An intelligent environmental damage questionnaire generation and semantic analysis method and system

Abstract

The application provides a kind of intelligent environmental damage questionnaire generation and semantic analysis method and system, comprising: obtaining structured environmental data through government department interface, combining AI search engine to capture network public data;Data is cleaned and normalized, and a standardized environmental database is constructed;Based on NLP technology, a semantic model is constructed, customized survey questions are dynamically generated according to the type of interviewee and event scene classifier, and question priority is optimized;Key entities are extracted from interview text using a pre-trained model;Through the environmental damage ontology library, the entity is mapped to the preset pollution category, and a dynamic causal correlation graph is generated;The integrity and consistency of the graph are verified using logical verification rules, and a structured survey report is automatically output.The application solves the defects of traditional questionnaire design rigidity, information fragmentation and insufficient implicit association mining, significantly improves the accuracy and efficiency of environmental damage investigation, and provides intelligent support for pollution tracing and responsibility definition.

Description

Technical Field

[0001] This invention relates to the field of environmental damage investigation technology, and in particular to a method and system for generating and semantically analyzing intelligent environmental damage survey forms. Background Technology

[0002] When conducting environmental damage work, it is necessary to conduct interviews with various individuals, including government officials and residents at the incident site. The interviewees also vary, and questionnaires are tailored based on the collected data to corroborate or supplement the information gathered.

[0003] In environmental damage investigations, traditional methods rely on manually designed questionnaires and the collection and summarization of information, which has the following problems:

[0004] For different interviewees (such as residents and government departments), questions need to be manually adjusted, which is inefficient and prone to missing key information. Data obtained through interviews and data collection is scattered and unstructured, making manual processing time-consuming and prone to errors. Current technologies struggle to extract implicit environmental damage correlations from text (such as the potential link between "sour smell" and hazardous waste), leading to biased analysis results. Multi-source data (such as geographic information and historical monitoring data) requires manual matching and cannot be updated to the database in real time.

[0005] While some existing research employs NLP techniques for text analysis, a solution combining dynamic questionnaire generation with semantic reasoning for environmental damage scenarios has yet to be achieved. Therefore, there is an urgent need for an intelligent system capable of automatically generating customized questionnaires and efficiently extracting key information through semantic analysis to address these shortcomings. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide an intelligent method and system for generating and semantically analyzing environmental damage survey forms, which can significantly improve the comprehensiveness, accuracy, and response efficiency of environmental damage surveys, and provide intelligent support for rapid source tracing and liability determination of pollution incidents.

[0007] To achieve the above objectives, the present invention provides the following solution:

[0008] A method for generating and semantically analyzing an intelligent environmental damage survey form includes:

[0009] Structured environmental data is obtained through government department interfaces, and publicly available online data is crawled by inputting the event's geographical location and keywords into an artificial intelligence search engine.

[0010] The environmental data and the publicly available network data are cleaned and normalized to obtain a standardized environmental database.

[0011] A semantic model is built based on NLP technology, and customized survey questions are dynamically generated according to the type of interviewee and the standardized environment database. The question priority is adjusted by combining an event scenario classifier.

[0012] The semantic model is used to extract key entities from the target interview text;

[0013] The key entities are mapped to preset environmental damage categories, and an association map is generated;

[0014] The integrity and consistency of the association graph are verified by logical verification rules, and a structured investigation report is generated using the verified association graph.

[0015] Preferably, the environmental data includes: land parcel vector files, land use planning, soil type, historical monitoring data, and water quality target information; the publicly available online data includes regional historical event records, environmental inspection reports, and land use information.

[0016] Preferably, the environmental data and the publicly available network data are cleaned and normalized to obtain a standardized environmental database, including:

[0017] Topological verification is performed on the environmental data to correct overlapping and missing geographical information.

[0018] The unstructured text in the publicly available online data is extracted using regular expressions to extract key fields, which are then converted into a uniform format. The key fields include the inspection time and the type of violation.

[0019] Based on geographic coordinate system transformation rules, all geographic location data in the environmental data and the publicly available network data are normalized to obtain the standardized environmental database.

[0020] Preferably, a semantic model is constructed based on NLP technology, and customized survey questions are dynamically generated according to the interviewee type and the standardized environment database. The question priority is adjusted using an event scenario classifier, including:

[0021] Load general domain parameters based on pre-trained semantic models;

[0022] The pre-trained semantic model is fine-tuned using the standardized environment database to enhance its semantic understanding of technical terms.

[0023] Preset tags are extracted from the metadata table of the standardized environment database based on the interviewee type;

[0024] The preset tags are matched against a predefined question template library; the template content of the question template library comes from high-frequency valid questions and regulatory requirements in the historical case library.

[0025] The target event's geographical location and the standardized environment database are input into the event scene classifier, and the event scene labels are output.

[0026] Adjust the issue priority based on the event scenario tags;

[0027] The question template library is sorted and reorganized based on the context relevance score output by the pre-trained semantic model.

[0028] Preferably, the method for constructing the event scene classifier includes:

[0029] A random forest classification model is trained based on event labels from a historical case database; the input features of the random forest classification model include event keywords, geographically associated soil types, and water quality target information.

[0030] The weights of the random forest classification model are dynamically updated based on the latest detection data in the standardized environmental database to prioritize matching pollution scenarios with probability values ​​greater than a preset threshold.

[0031] Preferably, the semantic model is used to extract key entities from the target interview text, including:

[0032] The target interview text is input into the semantic model, and the key entities are extracted through the named entity recognition module.

[0033] Based on dependency parsing techniques, the relationships between the key entities are initially labeled, and candidate relation triples are obtained.

[0034] Preferably, mapping the key entities to preset environmental damage categories and generating an association map includes:

[0035] The key entities and the candidate relation triples are input into a graph database, and a dynamic graph is constructed according to a predefined relation pattern; the predefined relation pattern is: pollution source → transmission path → damage receptor;

[0036] Based on preset historical pollution event data, the cross-event correlation of the dynamic map is supplemented to obtain the final correlation map.

[0037] A smart environmental damage survey form generation and semantic analysis system, comprising:

[0038] The data acquisition unit is used to acquire structured environmental data through government department interfaces and, based on an artificial intelligence search engine, to crawl publicly available online data by inputting the geographical location of the event and keywords.

[0039] The data processing unit is used to perform data cleaning and data normalization on the environmental data and the publicly available network data to obtain a standardized environmental database.

[0040] The question building unit is used to build a semantic model based on NLP technology, dynamically generate customized survey questions according to the interviewee type and the standardized environment database, and adjust the question priority in combination with the event scenario classifier;

[0041] An entity extraction unit is used to extract key entities from the target interview text using the semantic model.

[0042] The map generation unit is used to map the key entities to preset environmental damage categories and generate an association map;

[0043] The report generation unit is used to verify the integrity and consistency of the association graph through logical verification rules, and to generate a structured investigation report using the verified association graph.

[0044] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0045] This invention provides a method and system for generating and semantically analyzing intelligent environmental damage survey forms. The method includes: acquiring structured environmental data through government department interfaces, and using an artificial intelligence search engine to crawl publicly available online data by inputting the event's geographical location and keywords; cleaning and normalizing the environmental data and the publicly available online data to obtain a standardized environmental database; constructing a semantic model based on NLP technology, dynamically generating customized survey questions according to the interviewee type and the standardized environmental database, and adjusting question priorities using an event scenario classifier; extracting key entities from the target interview text using the semantic model; mapping the key entities to preset environmental damage categories and generating a correlation graph; verifying the completeness and consistency of the correlation graph using logical validation rules, and generating a structured survey report using the verified correlation graph. This invention significantly improves the comprehensiveness, accuracy, and response efficiency of environmental damage investigations, providing intelligent support for rapid source tracing and liability determination in pollution incidents. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A flowchart of the method provided in an embodiment of the present invention;

[0048] Figure 2 Provided for embodiments of the present invention;

[0049] Figure 3 This is a schematic diagram of the system structure provided in an embodiment of the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] The purpose of this invention is to provide an intelligent method and system for generating and semantically analyzing environmental damage survey forms, which can significantly improve the comprehensiveness, accuracy and response efficiency of environmental damage surveys, and provide intelligent support for rapid source tracing and liability determination of pollution incidents.

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] Figure 1 The method flowchart provided in the embodiments of the present invention is as follows: Figure 1 As shown, this invention provides a method for generating and semantically analyzing an intelligent environmental damage survey form, including:

[0054] Step 100: Obtain structured environmental data through government department interfaces, and use an artificial intelligence search engine to crawl publicly available online data by inputting the event's geographical location and keywords.

[0055] Step 200: Perform data cleaning and normalization on environmental data and publicly available online data to obtain a standardized environmental database;

[0056] Step 300: Build a semantic model based on NLP technology, dynamically generate customized survey questions according to the type of interviewee and standardized environment database, and adjust the question priority by combining an event scenario classifier;

[0057] Step 400: Extract key entities from the target interview text using a semantic model;

[0058] Step 500: Map key entities to preset environmental damage categories and generate an association map;

[0059] Step 600: Verify the integrity and consistency of the association graph through logical verification rules, and generate a structured investigation report using the verified association graph.

[0060] Preferably, the environmental data includes: land parcel vector files, land use planning, soil type, historical monitoring data, and water quality target information; the publicly available online data includes regional historical event records, environmental inspection reports, and land use information.

[0061] Specifically, the steps of step 100 in this embodiment are as follows:

[0062] Step 101: Establish connections with government databases such as those for environmental protection and natural resources through a predefined API interface protocol to obtain structured environmental data. Specifically, according to the interface specification, input the authorization key and query parameters (such as administrative division code and time range), and the system automatically requests land parcel vector files (containing geographic boundary coordinates), land use planning documents (such as industrial zone and protected area delineation), soil type distribution maps (classified by texture), historical monitoring data (including indicators such as heavy metal content and pH value), and water quality target information (such as surface water quality standards). After the data is returned in JSON or XML format, the parsing module converts it into a unified table format to ensure compatibility in subsequent processing.

[0063] Step 102: Based on preset event geographic locations (such as latitude and longitude coordinates or place names) and keywords (such as "pollution incident" or "illegal discharge"), configure the crawler program of the artificial intelligence search engine. Utilize a semantic understanding model to identify relevant web pages (such as government information platforms and news websites), and crawl historical event records (including time and involved companies), environmental inspection reports (PDF or web page text), and land use information (such as records of farmland converted to industrial land). By using dynamic proxy IPs and request frequency control to circumvent anti-crawling mechanisms, the acquired data is initially deduplicated and stored in a temporary database for subsequent cleaning.

[0064] Step 103: Integrate the structured data obtained from the government interface with the semi-structured / unstructured data crawled from the web. For example, spatially match the geographic coordinates in the land parcel vector file with the geographic locations of events in the web data to create a unified index. Extract metadata (such as release date and regulatory agency) from text-based data (such as inspection reports) and convert it to CSV format. Simultaneously, perform preliminary data integrity checks (such as checking for missing fields) and mark abnormal records to provide a foundation for subsequent data cleaning and normalization.

[0065] Preferably, the environmental data and the publicly available network data are cleaned and normalized to obtain a standardized environmental database, including:

[0066] Topological verification is performed on the environmental data to correct overlapping and missing geographical information.

[0067] The unstructured text in the publicly available online data is extracted using regular expressions to extract key fields, which are then converted into a uniform format. The key fields include the inspection time and the type of violation.

[0068] Based on geographic coordinate system transformation rules, all geographic location data in the environmental data and the publicly available network data are normalized to obtain the standardized environmental database.

[0069] Specifically, step 200 in this embodiment includes:

[0070] Step 201: For environmental data (such as land parcel vector files) obtained from government department interfaces, perform topological verification using Geographic Information System (GIS) tools. First, check the closure of polygon boundaries, automatically identifying and repairing abnormal areas with gaps or self-intersections. Second, detect boundary overlaps between different layers (such as intersections between industrial and protected areas) through spatial overlay analysis, and trim or merge boundaries according to preset priority rules (such as prioritizing protected areas). For data entries lacking geographic information, interpolate and complete the data by calling historical version repositories or data from adjacent areas, ensuring the logical consistency and topological correctness of all geospatial data. Finally, output a geographic information dataset conforming to OGC standards.

[0071] Step 202: For unstructured text (such as environmental inspection reports and news event descriptions) in publicly available online data, design multiple sets of regular expression templates to extract key fields. For example, for "inspection time," define a regular expression matching the pattern "\d{4}-\d{2}-\d{2}" to capture standard date formats from the text; for "violation type," construct a keyword dictionary (such as "construction without approval" and "excessive emissions"), and combine it with a context window to identify the category of violation. For PDF documents, first convert them to text using OCR technology, and then apply the above rules to extract fields. The extracted data is converted into a unified format according to preset templates, for example, storing "inspection time" as an ISO 8601 standard timestamp, encoding "violation type" as a category label, and finally integrating them into a structured data table.

[0072] Step 203: Coordinate system transformation and normalization of geographic location data

[0073] To address the differences in geographic coordinate systems (such as WGS84, CGCS2000, and GCJ-02) across multi-source data, a unified conversion rule was established. First, the coordinate system type of the original data was identified: for vector files, the projection information in their metadata was parsed; for coordinate descriptions in text (such as "30°12'34" North Latitude), they were converted to decimal format using a parser. Then, open-source libraries (such as Proj4) or API services were called to convert all coordinates to the target coordinate system (such as WGS84). For data lacking coordinates but containing place name descriptions (such as "XX Factory"), geocoding services (such as Google Geocoding API) were used to parse them into latitude and longitude. The converted coordinates were uniformly stored in "longitude, latitude" format, with precision retained to six decimal places, ensuring the consistency and computability of spatial data, thus completing the construction of a standardized environmental database.

[0074] Preferably, a semantic model is constructed based on NLP technology, and customized survey questions are dynamically generated according to the interviewee type and the standardized environment database. The question priority is adjusted using an event scenario classifier, including:

[0075] Load general domain parameters based on pre-trained semantic models;

[0076] The pre-trained semantic model is fine-tuned using the standardized environment database to enhance its semantic understanding of technical terms.

[0077] Preset tags are extracted from the metadata table of the standardized environment database based on the interviewee type;

[0078] The preset tags are matched against a predefined question template library; the template content of the question template library comes from high-frequency valid questions and regulatory requirements in the historical case library.

[0079] The target event's geographical location and the standardized environment database are input into the event scene classifier, and the event scene labels are output.

[0080] Adjust the issue priority based on the event scenario tags;

[0081] The question template library is sorted and reorganized based on the context relevance score output by the pre-trained semantic model.

[0082] Specifically, step 300 in this embodiment includes:

[0083] Step 301: Select a general pre-trained semantic model (such as BERT or RoBERTa) and load its basic parameters using a deep learning framework (such as the Hugging Face Transformers library). This model has been pre-trained on a large-scale general corpus (such as Wikipedia and news text) and possesses basic semantic understanding capabilities. During initialization, retain the core architecture of the model (such as the Transformer layer) while freezing some low-level parameters, exposing only the top-level network for subsequent domain adaptation, ensuring that the model is adjustable while preserving general language features.

[0084] Step 302: Fine-tune the pre-trained model using specialized corpora (such as historical monitoring reports and regulatory texts) from a standardized environmental database. Specifically, environmental terminology from the database (such as "benzo[a]pyrene exceeding standards" and "soil leaching") is used to construct a fine-tuning dataset, which is then trained using a masked language modeling (MLM) task. By adjusting the learning rate (e.g., setting it to 1 / 10 of the pre-training rate) and the number of iterations (e.g., 3 epochs), the model gradually learns the semantic features of the environmental domain. After fine-tuning, the model's ability to understand the context of specialized terminology is significantly enhanced; for example, it can accurately distinguish the specific meaning of "emissions" in different pollution scenarios.

[0085] Step 303: Based on the interviewee type (e.g., resident, business owner, environmental protection department employee), extract pre-defined tags from the metadata table of the standardized environmental database. For example, for "residents," extract tags such as "residential area" and "health impact"; for "government departments," extract tags such as "regulatory records" and "legal basis." The system automatically matches tags by parsing the interviewee's attribute fields (e.g., role, affiliation) and generates a tag set, which serves as the basis for subsequent question template selection.

[0086] Step 304: The predefined question template library is derived from high-frequency, valid questions in the historical case library and current regulatory requirements (such as clauses in a certain Environmental Impact Assessment Law). Each template is associated with a specific tag combination; for example, the tag "water pollution + enterprise" corresponds to the question "Please describe the operation status of wastewater treatment facilities in the past year." Based on the extracted tag set, the system filters candidate question sets from the template library through key-value matching. Simultaneously, the template library supports dynamic expansion, allowing administrators to supplement templates based on new regulations or cases to ensure comprehensive question coverage.

[0087] Step 305: Input the geographical location (e.g., latitude and longitude) of the target event and related data (e.g., soil type, historical pollution events) from the standardized environmental database into the event scenario classifier. This classifier is built based on the random forest algorithm, and the input features include event keywords (e.g., "leakage" "odor"), geographically associated soil types (e.g., clay, sandy soil), and water quality targets (e.g., Class III water standard). The classifier outputs a probability distribution, and selects scenario labels (e.g., "chemical leak" "agricultural non-point source pollution") with probabilities exceeding a preset threshold (e.g., 0.7) for subsequent issue priority adjustment.

[0088] Step 306: Prioritize the candidate question set based on the event scenario label. For example, if the scenario label is "illegal discharge of industrial wastewater," questions related to "wastewater treatment processes" and "discharge monitoring records" will be prioritized; if the label is "soil heavy metal pollution," questions related to "historical land use" and "crop planting type" will be prioritized. The system has a built-in priority weight table, with weight coefficients for specific questions corresponding to different scenario labels. The final question order is generated through weighted calculation to ensure that key questions are presented first.

[0089] Step 307: Input the candidate questions into the fine-tuned semantic model and calculate their relevance score to the current interview context (such as collected data and event descriptions). For example, if the interview text mentions "nighttime odor," the model gives a high relevance score to the question "nighttime production situation of nearby factories." The system performs a secondary sorting of questions based on the scores, eliminating low-scoring questions (such as those with scores below the threshold of 0.5) and inserting high-scoring questions into the priority queue. Finally, through multi-level sorting based on label matching, scene weights, and relevance scores, a dynamically customized list of survey questions is generated to ensure that the questions are both targeted and logically coherent.

[0090] Preferably, the method for constructing the event scene classifier includes:

[0091] A random forest classification model is trained based on event labels from a historical case database; the input features of the random forest classification model include event keywords, geographically associated soil types, and water quality target information.

[0092] The weights of the random forest classification model are dynamically updated based on the latest detection data in the standardized environmental database to prioritize matching pollution scenarios with probability values ​​greater than a preset threshold.

[0093] Optionally, this embodiment extracts an input feature set from a standardized environmental database based on event tags (such as "chemical spill" and "agricultural non-point source pollution") marked in a historical case library. This feature set includes event keywords (extracted from report text using word segmentation tools, such as "leak" and "odor"), geographically associated soil types (such as clay and sandy soil, derived from plot vector files), and water quality target information (such as Class III surface water standards, derived from water quality target tables). These features are used to train a random forest classification model with 100 trees, and hyperparameters are optimized through cross-validation. After training, the classifier outputs the probability distribution of pollution scenarios. To adapt to dynamic changes in environmental data, the system periodically converts the latest detection data (such as new pollution event reports and real-time monitoring values) into feature vectors, updating the model weights incrementally. Priority is given to strengthening the classification boundaries of high-probability scenarios (such as "illegal industrial wastewater discharge" with a probability > 0.7), ensuring the model's responsiveness and classification accuracy to new pollution patterns.

[0094] Preferably, the semantic model is used to extract key entities from the target interview text, including:

[0095] The target interview text is input into the semantic model, and the key entities are extracted through the named entity recognition module.

[0096] Based on dependency parsing techniques, the relationships between the key entities are initially labeled, and candidate relation triples are obtained.

[0097] Specifically, in step 400 of this embodiment, the target interview text is input into a domain-fine-tuned semantic model (such as BERT or BiLSTM-CRF). Key entities are extracted using the built-in Named Entity Recognition (NER) module, including pollutant type (such as "benzo[a]pyrene"), pollution signs (such as "odor"), geographical location (such as "XX river section"), and time point (such as "spring 2023"). The model performs entity classification and boundary determination based on contextual semantics and environmental professional dictionaries (such as the "List of Hazardous Wastes"). Subsequently, dependency parsing tools (such as Stanford CoreNLP or spaCy) are used to parse the sentence structure, identify grammatical dependency relationships between entities (such as subject-verb, verb-object relationships), initially label the relationship type (such as "emission → leading to → water quality deterioration"), and form candidate relation triples (subject-relationship-object). For ambiguous dependency paths (such as the parallel structure "Factory A and B discharge wastewater"), the rule engine supplements logical connectors to ensure the semantic integrity of triples, and finally outputs a set of structured entities and relationships, providing basic data for subsequent causal graph construction.

[0098] Preferably, mapping the key entities to preset environmental damage categories and generating an association map includes:

[0099] The key entities and the candidate relation triples are input into a graph database, and a dynamic graph is constructed according to a predefined relation pattern; the predefined relation pattern is: pollution source → transmission path → damage receptor;

[0100] Based on preset historical pollution event data, the cross-event correlation of the dynamic map is supplemented to obtain the final correlation map.

[0101] Optionally, step 500 in this embodiment includes:

[0102] Step 501: Input key entities (such as pollution source "XX Chemical Plant", propagation path "wastewater discharge pipeline", and damage receptor "farmland soil") and candidate relation triples into a graph database (such as Neo4j). Create nodes and edges according to the predefined relation pattern "pollution source → propagation path → damage receptor". Specifically, assign a unique node to each entity. Node attributes include type, name, and additional information (such as the production process of the pollution source and the medium type of the propagation path). The subject and object and relation type in the candidate relation triples (such as "emission → through → pipeline") are converted into corresponding edges through pattern matching rules. For relations that do not directly match the predefined pattern (such as "odor → spread to → residential area"), call the rule engine to parse their semantics and map them to the closest pattern branch (such as "propagation path"), finally generating a dynamic graph with pollution events as the core and containing multi-level causal chains.

[0103] Step 502: Based on a pre-set historical pollution event database (such as ten years of pollution event records stored in MongoDB), extract data with similar characteristics to the current event (such as the same pollution source type, nearby geographical location). Using the graph database's association query function, identify nodes in historical events related to the current entity (such as other pollution sources under the same company name) and add cross-event association edges (such as "historical similar events" and "related responsible entities"). For example, if "XX Chemical Plant" has multiple violation records in historical data in the current event, establish an association path of "current pollution source → historical violation → historical penalty result". Simultaneously, perform cluster analysis on the propagation paths or receptor damage patterns in historical events to supplement potential risk paths in the current graph (such as "groundwater infiltration → cross-regional pollution"), ultimately forming a complete causal graph integrating multi-dimensional associations to support in-depth analysis of pollution source tracing and responsibility determination.

[0104] Specifically, step 600 in this embodiment includes:

[0105] Step 601: This embodiment pre-defines a multi-dimensional logical verification rule base to verify the integrity of the association graph. For example, the rule base requires that each pollution source node must be associated with at least one propagation path and a damage receptor node; the absence of any element triggers an integrity alarm. For the causal relationship chain, the timeline rationality (e.g., the pollution event must occur later than the pollution source's operational start time) and geospatial consistency (e.g., the pollution source coordinates must be located in the upstream region of the propagation path) are checked. Simultaneously, an isolated node (e.g., anomaly entities not connected to the main event chain) is detected using a graph traversal algorithm and marked as data to be supplemented. The verification results generate a report list, marking missing fields or contradictory relationships to ensure the graph covers core causal elements.

[0106] Step 602: Based on authoritative data in a standardized environmental database (such as government monitoring records and regulatory documents), verify the consistency of entity attributes in the graph. For example, compare the enterprise registration information of pollution sources with the business registration database to ensure that the name and business scope are consistent; verify whether the medium type of the transmission path (such as groundwater or atmosphere) conforms to local geological or meteorological data. If a contradiction is detected (such as a chemical plant being marked as a pollution source without registering hazardous waste treatment qualifications), the system automatically triggers an anomaly handling mechanism: inferring possible causes (such as unlicensed operation) by associating with historical data or marking it as a disputed node awaiting manual verification. The verified graph must meet all rule constraints; otherwise, iterative correction is performed until the logical loop is closed.

[0107] Step 603: The verified correlation graph is input into the report generation engine, which extracts key information according to a preset template. The template is divided into modules such as event overview (time, location, subject), causal chain (pollution source → transmission path → receptor), responsible parties (involved companies, regulatory departments), and recommended measures (remediation technology, legal basis). Natural Language Generation (NLG) technology is used to convert the structured data in the graph into a coherent narrative. For example, "Chemical Plant A → Wastewater Discharge → River B" is mapped to "Chemical Plant A caused the water quality of River B to exceed standards through wastewater discharge." The report automatically attaches evidence chain attachments (such as screenshots of test reports and geographic coordinate maps) and supports exporting to PDF, DOCX, and other formats, ensuring that the content complies with industry standards and can be directly used in administrative or legal processes.

[0108] Corresponding to the above methods, such as Figure 3 As shown, this embodiment also provides an intelligent environmental damage survey form generation and semantic analysis system, including:

[0109] The data acquisition unit is used to acquire structured environmental data through government department interfaces and, based on an artificial intelligence search engine, to crawl publicly available online data by inputting the geographical location of the event and keywords.

[0110] The data processing unit is used to perform data cleaning and data normalization on the environmental data and the publicly available network data to obtain a standardized environmental database.

[0111] The question building unit is used to build a semantic model based on NLP technology, dynamically generate customized survey questions according to the interviewee type and the standardized environment database, and adjust the question priority in combination with the event scenario classifier;

[0112] An entity extraction unit is used to extract key entities from the target interview text using the semantic model.

[0113] The map generation unit is used to map the key entities to preset environmental damage categories and generate an association map;

[0114] The report generation unit is used to verify the integrity and consistency of the association graph through logical verification rules, and to generate a structured investigation report using the verified association graph.

[0115] The beneficial effects of this invention are as follows:

[0116] (1) Based on NLP technology and standardized environmental database, this invention dynamically generates customized survey questions that are adapted to different interviewees (residents, government departments) and event scenarios (illegal landfill, water pollution), which solves the problems of rigid design of traditional questionnaires and low efficiency of manual adjustment, and improves the effective information extraction rate by more than 30%.

[0117] (2) This invention uses the BERT pre-trained model to accurately extract key entities (such as waste type and pollution signs) from unstructured interview texts, and establishes logical associations through the environmental damage ontology library, thus solving the problems of data dispersion and time-consuming manual sorting;

[0118] (3) This invention generates a dynamic causal relationship map based on graph neural network (GNN), which visualizes the implicit relationship such as "pollution signs-waste type-damage consequences", making up for the shortcomings of traditional methods in mining causal relationships in complex environments;

[0119] (4) This invention automatically verifies the consistency of geographic information, the rationality of the timeline and the authority of the data through data cleaning, normalization and logical verification rules, and generates a structured survey report, which solves the problems of low efficiency and easy error in manual matching of multi-source data, and shortens the survey report generation time by 50%.

[0120] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0121] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for generating and semantically analyzing an intelligent environmental damage survey form, characterized in that, include: Structured environmental data is obtained through government department interfaces, and publicly available online data is crawled by inputting the event's geographical location and keywords into an artificial intelligence search engine. The environmental data and the publicly available network data are cleaned and normalized to obtain a standardized environmental database. A semantic model is built based on NLP technology, and customized survey questions are dynamically generated according to the type of interviewee and the standardized environment database. The question priority is adjusted by combining an event scenario classifier. The semantic model is used to extract key entities from the target interview text; The key entities are mapped to preset environmental damage categories, and a correlation map is generated; The integrity and consistency of the association graph are verified by logical verification rules, and a structured investigation report is generated using the verified association graph. A semantic model is built based on NLP technology. Customized survey questions are dynamically generated according to the interviewee type and the standardized environment database. The question priority is adjusted by combining an event scenario classifier, including: Load general domain parameters based on pre-trained semantic models; The pre-trained semantic model is fine-tuned using the standardized environment database to enhance its semantic understanding of technical terms. Preset tags are extracted from the metadata table of the standardized environment database based on the interviewee type; The preset tags are matched against a predefined question template library; the template content of the question template library comes from high-frequency valid questions and regulatory requirements in the historical case library. The target event's geographical location and the standardized environment database are input into the event scene classifier, and the event scene labels are output. Adjust the issue priority based on the event scenario tags; The question template library is sorted and reorganized based on the context relevance score output by the pre-trained semantic model.

2. The method for generating and semantically analyzing an intelligent environmental damage survey form according to claim 1, characterized in that, The environmental data includes: land parcel vector files, land use planning, soil type, historical monitoring data, and water quality target information; the publicly available online data includes regional historical event records, environmental inspection reports, and land use information.

3. The method for generating and semantically analyzing intelligent environmental damage survey forms according to claim 1, characterized in that, The environmental data and the publicly available online data are cleaned and normalized to obtain a standardized environmental database, including: Topological verification is performed on the environmental data to correct overlapping and missing geographical information. The unstructured text in the publicly available online data is extracted using regular expressions to extract key fields, which are then converted into a uniform format. The key fields include the inspection time and the type of violation. Based on geographic coordinate system transformation rules, all geographic location data in the environmental data and the publicly available network data are normalized to obtain the standardized environmental database.

4. The method for generating and semantically analyzing an intelligent environmental damage survey form according to claim 1, characterized in that, The method for constructing the event scene classifier includes: A random forest classification model is trained based on event labels from a historical case database; the input features of the random forest classification model include event keywords, geographically associated soil types, and water quality target information. The weights of the random forest classification model are dynamically updated based on the latest detection data in the standardized environmental database to prioritize matching pollution scenarios with probability values ​​greater than a preset threshold.

5. The method for generating and semantically analyzing an intelligent environmental damage survey form according to claim 1, characterized in that, The semantic model is used to extract key entities from the target interview text, including: The target interview text is input into the semantic model, and the key entities are extracted through the named entity recognition module. Based on dependency parsing techniques, the relationships between the key entities are initially labeled, and candidate relation triples are obtained.

6. The method for generating and semantically analyzing an intelligent environmental damage survey form according to claim 5, characterized in that, Mapping the key entities to preset environmental damage categories and generating an association map includes: The key entities and the candidate relation triples are input into a graph database, and a dynamic graph is constructed according to a predefined relation pattern; the predefined relation pattern is: pollution source → transmission path → damage receptor; Based on preset historical pollution event data, the cross-event correlation of the dynamic map is supplemented to obtain the final correlation map.

7. A smart environmental damage survey form generation and semantic analysis system, characterized in that, include: The data acquisition unit is used to acquire structured environmental data through government department interfaces and, based on an artificial intelligence search engine, to crawl publicly available online data by inputting the geographical location of the event and keywords. The data processing unit is used to perform data cleaning and data normalization on the environmental data and the publicly available network data to obtain a standardized environmental database. The question construction unit is used to build a semantic model based on NLP technology, dynamically generate customized survey questions according to the interviewee type and the standardized environment database, and adjust the question priority in combination with the event scenario classifier; An entity extraction unit is used to extract key entities from the target interview text using the semantic model. The map generation unit is used to map the key entities to preset environmental damage categories and generate an association map; The report generation unit is used to verify the integrity and consistency of the association graph through logical verification rules, and to generate a structured investigation report using the verified association graph; A semantic model is built based on NLP technology. Customized survey questions are dynamically generated according to the interviewee type and the standardized environment database. The question priority is adjusted by combining an event scenario classifier, including: Load general domain parameters based on pre-trained semantic models; The pre-trained semantic model is fine-tuned using the standardized environment database to enhance its semantic understanding of technical terms. Preset tags are extracted from the metadata table of the standardized environment database based on the interviewee type; The preset tags are matched against a predefined question template library; the template content of the question template library comes from high-frequency valid questions and regulatory requirements in the historical case library. The target event's geographical location and the standardized environment database are input into the event scene classifier, and the event scene labels are output. Adjust the issue priority based on the event scenario tags; The question template library is sorted and reorganized based on the context relevance score output by the pre-trained semantic model.

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