Intelligent environmental damage questionnaire generation and semantic analysis method and system

Abstract

The invention provides an intelligent environment damage questionnaire generation and semantic analysis method and system, and the method comprises the steps: obtaining structured environment data through a government department interface, and capturing network public data in combination with an AI search engine; performing cleaning and normalization processing on the data, and constructing a standardized environment database; constructing a semantic model based on an NLP technology, dynamically generating a customized investigation problem according to an interview object type and an event scene classifier, and optimizing the priority of the problem; extracting a key entity from the interview text by adopting a pre-training model; mapping the entity to a preset pollution category through the environmental damage ontology library, and generating a dynamic causal association map; and verifying the integrity and consistency of the atlas by using a logic verification rule, and automatically outputting a structured survey report. According to the method, the defects that a traditional questionnaire is rigid in design, fragmented in information and insufficient in implicit association mining are overcome, the accuracy and efficiency of environmental damage investigation are remarkably improved, and intelligent support is provided for pollution traceability and responsibility definition.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental damage investigation, and in particular to a method and system for generating and semantically analyzing an intelligent environmental damage investigation form. Background Art

[0002] When conducting environmental damage research, it is necessary to conduct interviews with personnel, including government departments and residents at the site of the incident. The survey subjects are also different. Based on the results of the data collection, a customized questionnaire is used to support or supplement the content of the data collected.

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

[0004] Questions need to be manually adjusted for different interviewees (such as residents and government departments), which is inefficient and prone to missing key information. The data obtained through interviews and data collection is scattered and unstructured, and manual organization is time-consuming and has a high error rate. Existing technologies have difficulty extracting implicit environmental damage associations from text (such as the potential connection between "sour taste" and hazardous waste), resulting in one-sided analysis results. Multi-source data (such as geographic information and historical testing data) need to be manually matched and cannot be updated to the database in real time.

[0005] While some existing research uses NLP technology for text analysis, a technical solution combining dynamic questionnaire generation and semantic reasoning for environmental damage scenarios has yet to be implemented. Therefore, there is an urgent need for an intelligent system that can automatically generate customized questionnaires and efficiently extract key information through semantic analysis to address these pain points. Summary of the Invention

[0006] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide an intelligent environmental damage investigation form generation and semantic analysis method and system, which can significantly improve the comprehensiveness, accuracy and response efficiency of environmental damage investigations, and provide intelligent support for the rapid tracing of pollution incidents and the definition of responsibilities.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] An intelligent environmental damage survey form generation and semantic analysis method, comprising:

[0009] Obtain structured environmental data through government department interfaces and, based on an AI-powered search engine, input the event's geographic location and keywords to capture publicly available online data;

[0010] Performing data cleaning and data normalization on the environmental data and the network public data to obtain a standardized environmental database;

[0011] Building a semantic model based on NLP technology, dynamically generating customized survey questions based on the interviewee type and the standardized environment database, and adjusting question priorities in conjunction with an event scenario classifier;

[0012] extracting key entities from the target interview text using the semantic model;

[0013] Mapping the key entities to preset environmental damage categories and generating a correlation map;

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

[0015] Preferably, the environmental data includes: plot vector files, land use planning, soil types, historical detection data and water quality target information; the network public data includes regional historical event records, environmental inspection reports and land use information.

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

[0017] Performing topological verification on the environmental data to correct boundary overlap and missing geographic information;

[0018] Extract key fields from the unstructured text in the network public data using regular expressions and convert them into a unified format; the key fields include inspection time and violation type;

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

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

[0021] Loading common domain parameters based on pre-trained semantic models;

[0022] Using the standardized environment database to perform domain-adaptive fine-tuning on the pre-trained semantic model to enhance the semantic understanding capability of professional terminology;

[0023] Extracting preset tags from the metadata table of the standardized environment database according to the type of the interviewee;

[0024] Matching a predefined question template library based on the preset tags; the template content of the question template library is derived from high-frequency effective questions and regulatory requirements in the historical case library;

[0025] Inputting the target event geographic location and the standardized environment database into an event scene classifier, and outputting an event scene label;

[0026] Adjust the priority of the issue according to the event scenario label;

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

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

[0029] Training a random forest classification model based on event labels in a historical case library; the input features of the random forest classification model include event keywords, soil type associated with geographic location, and water quality target information;

[0030] The weights of the random forest classification model are dynamically updated according to the latest detection data in the standardized environmental database to preferentially match pollution scenes with probability values greater than a preset threshold.

[0031] Preferably, extracting key entities from the target interview text using the semantic model includes:

[0032] Input the target interview text into the semantic model and extract the key entities through the named entity recognition module;

[0033] Based on dependency syntactic analysis technology, the relationships between the key entities are preliminarily marked to obtain candidate relationship triples.

[0034] Preferably, the key entities are mapped to preset environmental damage categories and a correlation map is generated, including:

[0035] Input the key entities and the candidate relationship triples into a graph database, and construct a dynamic graph according to a predefined relationship pattern; the predefined relationship pattern is: pollution source → transmission path → damage receptor;

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

[0037] An intelligent environmental damage survey form generation and semantic analysis system, comprising:

[0038] The data acquisition unit is used to obtain structured environmental data through government department interfaces and input event geographic locations and keywords based on an artificial intelligence search engine to crawl online public data;

[0039] A data processing unit, configured to perform data cleaning and data normalization on the environmental data and the network public data to obtain a standardized environmental database;

[0040] A question construction unit, configured to construct a semantic model based on NLP technology, dynamically generate customized survey questions based on the type of interviewee and the standardized environment database, and adjust question priorities in combination with an event scenario classifier;

[0041] An entity extraction unit, configured to extract key entities from the target interview text using the semantic model;

[0042] A graph generation unit, configured to map the key entities to preset environmental damage categories and generate a correlation graph;

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

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

[0045] The present invention provides a method and system for generating and semantically analyzing intelligent environmental damage survey forms. The method comprises: obtaining structured environmental data through a government department interface, and using an artificial intelligence search engine to input the event's geographic location and keywords to capture publicly available online data; performing data cleaning and normalization on the environmental data and publicly available online data to obtain a standardized environmental database; constructing a semantic model based on NLP technology, dynamically generating customized survey questions based on the interviewee type and the standardized environmental database, and adjusting question priorities in conjunction with an event scenario classifier; utilizing the semantic model to extract key entities from the target interview text; mapping the key entities to preset environmental damage categories and generating an association graph; verifying the integrity and consistency of the association graph through logical verification rules, and generating a structured survey report using the verified association graph. The present invention significantly improves the comprehensiveness, accuracy, and response efficiency of environmental damage investigations, providing intelligent support for the rapid tracing of pollution incidents and the definition of responsibilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0048] Figure 2 Provided in accordance with an embodiment of the present invention;

[0049] Figure 3 A schematic diagram of the system structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] The purpose of the present invention is to provide an intelligent environmental damage investigation form generation and semantic analysis method and system, which can significantly improve the comprehensiveness, accuracy and response efficiency of environmental damage investigations, and provide intelligent support for the rapid tracing of pollution incidents and the definition of responsibilities.

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] Figure 1 A flow chart of the method provided in the embodiment of the present invention is shown in FIG. Figure 1 As shown, the present invention provides an intelligent environmental damage survey form generation and semantic analysis method, including:

[0054] Step 100: Obtain structured environmental data through government department interfaces, and use an AI-powered search engine to input the event's geographic location and keywords to crawl online public data.

[0055] Step 200: Clean and normalize the environmental data and online public data to obtain a standardized environmental database;

[0056] Step 300: Build a semantic model based on NLP technology, dynamically generate customized survey questions based on the interviewee type and the standardized environment database, and adjust the question priority in combination with the 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 a correlation 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: plot vector files, land use planning, soil types, historical detection data and water quality target information; the network public data includes regional historical event records, environmental inspection reports and land use information.

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

[0062] Step 101: Establish connections with databases of government departments such as environmental protection and natural resources through predefined API protocols to obtain structured environmental data. Specifically, according to the interface specifications, enter the authorization key and query parameters (such as administrative division code and time range). The system automatically requests land parcel vector files (including geographic boundary coordinates), land use planning documents (such as industrial zone and protected area divisions), soil type distribution maps (classified by texture), historical test 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 for subsequent processing.

[0063] Step 102: Based on the preset event location (e.g., latitude and longitude coordinates or place names) and keywords (e.g., "pollution incident," "illegal discharge"), configure the AI search engine's crawler program. Using a semantic understanding model, it identifies relevant web pages (e.g., government announcement platforms, news websites), and crawls historical event records (including time and companies involved), environmental inspection reports (PDF or webpage text), and land use information (e.g., records of farmland converted to industrial use). Dynamic proxy IP and request frequency control are used to circumvent anti-crawling mechanisms. The acquired data is initially deduplicated and stored in a temporary database for subsequent cleaning.

[0064] Step 103: Associate and integrate the structured data obtained from the government interface with the semi-structured / unstructured data captured from the web. For example, spatially match the geographic coordinates in the plot vector file with the event location in the web data to create a unified index. Extract metadata (such as publication date and regulatory agency) from text data (such as inspection reports) and convert it to CSV format. At the same time, perform a preliminary data integrity check (such as checking whether there are missing fields in the data) and mark abnormal records to provide a foundation for subsequent data cleaning and normalization.

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

[0066] Performing topological verification on the environmental data to correct boundary overlap and missing geographic information;

[0067] Extract key fields from the unstructured text in the network public data using regular expressions and convert them into a unified format; the key fields include inspection time and violation type;

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

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

[0070] Step 201: For environmental data (such as land parcel vector files) obtained from government department interfaces, use geographic information system (GIS) tools to perform topological verification. First, check the closure of polygon boundaries, automatically identify and repair abnormal areas with gaps or self-intersections; second, detect boundary overlaps between different layers (such as the intersection of industrial areas and protected areas) through spatial overlay analysis, and perform boundary clipping or merging according to preset priority rules (such as priority for protected areas). For data entries with missing geographic information, call historical version libraries or adjacent area data for interpolation and completion to ensure the logical consistency and topological correctness of all geographic spatial data. Finally, output geographic information datasets that comply with OGC standards.

[0071] Step 202: For unstructured text in online public data (such as environmental inspection reports and news event descriptions), design multiple sets of regular expression templates to extract key fields. For example, for "inspection time", define a regular expression that matches the "\d{4}-\d{2}-\d{2}" pattern to capture the standard date format from the text; for "violation type", build a keyword dictionary (such as "construction without approval" and "exceeding emission standards"), and combine the context window to identify the category of violation behavior. For PDF documents, first convert them into text using OCR technology, and then apply the above rules to extract fields. The extracted data is converted into a unified format according to the preset template. For example, "inspection time" is stored as an ISO 8601 standard timestamp, and "violation type" is encoded as a classification label, and finally integrated into a structured data table.

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

[0073] In view of the differences in geographic coordinate systems in multi-source data (such as WGS84, CGCS2000, and GCJ-02), unified conversion rules are formulated. First, the coordinate system type of the original data is identified: for vector files, the projection information in its metadata is parsed; for coordinate descriptions in text (such as "30°12'34"" North Latitude), they are converted to decimal format through the parser. Subsequently, open source libraries (such as Proj4) or API services are called to convert all coordinates to the target coordinate system (such as WGS84). For data that lacks coordinates but contains place name descriptions (such as "XX Factory"), a geocoding service (such as Google GeocodingAPI) is called to parse it into longitude and latitude. The converted coordinates are uniformly stored in the "longitude, latitude" format, with the accuracy retained to six decimal places, to ensure the consistency and computability of spatial data and complete the construction of a standardized environmental database.

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

[0075] Loading common domain parameters based on pre-trained semantic models;

[0076] Using the standardized environment database to perform domain-adaptive fine-tuning on the pre-trained semantic model to enhance the semantic understanding capability of professional terminology;

[0077] Extracting preset tags from the metadata table of the standardized environment database according to the type of the interviewee;

[0078] Matching a predefined question template library based on the preset tags; the template content of the question template library is derived from high-frequency effective questions and regulatory requirements in the historical case library;

[0079] Inputting the target event geographic location and the standardized environment database into an event scene classifier, and outputting an event scene label;

[0080] Adjust the priority of the issue according to the event scenario label;

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

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

[0083] Step 301: Select a common 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 large-scale general-purpose corpora (such as Wikipedia and news text) and has basic semantic understanding capabilities. During initialization, the core architecture of the model (such as the Transformer layer) is retained, while some underlying parameters are frozen, leaving only the top-level network open for subsequent domain adaptation. This ensures that the model retains common language characteristics while maintaining adaptability.

[0084] Step 302: Fine-tune the pre-trained model using professional corpus in the standardized environmental database (such as historical inspection reports and regulatory texts). Specifically, the environmental professional terms in the database (such as "benzopyrene exceeds the standard" and "soil leaching") are constructed as a fine-tuning dataset, and the masked language modeling (MLM) task is used for training. By adjusting the learning rate (such as setting it to 1 / 10 of the pre-training) and the number of iterations (such as 3 epochs), the model gradually learns the semantic features of the environmental field. After fine-tuning, the model's ability to understand the context of professional terms 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 leader, environmental protection department staff), pre-set tags are extracted from the metadata table of the standardized environmental database. For example, for the "resident" subject, tags such as "residential area" and "health impact" are extracted; for the "government department" subject, tags such as "regulatory record" and "law enforcement basis" are extracted. The system automatically matches tags by parsing the interviewee's attribute fields (e.g., role, affiliation) and generates a tag set that serves as the basis for subsequent question template screening.

[0086] Step 304: The predefined question template library is derived from high-frequency and effective 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 of wastewater treatment facilities in the past year." Based on the extracted tag set, the system filters out candidate question sets from the template library through key-value matching. At the same time, the template library supports dynamic expansion, allowing administrators to supplement templates based on new regulations or cases to ensure comprehensive coverage of questions.

[0087] Step 305: The target event's geographic location (e.g., longitude and latitude) and associated data from a standardized environmental database (e.g., soil type, historical pollution events) are input into an event scenario classifier. This classifier is built using a random forest algorithm, and its input features include event keywords (e.g., "leak," "odor"), geographically associated soil types (e.g., clay, sandy soil), and water quality targets (e.g., Class III water standards). The classifier outputs a probability distribution and selects scenario labels (e.g., "chemical leak," "agricultural non-point source pollution") with a probability exceeding a preset threshold (e.g., 0.7) for subsequent problem prioritization.

[0088] Step 306: Prioritize the candidate question set based on the event scenario label. For example, if the scenario label is "illegal industrial wastewater discharge," questions related to "wastewater treatment processes" and "discharge monitoring records" are prioritized; if the label is "heavy metal contamination in soil," questions related to "historical land use" and "crop types" are prioritized. The system uses a built-in priority weighting table, which assigns weight coefficients to specific questions based on different scenario labels. This weighted calculation generates the final question order, ensuring that key issues are presented first.

[0089] Step 307: Input the candidate questions into the fine-tuned semantic model and calculate their relevance scores with the current interview context (such as collected data, event descriptions). For example, if the interview text mentions "unusual smells at night", the model will give a higher relevance score to the question "production conditions at nearby factories at night". The system performs a secondary sorting of the questions based on the scores, eliminates low-scoring questions (such as those with scores below the threshold of 0.5), and inserts high-scoring questions to the front of the priority queue. Finally, through multi-level sorting of label matching, scenario weights, and relevance scores, a dynamic customized survey question list 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] Training a random forest classification model based on event labels in a historical case library; the input features of the random forest classification model include event keywords, soil type associated with geographic location, and water quality target information;

[0092] The weights of the random forest classification model are dynamically updated according to the latest detection data in the standardized environmental database to preferentially match pollution scenes 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 labels annotated in a historical case library (such as "chemical leak" and "agricultural non-point source pollution"), including event keywords (extracted from the report text through a word segmentation tool, such as "leakage" and "odor"), soil types associated with geographical locations (such as clay and sandy soil, derived from plot vector files), and water quality target information (such as surface water Class III standards, derived from the water quality target table). These features are used to train a random forest classification model, the number of trees is set to 100, and the hyperparameters are optimized through cross-validation. After training, the classifier outputs the probability distribution of the pollution scenario. In order to adapt to the dynamic changes in environmental data, the system regularly converts the latest detection data (such as new pollution event reports and real-time monitoring values) into feature vectors, updates the model weights in an incremental learning manner, and prioritizes strengthening the classification boundaries of high-probability scenarios (such as "industrial wastewater illegal discharge" with a probability > 0.7) to ensure the model's responsiveness to new pollution patterns and classification accuracy.

[0094] Preferably, extracting key entities from the target interview text using the semantic model includes:

[0095] Input the target interview text into the semantic model and extract the key entities through the named entity recognition module;

[0096] Based on dependency syntactic analysis technology, the relationships between the key entities are preliminarily marked to obtain candidate relationship triples.

[0097] Specifically, in step 400 of this embodiment, the target interview text is input into a semantic model that has been fine-tuned in the domain (such as BERT or BiLSTM-CRF), and key entities are extracted through the built-in named entity recognition (NER) module, including pollutant types (such as "benzopyrene"), signs of pollution (such as "odor"), geographical locations (such as "XX river section") and time nodes (such as "Spring 2023"). The model performs entity classification and boundary determination based on contextual semantics and environmental professional dictionaries (such as the "Hazardous Waste List"). Subsequently, a dependency syntax analysis tool (such as Stanford CoreNLP or spaCy) is used to parse the sentence structure, identify the grammatical dependency relationships between entities (such as subject-predicate, verb-object relationships), preliminarily mark the relationship types (such as "emission → leads to → water quality deterioration"), and form candidate relationship triples (subject-relationship-object). For ambiguous dependency paths (such as the parallel structure "factories A and B discharge wastewater"), the rule engine is used to supplement logical connectives to ensure the semantic integrity of the triples, and finally output a set of structured entities and relationships to provide basic data for the subsequent construction of causal graphs.

[0098] Preferably, the key entities are mapped to preset environmental damage categories and a correlation map is generated, including:

[0099] Input the key entities and the candidate relationship triples into a graph database, and construct a dynamic graph according to a predefined relationship pattern; the predefined relationship pattern is: pollution source → transmission path → damage receptor;

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

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

[0102] Step 501: Input key entities (such as the pollution source "XX Chemical Plant", the propagation path "wastewater discharge pipeline", and the damage receptor "farmland soil") and candidate relationship triples into a graph database (such as Neo4j), and create nodes and edges based on the predefined relationship pattern "pollution source → propagation path → damage receptor". Specifically, a unique node is assigned to each entity, and the 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 relationship type (such as "discharge → through → pipeline") in the candidate relationship triples are converted into corresponding edges through pattern matching rules. For relationships that do not directly match the predefined pattern (such as "odor → spread to → residential area"), the rule engine is called to parse their semantics and map them to the closest pattern branch (such as "propagation path"), ultimately generating a dynamic graph with the pollution event as the core and containing a multi-level causal chain.

[0103] Step 502: Based on the preset 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 locations). Through the association query function of the graph database, identify the nodes related to the current entity in the historical event (such as other pollution sources under the name of the same enterprise), and add cross-event association edges (such as "historical similar events" and "associated responsible parties"). For example, if the "XX Chemical Plant" in the current event has multiple violation records in the historical data, then establish an association path of "current pollution source → historical violation → historical penalty result". At the same time, perform cluster analysis on the transmission path or receptor damage pattern in the historical event to supplement the potential risk path in the current map (such as "groundwater infiltration → cross-regional pollution"), and finally form a complete causal map integrating multi-dimensional associations to support in-depth analysis of pollution traceability and responsibility definition.

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

[0105] Step 601: This embodiment presets a multi-dimensional logic 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. If any element is missing, an integrity alarm will be triggered. For the causal chain, check the rationality of the timeline (such as the time when the pollution incident occurred must be later than the start time of the pollution source operation) and the geographic space consistency (such as the coordinates of the pollution source must be located in the upstream area of the propagation path). At the same time, isolated nodes (such as abnormal entities that are not connected to the main event chain) are detected through the 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 that the graph covers the core causal elements.

[0106] Step 602: Based on the authoritative data in the standardized environmental database (such as government monitoring records and regulatory documents), verify the consistency of the attributes of the entities in the graph. For example, compare the corporate registration information of the pollution source with the industrial and commercial database to ensure that the name and business scope are consistent; verify whether the medium type of the propagation path (such as groundwater, atmosphere) is consistent with the local geological or meteorological data. If a contradiction is detected (such as a chemical plant that has not registered for hazardous waste treatment qualifications but is marked as a pollution source), the system automatically triggers the exception handling mechanism: infer the possible cause (such as unlicensed operation) by associating historical data or mark it as a dispute node to be manually verified. The verified graph must meet all rule constraints, otherwise it will be iteratively corrected until the logical loop is closed.

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

[0108] Corresponding to the above method, 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 obtain structured environmental data through government department interfaces and input event geographic locations and keywords based on an artificial intelligence search engine to crawl online public data;

[0110] A data processing unit, configured to perform data cleaning and data normalization on the environmental data and the network public data to obtain a standardized environmental database;

[0111] A question construction unit, configured to construct a semantic model based on NLP technology, dynamically generate customized survey questions based on the type of interviewee and the standardized environment database, and adjust question priorities in combination with an event scenario classifier;

[0112] An entity extraction unit, configured to extract key entities from the target interview text using the semantic model;

[0113] A graph generation unit, configured to map the key entities to preset environmental damage categories and generate a correlation graph;

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

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

[0116] (1) Based on NLP technology and a standardized environmental database, this invention dynamically generates customized survey questions adapted to different interview subjects (residents, government departments) and event scenarios (illegal landfill, water pollution), solving the problems of rigid traditional questionnaire design and low efficiency of manual adjustment, and improving the effective information extraction rate by more than 30%;

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

[0118] (3) The present invention generates a dynamic causal association map based on a graph neural network (GNN), which visualizes implicit relationships such as “pollution signs-waste types-damage consequences”, thus making up for the shortcomings of traditional methods in mining complex environmental causal relationships.

[0119] (4) The present invention automatically verifies the consistency of geographic information, the rationality of the timeline, and the authority of data through data cleaning, normalization, and logical verification rules, and generates a structured survey report, thus solving the problem of low efficiency and easy errors in manual matching of multi-source data, and shortening the time for survey report generation by 50%.

[0120] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0121] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for generating and semantically analyzing an intelligent environmental damage survey form, characterized in that: include: Obtain structured environmental data through government department interfaces and, based on an AI-powered search engine, input the event's geographic location and keywords to capture publicly available online data; Performing data cleaning and data normalization on the environmental data and the network public data to obtain a standardized environmental database; Building a semantic model based on NLP technology, dynamically generating customized survey questions based on the interviewee type and the standardized environment database, and adjusting question priorities in conjunction with 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 map; The integrity and consistency of the association map are verified through logical verification rules, and a structured investigation report is generated using the verified association map.

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: plot vector files, land use planning, soil types, historical testing data and water quality target information; the online public data includes regional historical event records, environmental inspection reports and land use information.

3. The method for generating and semantically analyzing an intelligent environmental damage survey form according to claim 1, characterized in that: The environmental data and the online public data are cleaned and normalized to obtain a standardized environmental database, including: Performing topological verification on the environmental data to correct boundary overlap and missing geographic information; Extract key fields from the unstructured text in the network public data using regular expressions and convert them into a unified format; the key fields include inspection time and violation type; Based on geographic coordinate system conversion rules, the environmental data and all geographic location data in the network public 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: A semantic model is built based on NLP technology to dynamically generate customized survey questions based on the interviewee type and the standardized environment database. The priority of the questions is adjusted in conjunction with the event scenario classifier, including: Loading common domain parameters based on pre-trained semantic models; Using the standardized environment database to perform domain-adaptive fine-tuning on the pre-trained semantic model to enhance the semantic understanding capability of professional terminology; Extracting preset tags from the metadata table of the standardized environment database according to the type of the interviewee; Matching a predefined question template library based on the preset tags; the template content of the question template library is derived from high-frequency effective questions and regulatory requirements in the historical case library; Inputting the target event geographic location and the standardized environment database into an event scene classifier, and outputting an event scene label; Adjust the priority of the issue according to the event scenario label; The question template library is sorted and reorganized in combination with the context relevance score output by the pre-trained semantic model.

5. 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: Training a random forest classification model based on event labels in a historical case library; the input features of the random forest classification model include event keywords, soil type associated with geographic location, and water quality target information; The weights of the random forest classification model are dynamically updated according to the latest detection data in the standardized environmental database to preferentially match pollution scenes with probability values greater than a preset threshold.

6. 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: Input the target interview text into the semantic model and extract the key entities through the named entity recognition module; Based on dependency syntactic analysis technology, the relationships between the key entities are preliminarily marked to obtain candidate relationship triples.

7. The method for generating and semantically analyzing an intelligent environmental damage survey form according to claim 6, characterized in that: Map the key entities to pre-set environmental damage categories and generate a correlation map, including: Input the key entities and the candidate relationship triples into a graph database, and construct a dynamic graph according to a predefined relationship pattern; the predefined relationship pattern is: pollution source → transmission path → damage receptor; Based on the preset historical pollution event data, the cross-event association of the dynamic map is supplemented to obtain the final association map.

8. An intelligent environmental damage survey form generation and semantic analysis system, characterized in that: include: The data acquisition unit is used to obtain structured environmental data through government department interfaces and input event geographic locations and keywords based on an artificial intelligence search engine to crawl online public data; A data processing unit, configured to perform data cleaning and data normalization on the environmental data and the network public data to obtain a standardized environmental database; A question construction unit, configured to construct a semantic model based on NLP technology, dynamically generate customized survey questions based on the type of interviewee and the standardized environment database, and adjust question priorities in combination with an event scenario classifier; An entity extraction unit, configured to extract key entities from the target interview text using the semantic model; A graph generation unit, configured to map the key entities to preset environmental damage categories and generate a correlation graph; The report generating unit is used to verify the integrity and consistency of the association map through logical verification rules, and generate a structured investigation report using the verified association map.

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