An intelligent analysis method and system for an environmental protection dispute case of a power grid

Abstract

This invention discloses an intelligent analysis method and system for power grid environmental disputes, comprising: acquiring multi-source power grid environmental dispute case data and preprocessing the data; using natural language processing technology to intelligently analyze the preprocessed data and extract key information elements; constructing a power grid environmental dispute knowledge graph based on the key information elements; and performing case analysis based on the knowledge graph to obtain analysis results. Through in-depth analysis of historical cases, this invention enables users to better understand the development patterns of power grid environmental disputes and formulate more effective prevention and response strategies; by using knowledge graph technology, implicit knowledge is made explicit, facilitating knowledge sharing and inheritance; and through in-depth analysis of power grid environmental dispute cases, environmental risk points in power grid construction and operation can be identified, providing a reference for power grid companies to improve environmental protection measures and optimize project design.

Description

Technical Field

[0001] This invention relates to the field of power grid environmental protection data processing technology, and more specifically, to an intelligent analysis method and system for power grid environmental disputes. Background Technology

[0002] With the continuous expansion of power grid construction and the increasing public awareness of environmental protection, environmental disputes related to power grids are becoming increasingly prominent. Environmental disputes arising from power grid construction are on the rise. The main types of power grid environmental disputes include electromagnetic radiation disputes, noise pollution disputes, ecological damage disputes, and land expropriation disputes. Typical power grid environmental disputes also include public interest litigation cases concerning "wind and solar power curtailment." These cases reflect the environmental responsibility issues faced by power grid companies in the consumption of new energy sources. In addition, "tree-line conflicts" are also a common type of power grid environmental dispute.

[0003] In recent years, while the power industry has made progress in environmental monitoring and data management, the focus has primarily been on carbon emission monitoring, without systematically analyzing and processing environmental disputes. Analysis of existing technologies reveals the following major shortcomings: 1) Insufficient case data processing capabilities: Most existing systems use traditional database storage methods and lack the ability to intelligently process unstructured case texts, making it impossible to automatically extract key information and legal elements from cases.

[0004] 2) Weak intelligent analysis capabilities: The existing system mainly remains at the stage of simple case storage and retrieval, lacking advanced functions such as case analysis, similar case matching, and trend prediction based on artificial intelligence technology.

[0005] 3) Inadequate representation of professional knowledge: The existing system fails to fully consider the professional characteristics of the power grid environmental protection field and lacks structured representation and in-depth mining of power grid environmental protection professional knowledge.

[0006] 4) Low system integration: Most existing systems are single-function modules, lacking integrated management of the entire process of case collection, processing, analysis, retrieval, and management.

[0007] 5) Insufficient real-time and dynamic capabilities: Existing systems are often static databases, lacking the ability to update and dynamically analyze the latest cases in real time.

[0008] Therefore, there is an urgent need for an intelligent analysis method specifically for power grid environmental disputes to achieve comprehensive collection, in-depth analysis, and efficient utilization of case data. Summary of the Invention

[0009] This invention proposes an intelligent analysis method and system for power grid environmental disputes, in order to solve the problem of how to efficiently conduct intelligent analysis of power grid environmental disputes.

[0010] To address the aforementioned problems, according to one aspect of the present invention, an intelligent analysis method for power grid environmental disputes is provided, the method comprising: Acquire multi-source data on power grid environmental disputes and preprocess the data. Natural language processing technology is used to intelligently analyze pre-processed data on power grid environmental disputes and extract key information elements. Based on the aforementioned key information elements, a knowledge graph of power grid environmental disputes is constructed; Case analysis was conducted based on the aforementioned knowledge graph of power grid environmental disputes, and the analysis results were obtained.

[0011] Preferably, the power grid environmental dispute case data includes at least one of the following: court judgments, environmental protection department penalty decisions, media reports, and internal corporate cases.

[0012] Preferably, the preprocessing of the power grid environmental dispute case data includes: The data on power grid environmental disputes were sequentially processed through format conversion, noise filtering, and quality checks.

[0013] Preferably, natural language processing technology is used to intelligently analyze the preprocessed power grid environmental dispute case data to extract key information elements, including: The pre-processed data on power grid environmental disputes is automatically segmented, entity identified, relation extracted, and semantically understood. Key information is extracted, including basic case information, party information, case description, legal basis, judgment results, and evidence.

[0014] Preferably, the construction of a knowledge graph of power grid environmental disputes based on the key information elements includes: Based on the aforementioned key information elements, various entities in power grid environmental disputes are identified and classified into corresponding conceptual levels; Extract relationships and attribute information between entities; The extracted knowledge will be integrated to construct a knowledge graph of power grid environmental disputes.

[0015] Preferably, the case analysis based on the power grid environmental dispute knowledge graph, and the resulting analysis, include: Case classification analysis includes: classifying, statistically analyzing, and categorizing cases based on their nature, type, region, and time dimension; Similar case retrieval includes: retrieving historical cases similar to the target case through semantic similarity calculation, providing a reference for case handling.

[0016] Conduct trend forecasting analysis, including using methods such as time series analysis and association rule mining to predict the development trend of power grid environmental disputes.

[0017] Identify hot issues, including: identifying current hot issues and development trends in power grid environmental disputes.

[0018] Preferably, the method further includes: Random forest or support vector machine algorithms are used for case classification analysis; The similarity between cases is calculated using cosine similarity or edit distance algorithms; Trend prediction can be performed using time series analysis or neural network algorithms. Association rule mining algorithms are used to mine association rules and patterns in cases in order to identify hot issues.

[0019] According to another aspect of the present invention, an intelligent analysis system for power grid environmental disputes is provided, the system comprising: The data acquisition module is used to acquire multi-source power grid environmental dispute case data and preprocess the power grid environmental dispute case data; The information extraction module is used to intelligently analyze pre-processed power grid environmental dispute case data using natural language processing technology to extract key information elements. A knowledge graph construction module is used to construct a knowledge graph of power grid environmental disputes based on the key information elements. The analysis application module is used to perform case analysis based on the power grid environmental dispute knowledge graph and obtain analysis results.

[0020] Based on another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the steps in an intelligent analysis system for power grid environmental dispute cases.

[0021] According to another aspect of the present invention, the present invention provides an electronic device, comprising: The aforementioned computer-readable storage medium; and One or more processors for executing a program in the computer-readable storage medium.

[0022] This invention provides an intelligent analysis method and system for power grid environmental disputes, comprising: acquiring multi-source power grid environmental dispute case data and preprocessing the data; using natural language processing technology to intelligently analyze the preprocessed data and extract key information elements; constructing a power grid environmental dispute knowledge graph based on the key information elements; and performing case analysis based on the knowledge graph to obtain analysis results. Through in-depth analysis of historical cases, this invention enables users to better understand the development patterns of power grid environmental disputes and formulate more effective prevention and response strategies; it centralizes power grid environmental dispute cases scattered across different departments and regions, forming a rich knowledge resource database; through knowledge graph technology, it makes tacit knowledge explicit, facilitating knowledge sharing and inheritance; and through in-depth analysis of power grid environmental dispute cases, it can identify environmental risk points in power grid construction and operation, providing a reference for power grid companies to improve environmental protection measures and optimize project design. Attached Figure Description

[0023] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures: Figure 1 A flowchart of an intelligent analysis method 100 for power grid environmental dispute cases according to an embodiment of the present invention; Figure 2 A flowchart illustrating the knowledge graph construction process according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a knowledge graph of power grid environmental disputes according to an embodiment of the present invention; Figure 4 This is a hierarchical architecture diagram of the intelligent collection and knowledge graph data processing system for power grid environmental disputes according to an embodiment of the present invention; Figure 5 This is a functional architecture diagram of the intelligent collection and knowledge graph data processing system for power grid environmental disputes according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of an intelligent analysis system 600 for power grid environmental dispute cases according to an embodiment of the present invention. Detailed Implementation

[0024] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0025] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0026] The technical principle of this invention is based on a "data acquisition-intelligent processing-knowledge construction-analysis and application" technical architecture. Through multimodal data fusion, natural language processing, knowledge graph construction, and machine learning analysis, it achieves intelligent processing and analysis of power grid environmental disputes. The core technical principles of the system include: 1) Multimodal data fusion technology: The system can process case materials in various formats such as text, images, audio, and video, and realize the fusion processing of different types of data through a unified data interface.

[0027] 2) Natural Language Processing Technology: Using deep learning-based natural language processing technology, the case text is automatically segmented, entity recognized, relation extracted, and semantically understood to extract key information such as the parties involved, points of contention, legal basis, and judgment results.

[0028] 3) Knowledge graph construction technology: Based on professional knowledge in the field of power grid environmental protection, a knowledge graph containing entities, relationships and attributes is constructed to realize the structured representation and association analysis of case knowledge.

[0029] 4) Machine learning analysis technology: Utilize machine learning technologies such as classification algorithms, clustering algorithms, and association rule mining to achieve functions such as intelligent case classification, similar case matching, and trend prediction.

[0030] 5) Visualization technology: Through charts, graphs and other visualization methods, the distribution characteristics, development trends and relationships of cases are displayed intuitively, providing decision support for users.

[0031] Figure 1 This is a flowchart of an intelligent analysis method 100 for power grid environmental dispute cases according to an embodiment of the present invention. Figure 1 As shown, the intelligent analysis method 100 for power grid environmental disputes provided by the embodiment of the present invention starts from step 101. In step 101, multi-source power grid environmental disputes data are acquired, and the power grid environmental disputes data are preprocessed.

[0032] Preferably, the power grid environmental dispute case data includes at least one of the following: court judgments, environmental protection department penalty decisions, media reports, and internal corporate cases.

[0033] Preferably, the preprocessing of the power grid environmental dispute case data includes: The data on power grid environmental disputes were sequentially processed through format conversion, noise filtering, and quality checks.

[0034] In this invention, the first step is to collect and preprocess case data. Specifically, data on power grid environmental disputes is collected through various channels, including court judgments, environmental protection department penalty decisions, media reports, and internal company cases. The collected data undergoes preprocessing steps such as format conversion, noise filtering, and quality checks to ensure data integrity and accuracy.

[0035] In step 102, natural language processing technology is used to perform intelligent analysis on the preprocessed power grid environmental dispute case data to extract key information elements.

[0036] Preferably, natural language processing technology is used to intelligently analyze the preprocessed power grid environmental dispute case data to extract key information elements, including: The pre-processed data on power grid environmental disputes is automatically segmented, entity identified, relation extracted, and semantically understood. Key information is extracted, including basic case information, party information, case description, legal basis, judgment results, and evidence.

[0037] In this invention, in the intelligent information extraction step, natural language processing technology is used to intelligently analyze the preprocessed case text and extract key information elements, including: 1) Basic case information: case number, case type, court of trial, trial date, etc.; 2) Party information: Information on the plaintiff, defendant, third party, etc.; 3) Case description: the cause, process, and points of contention in the dispute; 4) Legal basis: Applicable laws, regulations, judicial interpretations, etc.; 5) Judgment results: court judgments, mediation results, enforcement status, etc.; 6) Evidence information: documentary evidence, physical evidence, witness testimony, etc.

[0038] In step 103, a knowledge graph of power grid environmental disputes is constructed based on the key information elements.

[0039] Preferably, the construction of a knowledge graph of power grid environmental disputes based on the key information elements includes: Based on the aforementioned key information elements, various entities in power grid environmental disputes are identified and classified into corresponding conceptual levels; Extract relationships and attribute information between entities; The extracted knowledge will be integrated to construct a knowledge graph of power grid environmental disputes.

[0040] In this invention, a knowledge graph of power grid environmental disputes is constructed based on extracted case information. Combined with... Figure 2 As shown, the knowledge graph construction process includes: 1) Entity identification and classification: Identify various entities in the case, such as parties involved, power grid facilities, environmental elements, legal provisions, etc., and classify them into the corresponding conceptual levels.

[0041] 2) Relationship extraction: Extracting relationships between entities, such as "plaintiff sues defendant", "case involves power grid facilities", "applicable legal provisions", etc.

[0042] 3) Attribute extraction: Extract the attribute information of entities, such as the basic information of the parties involved, the technical parameters of power grid facilities, the degree of environmental damage, etc.

[0043] 4) Knowledge integration: Integrate knowledge from different cases, eliminate conflicts and redundancies, and form a unified knowledge graph.

[0044] like Figure 3 The image shown is a knowledge graph of a power grid environmental dispute case.

[0045] In step 104, case analysis is performed based on the power grid environmental dispute knowledge graph to obtain analysis results.

[0046] Preferably, the case analysis based on the power grid environmental dispute knowledge graph, and the resulting analysis, include: Case classification analysis includes: classifying, statistically analyzing, and categorizing cases based on their nature, type, region, and time dimension; Similar case retrieval includes: retrieving historical cases similar to the target case through semantic similarity calculation, providing a reference for case handling.

[0047] Conduct trend forecasting analysis, including using methods such as time series analysis and association rule mining to predict the development trend of power grid environmental disputes.

[0048] Identify hot issues, including: identifying current hot issues and development trends in power grid environmental disputes.

[0049] Preferably, the method further includes: Random forest or support vector machine algorithms are used for case classification analysis; The similarity between cases is calculated using cosine similarity or edit distance algorithms; Trend prediction can be performed using time series analysis or neural network algorithms. Association rule mining algorithms are used to mine association rules and patterns in cases in order to identify hot issues.

[0050] In this invention, during the case analysis and mining steps, based on the constructed knowledge graph, the system provides various analytical functions, including: 1) Case classification analysis: Cases are classified, statistically analyzed, and categorized according to their nature, type, region, time, and other dimensions.

[0051] 2) Similar case retrieval: By calculating semantic similarity, historical cases similar to the target case are retrieved to provide a reference for case handling.

[0052] 3) Trend prediction analysis: Through time series analysis, association rule mining and other methods, predict the development trend of power grid environmental disputes.

[0053] 4) Hot Issue Identification: Identify the hot issues and development trends of current power grid environmental disputes.

[0054] In this invention, the aforementioned functional modules can be integrated into a unified system platform, providing a web interface and API interfaces to support collaborative work among multiple users and roles. The system also offers functions such as data export, report generation, and visualization to meet the needs of different users.

[0055] In this invention, the system constructed based on the method of this invention is as follows: Figure 4 As shown: (1) System architecture design The system adopts a distributed microservice architecture, dividing it into multiple microservice modules such as data acquisition service, data processing service, knowledge graph service, analysis application service, and user interface service. These modules communicate with each other through RESTful APIs to achieve high availability and scalability.

[0056] (2) Data storage design The architecture employs a multi-database fusion approach, including: 1) Relational databases (such as MySQL): Store structured case metadata 2) NoSQL databases (such as MongoDB): Store unstructured case text and attachments. 3) Graph databases (such as Neo4j): Store knowledge graph data 4) Object storage (such as MinIO): Stores multimedia data such as images, audio, and video. (3) Intelligent processing technology 1) Natural Language Processing Module: Employs a BERT-based Chinese natural language processing model to intelligently parse power grid environmental protection case texts. The model has been fine-tuned using professional corpus data in the power grid environmental protection field, enabling it to accurately identify professional terms and concepts related to power grid environmental protection.

[0057] 2) Optical Character Recognition (OCR) Technology: Integrating an advanced OCR engine, it can automatically recognize scanned case documents and supports the recognition of multiple languages ​​and fonts.

[0058] 3) Information extraction technology: The information extraction method, which combines rule-based and machine learning, can accurately extract key information elements from the case.

[0059] (4) Knowledge graph construction technology 1) Ontology Design: Design ontology models for the field of power grid environmental protection, including power grid facility ontology, environmental element ontology, legal text ontology, case ontology, etc.

[0060] 2) Knowledge extraction: Automatically extract entity, relation and attribute information from case text and populate it into the knowledge graph.

[0061] 3) Knowledge Reasoning: Based on knowledge graphs, logical reasoning is used to discover implicit knowledge relationships and patterns.

[0062] 4) Knowledge update: Establish an automatic update mechanism for the knowledge graph to reflect the latest laws, regulations and case information in a timely manner.

[0063] (5) Machine learning algorithms 1) Case classification algorithm: Random forest, support vector machine and other algorithms are used to achieve automatic case classification.

[0064] 2) Similarity calculation algorithm: The similarity between cases is calculated using algorithms such as cosine similarity and edit distance.

[0065] 3) Trend prediction algorithm: Using time series analysis, neural network and other algorithms, the development trend of power grid environmental disputes is predicted.

[0066] 4) Association rule mining algorithm: Apriori algorithm and other algorithms are used to mine association rules and patterns in the case.

[0067] (6) Security and Access Control 1) Identity Authentication: The OAuth 2.0 protocol is used to achieve unified identity authentication and authorization management.

[0068] 2) Data encryption: Sensitive data is stored and transmitted in encrypted form to ensure data security.

[0069] 3) Access control: Set different access permissions according to user roles to achieve hierarchical protection of data.

[0070] 4) Audit Log: Records all system operation logs to facilitate security auditing and problem tracing.

[0071] Compared with the prior art, the main technical improvements of this invention include: (1) Higher level of intelligence Existing technologies mainly rely on manual input and simple keyword retrieval, while this invention utilizes artificial intelligence technologies such as natural language processing, knowledge graphs, and machine learning to achieve advanced functions such as automatic case analysis, intelligent classification, and similar case recommendation, greatly improving the system's intelligence level.

[0072] (2) Professional knowledge is more accurately represented This invention designs a professional knowledge representation model specifically for the field of power grid environmental protection, fully considering the relationships between professional concepts such as power grid facilities, environmental factors, and legal provisions, and constructs a complete knowledge system for power grid environmental protection.

[0073] (3) Stronger multimodal data processing capabilities This invention can not only process traditional text data, but also case materials in various formats such as images, audio, and video. Through multimodal data fusion technology, it provides richer case information.

[0074] (4) Higher system integration This invention adopts a microservice architecture, integrating data collection, processing, analysis, and application functions into a unified platform, thereby achieving automation and intelligence of business processes.

[0075] (5) Better real-time performance and dynamism This invention establishes a real-time data update mechanism that can collect and process the latest case information in a timely manner, and provide dynamic data analysis and visualization.

[0076] (6) More scalable This invention adopts a modular design, allowing each functional module to be developed and deployed independently, facilitating system functional expansion and upgrades. Simultaneously, the system provides open API interfaces, supporting integration with other systems.

[0077] This invention, through automated case information extraction and intelligent analysis, reduces traditional manual processing time from hours to minutes, significantly improving efficiency. Statistics show that systems using similar technologies can improve information processing efficiency by eight times. Through natural language processing and knowledge graph analysis, key information and legal elements in cases can be accurately identified, avoiding omissions and errors that may occur during manual processing. In the legal field, contract review efficiency has increased 20 times, and the omission rate of key clauses has decreased from 15% to 0.3%. The system's intelligent retrieval, similar case recommendation, and trend analysis functions provide scientific decision support for power grid companies, environmental protection departments, and judicial organs. In-depth analysis of historical cases allows users to better understand the development patterns of power grid environmental disputes and formulate more effective prevention and response strategies. The system centrally processes power grid environmental dispute cases scattered across different departments and regions, forming a rich knowledge resource database. Knowledge graph technology makes tacit knowledge explicit, facilitating knowledge sharing and inheritance. In-depth analysis of power grid environmental dispute cases can identify environmental risk points in power grid construction and operation, providing references for power grid companies to improve environmental protection measures and optimize project design. Simultaneously, the system can provide data support for formulating power grid environmental protection policies. By providing accurate case references and intelligent analysis tools, it can help relevant departments handle power grid environmental disputes more quickly and accurately, reducing the time and economic costs of dispute resolution. The application of the method of this invention helps to unify the standards for handling power grid environmental disputes, promote "consistent judgments in similar cases," and advance the rule of law in the field of power grid environmental protection.

[0078] In an embodiment of the invention, the technology is deployed and applied within the environmental management department of a provincial power company. This department is responsible for environmental protection management during the construction and operation of the provincial power grid, and handles a large number of power grid environmental disputes annually.

[0079] The specific configuration of the implementation environment is as follows: (1) Hardware environment: Servers: 4 high-performance servers (CPU: Intel Xeon Gold 6248, memory: 256GB, hard drive: 10TB SSD); Storage device: Distributed storage system (total capacity: 100TB); Network environment: Gigabit Ethernet, supporting load balancing and redundancy backup.

[0080] (2) Software environment: Operating system: CentOS 7.9; Databases: MySQL 8.0 (relational database), MongoDB 4.4 (unstructured database), Neo4j 4.4 (graph database); Middleware: Tomcat 9.0, Redis 6.0; Development frameworks: Spring Boot 2.5, Spring Cloud; AI frameworks: TensorFlow 2.5, PyTorch 1.9; Search engine: Elasticsearch 7.10.

[0081] The system deployment adopts containerization technology, using Docker and Kubernetes for container orchestration and management to ensure high availability and scalability.

[0082] (1) System deployment steps: 1) Basic environment setup: Install Docker and Kubernetes on the server and configure the container runtime environment.

[0083] 2) Database deployment: Deploy a MySQL cluster to store structured case metadata; Deploy a MongoDB cluster to store unstructured case text and attachments; Deploy a Neo4j cluster to store knowledge graph data; Deploy a Redis cluster for caching and message queuing; 3) Application service deployment: Package each microservice into a Docker image; Deploy application services using Kubernetes, and configure load balancing and automatic scaling. Deploy an Elasticsearch cluster to provide full-text search functionality; 4) AI model deployment: Deploy the trained natural language processing model as a standalone service; Configure version management and hot update mechanisms for the model; Configure the model's concurrent processing capabilities and resource limits; 5) System Configuration: Configure system parameters, including database connection, caching strategy, log level, etc. Set user roles and permissions, and configure identity authentication and authorization mechanisms; Configure data collection sources, including court websites, environmental protection department systems, media platforms, etc.

[0084] Combination Figure 5 As shown, the system implements the following core functional modules: (1) Intelligent case collection and processing The system collects the latest power grid environmental disputes from multiple data sources daily, including: the Supreme People's Court Judgments Online website, official websites of courts in various provinces and cities, official websites of ecological and environmental departments, mainstream media reports, and internal case databases of enterprises.

[0085] The collected cases undergo the following processing flow: Format conversion: Convert PDF, image, scanned document, and other formats into editable text; Content Analysis: Using OCR technology to recognize text in images and scanned documents; Information extraction: Automatically extract basic case information, party information, case description, legal basis, judgment results, etc. through natural language processing technology; Quality check: Automatically detects the completeness and accuracy of information and marks the parts that require manual review.

[0086] (2) Knowledge Graph Construction The system has constructed a knowledge graph of power grid environmental disputes that includes the following elements: 1) Entity type: Parties involved: power grid companies, environmental organizations, individual users, government departments, etc. Power grid facilities: substations, transmission lines, power distribution equipment, new energy facilities, etc.; Environmental factors: electromagnetic radiation, noise, wastewater, exhaust gas, solid waste, etc. Legal provisions: laws and regulations, judicial interpretations, departmental rules, etc.; Case studies: specific dispute cases; 2) Relationship type: Relationship between parties: plaintiff suing defendant, enterprise constructing facilities, department supervising enterprise, etc. Causal relationship: facilities cause pollution, pollution causes damage, damage leads to disputes, etc. Applicable relationships: applicable law, legal basis provisions, etc. Spatiotemporal relationships: The case occurred in a certain place, the case occurred at a certain time, etc.; The system provides a visual management interface for the knowledge graph, allowing users to view the relationship network between entities and perform knowledge queries and reasoning.

[0087] (3) Intelligent retrieval and analysis The system offers multiple intelligent search methods: Keyword search: Supports fuzzy search, exact search, and combined search; Semantic retrieval: Understanding the user's query intent and returning the most relevant cases; Case similarity retrieval: Recommends similar cases based on the similarity of case content; Knowledge graph query: Query related entities and relationships through graph structure; The system provides the following analysis functions: Case classification statistics: statistics are compiled according to dimensions such as case type, region, time, and handling result; Trend Analysis: Analyzing the development trends and hot issues in power grid environmental disputes; Association analysis: Discovering the relationships and patterns between cases; Predictive analysis: Based on historical data, predict the types and trends of disputes that may occur in the future.

[0088] (4) Decision support and application The system provides customized decision support functions for different user roles: 1) For power grid companies: Provide experience and strategy suggestions for handling similar cases; analyze environmental risk points of power grid facilities; predict potential environmental disputes; and provide environmental compliance inspection tools; 2) For environmental regulatory departments: Analyze environmental violation trends in the power grid industry; assess the effectiveness of environmental policies; identify weaknesses in environmental regulation; and provide case studies for environmental enforcement. 3) For judicial users: Provide judgments for similar cases as reference; analyze the consistency of legal application; identify the development trend of adjudication rules; and assist in the formulation of relevant judicial interpretations.

[0089] After actual operation, the system has achieved the following significant results: (1) Case processing efficiency has been greatly improved The system automatically processes approximately 10-30 new cases daily, reducing processing time from an average of 2 hours per case to 5 minutes per case, a 24-fold increase in efficiency. Manual review workload has been reduced by 80%, allowing reviewers to focus more on handling complex cases.

[0090] (2) The quality of case analysis has been significantly improved. Through intelligent information extraction and knowledge graph analysis, the completeness of case information has increased from 70% to over 95%, and the accuracy has increased from 80% to over 98%. The system can automatically identify missing key information in cases and prompt for manual supplementation.

[0091] (3) Knowledge sharing effect is obvious The system has collected and processed over 800 cases of power grid environmental disputes, forming a rich case knowledge base. Through knowledge graph technology, the scattered case knowledge is organically integrated, achieving efficient knowledge sharing and utilization.

[0092] (4) Enhanced decision support capabilities The system's intelligent analysis capabilities provide strong support for decision-making by relevant departments. For example, analysis of historical cases revealed high-incidence areas and seasonal patterns of "electromagnetic radiation" disputes, providing a basis for power grid companies to formulate preventative measures. Trend analysis predicted new types of environmental disputes that may arise from the grid connection of new energy sources, providing a reference for policy formulation.

[0093] Figure 6 This is a schematic diagram of the intelligent analysis system 600 for power grid environmental dispute cases according to an embodiment of the present invention. Figure 6 As shown, the intelligent analysis system 600 for power grid environmental disputes provided in this embodiment of the invention includes: a data acquisition module 601, an information extraction module 602, a knowledge graph construction module 603, and an analysis and application module 604.

[0094] Preferably, the data acquisition module 601 is used to acquire multi-source power grid environmental dispute case data and preprocess the power grid environmental dispute case data.

[0095] Preferably, the power grid environmental dispute case data includes at least one of the following: court judgments, environmental protection department penalty decisions, media reports, and internal corporate cases.

[0096] Preferably, the data acquisition module 601 preprocesses the power grid environmental dispute case data, including: The data on power grid environmental disputes were sequentially processed through format conversion, noise filtering, and quality checks.

[0097] Preferably, the information extraction module 602 is used to perform intelligent analysis on the preprocessed power grid environmental dispute case data using natural language processing technology to extract key information elements.

[0098] Preferably, the information extraction module 602 uses natural language processing technology to intelligently analyze the preprocessed power grid environmental dispute case data and extract key information elements, including: The pre-processed data of power grid environmental disputes are automatically segmented, entity recognized, relation extracted, and semantically understood. The basic information of the case, the parties involved, the case description, the legal basis, the judgment result, and the evidence information are extracted as key information.

[0099] Preferably, the knowledge graph construction module 603 is used to construct a knowledge graph of power grid environmental disputes based on the key information elements.

[0100] Preferably, the knowledge graph construction module 603 constructs a knowledge graph of power grid environmental disputes based on the key information elements, including: Based on the aforementioned key information elements, various entities in power grid environmental disputes are identified and classified into corresponding conceptual levels; Extract relationships and attribute information between entities; The extracted knowledge will be integrated to construct a knowledge graph of power grid environmental disputes.

[0101] Preferably, the analysis application module 604 is used to perform case analysis based on the power grid environmental dispute knowledge graph and obtain analysis results.

[0102] Preferably, the analysis application module 604 performs case analysis based on the power grid environmental dispute knowledge graph to obtain analysis results, including: Case classification analysis includes: classifying, statistically analyzing, and categorizing cases based on their nature, type, region, and time dimension; Similar case retrieval includes: retrieving historical cases similar to the target case through semantic similarity calculation, providing a reference for case handling.

[0103] Conduct trend forecasting analysis, including using methods such as time series analysis and association rule mining to predict the development trend of power grid environmental disputes.

[0104] Identify hot issues, including: identifying current hot issues and development trends in power grid environmental disputes.

[0105] Preferably, the analysis application module 604 further includes: Random forest or support vector machine algorithms are used for case classification analysis; The similarity between cases is calculated using cosine similarity or edit distance algorithms; Trend prediction can be performed using time series analysis or neural network algorithms. Association rule mining algorithms are used to mine association rules and patterns in cases in order to identify hot issues.

[0106] The intelligent analysis system 600 for power grid environmental disputes in this embodiment of the invention corresponds to the intelligent analysis method 100 for power grid environmental disputes in another embodiment of the invention, and will not be described again here.

[0107] Based on another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the steps in an intelligent analysis method for power grid environmental dispute cases.

[0108] According to another aspect of the present invention, the present invention provides an electronic device, comprising: The aforementioned computer-readable storage medium; and One or more processors for executing a program in the computer-readable storage medium.

[0109] The present invention has been described with reference to a few embodiments. However, it will be apparent to those skilled in the art that other embodiments besides those disclosed above fall equivalently within the scope of the present invention.

[0110] Generally, all terms used in this invention are interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to “a / the / the [device, component, etc.]” ​​are openly interpreted as at least one instance of said device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed, unless explicitly stated otherwise.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for intelligent analysis of environmental protection disputes of power grid, characterized in that, The method includes: Acquire multi-source data on power grid environmental disputes and preprocess the data. Natural language processing technology is used to intelligently analyze pre-processed data on power grid environmental disputes and extract key information elements. Based on the aforementioned key information elements, a knowledge graph of power grid environmental disputes is constructed; Case analysis was conducted based on the aforementioned knowledge graph of power grid environmental disputes, and the analysis results were obtained.

2. The method of claim 1, wherein, The data on power grid environmental disputes includes at least one of the following: court judgments, environmental protection department penalty decisions, media reports, and internal corporate cases.

3. The method of claim 1, wherein, The data on power grid environmental disputes is preprocessed, including: The data on power grid environmental disputes were sequentially processed through format conversion, noise filtering, and quality checks.

4. The method of claim 1, wherein, Natural language processing technology is used to intelligently analyze pre-processed power grid environmental dispute case data to extract key information elements, including: The pre-processed data on power grid environmental disputes is automatically segmented, entity identified, relation extracted, and semantically understood. Key information is extracted, including basic case information, party information, case description, legal basis, judgment results, and evidence.

5. The method of claim 1, wherein, The construction of a knowledge graph of power grid environmental disputes based on the aforementioned key information elements includes: Based on the aforementioned key information elements, various entities in power grid environmental disputes are identified and classified into corresponding conceptual levels; Extract relationships and attribute information between entities; The extracted knowledge will be integrated to construct a knowledge graph of power grid environmental disputes.

6. The method according to claim 1, characterized in that, Case analysis was conducted based on the aforementioned knowledge graph of power grid environmental disputes, and the analysis results were obtained, including: Case classification analysis includes: classifying, statistically analyzing, and categorizing cases based on their nature, type, region, and time dimension; Similar case retrieval includes: retrieving historical cases similar to the target case through semantic similarity calculation, providing a reference for case handling. Conduct trend forecasting analysis, including using methods such as time series analysis and association rule mining to predict the development trend of power grid environmental disputes. Identify hot issues, including: identifying current hot issues and development trends in power grid environmental disputes.

7. The method according to claim 6, characterized in that, The method further includes: Random forest or support vector machine algorithms are used for case classification analysis; The similarity between cases is calculated using cosine similarity or edit distance algorithms; Trend prediction can be performed using time series analysis or neural network algorithms. Association rule mining algorithms are used to mine association rules and patterns in cases in order to identify hot issues.

8. An intelligent analysis system for power grid environmental disputes, characterized in that, The system includes: The data acquisition module is used to acquire multi-source power grid environmental dispute case data and preprocess the power grid environmental dispute case data; The information extraction module is used to intelligently analyze pre-processed power grid environmental dispute case data using natural language processing technology to extract key information elements. A knowledge graph construction module is used to construct a knowledge graph of power grid environmental disputes based on the key information elements. The analysis application module is used to perform case analysis based on the power grid environmental dispute knowledge graph and obtain analysis results.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-7.

10. An electronic device, characterized in that, include: The computer-readable storage medium as described in claim 9; as well as One or more processors for executing a program in the computer-readable storage medium.

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