graphic operation triggering data response; A business application layer comprising: A low code orchestration platform for customizing management scenario pages and work order processes through drag-and-drop components and templates; A number Figure One A virtual display module for displaying the multi-source power grid data on the same screen; A closed-loop work order management module for automatically generating and distributing management work orders based on data exceptions.

[0006] The application establishes a two-way linkage logic of "data-graphics" through a deep number map fusion mechanism, realizes automatic association of graphics and annotation of data exceptions, graphic operation triggering data response, breaks the separation mode of traditional "data form + independent graphics", innovates the scenario-based management interaction of "looking at the graph to manage the number and verifying the graph to manage the number", through the "double driving" architecture design, the rule engine supports visual configuration of data checking, number map association and exception handling rules, realizes pluggable management logic, enhances adaptability, and the low code platform provides power grid dedicated components and scenario templates, without the need for a large amount of code development, shortens the response cycle of new business scenarios, solves the technical problems of existing power grid data management platforms and methods, such as number map separation and poor adaptability, realizes power grid data "visual management, scenario-based control, and secure traceability", improves the efficiency of exception positioning and management, realizes automatic management and control and full-process traceability in the management process, and guarantees continuous improvement of data quality.

[0007] As a further improvement of the above scheme, the micro-service cluster comprises a plurality of micro-service modules independently deployed and communicated through an API or an event bus; the containerized deployment module is used for containerizing, packaging and orchestrating the micro-services, and realizing visual monitoring and operation and maintenance of the containers; the distributed storage and cache adopts a hybrid storage architecture of a time series database, a relational database, a graph database and a distributed file system.

[0008] As a further improvement of the above-mentioned scheme, the multi-source power grid data includes structured data, unstructured data, and spatio-temporal graphic data including the power grid GIS topological graph.

[0009] As a further improvement of the above-mentioned scheme, the data layer includes; A multi-source data access module is configured to access the multi-source power grid data, realize data format conversion and data cleaning, and push the cleaned data to a corresponding storage system; the multi-source power grid data includes real-time stream data, offline batch data, and heterogeneous interface data; A spatio-temporal data management module is configured to integrate a GIS engine, manage the power grid GIS topological graph and device spatial coordinate data, and provide spatial query and analysis functions; the spatio-temporal data management module supports access and association of graphic data including unmanned aerial vehicle inspection images and dispatching graphs, and establishes a mapping relationship between spatio-temporal graphic data and device entities.

[0010] Further, the rule engine module builds a governance rule center based on a Drools rule engine, and automatically generates a DRL script after rule configuration; the data quality diagnosis algorithm module automatically identifies data quality abnormalities and labels abnormal levels by loading data verification rules configured in the rule engine, performing four-dimensional feature verification on the structured data, and performing consistency verification on numerical and graphic association relationships; the numerical and graphic fusion driving engine module automatically locates corresponding graphic nodes, labels abnormalities with colors / icons and pops up abnormal detail bubbles, and triggers data query, topological verification, and batch verification when a user clicks / drag a graphic node.

[0011] As a further improvement of the above-mentioned scheme, the abnormal levels include serious, general, and slight; the serious abnormality is labeled with red, the general abnormality is labeled with yellow, and the slight abnormality is labeled with orange; the numerical and graphic fusion driving engine module pops up the abnormal detail bubbles beside the graphic nodes to display abnormal types, current values, standard values, and rectification suggestions.

[0012] As a further improvement of the above-mentioned scheme, the low-code orchestration platform provides a variety of power grid dedicated components and scene templates, and supports customization of governance scene pages and work order processes through a drag-and-drop manner; the numerical and graphic fusion driving engine module supports one-click query of abnormal nodes, one-click query of abnormal nodes, and one-click query of abnormal nodes. Figure One The body display module also supports flying line coloring, topological abnormality positioning, and one-key penetration query function; the closed-loop work order management module is built based on a workflow engine, and supports automatic creation, distribution, tracking, review, and archiving of work orders.

[0013] As a further improvement of the above-mentioned scheme, the business application layer further includes: A security desensitization module is configured to implement a dynamic desensitization strategy according to data sensitivity, user role and operation scenario, and / or add noise to aggregated statistical data using differential privacy technology; An audit log and report module is configured to collect and store user operation, system operation and data change logs in a non-tamperable manner, wherein the logs include at least one of operator, time, operation type, operation object and operation content.

[0014] As a further improvement of the above-mentioned scheme, the power grid number map fusion management platform further comprises a user layer, wherein the user layer comprises: A role-based operation interface is configured to provide an operation interface for operation and maintenance personnel; A message notification module is configured to push work order reminders, abnormal alarms and report notifications through short messages, emails and system messages, and support message read / unread marking and historical message query.

[0015] The application further provides a rule engine and low-code dual-driven power grid number map fusion management method, which applies the rule engine and low-code dual-driven power grid number map fusion management platform as described above. A data access and preprocessing step: access multi-source power grid data, and store the preprocessed data by type; A number map fusion and quality diagnosis step: associate and fuse structured data and power grid graphic data, and perform data quality diagnosis based on preconfigured rules, and mark the diagnosed abnormalities on the graphic interface; A closed-loop work order processing step: automatically generate and distribute management work orders based on data abnormalities, track the rectification process, and review and archive the rectification results; A security control and traceability step: perform dynamic desensitization and differential privacy protection on sensitive data during the whole management process, and record the whole process audit log.

[0016] Compared with existing power grid data management platforms and methods, the rule engine and low-code dual-driven power grid number map fusion management platform and method of the application have the following beneficial effects: 1. The rule engine and low-code double-driven power grid number-graph fusion governance platform, through a deep number-graph fusion mechanism, establishes a "data-graph" two-way linkage logic, realizes automatic association of data anomalies and graph annotation, and triggers data response through graph operation, breaks the traditional separation mode of "data form + independent graph", innovates the scene-based governance interaction of "graph-based governance and graph-based verification", through the "double-driven" architecture design, the rule engine supports visual configuration of data verification, number-graph association and exception handling rules, realizes pluggable governance logic, and enhances adaptability, while the low-code platform provides power grid dedicated components and scene templates, without the need for large amounts of code development, shortens the response cycle of new business scenarios, solves the technical problems of existing power grid data governance platforms and methods, such as number-graph separation and poor adaptability, realizes power grid data "visual governance, scene-based control, and secure traceability", improves anomaly positioning and governance efficiency, realizes automatic control and full-process traceability of the governance process, and ensures continuous improvement of data quality.

[0017] 2. The rule engine and low-code double-driven power grid number-graph fusion governance platform, through the construction of a full-process closed-loop management mechanism, based on a workflow engine and digital thread technology, can seamlessly connect "anomaly discovery, work order creation, responsibility allocation, rectification, review, and archiving" and completely associate "data, work order, graph, and rule" full-link information. This not only realizes automatic control and non-stop traceability of the governance process, makes rectification progress real-time visible and rectification results automatically verifiable, but also accurately locates governance responsibilities, thereby greatly improving data rectification completion rate from traditional methods and fundamentally ensuring continuous improvement and stable reliability of power grid data quality.

[0018] 3. The rule engine and low-code double-driven power grid number-graph fusion governance platform innovatively embeds security control deeply in the whole life cycle of data governance, through an integrated security mechanism of "dynamic de-sensitization, differential privacy, and full-process audit", effectively solving the persistent problem of disconnection between security and governance in traditional solutions. Dynamic de-sensitization flexibly adjusts the strategy according to user roles and operation scenarios, avoiding the "one-size-fits-all" drawbacks of static de-sensitization; differential privacy technology injects noise when aggregating and analyzing data, protecting individual privacy while ensuring the statistical analysis value of data; the full-process non-tamperable audit log meets compliance requirements, realizing traceability and auditability of governance operations.

[0019] 4、The rule engine and the low-code double-driven power grid number-graph fusion governance platform, in the system architecture and performance level, the platform adopts a hybrid distributed storage architecture based on microservices and containerization, with high availability and elastic expansion capability. The collaborative work of time series database, graph database, relational database and distributed file system perfectly meets the storage and efficient query needs of power grid multi-source heterogeneous data, and can support real-time governance operations of large-scale power grids with more than 1000 nodes, solving the performance bottleneck of traditional monolithic architecture and single database when facing massive spatiotemporal data and topological relationships.

[0020] 5、The rule engine and the low-code double-driven power grid number-graph fusion governance platform form a sustainable improvement closed loop of "governance-analysis-optimization". The platform can automatically generate multi-dimensional data governance effect report and system operation report, help administrators accurately locate weak links of governance, and then drive continuous optimization of governance rules and scene templates. This self-evolution ability based on data-driven decision-making enables the platform to continuously adapt to the dynamic development of power grid business, building a long-term data quality management system, and has long-term application value.

[0021] 6、The rule engine and the low-code double-driven power grid number-graph fusion governance method has the same beneficial effects as the power grid number-graph fusion governance platform described above, and will not be described here. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 The system framework diagram of the rule engine and the low-code double-driven power grid number-graph fusion governance platform of embodiment 1 of the application.

[0023] Figure 2 The flowchart of the rule engine and the low-code double-driven power grid number-graph fusion governance method of embodiment 2 of the application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical scheme and advantages of the application clearer, the application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the application and do not limit the application.

[0025] The current mainstream power grid data governance scheme mainly relies on traditional data governance tools, and is developed around structured data table operations, supplemented by independent graph display systems. The main technologies are as follows.

[0026] 1. Data governance tool. The tool takes the database management system, ETL tool, and data quality checking software as the core, and realizes data entry, cleaning, checking, and integration through the tabular interface. For example, abnormal data is filtered through SQL statements, data quality is checked based on preset rules (such as numerical range, field non-empty), and an Excel format quality report is generated.

[0027] 2. Graphical display system. Independent GIS platform and dispatching graph module system are used to display power grid graphical data. For example, the GIS system is used to present the spatial position and topological connection relationship of equipment, and the dispatching graph module system is used to display the operation state of the power grid. Graphical data and structured data are stored in different systems, and the graphical system only serves as a visual presentation carrier and does not participate in the data governance logic.

[0028] 3. Data correlation method. The loose correlation between structured data and graphical data is established through the key field of equipment ID, and the user needs to manually switch between the data governance tool and the graphical system for query. After locating the abnormal data in the data tool, the equipment ID needs to be recorded, and then the ID is input into the graphical system to query the corresponding equipment graphical node, and the "data-graph" correlation check is completed.

[0029] 4. Process management. The governance process is promoted by relying on manual or simple work order system. After the data anomaly is found, the relevant personnel are notified of the rectification through email or paper documents, and the result is manually fed back after the rectification is completed. The whole process of "abnormality discovery-rectification-review-archiving" lacks automatic control and traceability, and is not linked with graphical data to record the graphical changes before and after the rectification.

[0030] 5. System scalability. New business scenarios (such as new energy station data governance and main grid OP interconnection data governance) need to modify the underlying code based on the traditional development mode. For example, new verification rules for new energy equipment data need to develop new verification modules, which has a long customized development cycle and is difficult to quickly respond to changes in power grid business.

[0031] For example, in traditional power grid data governance, if it is found that the topological connection of a 110kV line is incorrect, the operation and maintenance personnel need to first find the equipment ID of the line in the data quality report, then switch to the GIS system to input the ID to locate the line graph, manually compare the graphical topology with the actual power grid connection relationship, and notify the operation and maintenance team of the rectification through email after confirming the error. After the rectification is completed, the "data query-graphical check" process needs to be repeated again to verify the effect. The whole process is cumbersome, frequent cross-system switching, and cannot track the rectification progress in real time.

[0032] The above power grid data governance technical solution has four defects in the following aspects: insufficient data and graph fusion depth, low governance efficiency, weak business adaptability, and disconnection between safety and governance.

[0033] 1. Shallow integration of data and graphs leads to inefficient governance interaction. In existing solutions, data and graphs are stored and displayed separately, requiring users to manually link "data-graph" information across systems, making it impossible to intuitively obtain an integrated "graph-data" view. Data quality anomalies (such as topology errors or parameter mismatches) require form filtering and manual comparison of graphs to pinpoint the problem, which is time-consuming and error-prone, making it difficult to achieve an efficient "data governance by graph analysis" model.

[0034] 2. The governance process is not closed-loop, and traceability is weak. There is a lack of an automated, end-to-end closed-loop management mechanism. Each stage of data anomaly detection, rectification, and review relies on manual coordination. Rectification progress is not monitored in real time, and rectification results are not automatically verified. Furthermore, the "data-work order-graphics-rules" linkage has not been established, making it impossible to trace the governance process of abnormal data (such as changes in data values ​​before and after modification, and changes in related graphics), and making it difficult to pinpoint governance responsibility.

[0035] 3. Poor business adaptability and high expansion costs: Under the traditional development model, adding new governance scenarios (such as data access to new energy power plants and data integration of distribution network operation and dispatch) requires a lot of customized development, including adding data verification rule code, developing dedicated graphical display interfaces, and adjusting work order process logic. The average response cycle is more than 2 months, which cannot meet the needs of rapid changes in power grid business. Some low-code technology applications only cover the front-end page, and the back-end governance rules and processes still need to be developed with code, which limits scalability.

[0036] 4. Disconnect between security and governance, and insufficient compliance: Sensitive data (such as 500kV main network topology and user electricity consumption information) lacks dynamic protection throughout the entire governance process. Static desensitization is often used (such as fixed hiding of the last 4 digits of the ID card), which cannot flexibly adjust the desensitization strategy according to user roles and operation scenarios. This can easily lead to "over-desensitization affecting data availability and insufficient desensitization causing security risks." Audit logs only record basic operations (such as login time and operator) and do not associate them with data governance details, which cannot meet the "data lifecycle traceability" requirement of the Information Security Protection Standard 2.0.

[0037] Based on this, the present invention provides the following embodiments to solve the above problems, which can deeply integrate data graphs, realize closed-loop governance, have rapid expansion capabilities, and embed security mechanisms.

[0038] Example 1 Please see Figure 1 This embodiment provides a power grid data-graph fusion governance platform driven by both a rule engine and low-code. The platform mainly consists of a five-layer architecture: infrastructure layer, data layer, algorithm engine layer, business application layer, and user layer. Each layer collaborates to achieve data-graph fusion governance functions. Of course, in other embodiments, the platform may also include other logical architecture layers depending on actual needs.

[0039] The infrastructure layer includes a microservice cluster and a containerized deployment module, and provides hybrid distributed storage and cache to support system elasticity expansion. In the embodiment, the microservice cluster includes a plurality of microservice modules independently deployed and communicating through an API or an event bus. The containerized deployment module is used for containerizing and packaging the microservice modules and realizing visual monitoring and operation and maintenance of the containers. The distributed storage and cache adopt a hybrid storage architecture of a time series database, a relational database, a graph database and a distributed file system.

[0040] Specifically, the plurality of microservice modules can be split into 12 core microservice modules based on a SpringCloud framework, and each module is independently deployed and communicates through a REST API / event bus, including a user authentication service, a data access service, a number-graph fusion service, a rule engine service, a low-code orchestration service, a work order management service, a security desensitization service, an audit log service, a space-time data service, a report statistics service, a message notification service and a system management service.

[0041] The containerized deployment module can containerize and package the microservice modules using Docker, implement multi-container orchestration through Docker Compose, define container startup sequence (such as starting the space-time data service before the number-graph fusion service), network configuration and data volume mounting, realize visual monitoring and operation and maintenance of the containers through Portainer, and support CPU / memory usage viewing, log retrieval and container restart.

[0042] The distributed storage and cache can adopt a hybrid storage architecture of “time series database (InfluxDB) + relational database (MySQL) + graph database (Neo4j) + distributed file system (MinIO)”. InfluxDB stores real-time running data such as current and voltage, MySQL stores structured data such as device parameters and work order information, Neo4j stores power grid topology relationship data, and MinIO stores unstructured data such as device images and inspection videos. Redis is used to realize distributed cache, cache user permissions and high-frequency query data, and reduce database access pressure.

[0043] The data layer is used to access and manage multi-source power grid data including a power grid GIS topology graph. The multi-source power grid data includes structured data, unstructured data and space-time graphic data including the power grid GIS topology graph. The data layer can realize hybrid storage and management of multi-source power grid data (structured / unstructured / space-time data). In the embodiment, the data layer includes a multi-source data access module and a space-time data management module.

[0044] The multi-source data access module is used to access multi-source power grid data, perform data format conversion and data cleaning, and push the cleaned data to the corresponding storage system. Here, multi-source power grid data includes real-time streaming data (such as Kafka streams), offline batch data (such as CSV files), and heterogeneous interface data (such as GIS system APIs and EMS system interfaces). The multi-source data access module supports multi-source power grid data, performs data format conversion (such as JSON to XML conversion), preliminary cleaning (such as removing duplicate data and completing missing fields), and pushes the data to the corresponding storage system.

[0045] The spatiotemporal data management module integrates with the GIS engine to manage power grid GIS topology maps and equipment spatial coordinate data. It also provides spatial query functions (such as filtering equipment by region) and analytical functions (such as equipment density statistics and topology connectivity analysis). The spatiotemporal data management module can support the access and association of graphic data, including UAV inspection images and dispatch maps, and establish a mapping relationship between spatiotemporal graphic data and equipment entities (such as binding equipment IDs to GIS node IDs).

[0046] The algorithm engine layer includes a rules engine module, a data quality diagnostic algorithm module, and a data-graph fusion driving engine module, providing core capabilities for governance logic.

[0047] The rules engine module is used to configure and execute data validation, data-graph association, and anomaly handling rules through a visual interface. In this embodiment, the rules engine module can build a governance rule center based on the Drools rules engine and automatically generate DRL scripts after rule configuration. Data validation rules allow drag-and-drop selection of value / rule / relationship / attribute validation dimensions, configuring logic such as "current > 2000A" and "missing topology connection." Data-graph association rules can define association logic such as "matching device voltage level with graphic layer." Anomaly handling rules can set strategies such as "automatically correcting minor numerical anomalies to historical averages" and "generating rectification work orders for serious anomalies." These three types of rules automatically generate DRL scripts after configuration and load and take effect in real time.

[0048] The data quality diagnostic algorithm module is used to perform rule-based data quality diagnosis to identify data anomalies. It integrates a four-dimensional feature verification algorithm, supporting cross-system data correlation verification (such as consistency verification between dispatch EMS and distribution network GIS data) and real-time map-model comparison (such as difference identification between dispatch map models and GIS map models). It automatically identifies data quality anomalies and labels their anomaly levels. Specifically, the data quality diagnostic algorithm module can load data verification rules configured in the rule engine to perform four-dimensional feature verification (value features, rule features, relationship features, and attribute features) on structured data, perform consistency verification on data-map relationships, and automatically identify data quality anomalies and label their anomaly levels.

[0049] The data-graphics fusion engine module enables bidirectional interaction between data and graphics. This bidirectional interaction includes automatic graphic annotation of data anomalies and data response triggered by graphic operations, i.e., data change-graphic linkage and graphic operation-data response linkage. Specifically, the data-graphics fusion engine module can automatically locate corresponding graphic nodes, mark anomalies with colors / icons (e.g., red for excessive current, yellow for missing topology) and pop up an anomaly details bubble. When the user clicks / drags a graphic node, it triggers data queries (e.g., viewing associated device parameters), topology verification (e.g., updating GIS coordinates and verifying topology after adjusting position), and batch checks (e.g., selecting devices to verify voltage level consistency).

[0050] Traditional solutions require manual cross-system data-graphic association, and anomaly location relies on manual comparison. This embodiment, however, achieves bidirectional linkage through a data-graphic fusion engine—automatic annotation of graphic nodes for data anomalies and triggering data responses through graphic operations. Combined with a "one-click penetrating query" function, data from multiple systems can be retrieved simply by clicking on the graphic. In scenarios where operational and distribution data are integrated, the efficiency of locating "errors in associating routes with user profiles" is significantly improved compared to traditional manual comparison, completely revolutionizing the inefficient "form-based data retrieval" model.

[0051] In this way, a deep data-data fusion system is built to achieve integrated display and coordinated governance of "graphics and data", breaking the traditional separation mode of "data form + independent graphics" and transforming data governance from a form mode to a scenario-based interactive mode of "governing data by looking at graphics and verifying governance by graphics", thereby improving the efficiency of anomaly location and governance.

[0052] The anomaly levels are categorized as severe, moderate, and minor. Severe anomalies are marked in red, moderate anomalies in yellow, and minor anomalies in orange. The data-graphics fusion engine module displays an anomaly details bubble next to the graphics node, showing the anomaly type, current value, standard value, and rectification suggestions.

[0053] The business application layer includes low-code orchestration platforms, data... Figure One The integrated display module and closed-loop work order management module may also include a security masking module and an audit log and reporting module. The business application layer can build data through a low-code orchestration platform. Figure One Applications include integrated display, cross-system correlation verification, and closed-loop work order management.

[0054] The low-code orchestration platform is used to customize governance scenario pages and work order processes through drag-and-drop components and templates. Specifically, the platform provides a variety of power grid-specific components and scenario templates, and supports drag-and-drop customization of governance scenario pages and work order processes, solving the problems of slow response and weak scalability in traditional development models. In this embodiment, the low-code orchestration platform can be built on Vue + ElementPlus, providing 30+ power grid-specific components (such as topology diagram components, data table components, anomaly annotation components, work order components, and report components) and 10+ preset scenario templates (such as main grid equipment governance templates, distribution network operation and dispatch data integration templates, and new energy station access templates). Business personnel can customize governance scenario pages through drag-and-drop, configuring page layout, component attributes, and data association logic. The low-code orchestration platform supports the BPMN 2.0 standard process designer, allowing for visual configuration of work order processes (such as "anomaly detection → work order creation → responsibility assignment → rectification → review → archiving").

[0055] number Figure One The integrated display module is used to display multi-source power grid data on the same screen, supporting features such as flying line coloring, topology anomaly location, and one-click penetration query. Figure One The integrated display module enables multi-source data to be displayed on the same screen in one integrated manner, integrating GIS topology maps, equipment operation data, parameter data, and anomaly annotation information. It supports real-time coloring of flying lines (flying lines corresponding to abnormal data are highlighted in red), automatic location of topology anomalies (accidental connection edges are marked based on graph calculation), and multi-source map model split-screen comparison (differences between scheduling map model and GIS map model are displayed synchronously). It provides a "one-click penetrating query" function, which allows users to query multi-source data such as EMS, GIS, and marketing system by clicking on graphic nodes.

[0056] The closed-loop work order management module is used to automatically generate and allocate governance work orders based on data anomalies. This module can be built on a workflow engine (such as Activiti) and supports automatic work order creation (automatic generation based on data anomalies, manual initiation by users), allocation (based on "anomaly type - responsible department" mapping rules), tracking (recording data before and after modification, uploading supporting materials), review (calling data quality diagnostic algorithms for secondary verification), and archiving (generating rectification reports and storing them in the knowledge base). This design creates a full-process closed-loop management mechanism, combining digital thread technology to establish a "data-work order-graphics-rules" linkage, achieving automated control and full traceability of the governance process. The governance process is traceable, responsibility is identifiable, and continuous improvement in data quality is guaranteed.

[0057] The security masking module is used to execute dynamic masking strategies based on data sensitivity, user roles, and operational scenarios, and / or employ differential privacy technology to add noise to aggregated statistical data. The module can build a dynamic masking engine that identifies sensitive data through rule matching and machine learning, categorizing it into three levels based on sensitivity (Level 1: user privacy, mainnet topology; Level 2: core device parameters, power load; Level 3: non-core parameters, public information). Masking strategies are configured according to user roles and operational scenarios (e.g., hiding key nodes when ordinary maintenance personnel view Level 1 data, while administrators see the full data). The module can also use a Laplace mechanism to achieve differential privacy protection, adding minor noise to aggregated statistics (e.g., the number of abnormal devices in a region) to balance data security and availability.

[0058] The audit log and report module is used to collect and immutably store logs of user operations, system operations, and data changes. Logs include at least one of the following: operator, time, operation type, operation object, and operation content. The audit log and report module uses immutable storage and supports multi-dimensional log queries and security alerts (such as abnormal access to sensitive data or batch data modification). The audit log and report module can automatically generate data governance effectiveness reports (anomaly repair rate, data accuracy trend) and device status statistical reports, supporting export and visualization.

[0059] Traditional solutions employ static data masking, resulting in a disconnect between sensitive data protection and governance processes, and audit logs lacking detailed correlation. This embodiment addresses this by using dynamic data masking with differentiated protection based on roles / scenarios (e.g., hiding critical mainnet nodes for ordinary users), differential privacy to ensure aggregated data security, and full-process auditing recording of the "operation-data-graphics" correlation information, meeting the Level 3 requirements of the Cybersecurity Classified Protection 2.0 standard. This avoids the problems of "excessive masking affecting usability and insufficient masking causing risks," achieving a balance between data security and governance efficiency. Thus, through dynamic data masking (differentiated protection based on roles / scenarios), differential privacy (noise protection for aggregated data), and full-process auditing (unalterable logs), it achieves full lifecycle protection of sensitive data and traceable governance operations, covering the entire data governance lifecycle, balancing data availability and security, and meeting the requirements of the Cybersecurity Classified Protection 2.0 standard.

[0060] The user layer includes a role-based operation interface and a message notification module, which provides customized operation interfaces for users with different roles (operation and maintenance personnel, administrators, and schedulers).

[0061] The role-based operation interface is designed to provide operation and maintenance personnel with a user interface. It offers a graphical governance and work order processing interface for operations and maintenance personnel, supporting anomaly location and rectification. For administrators, it provides a rule configuration and system management interface, supporting rule maintenance and user permission management. Finally, for dispatchers, it provides a real-time monitoring and data query interface, supporting viewing operational data and cross-system data penetration queries.

[0062] The message notification module is used to push work order reminders, abnormal alarms, and report notifications via SMS, email, and system messages, and supports message read / unread marking and historical message query.

[0063] Traditional solutions require more than two months of customized development for adding new business scenarios. However, this embodiment uses a dual-drive architecture of "rule engine + low code" to support visual configuration of rules and drag-and-drop customization of scenarios. The newly added "new energy power station data governance" scenario has been tested and found to take only 10 days, shortening the cycle by 83%. The rules take effect in real time after configuration without restarting the system, which can quickly respond to changes in power grid business (such as the need to verify data of new energy storage devices) and solve the pain points of "difficult code modification and slow expansion" in traditional architectures.

[0064] Traditional solutions rely on manual coordination of the governance process, with no automatic verification of rectification results, resulting in a completion rate of only 75% or less. In contrast, this embodiment achieves full-process automation based on a workflow engine—automatic generation of work orders for anomalies, automatic allocation of work orders, and automatic review after rectification. Combined with digital thread technology to link the "data-work order-graphics" chain, it is possible to trace changes in data and graphics before and after rectification. The rectification completion rate has been tested and increased from 75% to 98.5%, ensuring continuous optimization of data quality.

[0065] In the current construction of digital twin power grids, "separation of data and maps, inefficient governance, poor adaptability, and disconnect from security" are common challenges faced by the industry. Companies like State Grid and China Southern Power Grid invest billions of yuan annually in data governance, but traditional solutions have limited effectiveness. The governance platform in this embodiment can be directly applied to core scenarios such as the construction of a unified power grid map, the integration of operation, distribution, and dispatch data, and the access of new energy power station data, indicating a clear and urgent market demand. Graphical governance reduces cross-system operations, improves anomaly location efficiency by 80%, and reduces manual verification workload by 50%. Taking a provincial power grid company as an example, it is estimated that it can save over 2 million yuan in operation and maintenance manpower costs annually.

[0066] In addition, it should be noted that the power grid data governance platform provided in this embodiment is not only applicable to the data governance of power transmission and transformation equipment, but can also be extended to scenarios such as distribution networks, new energy power plants (photovoltaic / wind power), and energy storage systems. By adding components and templates through the low-code platform, it can quickly adapt to future business needs such as "digital twin substations" and "intelligent power grid dispatching". At the same time, the core technologies (data-map fusion, dynamic desensitization) can be migrated to asset data governance in fields such as rail transit and municipal pipeline networks, and the market boundaries can be further expanded.

[0067] This power grid data governance platform integrates technologies from multiple fields, including microservices, low-code, graph computing, and data security, forming a technological barrier of "deep data-graph collaboration + dual-drive architecture + integrated security and governance." Compared to traditional data governance tools, this embodiment is more aligned with the characteristics of power grid operations (such as GIS topology association and power grid equipment parameter verification), making it difficult to replace with general-purpose tools. Furthermore, this embodiment provides high-quality data input for the digital twin power grid, ensuring the accuracy of power grid simulation and the reliability of real-time status perception. It can serve as a core support platform for the digital transformation of the power grid, helping power grid companies achieve the goals of "source-grid-load-storage" coordination and carbon neutrality.

[0068] It should be noted that if the new power grid data governance platform is designed using the following workarounds, there will also be compromises in terms of technical effectiveness or compatibility, as detailed in the following analysis.

[0069] (1) Data-Graph Integration Avoidance Solution. Instead of deep integration with "two-way linkage", this solution uses a superficial association of "passive data display in graphics"—only embedding a data query entry point in the graphics system. Users need to manually click on the graphics nodes to trigger the data query. There is no automatic annotation for data anomalies, and the graphics operation triggers the data response function. Although this solution avoids the protection point of "two-way linkage", the governance still relies on manual operation. The efficiency is not fundamentally different from the traditional solution, and it cannot achieve the core goal of "governing data by looking at graphics".

[0070] (2) Rule and Low-Code Architecture Bypass Solution. Instead of using the dual-drive architecture of "visual rule engine + low-code platform", a "code-based rule + fixed page" mode is adopted. New validation rules require writing Java / Python code, and new scenarios require developing dedicated pages; or only low-code front-end customization is retained, and back-end rules still require code development. Although this solution avoids the protection point of "dual drive", the business response cycle returns to more than 2 months in the traditional way, which cannot quickly adapt to changes in power grid business and has weak scalability.

[0071] (3) Closed-loop management mechanism avoidance scheme. Instead of adopting the "digital thread + automatic review" closed loop, a "manual work order + manual verification" mode is used. After the work order is created, it needs to be manually assigned. After the rectification is completed, the data and graphics need to be manually compared to confirm the effect. There is no automatic review and full-link traceability. Although this scheme avoids the protection point of "automated closed loop", the rectification completion rate drops to about 75%, which cannot guarantee the stability of data quality. Moreover, there is no detailed traceability of the governance process, making it difficult to locate responsibility.

[0072] (4) Security control technology avoidance scheme. Instead of "dynamic desensitization + differential privacy", a "static desensitization + basic audit" mode is adopted - sensitive data fields are fixedly hidden (such as the last 4 digits of ID card, the last 2 digits of core device parameters), and the audit log only records the operator and time, without data modification values ​​and graphic association information. Although this scheme avoids the protection point of "dynamic security control", it is prone to "excessive desensitization affecting data availability (such as hiding key parameters, making it impossible to judge the device status)" or "insufficient desensitization causing risks (such as the main network topology being fully displayed to ordinary users)", and it is impossible to trace the details of data modification, which does not meet the compliance requirements.

[0073] (5) Storage and Architecture Avoidance Solutions. Instead of adopting the "hybrid storage + microservices" architecture, a "single relational database + monolithic application" model is used—all data (real-time / structured / graph) is stored in MySQL, and the system is deployed as a monolith; or instead of using a graph database to store topology relationships, relational database tables are used to simulate the topology. Although this solution avoids the protection point of "hybrid storage + microservices", the real-time data write performance is insufficient (InfluxDB writes tens of thousands per second vs MySQL writes thousands per second), the topology query efficiency is low (Neo4j graph query vs relational database multi-table joins), the system cannot support 1000+ users online at the same time, and it is not suitable for the needs of large-scale power grid governance.

[0074] In summary, designing a power grid data governance platform based on the above five aspects cannot achieve the technical effects obtained in this embodiment. Therefore, compared with existing power grid data governance platforms, the power grid data fusion governance platform driven by both a rule engine and low-code has the following significant advantages: 1. This rule engine and low-code dual-driven power grid data-graph fusion governance platform establish a two-way linkage logic between data and graph through a deep data-graph fusion mechanism. This enables automatic association of data anomalies with graph annotations and triggering data responses through graph operations. It breaks the traditional separation mode of "data forms + independent graphs" and innovates into a scenario-based governance interaction of "managing data through graphs and verifying governance through graphs". Through the "dual-drive" architecture design, the rule engine supports visual configuration of data verification, data-graph association, and anomaly handling rules, making the governance logic pluggable and enhancing adaptability. The low-code platform provides power grid-specific components and scenario templates, eliminating the need for extensive code development and shortening the response cycle for new business scenarios. It solves the technical problems of data-graph separation and poor adaptability in existing power grid data governance platforms and methods, realizing "visualized governance, scenario-based control, and secure traceability" of power grid data. This improves anomaly location and governance efficiency, achieves automated control and full traceability of the governance process, and ensures continuous improvement in data quality.

[0075] 2. This rule engine and low-code-driven power grid data fusion governance platform, through the construction of a closed-loop management mechanism covering the entire process, and based on workflow engine and digital thread technology, seamlessly connects each stage of "anomaly detection - work order creation - responsibility allocation - rectification - review - archiving," and fully associates the entire chain of information "data - work order - graph - rule." This not only achieves automated control and seamless traceability of the governance process, making rectification progress visible in real time and rectification results automatically verified, but also accurately identifies governance responsibilities, thereby significantly improving the data rectification completion rate compared to traditional methods, fundamentally ensuring the continuous improvement and stable reliability of power grid data quality.

[0076] 3. This rule engine, coupled with a low-code-driven power grid data fusion governance platform, innovatively embeds security controls deep into the entire data governance lifecycle. Through an integrated security mechanism of "dynamic desensitization + differential privacy + full-process auditing," it effectively solves the persistent problem of security and governance being disconnected in traditional solutions. Dynamic desensitization flexibly adjusts strategies based on user roles and operational scenarios, avoiding the "one-size-fits-all" drawbacks of static desensitization. Differential privacy technology injects noise during aggregation analysis, protecting individual privacy while ensuring the statistical analysis value of the data. The fully tamper-proof audit logs meet compliance requirements, achieving traceability and auditability of governance operations.

[0077] 4. This rule engine and low-code-driven power grid data-graph fusion governance platform, in terms of system architecture and performance, adopts a hybrid distributed storage architecture based on microservices and containerization, possessing both high availability and elastic scalability. The collaborative work of time-series databases, graph databases, relational databases, and distributed file systems perfectly meets the storage and efficient querying needs of multi-source heterogeneous data in the power grid, enabling real-time governance operations for large-scale power grids with thousands of nodes or more. This solves the performance bottleneck of traditional monolithic architectures and single databases when facing massive spatiotemporal data and topological relationships.

[0078] 5. This rule engine, coupled with the low-code-driven power grid data fusion governance platform, forms a sustainable improvement loop of "governance-analysis-optimization." The platform can automatically generate multi-dimensional data governance effect reports and system operation reports, helping administrators accurately identify weak points in governance and thus driving continuous optimization of governance rules and scenario templates. This self-evolving capability based on data-driven decision-making enables the platform to continuously adapt to the dynamic development of power grid operations, building a long-term data quality management system with far-reaching application value.

[0079] Example 2 Please see Figure 2 This embodiment provides a power grid data graph fusion governance method driven by both a rule engine and low-code. This power grid data graph fusion governance method applies the power grid data graph fusion governance platform driven by both a rule engine and low-code in Embodiment 1. The power grid data graph fusion governance method includes the following steps.

[0080] Data access and preprocessing steps: Access multi-source power grid data, perform preprocessing, and store them according to type.

[0081] Data fusion and quality diagnosis steps: The structured data and power grid graphic data are associated and fused, and data quality diagnosis is performed based on pre-configured rules. The diagnosed anomalies are marked on the graphical interface.

[0082] Closed-loop work order processing steps: Automatically generate and assign governance work orders based on data anomalies, track the rectification process, and review and archive the rectification results.

[0083] Security control and traceability steps: Throughout the entire governance process, sensitive data is dynamically anonymized and differentially protected for privacy, and a full-process audit log is recorded.

[0084] Example 3 This embodiment provides a power grid data graph fusion governance method driven by both a rule engine and low-code, which applies the power grid data graph fusion governance platform driven by both a rule engine and low-code in Embodiment 1. The power grid data graph fusion governance method includes the following steps.

[0085] Data access and preprocessing steps include: (1) Connect to the data sources of the power grid EMS system, GIS system and marketing system to receive real-time operation data, equipment parameter data, graphic data (GIS topology map, equipment image) and offline batch data; (2) Perform format conversion (e.g., convert XML format data output by the GIS system to JSON format) and data cleaning (remove duplicate data and fill in non-critical missing fields with default values) on the received data to obtain preprocessed data; (3) Push the preprocessed data to the corresponding storage system according to the type; among them, real-time data is written to the time series database: InfluxDB, structured data is written to the relational database: MySQL, topological relational data is written to the graph database: Neo4j, unstructured data is written to the distributed file system: MinIO, and graph data is associated with the device ID and then stored in the spatiotemporal data management module of the data layer.

[0086] The steps for data-image fusion and quality diagnosis include: (1) The GIS topology map of the power grid is loaded by calling the GIS engine through the spatiotemporal data management module. The data fusion service associates the equipment parameter data in MySQL and the real-time running data in InfluxDB with the graphic node according to the mapping relationship of "equipment ID-graphic node ID" to realize the integrated display of "graphics-data". (2) Load the data verification rules configured in the rule engine through the data quality diagnosis algorithm, perform four-dimensional feature verification on the structured data (such as verifying whether the voltage value is within a reasonable range and whether the equipment topology connection is complete), and perform consistency verification on the data-graph association relationship (such as whether the equipment voltage level matches the graphic layer). (3) Receive quality diagnosis results through the data fusion driving engine module. If data abnormality is found, the corresponding graphic node is automatically located. Serious abnormalities are marked in red, general abnormalities are marked in yellow, and minor abnormalities are marked in orange. At the same time, an abnormality details bubble pops up next to the graphic node, showing "abnormality type, current value, standard value, and rectification suggestions". (4) Synchronize abnormal data information to the work order management module and trigger the work order creation process: serious abnormalities generate work orders immediately, and general / minor abnormalities generate work orders in batches every hour. The work order automatically carries "abnormal data ID, graphic location information, and abnormal description".

[0087] The rule configuration and scenario customization steps (this step is optional and should be performed as needed) include: (1) If you need to add a new governance scenario (such as data governance of new energy power stations), select “New Energy Power Station Access Template” through the low-code orchestration platform, and drag and drop the topology diagram component, data table component, and work order component to adjust the page layout; (2) Configure new energy equipment-specific verification rules (such as photovoltaic inverter power range, wind power equipment speed threshold) and data-graph association rules (such as photovoltaic array graph nodes and inverter data binding) in the rule engine center. (3) Adjust the work order process through the BPMN process designer and set relevant parameters such as "allocate new energy abnormal work orders to new energy operation and maintenance teams" and "rectification time limit 48 hours"; (4) Generate scenario configuration scripts through the low-code orchestration platform and synchronize them to the business application layer; new scenarios can take effect without restarting the system, and users can carry out governance work by selecting the corresponding scenario in the role-based operation interface.

[0088] The closed-loop work order processing steps specifically include: (1) The closed-loop work order management module automatically assigns work orders to the corresponding departments (e.g., topology anomalies are assigned to the operation and maintenance department, and parameter anomalies are assigned to the dispatch center) based on the mapping rule of “anomaly type-responsible department”, and supports the administrator to manually adjust the assignment results; (2) Send SMS / system messages to the person in charge of the work order through the message notification module at the user level, informing them of the "work order number, exception description, and processing time limit", and at the same time, send SMS / system messages to the person in charge of the work order through the message notification module at the user level. Figure One The integrated display interface indicates the status of the work order (such as "Pending" or "Processing"). (3) The person in charge logs into the operation and maintenance personnel interface, clicks on the abnormal node in the graph, and uses "one-click penetrating query" to view the associated multi-source data (such as historical operation data in the scheduling EMS and user profile data in the marketing system), and uses the rectification tools provided by the platform (data editing interface, batch correction template) to complete the modification of abnormal data; (4) Automatically record “data value before modification, data value after modification, modification time, and modifier” and supporting materials (such as on-site photos, test reports, etc.) through the audit log and report module of the business application layer; after rectification, submit a review application; (5) The data quality diagnosis algorithm is called through the closed-loop work order management module to perform secondary verification on the rectification data; if the verification passes, the work order enters the archiving node; if it fails, the work order is returned to the rectification node with review comments (such as "the voltage level is still not compatible with the line"); the review personnel can view the rectification records and graphic changes and manually confirm the rectification effect. (6) After the work order is archived, a rectification report is automatically generated, which includes "basic information of the work order, rectification effect (abnormal repair rate, data accuracy improvement), and related data changes". The report is stored in the work order knowledge base and supports query and statistics by abnormal type and processing department.

[0089] Safety control and traceability steps specifically include: (1) When a user accesses the system, the user authentication service completes identity verification based on the OAuth2.0 protocol and assigns operation permissions through the RBAC (role-based access control) model (e.g., maintenance personnel can only view sensitive data at level 2 and below, while administrators can access all data); when a user triggers a sensitive data access operation (e.g., viewing the 500kV main network topology or user electricity information), the security desensitization module of the business application layer automatically identifies the user role and operation scenario and calls the corresponding desensitization strategy to perform processing; for example, when ordinary maintenance personnel view a user's ID number, the middle 8 digits are hidden (format is 110101********1234), and key nodes are hidden when viewing the main network topology; when the administrator reviews the data, they can obtain the complete sensitive data; (2) When a user initiates an aggregation analysis request (such as statistics on “number of abnormal 110kV transformers in the western part of the city” or “monthly data rectification completion rate”), the noise value is calculated based on the preset privacy budget ε = 0.1-0.5 (the smaller the ε value, the stronger the privacy protection). Small noise is added to the aggregation result (such as returning 24 or 26 when the actual number of abnormalities is 25). At the same time, an independent privacy budget pool is maintained for each user. Each aggregation query consumes the corresponding budget (such as 0.1 budget for regional abnormality statistics). An application for replenishment must be submitted after the budget is exhausted. When the same query is repeated within 1 hour, the initial calculation result is reused to avoid repeated consumption of the budget. (3) Three types of logs are collected in real time through the audit log and report module of the business application layer: user operation logs (login / logout, rule configuration, work order creation / processing, data query / modification), system operation logs (data access, de-identification execution, rule loading, service start / stop), and data change logs (values ​​before / after abnormal data modification, modifier, modification time, associated device ID); the log fields include "operator, operation time, operation type, operation object, operation content, IP address, device information, operation result"; all logs are stored in a read-only database with timestamps to prevent tampering; (4) Security alarm rules are preset through the audit log and report module of the business application layer (such as "login failure ≥5 times within 10 minutes of the same IP address", "ordinary users attempt to access first-level sensitive data ≥3 times within 1 hour", "modification of ≥10 core parameters of the device in a single instance"). After the rules are triggered, the administrator is notified immediately via SMS and email, and a pop-up prompt is displayed on the system management interface. The logs are automatically analyzed weekly to identify potential risk operations and generate a "Security Analysis Report" which includes details of risk operations, users involved, and suggested optimization strategies.

[0090] The steps for performance statistics and continuous optimization include: (1) Periodically (e.g., daily / weekly / monthly) extract data from each storage system, obtain "number of abnormal work orders, rectification completion rate, and average processing time" from the work order management module, obtain "data accuracy and distribution of abnormal data types" from the data quality diagnosis module, and obtain "user operation frequency and sensitive data access records" from the audit log module; (2) Process the extracted data into visualization charts (line chart to show the data accuracy trend, bar chart to show the proportion of abnormal types, pie chart to show the distribution of work order processing departments), generate "Data Governance Effect Report" and "System Operation Report", and support export to Excel and PDF formats; (3) View reports through the system management interface, analyze weak links in governance (such as "high proportion of topology abnormal work orders" and "low data accuracy in distribution network areas"), and optimize the verification rules in the rule engine (such as adding secondary verification logic for topology connectivity) and adjust the low-code scenario template (such as optimizing the layout of distribution network data governance page components) to form a continuous improvement closed loop of "governance-analysis-optimization".

[0091] Example 4 This embodiment provides a computer terminal, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the power grid data-graph fusion governance method driven by both a rule engine and low-code in Embodiment 2 or 3.

[0092] When applying the methods of Embodiment 2 or 3, they can be implemented in software form, such as by designing them as stand-alone programs and installing them on a computer terminal, which can be a computer, smartphone, control system, or other IoT devices. Alternatively, the methods of Embodiment 2 or 3 can be designed as embedded programs and installed on a computer terminal, such as on a microcontroller.

[0093] Example 5 This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, it implements the steps of the power grid data-graph fusion governance method driven by both a rule engine and low-code in Embodiment 2 or 3.

[0094] When applying the method of Embodiment 2 or 3, it can be applied in the form of software, such as by designing it as a program that can run independently on a computer-readable storage medium. The computer-readable storage medium can be a USB flash drive, designed as a USB security token, and the program is designed to start the entire method through an external trigger.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A power grid data graph fusion governance platform driven by both a rule engine and low-code, characterized in that, It includes: The infrastructure layer includes microservice clusters and containerized deployment modules, and provides hybrid distributed storage and caching; The data layer is used to access and manage multi-source power grid data, including power grid GIS topology maps; The algorithm engine layer includes: The rules engine module is used to configure and execute data validation, data graph association, and anomaly handling rules through a visual interface. The data quality diagnostic algorithm module is used to perform rule-based data quality diagnostics to identify data anomalies. The data-graphics fusion driving engine module is used to realize bidirectional linkage between data and graphics. The bidirectional linkage includes automatic graphic annotation of data anomalies and data response triggered by graphic operations. The business application layer includes: A low-code orchestration platform that allows users to customize governance scenario pages and work order processes by dragging and dropping components and templates; The integrated data and graph display module is used to display the multi-source power grid data on the same screen. The closed-loop work order management module is used to automatically generate and allocate governance work orders based on data anomalies.

2. The power grid data fusion governance platform driven by both a rule engine and low-code as described in claim 1, characterized in that, The microservice cluster includes multiple independently deployed microservice modules that communicate with each other via API or event bus; the containerized deployment module is used to containerize and orchestrate microservices, and to realize the visual monitoring and operation and maintenance of containers; the distributed storage and caching adopts a hybrid storage architecture of time-series database, relational database, graph database and distributed file system.

3. The power grid data and graph fusion governance platform driven by both rule engine and low-code as described in claim 1, characterized in that, The multi-source power grid data includes structured data, unstructured data, and spatiotemporal graphic data including the power grid GIS topology map.

4. The power grid data and graph fusion governance platform driven by both rule engine and low-code as described in claim 1, characterized in that, The data layer includes; A multi-source data access module is used to access the multi-source power grid data, realize data format conversion, data cleaning, and push the cleaned data to the corresponding storage system; the multi-source power grid data includes real-time streaming data, offline batch data, and heterogeneous interface data. The spatiotemporal data management module is used to integrate a GIS engine, manage the power grid GIS topology map and equipment spatial coordinate data, and provide spatial query and analysis functions. The spatiotemporal data management module supports the access and association of graphic data, including UAV inspection images and dispatch maps, and establishes a mapping relationship between spatiotemporal graphic data and equipment entities.

5. The power grid data and graph fusion governance platform driven by both rule engine and low-code as described in claim 3, characterized in that, The rule engine module builds a governance rule center based on the Drools rule engine and automatically generates DRL scripts after rule configuration; the data quality diagnosis algorithm module performs four-dimensional feature verification on the structured data and consistency verification on the data graph relationship by loading the data verification rules configured in the rule engine, and automatically identifies data quality anomalies and marks the anomaly level. The data-graph fusion driving engine module automatically locates the corresponding graph nodes, marks anomalies with colors / icons and pops up an anomaly details bubble, and triggers data query, topology verification and batch verification when the user clicks / drags the graph node.

6. The power grid data fusion governance platform driven by both a rule engine and low-code as described in claim 5, characterized in that, The anomaly levels include severe, general, and minor; severe anomalies are marked in red, general anomalies in yellow, and minor anomalies in orange; the data-image fusion driving engine module pops up a bubble of anomaly details next to the image node, displaying the anomaly type, current value, standard value, and rectification suggestions.

7. The power grid data and graph fusion governance platform driven by both rule engine and low-code as described in claim 1, characterized in that, The low-code orchestration platform provides a variety of power grid-specific components and scenario templates, and supports the customization of governance scenario pages and work order processes through drag-and-drop. The integrated data and graph display module also supports flying line coloring, topology anomaly location, and one-click penetrating query functions. The closed-loop work order management module is built on a workflow engine and supports the automatic creation, allocation, tracking, review, and archiving of work orders.

8. The power grid data and graph fusion governance platform driven by both a rule engine and low-code as described in claim 1, characterized in that, The business application layer also includes: The security desensitization module is used to execute dynamic desensitization strategies based on data sensitivity, user roles and operation scenarios, and / or use differential privacy technology to add noise to aggregated statistical data; The audit log and report module is used to collect and immutably store logs of user operations, system operations, and data changes. The logs include at least one of the following: operator, time, operation type, operation object, and operation content.

9. The power grid data and graph fusion governance platform driven by both rule engine and low-code as described in claim 1, characterized in that, The power grid data map fusion governance platform also includes a user layer, which includes: A role-based user interface, which is used to provide an operation and maintenance interface for operation and maintenance personnel; The message notification module is used to push work order reminders, abnormal alarms, and report notifications via SMS, email, and system messages, and supports message read / unread marking and historical message query.

10. A power grid data graph fusion governance method driven by both a rule engine and low-code, characterized in that, Its application is the power grid data graph fusion governance platform driven by both a rule engine and low-code as described in any one of claims 1-9, wherein the power grid data graph fusion governance method includes: Data access and preprocessing steps: Access multi-source power grid data, perform preprocessing, and store them according to type; Data fusion and quality diagnosis steps: The structured data and power grid graphic data are associated and fused, and data quality diagnosis is performed based on pre-configured rules. The diagnosed anomalies are marked on the graphical interface. Closed-loop work order processing steps: Automatically generate and assign governance work orders based on data anomalies, track the rectification process, and review and archive the rectification results; Security control and traceability steps: Throughout the entire governance process, sensitive data is dynamically anonymized and differentially protected for privacy, and a full-process audit log is recorded.

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