A power distribution network operation and industry expansion engineering multi-source service data fusion method and system

Abstract

The application relates to a power distribution network operation and industry expansion engineering multi-source business data fusion method and system, which comprises the following steps: S1: a unified data access and adaptation platform is constructed to realize full-coverage access of power distribution network operation and industry expansion engineering related data; S2: based on the accessed data, an enterprise-level master data management system covering the whole domain of power distribution network operation and industry expansion engineering is established; S3: a knowledge graph in the field of power distribution network operation and industry expansion engineering is constructed; S4: based on the knowledge graph in the field of power distribution network operation and industry expansion engineering, a whole-process digital process model of power distribution network operation and industry expansion engineering is constructed based on business process management technology, and intelligent management of the business process is realized; and S5: based on the knowledge graph in the field of power distribution network operation and industry expansion engineering, a time-space integrated data fusion framework is constructed, business data, geographic space information and time sequence information are deeply fused, and a four-dimensional data model is formed. The application realizes whole-process data fusion and cooperation of power distribution network operation and industry expansion engineering.

Description

Technical Field

[0001] This invention relates to the field of data governance, and in particular to a method and system for fusing multi-source business data from power distribution network operations and business expansion projects. Background Technology

[0002] Against the backdrop of new power system construction, the distribution network, as a crucial component of the energy internet, has seen its digital transformation become a key path to improving the intelligence level of the power grid. Currently, distribution network operation management and business expansion project management, as two core business domains, face pain points in actual operation, such as severe data silos, fragmented business processes, and lagging information transmission. The traditional subsystem management model results in the inability to effectively connect the information of business expansion projects from project initiation to commissioning with the operation and maintenance of distribution network operations, leading to problems such as suboptimal resource allocation, low response efficiency, and unstable service quality. Summary of the Invention

[0003] To address the aforementioned issues, the present invention aims to provide a method and system for fusing multi-source business data from distribution network operations and business expansion projects, thereby achieving full-process data fusion and collaboration between distribution network operations and business expansion projects.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A method for fusing multi-source business data from power distribution network operations and business expansion projects includes the following steps;

[0006] S1: Construct a unified data access and adaptation platform, and achieve full coverage access to data related to power distribution network operations and business expansion projects by deploying intelligent data collectors and standardized interface adapters;

[0007] S2: Based on the accessed data, establish an enterprise-level master data management system covering the entire domain of distribution network operations and business expansion projects, and build a unified data standard, coding specification and governance process;

[0008] S3: Construct a knowledge graph in the fields of power distribution network operations and business expansion projects, and achieve deep data fusion across systems and businesses through semantic modeling and intelligent association technologies;

[0009] S4: Based on the knowledge graph of distribution network operation and business expansion project, and based on business process management technology, construct a full-process digital process model for distribution network operation and business expansion project to realize intelligent management of business processes;

[0010] S5: Based on the knowledge graph of power distribution network operation and business expansion project, construct a spatiotemporal integrated data fusion framework to deeply integrate business data with geospatial information and time series information to form a four-dimensional data model.

[0011] Furthermore, a unified data access and adaptation platform will be constructed. By deploying intelligent data collectors and standardized interface adapters, full coverage access to data related to power distribution network operations and business expansion projects will be achieved, as detailed below:

[0012] The unified data access and adaptation platform adopts a distributed microservice architecture, constructing a technical system comprising four major components: an intelligent data collector, a standardized interface adapter, a data routing gateway, and a protocol conversion engine. The intelligent data collector serves as a seamless interface for heterogeneous systems of front-end devices at the data source end. The standardized interface adapter is responsible for converting source data of different formats into a unified data format for the platform. In terms of data collection strategy, a hierarchical collection mechanism is established. For data with high timeliness requirements, such as equipment status, work order progress, and fault information, real-time streaming collection based on message middleware is adopted to ensure millisecond-level response. For equipment ledgers, project archives, and personnel information data, a near real-time incremental synchronization method is adopted to ensure timely data updates. For historical statistics and report analysis data, a timed batch extraction mode is adopted to optimize system performance.

[0013] Furthermore, based on the accessed data, an enterprise-level master data management system covering the entire distribution network operation and business expansion project will be established, and unified data standards, coding specifications, and governance processes will be constructed, as follows:

[0014] The enterprise-level master data management system adopts a centralized control and distributed application architecture, constructing a unified master data model covering core entity domains such as equipment assets, engineering projects, work orders, and customer files. Equipment asset master data focuses on the entire lifecycle management of equipment, establishing a complete information hierarchy including basic equipment attributes, technical parameters, operating status, and maintenance history. It employs a four-segment coding system based on equipment type, voltage level, geographical region, and serial number to ensure the global uniqueness and business readability of equipment identification. Engineering project master data follows the State Grid engineering construction standards, establishing a complete project file from project application, feasibility study approval, design review to final acceptance, using standardized coding rules based on year, region code, project type, and sequence number. Customer file master data integrates customer information from the marketing system and business expansion system, establishing a unified customer view with one file per customer.

[0015] The master data management system establishes a three-tiered data governance model: source standardization, process cleansing, and result verification. It ensures high-quality master data by formulating unified data standards and business rules. At the source standardization level, it clarifies the field definitions, value ranges, format requirements, and business rules for various types of master data. Data quality inspection components are deployed in the data entry stages of each business system to enforce data standards. During the process cleansing stage, data cleansing tools based on rule engines and machine learning algorithms are deployed to automatically identify and handle data duplication, missing data, format errors, and logical conflicts. For abnormal data that cannot be automatically processed, the system generates a data quality report and pushes it to the relevant responsible personnel for manual verification and processing.

[0016] Furthermore, the knowledge graph in the power distribution network operation and business expansion project domain adopts an ontology-driven modeling approach to construct a multi-layered semantic network with equipment, projects, work orders, customers, and spaces as core entities and business relationships, temporal relationships, and spatial relationships as the main associations. The knowledge graph employs intelligent entity recognition technology based on deep learning and rule fusion to achieve automatic discovery and accurate matching of the same entities in different business systems. A dynamic knowledge graph update mechanism based on streaming computing is constructed, which receives real-time data change events from various business systems through the Apache Kafka message queue and uses the Apache Flink stream processing engine to achieve incremental knowledge graph updates. When upstream master data changes, the recalculation of relevant entities and relationships is automatically triggered. By updating the graph structure locally, the performance overhead of full graph reconstruction is avoided, achieving near real-time synchronous updates of the knowledge graph.

[0017] Furthermore, intelligent entity recognition technology based on deep learning and rule fusion is adopted to achieve automatic discovery and accurate matching of the same entities in different business systems. Specifically, in terms of equipment entity association, intelligent matching of newly built equipment in business expansion projects and distribution network GIS equipment files is achieved by constructing equipment feature vectors and similarity calculations. In customer entity fusion, fuzzy matching algorithms based on edit distance and geocoding technology based on address resolution are used to associate customer information in the marketing system with application information in business expansion projects in multiple dimensions. A unified customer entity view is established through weighted fusion calculation of customer number, electricity address, and contact information. The system deploys a distributed graph computing engine based on Apache Spark, which supports large-scale graph data processing of tens of millions of entities and hundreds of millions of relationships. The GraphX ​​graph processing framework is used to implement complex graph algorithms for multi-hop path analysis, community discovery, and centrality calculation between entities, providing efficient graph computing support capabilities for business collaborative analysis.

[0018] Furthermore, in the ontology model design, the knowledge graph in the fields of distribution network operations and business expansion projects includes equipment entity classes such as transformers, switching equipment, lines, and metering devices. Each entity has a complete semantic description with four dimensions: identification attributes, description attributes, status attributes, and relationship attributes. The project entity covers new construction projects, renovation projects, and emergency repair projects, establishing a multi-dimensional association model between projects and involved equipment, participating personnel, and affected customers. The work order entity integrates distribution network maintenance, business expansion connection, and fault handling operation types, constructing a dynamic association relationship between work orders and equipment status, project progress, and customer needs. By introducing the W3C standard RDF and OWL description languages, a knowledge representation model conforming to the international semantic web standard is established, supporting the accurate expression and automatic reasoning of complex business semantics, and providing a unified semantic foundation and reasoning rule framework for cross-system data fusion.

[0019] Furthermore, a full-process digital workflow model for distribution network operations and business expansion projects is constructed, as detailed below:

[0020] Based on the domain knowledge graph built in the S3 phase, BPMN 2.0 is used to establish a full-process digital process model for distribution network operation and business expansion project, and a BPM platform architecture including four core components: process designer, execution engine, monitoring center and rule engine is constructed.

[0021] A distributed event-driven architecture based on the Apache Kafka message middleware is constructed to realize real-time perception and automatic response to business events of power distribution network operation and business expansion project, and a business event ontology model is defined; cross-system linkage is realized through enterprise service bus, and the relevant affected systems and business processes are automatically identified according to the business relationship in the knowledge graph, and data synchronization and process coordination between systems are realized through standardized message protocols to ensure end-to-end continuity of business processes.

[0022] Based on the semantic reasoning capabilities of knowledge graphs and machine learning algorithms, an intelligent decision support system for processes is constructed to achieve adaptive optimization and intelligent management of business processes. In the task allocation stage, based on personnel skill tags, workload status, and geographical location distribution information in the knowledge graph, an intelligent scheduling algorithm combining the Hungarian algorithm and genetic algorithm is adopted to achieve optimal allocation of job tasks.

[0023] Furthermore, the process designer employs a graphical modeling tool that supports drag-and-drop process node configuration. It can digitally abstract the entire business chain of business expansion projects, from customer application, site survey, solution design, project implementation to final acceptance, and establish standardized process templates that include user tasks, service tasks, gateway nodes, and event nodes. The execution engine is built on the Activiti workflow framework, supporting the dynamic creation of process instances, task allocation, status tracking, and exception handling. Through deep integration with the knowledge graph, it achieves intelligent association between process nodes and business entities. When a project is started, the system automatically matches the corresponding process template and initializes the process instance based on the project type, scope of impact, and resource requirements knowledge graph information, realizing a personalized process management mode of one process per event.

[0024] Furthermore, the spatiotemporal integrated data fusion framework is based on a four-dimensional data model of 3D space and 1D time, constructing a multi-dimensional data architecture that integrates geometric space, attribute space, time dimension, and semantic relationships. In the geometric space dimension, a three-dimensional GIS coordinate system is adopted to establish a spatial geometric object model containing points, lines, surfaces, and volumes, describing geographical elements such as the spatial location of distribution network equipment, the impact range of business expansion projects, and the execution area of ​​work orders. In the time dimension, a multi-granularity time model based on a time ontology is constructed, supporting three types of time semantic representation: moment, time period, and period, achieving full time scale coverage from second-level real-time status to annual statistical cycles. The four-dimensional data model adopts a spatiotemporal cube data organization method, representing business objects as a quintuple form of O(x,y,z,t,A), where (x,y,z) are three-dimensional spatial coordinates, t is a timestamp, and A is an attribute set. By establishing a spatiotemporal index structure, efficient storage and fast retrieval of massive spatiotemporal data are achieved. Combined with the knowledge graph constructed in the S3 phase... Based on spectral semantic information, a spatiotemporal semantic ontology model is established, defining the spatiotemporal semantics of device-space relationships and project-time relationships. A distributed database cluster supporting massive spatiotemporal data storage, indexing, and querying is constructed using a spatiotemporal database technology stack of PostgreSQL, PostGIS, and TimescaleDB. PostgreSQL, as the relational database foundation, provides ACID transaction guarantees and SQL standard support. The PostGIS extension provides complete OGC standard spatial data types and spatial function libraries, supporting complex spatial operations such as spatial buffer analysis, spatial join queries, and topological relationship determination. TimescaleDB, as a time-series database extension, utilizes time partitioning and data compression technologies. At the GIS engine level, GeoServer is integrated as a spatial data publishing service. MapProxy is deployed as a map caching proxy, establishing a spatiotemporal data ETL processing pipeline through tile caching and pre-rendering technologies. A visual data stream processing engine is built based on Apache NiFi to achieve real-time data synchronization from business systems to the spatiotemporal database.

[0025] A multi-source business data fusion system for distribution network operations and business expansion projects includes a data acquisition and perception layer, a data governance and fusion layer, a business collaboration layer, a security management layer, and a visualization layer. The data acquisition and perception layer constructs a unified data access and adaptation platform, achieving full-coverage access to data related to distribution network operations and business expansion projects through the deployment of intelligent data collectors and standardized interface adapters. The data governance and fusion layer, based on the accessed data, establishes an enterprise-level master data management system covering the entire domain of distribution network operations and business expansion projects, constructing unified data standards, coding specifications, and governance processes. The business collaboration layer constructs a knowledge graph in the field of distribution network operations and business expansion projects, achieving deep data fusion across systems and businesses through semantic modeling and intelligent association technologies. The security management layer, based on the knowledge graph in the field of distribution network operations and business expansion projects and based on business process management technology, constructs a full-process digital process model for distribution network operations and business expansion projects, achieving intelligent management of business processes. The visualization layer, based on the knowledge graph in the field of distribution network operations and business expansion projects, constructs a spatiotemporal integrated data fusion framework, deeply integrating business data with geospatial information and time-series information to form a four-dimensional data model, and constructs a Web-based... The dynamic spatiotemporal visualization platform based on GIS technology uses the Cesium.js 3D Earth engine and the D3.js time-series data visualization library to achieve immersive display of power distribution equipment, business expansion projects, and work orders in 3D geographic space and dynamic playback on the timeline.

[0026] The present invention has the following beneficial effects:

[0027] 1. This invention achieves full coverage and high-quality access to multi-source heterogeneous data from power distribution network operations and business expansion projects. Utilizing intelligent data collectors, standardized interface adapters, and a hierarchical collection mechanism, it can automatically adapt to and connect in real time to data from various business systems, equipment terminals, and external platforms, significantly improving the timeliness, consistency, and completeness of data acquisition. Based on an enterprise-level master data management system, the collected data is standardized, uniformly identified, and centrally managed, not only solving the historical problems of data silos and inconsistent standards, but also effectively improving data quality and management efficiency, providing core data support for enterprise digital transformation and data-driven management.

[0028] 2. This invention introduces domain knowledge graph technology, which breaks down data barriers between various business systems and different business processes, such as power distribution network operations and business expansion projects, through complex semantic modeling and intelligent entity association. It achieves deep data fusion and understanding across systems and businesses. Combined with business process management and event-driven mechanisms, it deeply integrates knowledge graphs with business processes to build a digital process model and intelligent management system for the entire process and all scenarios. It can automatically orchestrate, dynamically allocate, and optimize processes in real time based on business semantics, process status, and event triggering mechanisms. This greatly improves the intelligence, collaboration, and automation of business operations, reduces the complexity of manual operations and information flow, and improves overall business efficiency and customer experience.

[0029] 3. This invention achieves deep integration of business data with geospatial information and time series information, establishes a four-dimensional data model and dynamic spatiotemporal analysis system, and relies on a GIS engine and a high-performance spatiotemporal database to accurately locate the spatial distribution and evolution process of business objects such as power distribution equipment, business expansion projects, and work orders. Attached Figure Description

[0030] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0031] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0032] refer to Figure 1 In this embodiment, a method for fusing multi-source business data of distribution network operations and business expansion projects is provided, including the following steps;

[0033] S1: Construct a unified data access and adaptation platform, and achieve full coverage access to data related to power distribution network operations and business expansion projects by deploying intelligent data collectors and standardized interface adapters;

[0034] S2: Based on the accessed data, establish an enterprise-level master data management system covering the entire domain of distribution network operations and business expansion projects, and build a unified data standard, coding specification and governance process;

[0035] S3: Construct a knowledge graph in the fields of power distribution network operations and business expansion projects, and achieve deep data fusion across systems and businesses through semantic modeling and intelligent association technologies;

[0036] S4: Based on the knowledge graph of distribution network operation and business expansion project, and based on business process management technology, construct a full-process digital process model for distribution network operation and business expansion project to realize intelligent management of business processes;

[0037] S5: Based on the knowledge graph of distribution network operations and business expansion projects, a spatiotemporal integrated data fusion framework is constructed to deeply integrate business data with geospatial information and time series information to form a four-dimensional data model. A dynamic spatiotemporal visualization platform based on Web GIS technology is also constructed. The Cesium.js 3D Earth engine and D3.js time series data visualization library are used to realize the immersive display of distribution network equipment, business expansion projects, and work orders in three-dimensional geographic space and dynamic playback on the timeline.

[0038] In this embodiment, a unified data access and adaptation platform is constructed. By deploying intelligent data collectors and standardized interface adapters, full coverage access to data related to power distribution network operations and business expansion projects is achieved, as detailed below:

[0039] The unified data access and adaptation platform adopts a distributed microservice architecture, constructing a technical system comprising four major components: intelligent data collectors, standardized interface adapters, data routing gateways, and a protocol conversion engine. The intelligent data collector, as a front-end device at the data source end, supports multiple protocols and can adapt to mainstream communication protocols such as Modbus, IEC 61850, OPC UA, HTTP / HTTPS, and FTP, enabling seamless access to heterogeneous systems such as distribution network automation systems, business expansion management systems, GIS systems, marketing systems, and mobile operation terminals. The standardized interface adapter is responsible for converting source data of different formats into a unified data format for the platform, supporting standardized processing of various data types such as XML, JSON, CSV, and database tables, ensuring format consistency and semantic integrity during data transmission and storage. Regarding data acquisition strategies, a hierarchical acquisition mechanism is established. For data with high timeliness requirements, such as equipment status, work order progress, and fault information, real-time streaming acquisition based on message middleware is adopted to ensure millisecond-level response. For equipment ledgers, project archives, and personnel information data, a near real-time incremental synchronization method is used to ensure timely data updates. For historical statistics and report analysis data, a timed batch extraction mode is used to optimize system performance.

[0040] In this embodiment, based on the accessed data, an enterprise-level master data management system covering the entire domain of distribution network operations and business expansion projects is established, and unified data standards, coding specifications, and governance processes are constructed, as detailed below:

[0041] The enterprise-level master data management system adopts a centralized control and distributed application architecture, constructing a unified master data model covering core entity domains such as equipment assets, engineering projects, work orders, and customer files. Equipment asset master data focuses on the entire lifecycle management of equipment, establishing a complete information spectrum including basic equipment attributes, technical parameters, operating status, and maintenance history. It employs a four-segment coding system based on equipment type, voltage level, geographical region, and serial number to ensure the global uniqueness and business readability of equipment identification. Engineering project master data follows the State Grid engineering construction standards, establishing a complete project archive from project application, feasibility study approval, design review to final acceptance, using standardized coding rules based on year, region code, project type, and sequence number. Customer file master data integrates customer information from the marketing system and the business expansion system, establishing a unified customer view with one file per customer. This enables the integration of basic customer information, electricity usage records, and business expansion records, providing data support for precise service and differentiated management.

[0042] The master data management system establishes a three-tiered data governance model: source standardization, process cleansing, and result verification. It ensures high-quality master data by formulating unified data standards and business rules. At the source standardization level, it clarifies the field definitions, value ranges, format requirements, and business rules for various types of master data. Data quality inspection components are deployed in the data entry stages of each business system to enforce data standards. During the process cleansing stage, data cleansing tools based on rule engines and machine learning algorithms are deployed to automatically identify and handle data duplication, missing data, format errors, and logical conflicts. For abnormal data that cannot be automatically processed, the system generates a data quality report and pushes it to the relevant responsible persons for manual verification. In the result verification stage, a multi-dimensional data quality assessment system is established to quantitatively evaluate master data from four dimensions: completeness, accuracy, consistency, and timeliness. Data quality thresholds and monitoring and early warning mechanisms are set to ensure that master data quality remains consistently above the target level of 95%.

[0043] In this embodiment, the knowledge graph for power distribution network operations and business expansion projects adopts an ontology-driven modeling approach, constructing a multi-layered semantic network with equipment, projects, work orders, customers, and spaces as core entities, and business relationships, temporal relationships, and spatial relationships as the main associations. The knowledge graph employs intelligent entity recognition technology based on deep learning and rule fusion to achieve automatic discovery and accurate matching of identical entities across different business systems. A dynamic knowledge graph update mechanism based on streaming computing is constructed, receiving real-time data change events from various business systems via an Apache Kafka message queue and using the Apache Flink stream processing engine to implement incremental knowledge graph updates. When upstream master data changes, the system automatically triggers the recalculation of relevant entities and relationships, avoiding the performance overhead of full graph reconstruction through partial updates of the graph structure, achieving near real-time synchronous updates of the knowledge graph. The Neo4j graph database is deployed as a knowledge storage and query engine, utilizing its native graph storage capabilities and Cypher query language to support complex graph pattern matching and multi-hop association queries, with query response time controlled within seconds. Establish a knowledge graph API service layer to provide standardized service interfaces for entity query, relationship exploration, path analysis, and subgraph extraction, supporting business systems to access knowledge graph data through RESTful API or GraphQL interface.

[0044] In this embodiment, intelligent entity recognition technology based on deep learning and rule fusion is adopted to achieve automatic discovery and accurate matching of the same entities in different business systems. Specifically, in terms of equipment entity association, intelligent matching of newly built equipment in business expansion projects and distribution network GIS equipment files is achieved by constructing equipment feature vectors and similarity calculations. In terms of customer entity fusion, fuzzy matching algorithm based on edit distance and geocoding technology based on address resolution are adopted to associate customer information in the marketing system with application information in business expansion projects in multiple dimensions. A unified customer entity view is established through weighted fusion calculation of customer number, electricity address, and contact information. The system deploys a distributed graph computing engine based on Apache Spark, which supports large-scale graph data processing of tens of millions of entities and hundreds of millions of relationships. The GraphX ​​graph processing framework is used to realize complex graph algorithms for multi-hop path analysis, community discovery, and centrality calculation between entities, providing efficient graph computing support capabilities for business collaborative analysis.

[0045] In this embodiment, the knowledge graph in the field of distribution network operation and business expansion project is designed in the ontology model. The equipment entity class includes subclasses of transformer, switchgear, line, and metering device. Each entity has a complete semantic description with four dimensions: identification attribute, description attribute, status attribute, and relationship attribute. The project entity covers the types of new construction projects, renovation projects, and emergency repair projects, and establishes a multi-dimensional association model between projects and the equipment involved, the personnel involved, and the customers affected. The work order entity integrates the types of distribution network maintenance, business expansion connection, and fault handling operations, and constructs a dynamic association relationship between work orders and equipment status, project progress, and customer needs. By introducing the RDF and OWL description languages ​​of the W3C standard, a knowledge representation model that conforms to the international semantic web standard is established, which supports the accurate expression and automatic reasoning of complex business semantics, and provides a unified semantic foundation and reasoning rule framework for cross-system data fusion.

[0046] In this embodiment, a full-process digital workflow model for distribution network operations and business expansion projects is constructed, as detailed below:

[0047] Based on the domain knowledge graph built in the S3 phase, BPMN 2.0 (Business Process Model and Notation) is used to establish a full-process digital process model for power distribution network operations and business expansion projects, and a BPM platform architecture including four core components: process designer, execution engine, monitoring center, and rule engine is constructed.

[0048] A distributed event-driven architecture based on the Apache Kafka message middleware is constructed to realize real-time perception and automatic response to business events of power distribution network operations and business expansion projects. A business event ontology model is defined, which includes four core dimensions: event type, triggering conditions, scope of impact, and processing priority. A hierarchical event processing mechanism from simple events to complex events is established. At the simple event level, the system monitors status change messages from business systems, such as basic events like work order completion, equipment commissioning, and customer installation applications. At the complex event level, it uses CEP (Complex Event Processing) technology to perform event correlation analysis and pattern recognition. When the "business expansion project completion and acceptance passed" event is detected, the system automatically triggers a series of related business processes, such as establishing maintenance files, formulating inspection plans, and issuing meter reading tasks. Cross-system linkage is achieved through the Enterprise Service Bus. Based on the business relationships in the knowledge graph, it automatically identifies the affected related systems and business processes, and uses standardized message protocols to achieve data synchronization and process coordination between systems, ensuring end-to-end continuity of business processes. For example, when a new transformer is installed in a business expansion project, the system automatically pushes the equipment file information to the GIS system, synchronizes changes in customer electricity capacity with the marketing system, and updates the power grid topology with the distribution network dispatching system, achieving a collaborative working mode of "one-time entry, network-wide synchronization".

[0049] Based on the semantic reasoning capabilities of knowledge graphs and machine learning algorithms, an intelligent decision support system for processes is constructed to achieve adaptive optimization and intelligent management of business processes. In the task allocation stage, based on personnel skill tags, workload status, and geographical location distribution information in the knowledge graph, an intelligent scheduling algorithm combining the Hungarian algorithm and genetic algorithm is adopted to achieve optimal allocation of job tasks, improving task execution efficiency by more than 20%.

[0050] In this embodiment, the process designer uses a graphical modeling tool that supports drag-and-drop process node configuration. It can digitally abstract the entire business chain of a business expansion project, from customer application, site survey, scheme design, project implementation to final acceptance, and establish standardized process templates that include user tasks, service tasks, gateway nodes, and event nodes. The execution engine is built on the Activiti workflow framework and supports dynamic creation of process instances, task allocation, status tracking, and exception handling. Through deep integration with the knowledge graph, it realizes intelligent association between process nodes and business entities. When a project is started, the system automatically matches the corresponding process template and initializes the process instance based on the project type, scope of impact, and resource requirements knowledge graph information, realizing a personalized process management mode of one process per event.

[0051] In this embodiment, the spatiotemporal integrated data fusion framework is based on a four-dimensional data model of 3D space and 1D time. It constructs a multi-dimensional data architecture that integrates geometric space, attribute space, time dimension, and semantic relationships. In the geometric space dimension, a three-dimensional GIS coordinate system is used to establish a spatial geometric object model containing points, lines, surfaces, and volumes to describe geographical elements such as the spatial location of distribution network equipment, the impact range of business expansion projects, and the execution area of ​​work orders. In the time dimension, a multi-granularity time model based on time ontology is constructed, supporting three types of time semantic representation: moment, time period, and period, achieving full time scale coverage from second-level real-time status to annual statistical cycles. The four-dimensional data model adopts a spatiotemporal cube data organization method, representing business objects as a five-tuple form of O(x,y,z,t,A), where (x,y,z) are three-dimensional spatial coordinates, t is a timestamp, and A is an attribute set. By establishing a spatiotemporal index structure (such as a combined index of R-tree, Quad-tree, and B+-tree), efficient storage and fast retrieval of massive spatiotemporal data are achieved. Based on the semantic information of the knowledge graph constructed in phase S3, a spatiotemporal semantic ontology model is established, defining spatiotemporal semantics for device-space relationships (such as "located in", "near", "contains") and project-time relationships (such as "started at", "continued to", "occurred in"), providing a semantic reasoning foundation for complex spatiotemporal queries and analysis. A distributed database cluster supporting massive spatiotemporal data storage, indexing, and querying is constructed using a spatiotemporal database technology stack of PostgreSQL, PostGIS, and TimescaleDB. PostgreSQL, as the relational database foundation, provides ACID transaction guarantees and SQL standard support. The PostGIS extension provides complete OGC standard spatial data types and spatial function libraries, supporting complex spatial operations such as spatial buffer analysis, spatial join queries, and topological relationship determination. TimescaleDB, as a time-series database extension, utilizes time partitioning and data compression technologies. At the GIS engine level, GeoServer is integrated as a spatial data publishing service, supporting WMS (Web Service Management System). The system utilizes OGC standard protocols such as Map Service, Web Feature Service (WFS), and Web Coverage Service (WCS) to achieve standardized service output of spatiotemporal data. MapProxy is deployed as a map caching proxy, and a spatiotemporal data ETL processing pipeline is established through tile caching and pre-rendering technologies. A visual data stream processing engine is built based on Apache NiFi to achieve real-time data synchronization from business systems to the spatiotemporal database.

[0052] A multi-source business data fusion system for distribution network operations and business expansion projects includes a data acquisition and perception layer, a data governance and fusion layer, a business collaboration layer, a security management layer, and a visualization layer. The data acquisition and perception layer constructs a unified data access and adaptation platform, achieving full-coverage access to data related to distribution network operations and business expansion projects through the deployment of intelligent data collectors and standardized interface adapters. The data governance and fusion layer, based on the accessed data, establishes an enterprise-level master data management system covering the entire domain of distribution network operations and business expansion projects, constructing unified data standards, coding specifications, and governance processes. The business collaboration layer constructs a knowledge graph in the field of distribution network operations and business expansion projects, achieving deep data fusion across systems and businesses through semantic modeling and intelligent association technologies. The security management layer, based on the knowledge graph in the field of distribution network operations and business expansion projects and based on business process management technology, constructs a full-process digital process model for distribution network operations and business expansion projects, achieving intelligent management of business processes. The visualization layer, based on the knowledge graph in the field of distribution network operations and business expansion projects, constructs a spatiotemporal integrated data fusion framework, deeply integrating business data with geospatial information and time-series information to form a four-dimensional data model, and constructs a Web-based... This dynamic spatiotemporal visualization platform, based on GIS technology, utilizes the Cesium.js 3D Earth engine and the D3.js time-series data visualization library to achieve immersive display and dynamic playback of distribution network equipment, business expansion projects, and work orders in a 3D geographic space. Loosely coupled connections between layers are achieved through standardized interfaces and a service bus, ensuring both system flexibility and scalability while guaranteeing data consistency and business continuity. The overall architecture supports a combination of centralized cloud deployment and edge distributed computing, adapting to the management needs of distribution networks of different regions and scales.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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 fusing multi-source business data from power distribution network operations and business expansion projects, characterized in that, Includes the following steps; S1: Construct a unified data access and adaptation platform, and achieve full coverage access to data related to power distribution network operations and business expansion projects by deploying intelligent data collectors and standardized interface adapters; S2: Based on the accessed data, establish an enterprise-level master data management system covering the entire domain of distribution network operations and business expansion projects, and build a unified data standard, coding specification and governance process; S3: Construct a knowledge graph in the fields of power distribution network operations and business expansion projects, and achieve cross-system and cross-business data fusion through semantic modeling and intelligent association technology; S4: Based on the knowledge graph of distribution network operation and business expansion project, and based on business process management technology, construct a full-process digital process model for distribution network operation and business expansion project to realize intelligent management of business processes; S5: Based on the knowledge graph of power distribution network operation and business expansion project, construct a spatiotemporal integrated data fusion framework to deeply integrate business data with geospatial information and time series information to form a four-dimensional data model; The knowledge graph in the field of power distribution network operation and business expansion project adopts an ontology-driven modeling method to construct a multi-level semantic network with equipment, project, work order, customer, and space as core entities and business relationship, temporal relationship, and spatial relationship as the main associations; the knowledge graph adopts intelligent entity recognition technology based on deep learning and rule fusion. In terms of equipment entity association, by constructing equipment feature vectors and calculating similarity, the newly built equipment in the business expansion project is matched with the equipment files of the power distribution network GIS; in terms of customer entity fusion, a fuzzy matching algorithm based on edit distance and geocoding technology based on address parsing are used to associate the customer information of the marketing system with the application information of the business expansion project in multiple dimensions. Through weighted fusion calculation of customer number, electricity address and contact information, a unified customer entity view is established. Based on the semantic reasoning capabilities of knowledge graphs and machine learning algorithms, an intelligent decision support system for processes is constructed. In the task allocation stage, based on personnel skill tags, workload status, and geographical location distribution information in the knowledge graph, an intelligent scheduling algorithm combining the Hungarian algorithm and genetic algorithm is used to allocate job tasks.

2. The method for fusing multi-source business data of distribution network operation and business expansion project according to claim 1, characterized in that, The construction of a unified data access and adaptation platform, through the deployment of intelligent data collectors and standardized interface adapters, achieves full coverage access to data related to power distribution network operations and business expansion projects, as detailed below: The unified data access and adaptation platform adopts a distributed microservice architecture design and builds a technical system that includes four major components: intelligent data collector, standardized interface adapter, data routing gateway, and protocol conversion engine; the intelligent data collector serves as a front-end device at the data source end. The standardized interface adapter is responsible for converting source data of different formats into a unified data format for the platform. In terms of data acquisition strategy, a hierarchical acquisition mechanism is established. For data on equipment status, work order progress, and fault information, real-time streaming acquisition based on message middleware is adopted. For data on equipment ledgers, project archives, and personnel information, a real-time incremental synchronization method is adopted. For historical statistics and report analysis data, a timed batch extraction mode is adopted.

3. The method for fusing multi-source business data of distribution network operation and business expansion project according to claim 1, characterized in that, Based on the accessed data, an enterprise-level master data management system covering the entire domain of distribution network operations and business expansion projects is established. This system constructs unified data standards, coding specifications, and governance processes, specifically as follows: The enterprise-level master data management system adopts a centralized control and distributed application architecture, constructing a unified master data model covering core entity domains such as equipment assets, engineering projects, work orders, and customer files. Equipment asset master data focuses on the entire lifecycle management of equipment, establishing a complete information spectrum including basic equipment attributes, technical parameters, operating status, and maintenance history. A four-segment coding system based on equipment type, voltage level, geographical region, and serial number is adopted to ensure the global uniqueness of equipment identification and business readability. Engineering project master data uses standardized coding rules based on year, region code, project type, and sequence number. Customer file master data integrates customer information from the marketing system and the business expansion system, establishing a unified customer view with one file per customer. Establish a three-tiered data governance model encompassing source standardization, process cleansing, and result verification. Develop unified data standards and business rules. At the source standardization level, clarify the field definitions, value ranges, format requirements, and business rules for various master data types. Deploy data quality inspection components in the data entry stages of each business system to enforce data standards. During the process cleansing phase, deploy data cleansing tools based on rule engines and machine learning algorithms to automatically identify and handle data duplication, missing data, format errors, and logical conflicts. For abnormal data that cannot be automatically processed, the system will generate a data quality report and push it to the relevant responsible personnel for manual verification and processing.

4. The method for fusing multi-source business data of distribution network operation and business expansion project according to claim 1, characterized in that, A dynamic knowledge graph update mechanism based on stream computing is constructed. Real-time data change events from various business systems are received through the Apache Kafka message queue, and incremental knowledge graph updates are implemented using the Apache Flink stream processing engine. When the upstream master data changes, the recalculation of related entities and relationships is automatically triggered.

5. The method for fusing multi-source business data of distribution network operation and business expansion project according to claim 4, characterized in that, The system is deployed based on the Apache Spark distributed graph computing engine and uses the GraphX ​​graph processing framework to implement complex graph algorithms for multi-hop path analysis, community detection, and centrality calculation between entities.

6. The method for fusing multi-source business data of distribution network operation and business expansion project according to claim 4, characterized in that, In the ontology model design, the knowledge graph in the field of distribution network operation and business expansion project includes equipment entity classes such as transformers, switchgear, lines, and metering devices. Each entity has a semantic description with four dimensions: identification attribute, description attribute, status attribute, and relationship attribute. The project entity covers new construction projects, renovation projects, and emergency repair projects, and establishes a multi-dimensional association model between projects and the equipment involved, the personnel involved, and the customers affected. The work order entity integrates distribution network maintenance, business expansion connection, and fault handling operation types, and constructs a dynamic association relationship between work orders and equipment status, project progress, and customer needs. The knowledge representation model is established by introducing the W3C standard RDF and OWL description languages.

7. The method for fusing multi-source business data of distribution network operation and business expansion project according to claim 1, characterized in that, The detailed process model for constructing the entire digital workflow of power distribution network operations and business expansion projects is as follows: Based on the domain knowledge graph built in the S3 phase, BPMN 2.0 is used to establish a full-process digital workflow model for distribution network operations and business expansion projects, and a BPM platform architecture including a workflow designer, execution engine, monitoring center, and rules is constructed. A distributed event-driven architecture based on the Apache Kafka message middleware is constructed, and a business event ontology model is defined. Cross-system linkage is achieved through an enterprise service bus. Based on the business relationships in the knowledge graph, the affected related systems and business processes are automatically identified, and data synchronization and process coordination between systems are achieved through standardized message protocols.

8. The method for fusing multi-source business data of distribution network operation and business expansion project according to claim 7, characterized in that, The process designer uses a graphical modeling tool that supports drag-and-drop process node configuration. It can digitally abstract the entire business chain of business expansion projects, from customer application, site survey, scheme design, project implementation to final acceptance, and establish standardized process templates that include user tasks, service tasks, gateway nodes, and event nodes. The execution engine is built on the Activiti workflow framework and supports dynamic creation of process instances, task allocation, status tracking, and exception handling. Through integration with the knowledge graph, when a project starts, the system automatically and intelligently matches the corresponding process template and initializes the process instance based on the project type, scope of impact, and resource requirements.

9. The method for fusing multi-source business data of distribution network operation and business expansion project according to claim 7, characterized in that, The spatiotemporal integrated data fusion framework is based on a four-dimensional data model of 3D space and 1D time. It constructs a multi-dimensional data architecture that integrates geometric space, attribute space, time dimension, and semantic relationships. In the geometric space dimension, a three-dimensional GIS coordinate system is used to establish a spatial geometric object model containing points, lines, surfaces, and volumes, describing the spatial location of distribution network equipment, the impact range of business expansion projects, and the geographical elements of the execution area of ​​work orders. In the time dimension, a multi-granularity time model based on a time ontology is constructed, supporting three time semantic representations: moment, time period, and period. The four-dimensional data model adopts a spatiotemporal cube data organization method, integrating business object tables... It is represented as a quintuple of O(x,y,z,t,A), where (x,y,z) are three-dimensional spatial coordinates, t is a timestamp, and A is a set of attributes, establishing a spatiotemporal index structure; combined with the semantic information of the knowledge graph constructed in stage S3, a spatiotemporal semantic ontology model is established, defining the spatiotemporal semantics of device-space relationships and project-time relationships; using the spatiotemporal database technology stack of PostgreSQL, PostGIS, and TimescaleDB, a distributed database cluster supporting spatiotemporal data storage, indexing, and querying is constructed; at the GIS engine level, GeoServer is integrated as a spatial data publishing service; MapProxy is deployed as a map caching proxy. Through tile caching and pre-rendering technologies, a spatiotemporal data ETL processing pipeline is established, and a visualization data stream processing engine is built based on Apache NiFi.

10. A system for the multi-source business data fusion method of distribution network operation and business expansion project as described in any one of claims 1-9, characterized in that, The system comprises a data acquisition and perception layer, a data governance and fusion layer, a business collaboration layer, a security management layer, and a visualization layer. The data acquisition and perception layer constructs a unified data access and adaptation platform, achieving full coverage access to data related to power distribution network operations and business expansion projects through the deployment of intelligent data collectors and standardized interface adapters. The data governance and fusion layer, based on the accessed data, establishes an enterprise-level master data management system covering the entire domain of power distribution network operations and business expansion projects, constructing unified data standards, coding specifications, and governance processes. The business collaboration layer constructs a knowledge graph in the field of power distribution network operations and business expansion projects, achieving deep data fusion across systems and businesses through semantic modeling and intelligent association technologies. The security management layer, based on the knowledge graph in the field of power distribution network operations and business expansion projects and using business process management technology, constructs a full-process digital process model for power distribution network operations and business expansion projects, achieving intelligent management of business processes. The visualization layer constructs a spatiotemporal integrated data fusion framework based on the knowledge graph of power distribution network operations and business expansion projects. It deeply integrates business data with geospatial information and time series information to form a four-dimensional data model. It also builds a dynamic spatiotemporal visualization platform based on Web GIS technology, using the Cesium.js 3D Earth engine and the D3.js time series data visualization library to realize immersive display of power distribution network equipment, business expansion projects, and work orders in 3D geographic space and dynamic playback on the timeline.

Images