A method and system for generating an environmental impact assessment report

Abstract

The application relates to the technical field of environmental impact assessment analysis, and discloses an environmental impact assessment report generation method and system, which comprises the following steps: collecting water environment evaluation multi-source basic data and forming an environmental protection rule instruction set; constructing an environmental evaluation data set with time sequence alignment and unified spatial coordinates; extracting a pollution factor characteristic vector and constructing a pollution knowledge triple; constructing an environmental protection evaluation constraint graph based on the environmental protection rule instruction set, screening a compliance evaluation path, and compiling and generating a prediction parameter set; obtaining a prediction result; assembling the pollution knowledge triple, the prediction result and a compliance evaluation conclusion into a report draft according to a preset report chapter framework; extracting cross-chapter correlation characteristic items from the report draft and performing consistency comparison and logic checking, and outputting a final report after triggering deviation correction. Therefore, semantic alignment of multi-source heterogeneous data is realized, consistency between technical conclusions and supporting evidence is ensured, and the generation efficiency and intelligent level of the environmental impact assessment report are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of environmental impact assessment analysis technology, and more specifically, to a method and system for generating environmental impact assessment reports. Background Technology

[0002] With the compression of report preparation timelines, the reuse of batch projects, and the digital upgrading of public participation, the demand for efficient, standardized, and intelligent preparation of water environment impact assessment reports is becoming increasingly prominent. Currently, the well-known methods for preparing water environment impact assessment reports mainly rely on manual guidance and template assistance, combined with model software prediction outputs, geographic information system spatial mapping, and multi-level manual review. While these methods have achieved some success in terms of environmental rule compliance, output format adaptation to administrative review, the report generation process involves the coupling effects of heterogeneous information from multiple sources, including environmental rules and HJ series guidelines, project source strength and waste characteristic parameters, multi-source monitoring and ledger data such as surface water and groundwater, prediction model results, and public participation materials. The existing preparation model still has systemic shortcomings in areas such as immediate response to updates to environmental rules and guidelines, semantic alignment and structured integration of heterogeneous multi-source data, and verifiable reasoning and consistency verification of cross-chapter technical logic. It cannot fully depict the traceable connections in the closed-loop chain of water-related project emissions, water body response, compliance determination, and measures, thus limiting the improvement of the level of refined and intelligent management of report quality.

[0003] Therefore, how to effectively integrate rule-based mechanisms with data-driven methods to build an intelligent water environment impact assessment system with machine-readable parsing of environmental rules, collaborative alignment of multi-source data, and closed-loop consistency verification capabilities has become a pressing technical challenge. Summary of the Invention

[0004] This invention provides a method and system for generating environmental impact assessment reports, which solves the technical problems in the prior art, such as the report preparation relying heavily on human experience, resulting in delayed response to environmental protection regulations, high rework rate, low efficiency in integrating multi-source heterogeneous data and lack of a unified semantic parsing and structured data storage mechanism, and the lack of a verifiable knowledge reasoning chain in the report generation process, leading to a disconnect between technical conclusions and supporting evidence.

[0005] This invention provides a method and system for generating environmental impact assessment reports, comprising: Firstly, a method for generating an environmental impact assessment report includes the following steps: Collect multi-source basic data for water environment assessment to obtain data related to the boundary between water environment and hydrology and water quality, and form the environmental protection rule instruction set; The multi-source basic data are subjected to hydrological and water quality time series alignment and unified processing of spatial coordinates and elevation benchmarks to construct an environmental assessment dataset; Pollution factor feature vectors are extracted from the environmental assessment dataset, and the pollution knowledge triples are constructed using a preset water environment semantic parsing engine. Based on the aforementioned environmental protection rule instruction set, an environmental evaluation constraint graph is constructed with water-related pollutants, concentration limits, total quantity indicators, monitoring frequency, ecological flow and protection targets as nodes, and migration association, judgment logic chain and causal path as directed edges. Then, the compliant evaluation path is screened out using pollution knowledge triples, and a set of predicted parameters for water environment impact is compiled and non-compliant evaluation paths are marked. The predicted parameter set is used to drive a coupled simulation operator of water environment dynamics and water quality to calculate the predicted results of the water environment pollution impact. Based on the pre-set water-related report chapter framework, the pollution knowledge triplet, prediction results, and compliance evaluation path judgment conclusions are assembled into the initial draft of the report; Extract water-related feature items across chapters from the initial draft of the report and perform cross-chapter consistency comparison and logical verification to obtain the logical verification result. When the logical verification result indicates that there is a deviation or conflict, trigger the deviation correction instruction and output the final report.

[0006] Furthermore, the multi-source basic data includes pollution source and hydrological and water quality boundary characteristic parameters, surface water and groundwater environmental quality monitoring data, water catchment and drainage topology data, regional water environment functional zones and sewage discharge permit constraint indicators, as well as applicable surface water, groundwater and sewage discharge control requirements, to form an environmental protection rule instruction set; Among them, corresponding to the boundary characteristic parameters of the pollution source and hydrological and water quality, the environmental impact assessment text, water balance test data and water discharge accounting materials are scanned by the optical character recognition component, and the relationship between the sewage discharge area and the water body adjacent to the shoreline, the layout of water intake and drainage facilities and sewage outlets, the water intake and drainage volume, the reuse rate, the generation links of industrial wastewater and domestic sewage, the process and operation constraint parameters of the pollution treatment unit, and the volume of the emergency pool are extracted by the natural language processing unit. Corresponding to the surface water and groundwater environmental quality monitoring data, the data interface is connected to the environmental monitoring center station, the river chief system information platform or the watershed water quality automatic monitoring network to obtain the regular and intensive monitoring sequences of surface water control sections, upstream and downstream of sewage outlets and bank zones within the preset water catchment units or river buffer zones within the evaluation scope, as well as the stratified monitoring sequences of groundwater unconfined aquifers and confined aquifers. Based on the aforementioned water catchment and drainage topology data, the water catchment area and flow path are extracted by linking the digital elevation model with the geographic information system bus, the river network connectivity and the spatial connection between the sewage outlet and the receiving water body are retrieved, and the drainage network topology and sewage discharge direction data are obtained by connecting with urban drainage related information. Corresponding to the water environment functional zones and sewage discharge permit constraints of the region, the spatial vector boundary data of water environment functional zones, drinking water source protection zones, important wetlands and shoreline control zones, groundwater pollution prevention and control zones and ecological red lines are retrieved through the geographic information system bus, and connected to the urban drainage and sewage discharge permit database to obtain wastewater categories, permitted discharge concentrations and total amounts, water environment monitoring plans and water environment monitoring results; Corresponding to the environmental protection rule instruction set applicable to the control of surface water, groundwater and sewage discharge, the unstructured water-related environmental protection rules, emission standards, total quantity control and sewage discharge permit regulations are transformed into machine-readable instructions with Boolean logic and water period phase characteristics using an environmental protection rule translation engine, forming the environmental protection rule instruction set. The environmental protection rule instruction set has the same meaning as the environmental protection rule machine-readable instruction set.

[0007] Furthermore, the multi-source basic data undergoes hydrological and water quality time-series alignment and spatial coordinate and elevation benchmark unification processing to construct an environmental assessment dataset, including: Identify the original timestamps and water season division markers of each data source, and use the representative periods of the preset hydrological scheduling or hydrological year as a benchmark to perform missing data interpolation and outlier smoothing on the water quality monitoring sequence, and perform time-series registration with precipitation runoff process or gate scheduling logs to eliminate the misalignment of monitoring at different cross sections and sewage outlets on the time axis. In conjunction with the time-series registration, the spatial coordinates and elevation benchmarks are uniformly processed. By identifying the projected coordinate systems and elevation benchmarks of each spatial data and hydrological element, the spatial boundaries of the water environment assessment, sewage outlets and their distances from the shore, river cross-sections and lake / reservoir shorelines, groundwater monitoring wells and hydrogeological unit boundaries are uniformly mapped to the preset geographic coordinate system and national elevation benchmark through coordinate and elevation transformation. This ensures that the environmental assessment dataset includes river network connectivity, water catchment paths and spatial adjacency relationships between surface water and groundwater. The time-registered hydrological and water quality sequences are attached to the geometric representation of the monitoring points and spatiotemporally fused with the river network connectivity, water catchment paths, and spatial adjacency relationships between surface water and groundwater to construct the environmental assessment dataset.

[0008] Furthermore, pollution factor feature vectors are extracted from the environmental assessment dataset, and the pollution knowledge triples are constructed using a pre-defined water environment semantic parsing engine, including: The pollution factor feature vector includes wastewater generation, pollutant concentration and composition, pollution treatment unit efficiency, discharge intensity of sewage outlets, and geographical location information of water bodies. The water environment semantic parsing engine is a semantic parsing engine that has been fine-tuned with water environment domain corpus; The water environment semantic parsing engine uses a pre-defined bidirectional long short-term memory network model to identify the entity chains of pollution sources, pollutants, discharge methods, receiving water bodies, and drainage destinations in the water environment pollution description text. It also extracts a list of characteristic pollutants and industry characteristic factors to generate the initial structure of entities and relationships in the pollution knowledge triple. The initial structure is used to establish the identifiers of pollution source entities, discharge outlet entities, pollutant components, and discharge-receiving relationship edges, serving as the topological skeleton for attribute mounting and numerical verification. On the pollution source entity, sewage outlet entity and entity chain defined by the topological skeleton, the statistical characteristic values ​​of wastewater volume and pollutant discharge rate and discharge concentration of each discharge link are calculated according to water balance and concentration balance. The statistical characteristic values ​​are attached as quantitative attributes to the corresponding entity and corresponding relation edge, so that the initial structure obtains the source strength, water volume and concentration characterization consistent with the discharge link. Using the quantitative attributes and the characteristic vectors of the pollutants as constraints, the numerical logical relationship between raw material input, product output, loss and the amount of characteristic pollutants generated in wastewater is verified by material balance and water-salt ion balance. When the deviation rate exceeds the preset deviation threshold, the water balance and source strength recalculation command is triggered, and the recalculated attributes are written back to the corresponding entities and relationships in the topology skeleton. For toxic, hazardous, or persistent pollutants, bioaccumulation or ecological risk label attributes are added to the pollutant components that pass the verification. After the semantic initial structure, the quantitative attributes, and the material and water salt verification results are consistent with each other, they are aggregated to form the pollution knowledge triplet, which is composed of entities, relations, and attributes; the entities include at least pollution sources, pollutant components, emission pathways, wastewater treatment units, and receiving water bodies.

[0009] Furthermore, the environmental assessment constraint map uses water-related pollutants, concentration limits at cross sections or discharge outlets, total quantity control indicators, monitoring frequency, ecological flow or hydrological situation constraints, and drinking water source protection target types as nodes, and uses surface water-groundwater migration correlation, compliance judgment logic chain, applicable rules for mixed zones, spatial buffer inclusion relationship, and upstream and downstream causal paths as directed connection edges, and configures matching weights and priority parameters for each node; Using the pollution knowledge triples as query conditions, evaluation paths that satisfy the evaluation logic chain are selected from the environmental evaluation constraint graph. The constraint nodes on the evaluation paths are parameterized and compiled to generate a set of prediction parameters for water environment impact assessment that includes hydrological and water quality boundaries, initial and background water quality fields, pollution non-point source and point source strengths and degradation coefficients. Based on the candidate compliance evaluation path, non-compliant evaluation paths are determined according to the environmental evaluation constraint graph and the environmental rule instruction set. If any constraint node fails to meet the standard or the deduction premise of any directed connection edge contradicts the judgment output, the path is a non-compliant evaluation path.

[0010] Furthermore, the non-compliance evaluation path includes at least one of the following scenarios: The predicted concentration or calculated total emission exceeds the concentration limit node of the section or discharge outlet, the total control index node, or the emission standard limit node mapped to the discharge permit. The spatial location of the candidate route is inconsistent with the spatial buffer inclusion relationship of drinking water source protection areas, shoreline buffer zones or groundwater pollution prevention and control zoning nodes, or falls under the prohibited control requirements. The output path following the compliance judgment logic does not match the more stringent evaluation level branch or additional mitigation measure branch given by the rule reasoning engine; Even if the applicable conditions for the rule nodes or the surface water-groundwater migration correlation edges in the mixed zone are not met, the deduction is still completed along the directed edges; the conditions for the monitoring frequency nodes, ecological flow nodes, or hydrological situation constraint nodes are not met by the candidate paths. Paths deemed non-compliant are marked with a non-compliant label, and the constraint node type, connection edge type, and deviation from the limit value that triggered the judgment are recorded.

[0011] Furthermore, based on the pre-defined water-related report chapter framework, the pollution knowledge triplet, prediction results, and compliance evaluation path determination conclusions are assembled into the initial draft of the report, including: A pre-set standardized water-related report chapter template is provided. The water-related report chapter template defines the titles, paragraph formats and data reference tags of chapters such as pollution source strength and water discharge characteristics, surface water environmental impact, groundwater environmental impact, environmental protection measures and monitoring plans. At the same time, it establishes a unified data reference tag key across chapters. The data reference tag key is used to uniquely associate the sewage outlet, control section, characteristic pollutant and environmentally sensitive target. According to the data reference tag key and tag mapping relationship, the wastewater source strength, treatment efficiency and discharge pattern corresponding to each tag key in the pollution knowledge triple are converted into descriptive text and filled into the pollution source strength and water discharge characteristics chapter, so that each data reference tag in the water discharge characteristics chapter corresponds one-to-one with the source strength object in the pollution knowledge triple; Based on the consistency of the data reference tags, the concentration distribution along the route, cross-sectional compliance analysis, lake and reservoir pollution zone range or groundwater pollution halo evolution map corresponding to each tag in the prediction results, as well as the concentration and standard comparison table at sensitive targets, are automatically rendered as report illustrations and appendices and filled into the impact prediction section of surface water environmental impact and groundwater environmental impact, so that the impact prediction display is consistent with the emission objects and receptor objects referenced in the pollution source strength section; Using the predicted concentrations, standard comparison tables, and map tags filled in the impact prediction section as input, the time-matching relationship between the predicted concentrations and water environmental quality standards, emission standards, and discharge permit limits is compared using the compliance evaluation path determination principle. Conclusive statements, including compliance demonstrations and mitigation measures requirements for exceeding standards, are generated and filled into the evaluation conclusion section, ensuring that the evaluation conclusions maintain a consistent reference relationship with the impact predictions and pollution source intensity analysis on the data reference tags.

[0012] Furthermore, water-related feature items across chapters are extracted from the initial draft report, and cross-chapter consistency comparison and logical verification are performed to obtain the logical verification result, including: Define a cross-chapter association matrix, which uses water-related association features as rows and report chapters as columns. The matrix cells are used to record the text content and values ​​of the same feature in each chapter. The water-related association features include at least the sewage outlet number and coordinates, wastewater discharge volume and water quality concentration, receiving water body name and water function zone target, hydrogeological unit number, protection distance, emergency pool volume and water reuse rate that appear repeatedly in different chapters. The row identifier of the cross-chapter association matrix is ​​associated with the object key in the data reference tag key that has a cross-chapter reference relationship, so that the description of the same discharge outlet, control section or sensitive target in different chapters is mapped to the same matrix row; Based on the row and column structure of the matrix, the scanning steps are performed. The initial draft of the report is traversed according to the feature item identifier corresponding to the matrix row. The associated feature items and values ​​that match the row identifier in each chapter are extracted and filled into the corresponding matrix unit to complete the cross-chapter matrix filling. After the matrix is ​​filled, consistency checks are performed on cross-chapter column units row by row: for the same matrix row, the differences between different chapter columns are calculated using string matching and numerical comparison algorithms; for concentration indicators, unit normalization is performed before comparison. When any row has a non-zero difference or semantic inconsistency between any two chapter columns, the associated feature item is located in the chapter position of the initial draft report based on the current row identifier and column identifier and marked as a logical conflict point, and the row identifier and column identifier are retained as a source index.

[0013] Furthermore, when the logical verification result indicates a deviation or conflict, a deviation correction instruction is triggered and a final report is output, including: The logical conflict points are sent to the data source tracing engine to compare the discrepancies between the conflict items and the original monitoring, permitting or verification ledger data of the multi-source basic data of water environment impact assessment. If the data in a certain chapter does not match the original values ​​in the dataset, the overwrite instruction is executed, and the text generation operator for that chapter is retried using the original data; Calculate the relative deviation rate using text generation operators; The relative deviation rate is calculated by multiplying the absolute value of the difference between the feature item values ​​in the report chapter and the original benchmark values ​​in the water environment environmental impact assessment basic dataset into the numerator, and the original benchmark value into the denominator. A first preset deviation threshold and a second preset deviation threshold are preset, wherein the first preset deviation threshold is greater than the second preset deviation threshold, and the second preset deviation threshold is greater than or equal to zero; When the relative deviation rate exceeds the first preset deviation threshold, the data rollback and reconstruction steps are triggered. When the relative deviation rate is greater than the second preset deviation threshold and less than or equal to the first preset deviation threshold, the local text regeneration step of the affected chapter is triggered, and a re-examination is performed on the row of the corresponding feature item in the cross-chapter association matrix. When the relative deviation rate is less than or equal to the second preset deviation threshold, a low-risk consistency prompt record is generated and the data rollback and reconstruction steps are not triggered.

[0014] Secondly, an environmental impact assessment report generation system includes: The data acquisition module is used to collect multi-source basic data for water environment assessment, so as to obtain data related to the boundary between water environment and hydrology and water quality and form the environmental protection rule instruction set. The semantic processing module is used to perform hydrological and water quality time-series alignment and spatial coordinate and elevation benchmark unification processing on the multi-source basic data to construct an environmental assessment dataset. The pollution element extraction module is used to extract pollution factor feature vectors from the environmental assessment dataset and construct the pollution knowledge triples using a preset water environment semantic parsing engine. The constraint graph construction module is used to construct an environmental evaluation constraint graph based on the environmental protection rule instruction set. The graph consists of water-related pollutants, concentration limits, total quantity indicators, monitoring frequency, ecological flow and protection targets as nodes, and migration association, judgment logic chain and causal path as directed edges. The module also uses pollution knowledge triples to screen out compliant evaluation paths, and then compiles and generates a set of predicted parameters for water environment impact and marks non-compliant evaluation paths. The prediction model driving module is used to drive the coupled simulation operator of water environment dynamics and water quality using the prediction parameter set, and calculate the prediction results; The structured assembly module is used to assemble the pollution knowledge triplet, the prediction results, and the compliance evaluation path judgment conclusions into the initial draft of the report according to the preset water-related report chapter framework. The verification and optimization module is used to extract water-related feature items across chapters from the initial draft of the report and perform cross-chapter consistency comparison and logical verification to obtain the logical verification result. When the logical verification result indicates that there is a deviation or conflict, a deviation correction instruction is triggered and the final report is output.

[0015] The beneficial effects of this invention are as follows: This invention, by collecting multi-source basic data and utilizing an environmental rule translation engine to transform unstructured environmental rules into machine-readable instructions, achieves machine-readable environmental rule texts and solves the problem of delayed response to environmental rule updates in existing technologies; by performing hydrological and water quality time-series alignment and unified processing of spatial coordinates and elevation benchmarks on multi-source basic data, and combining this with a water environment semantic parsing engine to construct pollution knowledge triples, it achieves semantic alignment and structured data storage of multi-source heterogeneous data, effectively solving the technical problems of low data integration efficiency and lack of unified semantic parsing in existing technologies; by constructing an environmental assessment constraint graph and utilizing pollution knowledge... The system uses triplet recognition for compliance path screening, enabling graph-based reasoning of technical logic and solving the technical problem of lacking verifiable knowledge reasoning chains in existing technologies. By defining cross-chapter association matrices and performing cross-chapter consistency comparisons and logical verifications, it achieves automatic generation and consistency verification of report content, effectively solving the problem of disconnect between technical conclusions and supporting evidence in existing technologies. By feeding back logical verification results to the data source and triggering deviation correction instructions, it constructs an adaptive closed-loop report generation system, enabling the report generation system to continuously improve generation accuracy as application scenarios accumulate, achieving refined and intelligent generation of environmental impact assessment reports. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of an environmental impact assessment report generation method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of an environmental impact assessment report generation system module provided in an embodiment of the present invention. Detailed Implementation

[0017] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0018] At least one embodiment of the present invention discloses a method and system for generating environmental impact assessment reports, comprising: like Figure 1 As shown, a method for generating an environmental impact assessment report includes the following steps: Step 1: Collect multi-source basic data for water environment assessment to obtain data related to the boundary between water environment and hydrology and water quality, and form the environmental protection rule instruction set; Step 2: Perform hydrological and water quality time series alignment and spatial coordinate and elevation benchmark unification processing on the multi-source basic data to construct an environmental assessment dataset; Step 3: Extract the feature vectors of pollution factors from the environmental assessment dataset, and construct the pollution knowledge triples using a preset water environment semantic parsing engine; Step 4: Based on the environmental protection rule instruction set, construct an environmental evaluation constraint graph with water-related pollutants, concentration limits, total quantity indicators, monitoring frequency, ecological flow and protection targets as nodes, and migration association, judgment logic chain and causal path as directed edges. Then, use pollution knowledge triples to screen out compliant evaluation paths, compile and generate a set of prediction parameters for water environment impact and mark non-compliant evaluation paths. Step 5: Use the predicted parameter set to drive the coupled simulation operator of water environment dynamics and water quality, and calculate the prediction results; Step 6: Assemble the pollution knowledge triplet, the prediction results, and the compliance evaluation path judgment conclusions into the first draft of the report according to the preset water-related report chapter framework; Step 7: Extract water-related feature items across chapters from the initial draft of the report and perform cross-chapter consistency comparison and logical verification to obtain the logical verification result. When the logical verification result indicates that there is a deviation or conflict, trigger the deviation correction instruction and output the final report.

[0019] like Figure 2 As shown, an environmental impact assessment report generation system includes: The data acquisition module is used to collect multi-source basic data for water environment assessment, so as to obtain data related to the boundary between water environment and hydrology and water quality and form the environmental protection rule instruction set. The semantic processing module is used to perform hydrological and water quality time-series alignment and spatial coordinate and elevation benchmark unification processing on the multi-source basic data to construct an environmental assessment dataset. The pollution element extraction module is used to extract pollution factor feature vectors from the environmental assessment dataset and construct the pollution knowledge triples using a preset water environment semantic parsing engine. The constraint graph construction module is used to construct an environmental evaluation constraint graph based on the environmental protection rule instruction set. The graph consists of water-related pollutants, concentration limits, total quantity indicators, monitoring frequency, ecological flow and protection targets as nodes, and migration association, judgment logic chain and causal path as directed edges. The module also uses pollution knowledge triples to screen out compliant evaluation paths, and then compiles and generates a set of predicted parameters for water environment impact and marks non-compliant evaluation paths. The prediction model driving module is used to drive the coupled simulation operator of water environment dynamics and water quality using the prediction parameter set, and calculate the prediction results; The structured assembly module is used to assemble the pollution knowledge triplet, the prediction results, and the compliance evaluation path judgment conclusions into the initial draft of the report according to the preset water-related report chapter framework. The verification and optimization module is used to extract water-related feature items across chapters from the initial draft of the report and perform cross-chapter consistency comparison and logical verification to obtain the logical verification result. When the logical verification result indicates that there is a deviation or conflict, a deviation correction instruction is triggered and the final report is output.

[0020] The environmental impact assessment report generation system provided in this embodiment of the invention includes seven main components: a data acquisition module, a semantic processing module, a pollution element extraction module, a constraint map construction module, a prediction model driving module, a structured assembly module, and a verification and optimization module. The modules transmit data unidirectionally through preset data interfaces, forming a complete processing pipeline from raw data acquisition to final report output. Specifically, the output of the data acquisition module is connected to the input of the semantic processing module via the first data bus, transmitting pollution source data, water quality data, spatial topology data, and environmental protection rule instruction sets to the semantic processing module. After completing time alignment and coordinate benchmark unification, the semantic processing module transmits the semantic data to the pollution element extraction module via the second data bus. The pollution knowledge triples constructed by the pollution element extraction module are simultaneously transmitted to the constraint graph construction module and the prediction model driving module via the third data bus, realizing the coordinated driving of two parallel processing paths. The compliance evaluation path judgment conclusion output by the constraint graph construction module is transmitted to the structured assembly module via the fourth data bus, and the prediction result set output by the prediction model driving module is also transmitted to the structured assembly module via the fifth data bus. After the structured assembly module completes the report assembly, the draft report is transmitted to the verification and optimization module located at the end of the system via the sixth data bus, which performs cross-chapter consistency verification and deviation correction before outputting the final environmental impact assessment report.

[0021] The data acquisition module is located at the beginning of the entire system. It includes four sub-components: an optical character recognition component, a data interface, a geographic information system bus, and an environmental protection rule translation engine. The input ends of each sub-component are connected to the corresponding external data sources, and the output ends are aggregated and processed through format standardization to form the module output. The optical character recognition (OCR) component connects to a scanner or image file storage device at its input end. Its internal image preprocessing unit performs grayscale conversion, noise reduction, and tilt correction on the input scanned image. The character segmentation unit divides the preprocessed image into independent character regions. The feature extraction unit extracts stroke features, contour features, and structural features from each character region. The pattern matching unit compares the extracted features with a pre-built character feature library and identifies the corresponding text information. The final output includes structured information such as a pollution source list, coordinates of the enterprise's discharge outlet, discharge outlet number, discharge pipe diameter, and discharge height, as well as unstructured text such as copies of discharge permits and old environmental impact reports. The data interface has multiple standardized data access ports, connecting to the monitoring network server's database access interface, real-time monitoring data push interface, and historical data query interface. Its internal protocol conversion unit converts received HTTP, FTP, and MQTT protocol data into an internally unified data format. The data format parsing unit parses JSON, XML, and CSV format data into structured records. The data verification unit performs a complete verification of the parsed data. The system performs checks on water quality, including numerical range verification and timestamp validity verification, and outputs water quality monitoring data for each monitoring point, such as water temperature, pH value, dissolved oxygen concentration, permanganate index, five-day biochemical oxygen demand, ammonia nitrogen concentration, total phosphorus concentration, and total nitrogen concentration. The spatial data access unit within the Geographic Information System (GIS) bus acquires surface water system distribution data, drainage network topology data, sensitive target distribution data, and topographic data from external GIS systems via the OGC standard interface. The coordinate transformation unit uniformly converts the received spatial data from different coordinate systems, such as the Beijing 54 coordinate system, Xi'an 80 coordinate system, and WGS84 coordinate system, into a unified format. The CGCS2000 coordinate system and the topology construction unit construct the upstream and downstream relationships of rivers, the connectivity of drainage pipe networks, and the spatial adjacency relationships between pollution sources and receiving water bodies based on the transformed spatial data, outputting a spatial topology dataset; the environmental rule translation engine's environmental rule database storage unit stores the full text of various water pollution prevention and control environmental rules; the natural language processing unit uses the Bert-BiLSTM-CRF model to perform semantic parsing on the input environmental rule query requests, identify entity information such as pollutant names, concentration limits, time periods, and applicable scope in the environmental rule clauses, and locate relevant clauses;The rule instruction generation unit converts the parsed results into a machine-readable environmental rule instruction set in JSON format. Each instruction contains six fields: rule-id (rule number), pollutant (pollutant name), limit-type (limit type: concentration / total), limit-value (limit value), time-period (water season: high water / normal water / dry water), and condition (trigger condition). Multi-condition combination logic is represented using the AND, OR, and NOT Boolean operators. For example, when the pollutant is COD, the limit type is concentration, the limit is 50 mg / L, and the water season is normal water, the corresponding Boolean instruction is expressed as: IF pollutant = COD - AND limit-type = concentration - AND limit-value = 50 mg / L - AND time-period = normal water season - THEN - pass. This achieves accurate conversion of environmental rule texts into machine-readable instructions with Boolean logic and water season characteristics, outputting a complete environmental rule instruction set.

[0022] After the multi-source data output by the data acquisition module is transmitted to the semantic processing module via the first data bus, the data preprocessing is completed sequentially by the time alignment unit and the coordinate reference unification unit set in series. The time-series alignment unit's internal timestamp standardization subunit converts various time formats in the input data, such as ISO8601, Chinese standard time, and UNIX timestamps, into a standard date and time type. The sampling frequency analysis subunit analyzes the sampling intervals of various monitoring data to identify hourly, daily, and monthly data. The interpolation synchronization subunit performs linear or spline interpolation on low-frequency data based on the analysis results, unifying all data types into a daily time series and outputting a time-series aligned structured dataset. The coordinate reference unification unit's input is connected to the output of the time-series alignment unit. Its internal coordinate system identification subunit identifies the coordinate system type used in the input spatial data. The projection transformation subunit converts coordinate data from different projection methods, such as Mercator, Gauss-Kruger, and UTM projections, into coordinate data from a unified projection system. The accuracy verification subunit verifies the accuracy of the converted coordinate data and marks abnormal data points with accuracy losses exceeding 0.5 meters, outputting a coordinate reference unified dataset. After the above processing, the semantic processing module transmits the time-series aligned and spatially referenced structured data to the pollution element extraction module via a second data bus.

[0023] After receiving semantic data, the pollution element extraction module constructs pollution knowledge triplets through three interconnected sub-units: the water environment semantic parsing engine, the pollution factor feature vector extraction unit, and the material balance verification unit. The input of the water environment semantic parsing engine connects to the output of the semantic processing module. Its core entity recognition and relation extraction model employs a BiLSTM-CRF architecture combining a bidirectional long short-term memory network and a conditional random field: the input layer receives the segmented text sequence, the hidden layer uses a two-layer LSTM structure with 128 hidden nodes per layer, and the output layer decodes the sequence labels using a conditional random field (CRF). The model is fine-tuned using 3000 pre-labeled training corpora in the water environment domain, with 50 training iterations and a learning rate of 0.001. Entity recognition covers four entity types: pollution sources, pollutants, discharge outlets, and receiving water bodies. The relation extraction covers three relation types: emission relations, reception relations, and treatment relations. A softmax classifier outputs the probability distributions of entity types and relation types, and the highest probability is selected as the identification result. The domain dictionary loading unit loads a pre-built pollutant dictionary (covering 156 conventional pollutants such as chemical oxygen demand, ammonia nitrogen, and total phosphorus, and 89 characteristic pollutants such as volatile phenols, benzene series compounds, and heavy metals), an emission industry dictionary, and a receiving water body dictionary. For words not covered by the dictionaries, the TF-IDF algorithm is used to extract high-frequency feature words, combined with manual annotation for confirmation. The industry feature factor library covers 62 industry categories, with each category containing 3 to 8 items. Featured pollutants; the relation extraction unit extracts the ternary relations between pollution source-emission-pollutant based on the above identification results, generating pollution knowledge triples in the format (entity 1, relation, entity 2), such as (a chemical plant, emission, wastewater), (wastewater, contains, ammonia nitrogen), (ammonia nitrogen, discharged into, a river), and outputs the semantic parsing result dataset; the input of the pollution factor feature vector extraction unit is connected to the output of the water environment semantic parsing engine, and its internal feature definition subunit defines the physicochemical feature dimensions of pollution factors, including emission concentration, emission amount, emission water temperature, emission pH value, emission form (dissolved or particulate), and emission toxicity category. The degree mapping subunit maps the input pollution source attribute data to the defined feature dimensions. The vector generation subunit generates a multi-dimensional feature vector for each pollution source entity and outputs a set of pollution factor feature vectors. The input end of the material balance verification unit is connected to the output end of the pollution factor feature vector extraction unit. Its internal input item summary subunit counts the total amount of material inputs such as fresh water consumption M1, circulating water consumption M2, and water brought in by raw and auxiliary materials M3 for each pollution source. The output item summary subunit counts the total amount of material outputs such as wastewater discharge W1, exhaust gas discharge W2, and solid waste generation W3 for each pollution source, and also takes into account loss items such as leakage L1 and evaporation loss L2.The balance calculation subunit calculates the material balance based on the mass conservation equation ∑Input - ∑Output - ∑Loss = 0, where ∑Input is the sum of M1, M2, and M3, ∑Output is the sum of W1, W2, and W3, and ∑Loss is the sum of L1 and L2. The specific formula for calculating the balance rate is: Balance Rate = (∑Input - ∑Output) / ∑Input × 100%. When the balance rate is within the range of 95% to 105%, the material balance check is considered passed. Based on this, the water-salt ion balance subunit further verifies the cation and anion balance in the water body using the ion concentration conservation equation, primarily verifying the chloride ion (Cl-) balance. Sulfate ions (SO42-), sodium ions (Na+), potassium ions (K+), calcium ions (Ca2+), and magnesium ions (Mg2+) are used as the criterion, with the sum of cation concentrations equal to the sum of anion concentrations. The ion balance deviation threshold is set to 10%. The deviation processing subunit marks pollution sources whose balance rates exceed the preset range as abnormal, triggers water balance and source strength recalculation instructions, and writes the recalculated attributes back to the corresponding entities and relationships in the topology skeleton. Finally, it outputs a pollution knowledge triple consisting of the verified pollution source entity, pollutant entity, emission pathway entity, and receiving water body entity. The pollution element extraction module transmits the pollution knowledge triple to the constraint graph construction module and the prediction model driving module respectively through the third data bus.

[0024] After receiving the pollution knowledge triple, the constraint graph construction module, in collaboration with the environmental assessment constraint graph, compliance path screening unit, and prediction parameter set compilation unit, completes the compliance path determination and prediction parameter compilation. The environmental assessment constraint graph adopts a dual-table structure: a node storage table and an edge storage table. The node storage table stores various types of node data in the graph. Each node record includes a node identifier field, a node type field, a node attribute field, and a node weight field. Node types cover six categories: water-related pollutant nodes, concentration limit nodes, total quantity index nodes, monitoring frequency nodes, ecological flow nodes, and protection target nodes. The edge storage table stores various types of edge data in the graph. Each edge record includes a start node identifier field, an end node identifier field, an edge type field, and an edge weight field. Edge types cover four categories: constraint-related edges, upstream-downstream relationship edges, monitoring-dependent edges, and ecological flow-dependent edges. The graph construction process first determines water-related pollution factor nodes based on the input pollution knowledge triplet. Then, it determines concentration limit nodes, total quantity index nodes, monitoring frequency nodes, and ecological flow nodes based on the environmental rule instruction set generated by the environmental rule translation engine. Finally, it determines the protection target nodes and the edge connection relationships between each node based on the spatial topology data output by the geographic information system bus, forming a complete environmental assessment constraint graph. The input of the compliance path screening unit is connected to the environmental assessment constraint graph. Its internal path search subunit uses a depth-first search algorithm to search for a complete path from the pollution source node to the receiving water body node in the graph. The constraint matching subunit performs constraint matching on each searched path to verify whether the attributes of each node on the path meet the corresponding environmental protection rules. The path marking subunit marks the verified paths as compliant paths and the unverified paths as non-compliant paths according to the matching results, and marks the specific reasons for non-compliance, and outputs the compliance assessment path judgment conclusion. The prediction parameter set compilation unit takes the compliance assessment path judgment conclusion as input. The parameter extraction subunit extracts the hydrological parameters, water quality parameters, pollution source parameters and boundary condition parameters required by the prediction model. The parameter encapsulation subunit encapsulates the parameters according to the input format requirements of the prediction model driving module, outputs the prediction parameter set and transmits it to the prediction model driving module.

[0025] The prediction model-driven module takes pollution knowledge triples from the pollution element extraction module and prediction parameter sets from the constraint graph construction module as dual inputs, and completes the simulation prediction by cascading water environment dynamics coupled simulation operators and water quality coupled simulation operators. The hydrological process simulation sub-operator within the water environment dynamics coupled simulation operator uses the linearized approximation form of the Saint-Venant equations, namely the kinematic wave equation, to simulate the flow change process of the river. During the solution process, the time step Δt is set to 1 hour, and the spatial step Δx is set to 100 meters. The spatiotemporal discretization of the equations uses the Preissmann implicit difference scheme to ensure computational stability, outputting the flow process lines for each cross-section. The sediment transport simulation sub-operator uses the suspended sediment transport equation, dividing the sediment into five particle sizes according to the representative particle size method, calculating the transport and deposition of sediment in each particle size size, and outputting the sediment concentration process lines for each cross-section. The water temperature simulation sub-operator uses the one-dimensional heat transport equation to simulate the river water temperature change process, where the solar radiation heat absorption is calculated using the exponential decay formula, outputting the water temperature process lines for each cross-section. The coupling interface sub-operator synchronizes the hydrological, sediment, and water temperature simulation results in the time and spatial dimensions through spatiotemporal data interpolation, generating the water environment dynamics coupled simulation results. The input of the water quality coupled simulation operator is connected to the output of the water environment dynamics coupled simulation operator. Using the dynamics coupled simulation results as the driving field, its internal pollutant transport simulation sub-operator solves the convection-diffusion-attenuation equations using the finite volume method. The convection term is processed using a first-order upwind scheme to ensure numerical stability, the diffusion term is processed using a central difference scheme, and the attenuation term is calculated using a first-order reaction kinetic model, outputting the concentration distribution fields of each pollutant. The biochemical reaction simulation sub-operator uses the BOD-DO coupled equations to simulate the oxygen consumption and reoxygenation biochemical reaction processes in the water body, outputting the dissolved oxygen concentration distribution field and the biochemical oxygen demand concentration distribution field. The attenuation process simulation sub-operator uses a first-order attenuation equation to simulate the natural attenuation process of each pollutant. The water quality coupling interface sub-operator couples and integrates the pollutant transport simulation results, biochemical reaction simulation results, and attenuation process simulation results through spatiotemporal data interpolation, ensuring temporal synchronization and spatial alignment of the calculation results between each sub-operator, generating the water quality coupled simulation results. The prediction model-driven module summarizes the coupled simulation results of water environment dynamics and water quality, and outputs a set of prediction results containing the temporal and spatial distribution data of parameters such as flow rate, sediment concentration, water temperature, dissolved oxygen concentration, biochemical oxygen demand concentration, ammonia nitrogen concentration, total phosphorus concentration and total nitrogen concentration for each prediction period. The results are then transmitted to the structured assembly module via the fifth data bus.

[0026] The structured assembly module uses the water-related report chapter template as a framework and text generation operators and report illustration rendering units as the execution engine to automate the assembly of report content. The water-related report chapter template adopts a tree structure in XML format, containing ten chapter nodes: report cover element, report summary element, project overview, environmental quality status survey, pollution source analysis, environmental impact prediction, environmental impact assessment, environmental protection measures, environmental management and monitoring plan, and conclusions and recommendations. Each chapter node has several child nodes corresponding to specific paragraph titles, and placeholder markers are set in key positions to indicate the location where the text generation operators fill in the content. The text generation operator's input terminals are connected to the pollution element extraction module, the prediction model driving module, and the constraint graph construction module, respectively. Its internal content mapping operator maps the input pollution knowledge triples, prediction results, and compliance evaluation path judgment conclusions to the corresponding placeholder positions in the water-related report chapter template. During the mapping process, it establishes unified cross-chapter data reference tags for discharge outlets, control sections, characteristic pollutants, and environmentally sensitive targets. The tag keys adopt a hierarchical naming rule, with the format {data type}-{serial number}-{attribute type}. Discharge outlet numbers begin with P01 (e.g., P01-DISCHARGE represents the discharge volume of discharge outlet 01), control section numbers begin with S01 (e.g., S03-SECTION represents the concentration value of control section 03), characteristic pollutant numbers begin with C01 (e.g., C05-POLLCOD represents the COD concentration of characteristic pollutant 05), and environmentally sensitive target numbers begin with T01 (e.g., T02-TARGET represents the environmentally sensitive target number of environmentally sensitive target 02). (Target coordinates); Each tag key corresponds to a unique record identifier in the database. Cross-chapter data referencing is achieved through a foreign key association mechanism, ensuring the uniqueness and traceability of the same data object referenced in different chapters. The syntax generation sub-operator generates a sequence of sentences conforming to technical report specifications, containing subject, predicate, object, and necessary modifiers and adverbials, based on the mapping results. The format adjustment sub-operator further completes paragraph indentation processing, punctuation standardization processing, and number format unification processing, outputting a fully filled report text. The input end of the report illustration rendering unit is connected to the output end of the text generation operator. Its internal data binding sub-unit binds the monitoring data, prediction data, and statistical results referenced in the report text with the corresponding graphic data. The graphics drawing sub-unit draws report illustrations such as line charts, bar charts, contour maps, and flowcharts based on the bound graphic data. The graphic layout sub-unit aligns the drawn illustrations with the corresponding report text, determines the placement, size, and legend of the illustrations, and outputs a draft report containing illustrations. The structured assembly module transmits the draft report to the verification and optimization module through the sixth data bus.

[0027] The verification and optimization module, as the system's end unit, comprises three sub-units: a cross-chapter association matrix, a consistency verification unit, and a deviation correction unit. It is responsible for performing cross-chapter consistency verification and deviation correction on the initial draft report, ultimately outputting the environmental impact assessment report. The cross-chapter association matrix adopts a sparse matrix format, containing row index fields, column index fields, and association strength fields. The row and column indices correspond to different chapters and paragraphs in the report, respectively, while the association strength field records the degree of information association between two paragraphs. The matrix construction process first performs textual semantic analysis on the initial draft report to identify the core semantic themes of each paragraph. Then, it calculates the Jaccard similarity between different paragraphs based on the keyword set. When the Jaccard similarity of the keyword sets of two texts is not less than 0.5, it determines that there is a semantic association between the two paragraphs and records it in the association matrix; if it is less than 0.5, it is determined that there is no semantic association. Finally, a complete association matrix is ​​constructed based on this. The consistency verification unit's input is connected to both the cross-chapter association matrix and the initial report draft output by the structured assembly module. Its internal data extraction subunit extracts numerical, date, and reference data from each paragraph of the initial report draft. The comparison calculation subunit performs consistency comparisons between related paragraphs based on the association relationships determined by the cross-chapter association matrix. String comparison uses a full match method, distinguishing between uppercase and lowercase letters and full-width and half-width characters, and does not ignore spaces and punctuation. Numerical comparison uses a relative error calculation formula: Relative Error = |Value1 - Value2| ÷ max(|Value1|, |Value2|) × 100%, where Value1 and Value2 are the numerical values ​​of the same feature item in the two related chapter paragraphs, and max(|Value1|, |Value2|) takes the larger of their absolute values ​​as the normalization benchmark. The relative error is less than 0.Data consistency is determined at 1%; concentration indicators are uniformly converted to mg / L units before comparison (μg / L data divided by 1000, g / L data multiplied by 1000) to eliminate the impact of dimensional differences on the results; the anomaly marking subunit marks the identified inconsistent data, records the inconsistency type and location, and adds a data-origin attribute field to the corresponding data item to record the data source, collection time, and original value to retain a complete traceability index, and outputs a consistency verification report; the input of the deviation correction unit is connected to the output of the consistency verification unit, and its internal deviation classification subunit calculates the relative deviation rate of the feature item values ​​in the report chapter based on the original benchmark values ​​in the water environment environmental impact assessment basic dataset. The calculation method is: relative deviation rate = absolute value of the difference between the feature item value in the report chapter and the original benchmark value ÷ original benchmark value × 100%, and classifies the marked abnormal data into three types: numerical out-of-range deviation, numerical contradiction deviation, and reference error deviation; the correction strategy selection subunit performs graded correction based on the comparison result of the relative deviation rate and the preset threshold: when the relative deviation rate exceeds 5%, data is triggered. The rollback and reconstruction process involves several steps. The rollback operation records all occurrences and original values ​​of the feature item in the initial draft report to a rollback log (the log structure includes four fields: chapter number, paragraph number, data reference tag key, and original value). The reconstruction operation re-obtains the original baseline data for the feature item through the data source tracing engine and calls the text generation operator to regenerate the relevant descriptive text for the affected chapters, updating the corrected data in the initial draft report. When the relative deviation rate is greater than 2% and less than or equal to 5%, a local text regeneration step for the affected chapters is triggered, calling the paragraph update submodule of the text generation operator to generate updated paragraph text using the original data and target paragraph identifier as input, replacing the original content. The data correction subunit employs a threshold correction strategy for out-of-range deviations, a priority correction strategy for contradictory deviations, and a backtracking correction strategy for citation errors. Based on the selected strategy, it corrects the abnormal data and updates the corresponding data in the initial draft report. After correction, the data-origin tracing attribute is retained to support complete audit trail, and the final version of the validated and optimized environmental impact assessment report is output.

[0028] To further illustrate the system's operational mechanism, the following detailed explanation of its workflow is provided using two specific application scenarios. In the first scenario, when the system receives environmental impact assessment project data containing fifty pollution sources, the data acquisition module uses an optical character recognition (OCR) component to identify the distribution map of pollution sources in the project area and obtain the spatial coordinates of each pollution source. It also obtains water quality monitoring data from ten upstream and downstream monitoring points in the project area over the past year via a data interface; acquires data on the river topology, drainage network distribution, and sensitive target distribution in the project area via a geographic information system (GIS) bus; and obtains the concentration limits of pollutants corresponding to Class I to IV water bodies as specified in the "Surface Water Environmental Quality Standard" and the Class I to Class III discharge standard limits as specified in the "Integrated Wastewater Discharge Standard" via an environmental rule translation engine. The semantic processing module unifies the collected data to a time base from January 1, 2023 to December 31, 2023, and converts the spatial coordinates to the CGCS2000 coordinate system with a 3-degree projection. The pollution element extraction module extracts attribute information such as the name, location, industry category, production scale, wastewater discharge volume, and main pollutant discharge concentration of fifty pollution source entities, as well as the discharge path entity information such as the location of the discharge outlet, discharge method, and discharge destination of each pollution source, and the name, type, and water environment functional zoning information of the main receiving water bodies. It also verifies whether the balance between wastewater discharge volume and fresh water consumption of each pollution source conforms to the law of conservation of mass through material balance calculations. The constraint map construction module uses water-related pollutants such as ammonia nitrogen, total phosphorus, and dissolved oxygen as nodes, and constructs an environmental assessment constraint map based on the concentration limits in the "Surface Water Environmental Quality Standard," the total quantity indicators in the "Integrated Wastewater Discharge Standard," and the monitoring frequency requirements in the "Regulations on Discharge Permit Management." The compliance path screening unit searches the map for evaluation paths from each pollution source to the receiving water body, filters out compliant paths that meet all constraints, and marks pollution sources with a risk of exceeding standards and their exceeding factors. The prediction model-driven module, based on hydrological conditions of an annual average flow rate of 50 cubic meters per second, a river gradient of 0.5‰, and a river meander coefficient of 1.2, current water quality conditions of 2.5 mg / L ammonia nitrogen concentration and 7.0 mg / L dissolved oxygen concentration, and pollution source parameters of 30,000 tons of wastewater discharged daily from 50 pollution sources with an average ammonia nitrogen concentration of 15 mg / L, conducts a one-year water quality prediction simulation and outputs the predicted concentration values ​​for each monitoring point at different time periods. The structured assembly module assembles the above processing results into an environmental impact assessment report containing ten chapters. The text generation operator generates approximately 80,000 words of report text, and the report illustration rendering unit draws a total of fifteen report illustrations, including distribution maps of 50 pollution sources, line graphs of annual average concentration changes, and concentration contour maps.The verification and optimization module identified a total of 230 pairs of chapter paragraphs with data correlation in the report through the cross-chapter association matrix. The consistency verification unit found three data inconsistencies during the comparison process (for example, the total emissions described in the pollution source analysis chapter deviated from the input parameters used in the environmental impact prediction chapter). The deviation correction unit corrected the data by adopting the corresponding correction strategy according to the anomaly type and then output the final environmental impact assessment report.

[0029] In the second application scenario, a chemical industrial park plans to build a new wastewater treatment plant with a daily processing capacity of 50,000 tons. The project requires a water environment impact assessment report to apply for a discharge permit. The optical character recognition component of the data acquisition module scans and identifies existing feasibility study reports, old environmental impact reports, water balance test reports, and discharge outlet demonstration reports for the project. It extracts structured information such as the wastewater treatment plant's design scale, daily treatment capacity, influent water quality indicators (chemical oxygen demand, ammonia nitrogen, total phosphorus, total nitrogen, five-day biochemical oxygen demand, etc.), effluent water quality standards (Class A discharge standard), receiving water body name, and discharge outlet coordinates. The data interface connects to the database of the local environmental monitoring center to obtain monthly water quality monitoring data for the past three years from a monitoring section 5 kilometers downstream of the project area, including water temperature, pH value, dissolved oxygen, and dissolved oxygen levels. Conventional indicators such as oxygen, permanganate index, ammonia nitrogen, total phosphorus, and total nitrogen, as well as characteristic pollutants such as volatile phenols, anilines, and nitrobenzenes; the Geographic Information System (GIS) bus obtains spatial data such as river network topology, water diversion project distribution, drinking water source protection zone boundaries, and groundwater vulnerability zones in the project area from the watershed management agency through the OGC standard interface; the environmental protection rule translation engine retrieves water environment wastewater discharge standards and environmental protection rule standards from the environmental protection rule database, and converts the limits of chemical oxygen demand (COD) below 50 mg / L, ammonia nitrogen below 5 mg / L, and total nitrogen below 15 mg / L in the Class A discharge standard into executable concentration threshold instructions. The semantic processing module's time-series alignment unit identifies two sampling frequencies in the water quality monitoring data: monthly data (sampling interval of 30 days) and quarterly data (sampling interval of 90 days). It uses linear interpolation to interpolate the quarterly data into monthly data, unifying all monitoring data into a monthly time series and converting the timestamps to the ISO8601 standard format. The coordinate benchmark unification unit unifies the coordinates of the project's discharge outlet (Beijing 54 coordinate system) and the boundary coordinates of the drinking water source protection area (WGS84 coordinate system) into the CGCS2000 coordinate system and unifies the elevation data to the 1985 National Elevation Datum. It outputs a unified environmental assessment dataset that includes river network connectivity, water catchment paths, and the spatial adjacency between the discharge outlet and the receiving water body.The water environment semantic parsing engine of the pollution element extraction module performs semantic parsing on the evaluation text of the wastewater treatment plant, identifying entities such as the discharge outlet and receiving water body of the first phase of the wastewater treatment plant, as well as the discharge and reception relationships. The pollution factor feature vector extraction unit generates feature vectors containing information such as the designed treatment capacity of 50,000 tons per day, the designed influent chemical oxygen demand of 500 mg / L, the designed effluent chemical oxygen demand of 50 mg / L, and the removal rate of 90%. The material balance verification unit takes 35,000 tons of fresh water consumption and 18,000 tons of circulating water consumption as input, and 48,000 tons of wastewater discharge and 150 tons of sludge with a moisture content of 80% as output. The calculated water balance rate is 97.3%, which is within the preset deviation range of 95% to 105%. It is determined that the material balance verification is passed, and outputs a pollution knowledge triplet containing entities of the wastewater treatment plant, discharge outlet, various pollutants, receiving water body, and discharge relationship edges. The constraint graph construction module uses water-related pollutants such as chemical oxygen demand, ammonia nitrogen, total phosphorus, and total nitrogen as nodes, and uses the Class I discharge standard limits in the "Integrated Wastewater Discharge Standard," the Class IV water body limits in the "Surface Water Environmental Quality Standard," the total discharge indicators approved by the discharge permit, the monthly monitoring frequency, and the ecological flow of 10 cubic meters per second as constraint nodes. It constructs an environmental assessment constraint graph using the pollutant migration path from the discharge outlet to the downstream monitoring section and the compliance judgment logic chain as directed edges. The compliance path screening unit uses a depth-first search algorithm to search for the complete path from the discharge outlet to the downstream monitoring section. If the total phosphorus concentration limit node in the path does not meet the Class IV water body requirement of 0.1 mg / L, the path is marked as a non-compliant path with specific reasons for non-compliance. The prediction parameter set compilation unit extracts hydrological parameters (water temperature, flow rate, flow velocity), water quality parameters (initial concentration, background concentration), pollution source parameters (discharge volume, discharge concentration), and boundary condition parameters from the compliance assessment path judgment conclusions, compiles them into a prediction parameter set, and outputs it to the prediction model driving module. The water environment dynamics coupled simulation operator of the prediction model-driven module uses the Saint-Venant equations to simulate the flow change process of the receiving river. It comprehensively considers factors such as upstream inflow, sewage treatment plant discharge, and tributary inflow, and outputs the flow process lines of each section. At the same time, it uses the heat transport equation to simulate the water temperature change process considering the heat exchange between sewage treatment plant discharge and upstream inflow, and outputs the water temperature process lines of each section. After receiving the above dynamics coupled simulation results, the water quality coupled simulation operator uses the convection-diffusion attenuation equations to simulate the transport and transformation process of pollutants such as chemical oxygen demand, ammonia nitrogen, total phosphorus, and total nitrogen in the water body. It uses the BOD-DO coupled equations to simulate the oxygen consumption and reoxygenation process in the water body, and uses the first-order attenuation equation to simulate the natural attenuation process of pollutants. It outputs a set of prediction results containing the predicted concentration values ​​of chemical oxygen demand, ammonia nitrogen, total phosphorus, total nitrogen, and dissolved oxygen at each monitoring section downstream of the discharge outlet at different time periods.The text generation operator of the structured assembly module fills the information corresponding to each tag key in the pollution knowledge triplet into the pollution source analysis chapter, fills the concentration distribution data corresponding to each tag key in the prediction results into the environmental impact prediction chapter, and fills the compliance evaluation path determination conclusion into the environmental impact assessment chapter, based on the water-related report chapter template. The report illustration rendering unit draws the wastewater treatment plant process flow diagram, discharge outlet location diagram, receiving water body water quality status map, predicted concentration change along the process line graph, compliance analysis table, and other report illustrations and completes the text and image layout, forming a draft report containing ten chapters. The cross-chapter association matrix of the verification and optimization module performs textual semantic analysis on the initial draft report, identifying core semantic themes such as the daily treatment capacity of the wastewater treatment plant (50,000 tons), the daily discharge volume (48,000 tons), and the daily effluent quality meeting the Class A standard, and constructs an association matrix accordingly. The consistency verification unit compares the data between the pollution source analysis chapter and the environmental impact prediction chapter, finding that the former records a discharge volume of 48,000 tons per day while the latter uses a discharge parameter of 49,000 tons per day, resulting in a data inconsistency of 1,000 tons per day. The anomaly marking subunit marks this inconsistent data and records the location of the inconsistency. The deviation correction unit then performs deviation correction based on the data... The results showed a relative deviation rate of 2.08%, exceeding 2%, which triggered a local text regeneration step in the affected chapters. This involved calling the text generation operator to regenerate the emission data referenced in the environmental impact prediction chapter and updating the revised data to the initial draft of the report. The final output was a validated and optimized version of the environmental impact assessment report. This report included complete content such as wastewater treatment plant engineering analysis, regional water environment status survey, pollution source and pollution prevention and control measures analysis, surface water environmental impact prediction, groundwater environmental impact analysis, environmental risk assessment, environmental protection measures and monitoring plans, etc. The data in each chapter was consistent and logically self-consistent.

[0030] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A method for generating an environmental impact assessment report, characterized in that, include: Collect multi-source basic data for water environment assessment to obtain data related to the boundary between water environment and hydrology and water quality, and form the environmental protection rule instruction set; The multi-source basic data are subjected to hydrological and water quality time series alignment and unified processing of spatial coordinates and elevation benchmarks to construct an environmental assessment dataset; Pollution factor feature vectors are extracted from the environmental assessment dataset, and the pollution knowledge triples are constructed using a preset water environment semantic parsing engine. Based on the aforementioned environmental protection rule instruction set, an environmental evaluation constraint graph is constructed with water-related pollutants, concentration limits, total quantity indicators, monitoring frequency, ecological flow and protection targets as nodes, and migration association, judgment logic chain and causal path as directed edges. Then, the compliant evaluation path is screened out using pollution knowledge triples, and a set of predicted parameters for water environment impact is compiled and non-compliant evaluation paths are marked. The predicted parameter set is used to drive a coupled simulation operator of water environment dynamics and water quality to calculate the predicted results of the water environment pollution impact. Based on the pre-set water-related report chapter framework, the pollution knowledge triplet, prediction results, and compliance evaluation path judgment conclusions are assembled into the initial draft of the report; Extract water-related feature items across chapters from the initial draft of the report and perform cross-chapter consistency comparison and logical verification to obtain the logical verification result. When the logical verification result indicates that there is a deviation or conflict, trigger the deviation correction instruction and output the final report.

2. The method for generating an environmental impact assessment report according to claim 1, characterized in that, The multi-source basic data includes pollution source and hydrological and water quality boundary characteristic parameters, surface water and groundwater environmental quality monitoring data, water catchment and drainage topology data, regional water environment functional zones and sewage discharge permit constraint indicators, as well as applicable surface water, groundwater and sewage discharge control requirements, to form an environmental protection rule instruction set.

3. The method for generating an environmental impact assessment report according to claim 1, characterized in that, The multi-source basic data are subjected to hydrological and water quality time series alignment and spatial coordinate and elevation benchmark unification processing to construct an environmental assessment dataset, including: Identify the original timestamps and water season division markers of each data source, and use the representative periods of the preset hydrological scheduling or hydrological year as a benchmark to perform missing data interpolation and outlier smoothing on the water quality monitoring sequence, and perform time-series registration with precipitation runoff process or gate scheduling logs to eliminate the misalignment of monitoring at different cross sections and sewage outlets on the time axis. In conjunction with the time-series registration, the spatial coordinates and elevation benchmarks are uniformly processed. By identifying the projected coordinate systems and elevation benchmarks of each spatial data and hydrological element, the spatial boundaries of the water environment assessment, sewage outlets and their distances from the shore, river cross-sections and lake / reservoir shorelines, groundwater monitoring wells and hydrogeological unit boundaries are uniformly mapped to the preset geographic coordinate system and national elevation benchmark through coordinate and elevation transformation. This ensures that the environmental assessment dataset includes river network connectivity, water catchment paths and the spatial adjacency relationship between surface water and groundwater. The time-registered hydrological and water quality sequences are attached to the geometric representation of the monitoring points and spatiotemporally fused with the river network connectivity, water catchment paths, and spatial adjacency relationships between surface water and groundwater to construct the environmental assessment dataset.

4. The method for generating an environmental impact assessment report according to claim 1, characterized in that, Pollution factor feature vectors are extracted from the environmental assessment dataset, and the pollution knowledge triples are constructed using a pre-defined water environment semantic parsing engine, including: The pollution factor feature vector includes wastewater generation, pollutant concentration and composition, pollution treatment unit efficiency, discharge intensity of sewage outlets, and geographical location information of water bodies. The water environment semantic parsing engine is a semantic parsing engine that has been fine-tuned with water environment domain corpus; The water environment semantic parsing engine uses a pre-defined bidirectional long short-term memory network model to identify the entity chains of pollution sources, pollutants, discharge methods, receiving water bodies, and drainage destinations in the water environment pollution description text. It also extracts a list of characteristic pollutants and industry characteristic factors to generate the initial structure of entities and relationships in the pollution knowledge triple. The initial structure is used to establish the identifiers of pollution source entities, discharge outlet entities, pollutant components, and discharge-receiving relationship edges, serving as the topological skeleton for attribute mounting and numerical verification. On the pollution source entity, sewage outlet entity and entity chain defined by the topological skeleton, the statistical characteristic values ​​of wastewater volume and pollutant discharge rate and discharge concentration of each discharge link are calculated according to water balance and concentration balance. The statistical characteristic values ​​are attached as quantitative attributes to the corresponding entity and corresponding relation edge, so that the initial structure obtains the source strength, water volume and concentration characterization consistent with the discharge link. Using the quantitative attributes and the characteristic vectors of the pollutants as constraints, the numerical logical relationship between raw material input, product output, loss and the amount of characteristic pollutants generated in wastewater is verified by material balance and water-salt ion balance. When the deviation rate exceeds the preset deviation threshold, the water balance and source strength recalculation command is triggered, and the recalculated attributes are written back to the corresponding entities and relationships in the topology skeleton. For toxic, hazardous, or persistent pollutants, bioaccumulation or ecological risk label attributes are added to the pollutant components that pass the verification. After the semantic initial structure, the quantitative attributes, and the material and water salt verification results are consistent with each other, they are aggregated to form the pollution knowledge triplet, which is composed of entities, relations, and attributes; the entities include at least pollution sources, pollutant components, emission pathways, wastewater treatment units, and receiving water bodies.

5. The method for generating an environmental impact assessment report according to claim 1, characterized in that, The environmental assessment constraint map uses water-related pollutants, concentration limits at cross sections or discharge outlets, total quantity control indicators, monitoring frequency, ecological flow or hydrological situation constraints, and drinking water source protection target types as nodes. It uses surface water-groundwater migration correlation, compliance judgment logic chain, applicable rules for mixed zones, spatial buffer inclusion relationship, and upstream and downstream causal paths as directed connection edges, and configures matching weight and priority parameters for each node. Using the pollution knowledge triples as query conditions, evaluation paths that satisfy the evaluation logic chain are selected from the environmental evaluation constraint graph. The constraint nodes on the evaluation paths are parameterized and compiled to generate a set of prediction parameters for water environment impact assessment that includes hydrological and water quality boundaries, initial and background water quality fields, pollution non-point source and point source strengths and degradation coefficients. Based on the candidate compliance evaluation path, non-compliant evaluation paths are determined according to the environmental evaluation constraint graph and the environmental rule instruction set. If any constraint node fails to meet the standard or the deduction premise of any directed connection edge contradicts the judgment output, the path is a non-compliant evaluation path.

6. The method for generating an environmental impact assessment report according to claim 5, characterized in that, The non-compliance evaluation path includes at least one of the following situations: The predicted concentration or calculated total emission exceeds the concentration limit node of the section or discharge outlet, the total control index node, or the emission standard limit node mapped to the discharge permit. The spatial location of the candidate route is inconsistent with the spatial buffer inclusion relationship of drinking water source protection areas, shoreline buffer zones or groundwater pollution prevention and control zoning nodes, or falls under the prohibited control requirements. The output path following the compliance judgment logic does not match the more stringent evaluation level branch or additional mitigation measure branch given by the rule reasoning engine; Even if the applicable conditions for the rule nodes or the surface water-groundwater migration correlation edges in the mixed zone are not met, the deduction is still completed along the directed edges; the conditions for the monitoring frequency nodes, ecological flow nodes, or hydrological situation constraint nodes are not met by the candidate paths. Paths deemed non-compliant are marked with a non-compliant label, and the constraint node type, connection edge type, and deviation from the limit value that triggered the judgment are recorded.

7. The method for generating an environmental impact assessment report according to claim 1, characterized in that, Based on the pre-set water-related report chapter framework, the pollution knowledge triplet, prediction results, and compliance evaluation path determination conclusions are assembled into the initial draft of the report, including: A pre-set standardized water-related report chapter template is provided. The water-related report chapter template defines the titles, paragraph formats and data reference tags of chapters such as pollution source strength and water discharge characteristics, surface water environmental impact, groundwater environmental impact, environmental protection measures and monitoring plans. At the same time, it establishes a unified data reference tag key across chapters. The data reference tag key is used to uniquely associate the sewage outlet, control section, characteristic pollutant and environmentally sensitive target. According to the data reference tag key and tag mapping relationship, the wastewater source strength, treatment efficiency and discharge pattern corresponding to each tag key in the pollution knowledge triple are converted into descriptive text and filled into the pollution source strength and water discharge characteristics chapter, so that each data reference tag in the water discharge characteristics chapter corresponds one-to-one with the source strength object in the pollution knowledge triple; Based on the consistency of the data reference tags, the concentration distribution along the route, cross-sectional compliance analysis, lake and reservoir pollution zone range or groundwater pollution halo evolution map corresponding to each tag in the prediction results, as well as the concentration and standard comparison table at sensitive targets, are automatically rendered as report illustrations and appendices and filled into the impact prediction section of surface water environmental impact and groundwater environmental impact, so that the impact prediction display is consistent with the emission objects and receptor objects referenced in the pollution source strength section; Using the predicted concentrations, standard comparison tables, and map tags filled in the impact prediction section as input, the time-matching relationship between the predicted concentrations and water environmental quality standards, emission standards, and discharge permit limits is compared using the compliance evaluation path determination principle. Conclusive statements, including compliance demonstrations and mitigation measures requirements for exceeding standards, are generated and filled into the evaluation conclusion section, ensuring that the evaluation conclusions maintain a consistent reference relationship with the impact predictions and pollution source intensity analysis on the data reference tags.

8. The method for generating an environmental impact assessment report according to claim 1, characterized in that, The logical verification results are obtained by extracting water-related feature items across chapters from the initial draft of the report and performing cross-chapter consistency comparison and logical verification, including: Define a cross-chapter association matrix, which uses water-related association features as rows and report chapters as columns. The matrix cells are used to record the text content and values ​​of the same feature in each chapter. The water-related association features include at least the sewage outlet number and coordinates, wastewater discharge volume and water quality concentration, receiving water body name and water function zone target, hydrogeological unit number, protection distance, emergency pool volume and water reuse rate that appear repeatedly in different chapters. The row identifier of the cross-chapter association matrix is ​​associated with the object key in the data reference tag key that has a cross-chapter reference relationship, so that the description of the same discharge outlet, control section or sensitive target in different chapters is mapped to the same matrix row; Based on the row and column structure of the matrix, the scanning steps are performed. The initial draft of the report is traversed according to the feature item identifier corresponding to the matrix row. The associated feature items and values ​​that match the row identifier in each chapter are extracted and filled into the corresponding matrix unit to complete the cross-chapter matrix filling. After the matrix is ​​filled, consistency checks are performed on cross-chapter column units row by row: for the same matrix row, the differences between different chapter columns are calculated using string matching and numerical comparison algorithms; for concentration indicators, unit normalization is performed before comparison. When any row has a non-zero difference or semantic inconsistency between any two chapter columns, the associated feature item is located in the chapter position of the initial draft report based on the current row identifier and column identifier and marked as a logical conflict point, and the row identifier and column identifier are retained as a source index.

9. The method for generating an environmental impact assessment report according to claim 1, characterized in that, When the logical verification result indicates a deviation or conflict, a deviation correction instruction is triggered and a final report is output, including: The logical conflict points are sent to the data source tracing engine to compare the discrepancies between the conflict items and the original monitoring, permitting or verification ledger data of the multi-source basic data of water environment impact assessment. If the data in a certain chapter does not match the original values ​​in the dataset, the overwrite instruction is executed, and the text generation operator for that chapter is retried using the original data; Calculate the relative deviation rate using text generation operators; The relative deviation rate is calculated by multiplying the absolute value of the difference between the feature item values ​​in the report chapter and the original benchmark values ​​in the water environment environmental impact assessment basic dataset into the numerator, and the original benchmark value into the denominator. A first preset deviation threshold and a second preset deviation threshold are preset, wherein the first preset deviation threshold is greater than the second preset deviation threshold, and the second preset deviation threshold is greater than or equal to zero; When the relative deviation rate exceeds the first preset deviation threshold, the data rollback and reconstruction steps are triggered. When the relative deviation rate is greater than the second preset deviation threshold and less than or equal to the first preset deviation threshold, the local text regeneration step of the affected chapter is triggered, and a re-examination is performed on the row of the corresponding feature item in the cross-chapter association matrix. When the relative deviation rate is less than or equal to the second preset deviation threshold, a low-risk consistency prompt record is generated and the data rollback and reconstruction steps are not triggered.

10. An environmental impact assessment report generation system, used to execute an environmental impact assessment report generation method as described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to collect multi-source basic data for water environment assessment, so as to obtain data related to the boundary between water environment and hydrology and water quality and form the environmental protection rule instruction set. The semantic processing module is used to perform hydrological and water quality time-series alignment and spatial coordinate and elevation benchmark unification processing on the multi-source basic data to construct an environmental assessment dataset. The pollution element extraction module is used to extract pollution factor feature vectors from the environmental assessment dataset and construct the pollution knowledge triples using a preset water environment semantic parsing engine. The constraint graph construction module is used to construct an environmental evaluation constraint graph based on the environmental protection rule instruction set. The graph has water-related pollutants, concentration limits, total quantity indicators, monitoring frequency, ecological flow and protection targets as nodes, and migration association, judgment logic chain and causal path as directed edges. The module also uses pollution knowledge triples to screen out compliant evaluation paths, and then compiles and generates a set of predicted parameters for water environment impact and marks non-compliant evaluation paths. The prediction model driving module is used to drive the coupled simulation operator of water environment dynamics and water quality using the prediction parameter set, and calculate the prediction results; The structured assembly module is used to assemble the pollution knowledge triplet, the prediction results, and the compliance evaluation path judgment conclusions into the initial draft of the report according to the preset water-related report chapter framework. The verification and optimization module is used to extract water-related feature items across chapters from the initial draft of the report and perform cross-chapter consistency comparison and logical verification to obtain the logical verification result. When the logical verification result indicates that there is a deviation or conflict, a deviation correction instruction is triggered and the final report is output.

Images