Intelligent ecological environment pre-auditing method based on partition management and control and capacity control

Abstract

The invention discloses an ecological environment intelligent pre-auditing method based on partition management and control and capacity control, and relates to the technical field of ecological environment informatization management. According to the method, parallel superposition is carried out on multiple types of ecological environment sensitive layers, dimension reduction processing and weighted integration are carried out on the multi-dimensional space conflict factors through principal component analysis, a quantifiable ecological sensitivity risk score is generated, the single logic that a traditional method can only judge whether overlapping exists or not is effectively overcome, fine expression of multi-layer composite conflicts is achieved, and the risk assessment efficiency is improved. And the resolution and judgment basis of spatial analysis are enhanced. A pollution factor-control unit-emission period three-dimensional capacity comparison model is established, and for capacity accounting requirements of multiple pollutant types, multiple space areas and multiple time dimensions, limitation of static threshold comparison of a traditional method is broken through, and dynamic adaptability and accuracy of capacity evaluation under a complex emission structure are realized.

Description

Technical Field

[0001] This invention relates to the field of ecological and environmental information management technology, specifically to an intelligent pre-approval method for ecological and environmental protection of construction projects that combines geospatial analysis and environmental capacity calculation. It is applicable to environmental supervision scenarios such as project site selection pre-assessment and pre-determination of pollution discharge permits. Background Technology

[0002] As ecological civilization construction continues to advance, environmental impact assessments must be conducted in advance during the site selection and project approval stages of construction projects to avoid conflicts with ecological protection red lines, excessive pollutant emissions, and regional environmental risks and hazards, thus promoting the transformation of environmental governance from "end-of-pipe control" to "source prevention." Against this backdrop, some regions have successively launched pilot projects for environmental pre-approval systems for ecological and environmental projects, assisting ecological and environmental authorities in conducting pre-project environmental feasibility analyses and risk screenings.

[0003] Existing methods generally employ a static comparison mechanism combining layer overlay and planning judgment to analyze the site selection and emission status of construction projects, outputting preliminary conclusions on whether spatial conflicts or pollutant overload exist, serving as auxiliary technical references for approval processes such as planning site selection and pollution discharge permits. While these methods have played a positive role in improving preliminary review efficiency, their technology still has the following significant shortcomings: Spatial analysis is static and has a single dimension of judgment: Existing methods generally adopt the approach of "layers are superimposed one by one and rule judgments", which can only determine whether a project falls into a sensitive area. They cannot comprehensively consider the composite sensitivity between different layers, lack the ability to quantify the degree of risk, and are difficult to support the ranking of projects.

[0004] Rigidity and poor adaptability of capacity comparison: Capacity calculation is usually based on fixed thresholds and annual data, which fails to fully consider dynamic factors such as pollution type, emission time, and the coupling relationship of indicators. The accuracy and foresight of capacity comparison are insufficient, and it is difficult to identify boundary projects.

[0005] Lacking intelligent learning mechanisms, it is difficult to evolve and optimize: Existing methods are mainly based on static coding logic and do not embed supervised learning or rule mining capabilities driven by historical approval data. They lack experience transfer and model adaptive evolution mechanisms, making it difficult to summarize and reuse approval experience.

[0006] The preliminary review results are poorly structured and lack intelligence: the output mainly consists of qualitative descriptions or binary conclusions, lacking structured data support, which is not conducive to forming standardized report templates or supporting mandatory multi-item analysis.

[0007] The existing pre-screening methods are closed in structure and lack intelligent integration capabilities: they generally adopt linear processing flow, with high coupling between functional modules and a lack of flexible task scheduling and interface mechanisms. They are difficult to embed intelligent algorithm modules such as principal component analysis, supervised learning models, and scoring prediction algorithms, which limits the methods' ability to expand and evolve in terms of intelligent modeling, parameter adaptation, and result interpretability. Summary of the Invention

[0008] The purpose of this invention is to provide an intelligent pre-assessment method for the ecological environment based on zoning management and capacity control, addressing the aforementioned problems.

[0009] The technical solution of the present invention is as follows: An intelligent pre-assessment method for the ecological environment based on zoning management and capacity control includes the following steps: Project data access and spatial boundary import: Access project land boundary graphic data and project attribute information, and unify coordinate transformation and layer binding; Multi-layer ecological environment control element overlay analysis: Load multiple types of ecological environment sensitive layers, perform spatial overlay calculations with project boundaries, identify conflict areas, and form a structured output of layer conflicts; Principal component analysis model construction and ecological risk scoring: A factor matrix is ​​constructed based on the layer conflict index data. Principal component analysis is used to compress dimensions and weight factors to form a comprehensive ecological sensitivity scoring index for the project. Pollutant Emissions and Regional Capacity Mapping: Construct and continuously update the environmental capacity database of control units. Based on the main pollutants, emission intensity and emission cycle information declared in the project, establish a three-dimensional dynamic capacity calculation mechanism of pollutant factors-control units-time slices; Regional capacity comparison and preliminary feasibility assessment: The total emissions of the project are compared with the remaining environmental capacity of the target control unit, the permissible analysis level is output, and the comparison status, degree of exceedance and spatial unit of each pollutant are recorded. Supervised learning model training and permit prediction: Construct a pollution discharge permit prediction model based on a supervised learning mechanism, train it using historical approval data, extract the relationship between feature variables and permit determination, complete the classification judgment and probability prediction of the feasibility of new project permits, and assist manual evaluation; Results: Automatically generated structured project pre-screening report.

[0010] Furthermore, the multi-layer ecological environment management element overlay analysis includes: Load and call ecological and environmental control layer data for spatial overlay analysis: integrate and dynamically load multiple types of ecological and environmental spatial layers; call layer data through layer service interfaces; Layer data version synchronization mechanism: Regularly synchronize layer updates from the ecological and environmental authorities or business systems; Establish a spatial overlay relationship judgment mechanism between project boundaries and ecological environment control layers: call the spatial analysis engine to perform spatial calculations on the imported project boundary graphics and various layer data; Based on the spatial calculation results, a logical judgment operation is performed to determine whether there is a condition that "the area intersecting with the ecological protection red line is greater than 0 square meters", which is considered a spatial conflict. Set spatial judgment rule parameters: The platform has built-in default thresholds for commonly used judgment rules, and allows users to customize and modify conflict identification thresholds and sensitivity level classification standards in specific analysis tasks; Set layer conflict judgment weights and grading rules: Set conflict judgment weights and grading standards for various types of ecological environment layers, and set conflict impact levels and corresponding weight coefficients for each type of ecological environment layer; users can manually fine-tune the layer weight values ​​based on the ecological control requirements of specific management areas or the differences in the characteristics of actual projects; Generate spatial conflict analysis results and layer records: The spatial overlay analysis results of the construction project boundary and the ecological environment layer are extracted and recorded in a structured manner. Based on spatial relationship judgment and layer attribute parsing, structured data results that can describe the core content of conflict status, spatial overlap degree, layer feature information and conflict level are generated and stored and managed in a unified format. The spatial conflict analysis results are presented in the map visualization interface. Conflict areas can be highlighted in the geographic information interface, allowing users to view layers by category, filter conflict levels, control the visibility of layers, and perform multi-layer linked preview operations as needed. Constructing a layer conflict factor matrix: The spatial conflict analysis results of each layer are transformed into a modeling input form, specifically by constructing a two-dimensional feature matrix of "project × layer factor"; Outlier removal and field filtering are performed to eliminate noise factors or null values ​​that may interfere with subsequent analysis; the final factor matrix is ​​written to the cache or intermediate database table in real time and bound to the project number. Establish a version control and process backtracking mechanism for analysis results: Construct a version management and process backtracking mechanism for spatial overlay analysis results. This mechanism records and archives the execution process of each spatial overlay analysis task, covering task identifier, executor information, timestamp, version identifier of input layer, and corresponding snapshot elements of analysis results.

[0011] Furthermore, the principal component analysis model construction and ecological risk scoring include: Perform principal component analysis for dimensionality reduction: Perform principal component analysis on the constructed layer conflict factor matrix: Standardize the layer conflict factor matrix to convert all numerical fields into a standard format with zero mean and unit variance; calculate the covariance matrix between variables and extract its eigenvalues ​​and corresponding eigenvectors, thereby constructing the directions of each principal component axis in the multidimensional space; obtain the principal component indices. The dimensional truncation threshold is set based on the cumulative contribution rate. When the cumulative explained variance exceeds the set proportion, the required number of principal components is automatically determined to form the best approximate expression of the original combination of conflicting factors in a lower dimension. The principal component score is calculated by projecting the item onto the principal component axis. Record the core parameters of the principal component model, including the feature loading matrix used in modeling, the original factor contribution of each principal component, the layer field information used in analysis, and the model generation timestamp content; The principal component score for each item is bound to the item identifier and output in a structured format; An ecological sensitivity risk scoring model is constructed to quantify the spatial environmental risk of each project. Weights are automatically assigned based on the variance contribution rate of each principal component, and a linear combination method is used to weight the scores of all principal components to form a single comprehensive risk score. The scoring model is as follows: , in, Indicates project Sensitivity score, For the project in the Scores on principal components The weights of the principal components; The scoring results are normalized to a fixed range, with higher values ​​indicating higher ecological risks in the project's location area, reflecting the project's comprehensive sensitivity in terms of spatial conflict. The principal component weights can be adjusted according to local ecological management requirements, which can be achieved through a configuration interface or API call, and modification logs are automatically recorded to ensure traceability. The final scoring results will be associated with metadata information such as project identifier, analysis time, and principal component model version, and output as structured data. Set risk level classification and grading rules: Set risk level classification rules and classify projects; multiple classification strategies can be used, including static segmentation based on fixed thresholds and dynamic clustering based on data distribution characteristics; among them, the static segmentation strategy can divide levels according to preset scoring intervals, which facilitates standardization and interpretability of results; the dynamic clustering strategy uses an automatic classification algorithm to generate risk level boundaries based on historical project scoring distribution. Output ecological sensitivity scores and layer highlighting results: Load the ecological score values ​​and risk level results corresponding to each project into the map rendering engine, and set the layer color style according to the score range to form a highlighting visualization effect based on the level division.

[0012] Furthermore, the mapping of pollutant emissions to regional capacity includes: Extract pollutant information from the project and establish an emissions inventory: Based on the data submitted regarding pollutant type, emission scale, emission facilities, and emission cycle, emission factor parameters related to the project were extracted, and a standardized emission inventory was established. The extracted emission factors include... , Common water and air pollutants such as COD and ammonia nitrogen are recorded, along with their corresponding emission source type, emission intensity, emission method, and accounting unit. The entered fields are semantically parsed and standard fields are completed, and a structured list of data is formed using field mapping logic. Each emission information is uniquely indexed by "project number + pollutant factor" and written into a preset emission information table. Establish a mapping relationship between pollutants and emission type classifications, automatically label the category and emission control attributes through a pollutant factor dictionary, and bind it to the spatial boundary information of the project land use; Matching emission factor and control unit capacity data: Based on the spatial location of the project site, the ecological and environmental control unit where it is located is identified, and the capacity data of the various pollutants involved in the project are mapped to the corresponding control unit. The capacity data uses pollutant-regional unit as the primary key and stores fields including capacity limit, occupied capacity, remaining capacity, capacity accounting year, data source and update date. This information is retrieved from the regional environmental capacity database, and the pollutants in the emission inventory are matched with the same factors in the control unit. The matching process can automatically filter the control unit type according to the medium to which the pollutant belongs and supports matching multiple control units simultaneously. The matching result is represented by a triple structure of "project number-pollutant factor-control unit number", and the matching method, spatial coverage ratio, and capacity data integrity status metadata are recorded and written into the capacity mapping result table. Establish a three-dimensional mapping relationship between emissions and capacity across different time periods: By introducing the emission time dimension, a three-dimensional mapping structure of "pollutant-control unit-emission period" is established between pollutant emissions and regional environmental capacity: The method identifies information fields related to emission cycles in project application materials, including annual average emissions, quarterly emissions, monthly emissions, and emissions differences between wet and dry seasons. For projects with periodic or seasonal emission characteristics, the method breaks down their total emissions by time period to form time-period emission factor data. Retrieve time-of-use capacity data of the control unit from the regional capacity database and establish a corresponding time index field to ensure consistency between emission time and capacity time dimension; Construct a mechanism for comparing and determining pollutant intensity versus remaining capacity: The declared emission intensity of each pollutant is quantitatively compared with the remaining environmental capacity of the corresponding control unit to form a capacity occupancy rate index, and the feasibility of project emissions and whether there is an overload risk are judged accordingly. The capacity comparison calculation formula is based on the following: , in, This indicates the pollutant emission intensity of the project at a specific control unit and during a specific time period, expressed in tons per year or... ; This indicates the remaining environmental capacity of the unit during that time period; The comparison results are divided into different prediction levels according to the occupancy rate. The specific division rules adopt the following intervals: Utilization rate ≤ 0.6%: Sufficient capacity, approval recommended; 0.6 < occupancy rate ≤ 0.9: Capacity is approaching the critical level; a review is recommended. Utilization rate > 0.9: Capacity is tight or overloaded; warning is recommended. The comparison output results are stored in a structured format with fields such as "project number - pollution factor - time period - control unit - occupancy rate - judgment level". This is used to generate subsequent feasibility recommendations for permits and can be overlaid on a map to display the capacity pressure status in a graphical form. Output capacity alignment suggestion results and boundary judgment labels: Based on the grading thresholds set by the system, the occupancy rate results are divided into different levels of permission recommendation categories; The judgment results are bound to the project boundary graphic object, and graphic display attribute values ​​are assigned to each type of result to achieve spatial expression; boundary labels for map visualization are generated by converting the judgment level into layer rendering parameters. Control units in the critical range are marked as "sensitive boundary areas" as important reference sections for subsequent environmental management and optimization design.

[0013] Furthermore, the regional capacity comparison and preliminary assessment of permit feasibility include: Obtain the pollutant capacity occupancy rate calculation results: Based on the established ternary matching relationship of "project-pollutant-control unit-emission period", a data index structure of pollutant emission and remaining capacity of control unit is constructed, and the capacity comparison formula is called to complete the occupancy rate calculation. The pollutant emission intensity Eproj in the project pollution inventory data table t_proj_pollutant_info is called and a one-to-one mapping is established with the remaining capacity data Cremain of the corresponding control unit in the regional capacity database t_ctrlunit_capacity_remain. The capacity occupancy rate of pollutants in the corresponding control unit is calculated, and the result is written into the structured capacity occupancy rate calculation result table t_proj_capacity_ratio.

[0014] Set permission determination rules and tiered thresholds: Construct a set of permission judgment rules and set multi-level judgment thresholds to support the feasibility assessment of projects under environmental capacity constraints; The permit determination rules are set based on relevant environmental management regulations and regional pollutant control policies, and thresholds are divided using a tiered and graded approach; the pollutant emission occupancy rate (U) is set at the following permit determination levels: If 𝜂 < 0.6, it is judged as "recommended to pass", indicating that the project has little impact on the control unit and is feasible; If 0.6≤A<0.9, it is judged as “capacity critical”, indicating that the project is close to the regional capacity limit, and it is recommended to further optimize the emission intensity or conduct a risk assessment. If α ≥ 0.9, it is judged as an "overload warning", indicating that the project has significantly exceeded the capacity safety threshold and direct approval is not recommended; Generate capacity comparison conclusions and result labels: Based on the occupancy status and judgment results of each pollutant under different control units, the project's capacity comparison comprehensive conclusion is output, and information records with permission assessment labels are generated simultaneously.

[0015] Furthermore, the supervised learning model training and license prediction determination include: Training dataset for building the permission determination model: By tracing back the approval files, pollutant discharge permit records, and historical data of environmental impact assessment approval conclusions of past construction projects, structured feature information that is highly correlated with the permit results is extracted; Training a supervised learning model to achieve license prediction functionality: The supervised learning model ECP-Predict is used as the core algorithm framework; Training sample preparation: Historical approval data is cleaned and standardized to extract multi-dimensional features such as ecological space conflict factors, ecological sensitivity scores, pollutant emission intensity and capacity occupancy rates, industry categories, and regional codes, forming an input feature vector; the input feature vector for projects to be approved is: , in, Indicates the spatial overlay analysis factor. Indicates the ecological sensitivity score. Indicators representing pollutant emissions and capacity occupancy rate Indicates industry category characteristics, Indicates region coding features; Approval Result Label As an output variable, it is related to the input feature vector. Constructing standard supervised learning training sample pairs All samples are used to construct a unified training data table to ensure field consistency and data integrity. Model structure settings: The ECP-Predict model employs a multi-layered ensemble discriminant structure, consisting of the following core modules: Input Feature Layer: This layer receives multi-source input features from spatial overlay analysis, sensitivity scoring, capacity comparison, and industry categories. Through feature normalization and embedding mapping mechanisms, this layer unifies heterogeneous inputs into a single feature space, generating standardized input vectors. , Where Norm(•) represents the normalization and embedding mapping function; Risk Feature Fusion Layer: Based on the input vector, risk features generated by principal component analysis are introduced separately and then concatenated with the original features at multiple scales. The concatenation process is as follows: , in, This represents vector concatenation. This represents the dynamic fusion coefficient.

[0016] Partitioned Sub-model Layer: Based on the regional capacity level and industry category of the project, it is automatically divided into several sub-models. Each sub-model is trained independently and adapted to the corresponding scenario. Within each sub-model, a group of weak classifiers based on gradient boosting is used for residual fitting to learn the nonlinear discrimination rules for that specific scenario. , in, Indicates the area With the industry Sub-model, For the first A weak classifier, For the corresponding weights; Dynamic feature weight layer: The contribution of each input factor is calculated in real time during training, and the splitting strategy of the weak classifier in subsequent iterations is adjusted through the weight adjustment mechanism; Fusion discriminant layer: The output results of all sub-models are weighted and soft-voted for fusion; the fusion weights are dynamically updated by the performance evaluator inside the model, so that the overall prediction results have higher robustness and generalization ability in different regional and industry combination scenarios. Explanation and Feedback Layer: Before the final output, the built-in Shapley value explanation module is called to generate feature contribution ranking and decision path backtracking for the judgment results; Training and validation process: The platform uses a k-fold cross-validation mechanism to divide the training samples into training and validation sets, and performs multiple iterations of training to test the stability and generalization performance of the model on different data subsets. Results archiving and model finalization: After training is completed, the system archives and stores the model version information, hyperparameter configuration, training logs, and feature importance results, forming a traceable model evaluation record table. The supervised learning model ECP-Predict is invoked to predict the feasibility of obtaining a permit for projects awaiting approval. When a new construction project enters the pre-approval process, the system automatically extracts its spatial overlay indicators, ecological sensitivity score, pollutant emission intensity and capacity occupancy rate, principal component risk score, and key features of industry category, encodes them into a standard input vector, and inputs them into the trained ECP-Predict model; Establish a mechanism for integrating model prediction results with the pre-screening system: The model output results are standardized and written into the project evaluation result table, and the same structure is maintained with the layer conflict analysis, capacity comparison and sensitivity scoring modules; the model judgment results are automatically linked to the pre-review main process node for report generation and visualization. Continuous learning and updating mechanism for building permission prediction models: The continuous learning and dynamic update mechanism of the model enables the pre-screening method to have the intelligent evolution capability of "learning while running"; Continuously monitor the final approval conclusion of the project in the actual approval process, and collect the final permission opinions issued by the approval department through interface or database synchronization mechanism. Use these opinions as real labels to compare with the model output results, automatically label whether the prediction is accurate and record the model error. An incremental sample collection strategy is introduced to periodically include new project samples that have been verified through real approval into the training sample set. Data batching is managed through a sample data version control mechanism to ensure the representativeness, completeness and timeliness of the sample data.

[0017] Furthermore, the results are presented as follows: Summarize the preliminary review results from each module and construct a structured data form: After completing spatial overlay, sensitivity scoring, capacity comparison and permit prediction, the results are summarized into a structured data form, covering key fields such as conflict information, risk score, capacity occupancy rate and permit prediction conclusion, and the data source and calculation basis are bound to ensure interpretability and traceability. Generate preliminary review reports and visualizations: Based on the form data, the system automatically generates a standardized pre-approval report, covering project overview, conflict summary, sensitivity score, capacity comparison and permit recommendations, and integrates WebGIS rendering components to output intuitive spatial maps; Establish a source tracing and version control mechanism: During the report generation process, the system synchronously records the field source, model version, and parameter configuration; at the same time, it sets up version control and archiving mechanisms to achieve historical version retrieval, tamper prevention, and long-term retention. Provides support for scheduling, permissions, and log auditing: It is equipped with supporting task scheduling, hierarchical permission and log auditing functions, supports module process configuration, user permission boundary control and full-process operation traceability, and ensures the security and supervision of collaborative applications.

[0018] Furthermore, the construction of the two-dimensional feature matrix of "project × layer factor" includes: Standardize the extracted spatial conflict elements; Construct a two-dimensional matrix structure by using conflict indicators corresponding to multiple layers as columns and different items as rows; the matrix is ​​as follows: Row index: A unique identifier for the item; Column field: Conflict factor field; Cell value: The standardized factor value of the item in the corresponding layer.

[0019] Furthermore, the spatial operations include the following types of relational calculation methods: Intersection: Determines whether the project boundary overlaps with the layer object in space; Includes: Determine whether the project boundary is completely contained within a certain type of ecologically sensitive area, or vice versa; Buffer analysis: Constructs a buffer within a specified distance range and analyzes whether it intersects with the project range; Distance Calculation: Calculates the shortest distance between the project boundary and the nearest layer boundary.

[0020] Furthermore, the project data access and spatial boundary import include: Construct a multi-source graphic data import interface mechanism: upload project boundary space data files through the interface; call spatial data decoding operations to automatically extract graphic boundary coordinate point sets, attribute fields and projection information; convert multiple common coordinate systems into a unified standard coordinate system; identify and handle duplicate points, dangling points and non-closed boundary structural problems in graphic data through spatial verification functions; Perform spatial boundary and project master data binding operations: Based on project code, uploaded file name, or spatial overlap logic matching method, identify and establish the mapping relationship between graphic objects and corresponding project information; receive parameter files and extract project information fields; automatically identify and parse core field information in the file; match uploaded fields with platform standard fields through a preset field mapping table; Perform graphic attribute verification and structured conversion: Perform integrity and consistency verification on the uploaded graphic data and attribute data, convert the graphic data and attribute data into a standardized structure, and write it into an intermediate database table; Online drawing and manual data entry: Users can manually draw project boundary graphics on an interactive map interface, generating corresponding spatial layers in real time, and providing graphic editing and saving functions; project information can be entered through web forms, allowing users to manually fill in basic project information fields when standard drawings or attribute files are lacking; Visualization and multi-layer preview of import results: The imported project boundary graphics are loaded into the map view for visualization; by calling the GIS map service module, the project land boundary is presented in the interactive map interface, and overlaid with the preset ecological and environmental sensitive layer in the platform; users can adjust the transparency of each layer, adjust the layer order, and switch between multiple layers to check whether the project boundary has spatial overlap, occlusion, or offset with the sensitive area layer; project attribute fields are displayed in the interface in the form of structured tables, which users can browse, check, and manually edit; for detected missing fields or data anomalies, they are automatically marked and highlighted to assist users in completing information correction and completion during the import stage; Establish a full-process log recording and data version management mechanism: During the process of project data access and import, record log information of various operation behaviors and data status.

[0021] Compared with existing technologies, the advantages of this invention are: To improve the accuracy and expressive power of spatial analysis, this application employs parallel overlay of multiple types of ecologically sensitive layers and uses principal component analysis to reduce the dimensionality of multidimensional spatial conflict factors and perform weighted integration, generating a quantifiable ecological sensitivity risk score. This effectively overcomes the single logic of traditional methods that can only determine "whether there is overlap," achieving a refined expression of multi-layer composite conflicts and enhancing the resolution and judgment basis of spatial analysis. To enhance the adaptability and dynamism of the capacity comparison mechanism, this application establishes a three-dimensional capacity comparison model of pollutant factors, control units, and emission periods. This model addresses the capacity accounting needs of multiple pollutant types, multiple spatial regions, and multiple time dimensions, breaking through the limitations of static threshold comparison in traditional methods and achieving dynamic adaptability and accuracy of capacity assessment under complex emission structures. Finally, to improve the intelligence level of permit judgment, this application introduces a permit prediction model based on a supervised learning mechanism, utilizing historical data... The approval data is used for training and inference to achieve probabilistic output of licensing conclusions and feature contribution analysis, enhancing the method's ability to assist in judgment in complex scenarios and effectively reducing the uncertainty and consistency risks of manual rule judgment. The pre-review process achieves unified workflow and parameter reuse. Through a unified task scheduling mechanism, the spatial conflict analysis and capacity calculation processes are modularly encapsulated, constructing a visual, configurable, and reusable pre-review workflow system. This avoids repetitive data input and fragmented operations, improving overall execution efficiency and parameter consistency, and adapting to the rapid pre-review needs of various project types. Furthermore, the system's operational efficiency and module scalability are improved. Through a modular architecture and flowchart visualization configuration mechanism, this application can flexibly adapt to the deployment needs of different regions, departments, and business scenarios by customizing pre-review task parameters, embedding models, and accessing new layers, significantly improving the method's maintainability and cross-domain applicability. Attached Figure Description

[0022] Figure 1 This is a flowchart of the method described in this application.

[0023] Figure 2 This is a flowchart of the ECP-Predict model.

[0024] Figure 3 This is a flowchart illustrating the resource and environmental carrying capacity early warning method of the present invention. Detailed Implementation

[0025] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0026] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0027] Current preliminary assessment methods still rely primarily on rule matching and static layer comparison, lacking comprehensive modeling and fusion reasoning mechanisms for key elements such as ecological spatial conflicts, multidimensional emission factors, and regional environmental carrying capacity. Furthermore, spatial analysis and capacity comparison processes are independent of each other, failing to form a systematic and interconnected judgment logic, resulting in insufficient intelligent decision-making support capabilities. Therefore, there is an urgent need for an intelligent preliminary assessment method for project environments that integrates spatial overlay analysis and capacity comparison mechanisms, and possesses the ability to embed principal component analysis and supervised learning models. This would enable a transformation from "static comparison" to "intelligent judgment," enhancing the scientific rigor, flexibility, and modeling level of the preliminary assessment process.

[0028] Please see Figure 1 and Figure 2 An intelligent pre-assessment method for ecological environment based on zoning management and capacity control includes: Project Data Access and Spatial Boundary Import: This involves accessing the project boundary graphic data and structured project information, importing the spatial data files of the project land boundary into the pre-approval platform. Spatial data can use common formats such as GeoJSON, Shapefile, and DWG. Coordinate system standardization transformation, boundary element parsing, and graphic rendering are performed on the graphic data to ensure the accuracy and consistency of the spatial boundary data in subsequent spatial overlay analysis. Structured project information is imported, including fields such as project name, construction type, industry category, main pollutant types, and expected emission intensity and cycle. Through field recognition and standard field mapping mechanisms, attribute information is bound one-to-one with boundary graphic objects, thereby forming a unified spatial project information entity.

[0029] To enhance the adaptability of data input, the system provides a step-by-step guide for users to draw project boundary diagrams online, enabling manual input of spatial extents even in scenarios where graphic files are missing. It also allows for the retrieval and retrieval of historical project data, supporting rapid modeling and assisted judgment of similar projects based on past cases.

[0030] A unified data access mechanism for spatial overlay analysis and environmental capacity comparison has been established to ensure that the boundary and attribute information of the project has a unified format, consistent coordinates, and standardized structure during the technical processing, providing standardized input support for subsequent spatial analysis and model calculation.

[0031] A multi-source ecological environment layer spatial overlay analysis workflow is constructed: spatial overlay analysis is performed on the project boundary and ecological environment control layers. First, multiple types of ecological environment spatial layers are loaded, including: ecological protection red lines, water source protection areas, atmospheric functional zoning, water environment control units, and ecologically sensitive areas. These layers possess a unified coordinate system and standardized classification attributes. Further spatial computation analysis is performed, employing geospatial relationship calculation methods such as "intersection," "containment," "buffer analysis," and "distance calculation" to determine the spatial relationship between the project boundary and each layer. Based on the analysis results, spatial conflict information is extracted and structured result data is output, including: the type of conflicting layer, conflict area, proportion, spatial location relationship, and sensitivity level labeling. This structured output will serve as the core input data for subsequent quantitative ecological risk analysis (principal component scoring) and the use of permit prediction models.

[0032] This step enables the identification of the sensitivity of construction projects and the determination of the scope of risks in the ecological space dimension, providing a spatial information basis for site selection rationality evaluation and environmental early warning.

[0033] Principal component analysis (PCA) is introduced for risk factor scoring: The spatial overlay analysis results are dimensionality reduced and risk-scored to quantify the ecological sensitivity level of the project. First, a risk factor matrix is ​​constructed, converting extracted layer conflict indicators (conflict area, conflict ratio, layer type, sensitivity level, etc.) into a standardized multidimensional factor dataset, forming a two-dimensional data matrix with "project-layer variable" as the dimension. Then, PCA modeling is performed. By decomposing the factor matrix into a covariance matrix, the principal components with the largest explained variance are extracted, and effective factor combinations are determined based on the component contribution rate. The PCA process may optionally include normalization, variance screening, and principal component number control.

[0034] The project's ecological sensitivity score is further calculated, and the principal component results are combined with the layer factor weights to output a comprehensive score for ranking and risk assessment. This score supports setting risk level thresholds, enabling tiered and categorized management of different projects.

[0035] By introducing principal component analysis technology, the problems of redundant indicators, strong correlation, and high dimensionality among multiple layers are overcome, and the compressed expression and structured ranking of ecological space sensitivity risks are realized, providing clear and quantitative ecological background support for subsequent capacity comparison and permitting models.

[0036] Taking Lincang City as an example, in the proposed pharmaceutical factory project in the Fengqing County Industrial Park of Lincang City, the system calls upon the monitoring database of the "City-County (District) Integrated Ecological Environment Information Management Platform" of Lincang City, selecting the main ecologically sensitive layers involved in the project location, including the buffer zone of the secondary protection zone of drinking water sources, the periphery of nature reserves, the red line of basic farmland, and atmospheric environmental sensitive points. After standardizing the data of factors such as conflict area, conflict ratio, and sensitivity level, the system constructs a risk factor matrix. Through principal component analysis, the cumulative contribution rate of the first two principal components reached 82.6%, with the first principal component mainly reflecting water environment sensitivity (contribution rate 54.2%) and the second principal component mainly reflecting atmospheric and land use sensitivity (contribution rate 28.4%). After comprehensive weighted calculation, the system outputs an ecological sensitivity score of 0.42 (out of 1), which is judged as a "moderately sensitive area" after threshold comparison. This result is then used as an important input feature for subsequent capacity comparison and permit prediction.

[0037] Establish an emission information extraction and capacity control unit mapping mechanism: Extract emission information from construction projects and establish a correspondence between the emission information and regional environmental capacity control units based on a three-dimensional dynamic capacity calculation mechanism of pollutant factors, control units, and time slices. First, analyze key parameters such as pollutant type, expected emission intensity, emission mode, and emission cycle from the project application data. Pollutants may include COD, ammonia nitrogen, total phosphorus, etc. , The main pollutants in water and air are identified. Subsequently, by accessing capacity control unit information from the regional environmental database (e.g., water environment control units: watersheds, lake sub-units; or atmospheric environment functional zones: grid units, air quality compliance zones, etc.), and combining the project location with boundary overlay results, the capacity unit to which the project belongs is determined. A three-dimensional mapping relationship (pollutant factor, control unit, time slice) (i.e., (p, u, τ) ternary binding) is further constructed to achieve a structured binding between project emission characteristics and environmental capacity constraints. This mechanism supports parallel mapping of multiple pollutant factors, adapts to various emission cycle types such as annual average, quarterly peak, and concentrated period emissions, and can be normalized by combining pollutant emission concentration standards.

[0038] It can also be extended to support real-time updates, verification, and dynamic retrieval mechanisms for regional capacity data, ensuring the timeliness and accuracy of mapping results.

[0039] Taking Lincang City as an example, during the preliminary review process of the pharmaceutical factory project in the Fengqing County Industrial Park of Lincang City, the system automatically analyzed the pollutant emission information in the construction unit's application form. The project's designed annual emissions include: COD 50 tons, ammonia nitrogen 8 tons, 12 tons 15 tons were discharged, with wastewater treated and then discharged into the Fengqing River system, and exhaust gas emitted via elevated discharge into the Linxiang District atmospheric environmental functional zone. The system accessed the Lincang City environmental capacity database to determine that the water environment control unit corresponding to this project was the Lancang River Fengqing section sub-basin, and the atmospheric control unit was the Fengqing County urban grid unit. Subsequently, the method established a three-dimensional binding relationship between pollutant factors, control units, and time slices, such as (COD, Lancang River Fengqing section, year) and... The Fengqing urban area grid (quarterly) system further combines regional capacity limits and emission concentration standards to normalize different pollutants, obtaining the occupancy rates of each factor: COD occupies 62.5%, and ammonia nitrogen occupies 40%. 38% occupied This mapping relationship achieves a structured connection between project emission data and Lincang City's capacity units, and provides standardized input for subsequent capacity comparison and permit determination.

[0040] The system executes capacity comparison logic and permit feasibility assessment: It utilizes the results of a three-dimensional dynamic capacity calculation mechanism involving pollutant factors, control units, and time slices to compare the project's pollutant emission load with the remaining capacity of its corresponding environmental capacity control unit. Based on the comparison results, it outputs a permit feasibility assessment conclusion. First, the project's pollutant emission load is calculated. Based on extracted parameters such as emission intensity, cycle, and factor type, the annual total emission or periodic emission peak for each type of pollutant is determined. Parameter adjustments can be made based on different pollutant measurement standards, process stages, and industry emission characteristics. Then, the available capacity data of the corresponding control unit is retrieved and compared factor-by-factor with the project's emissions. The comparison logic supports setting capacity utilization thresholds, warning thresholds, and prohibited emission thresholds, outputting assessment results according to multiple permit levels such as "allowable," "capacity critical," and "overload warning." The comparison mechanism supports grouping rule configuration by industry category, emission method, and control unit category, enabling differentiated and flexible adaptation of the permit assessment logic. Structured result fields such as capacity percentage, remaining capacity, and permit recommendation level are output for report generation and model analysis module calls.

[0041] This step enables a direct comparison between pollutant emission intensity and ecological carrying capacity, providing a quantitative, tiered, and parameter-adjustable basis for permit determination. Compared to traditional static threshold-based comparison methods, it offers greater flexibility, applicability, and interpretive transparency, providing reliable intermediate analysis support for subsequent intelligent assessment and policy coordination.

[0042] Supervised learning model for permit prediction and assessment: Based on historical pollution discharge permit approval data, a proprietary supervised learning model, ECP-Predict, is constructed and invoked to intelligently predict and assist in the assessment of the feasibility of construction project permits. The model's training samples include: spatial location information, ecological layer overlay factors, principal component sensitivity scores, and capacity occupancy rate indicators derived from a three-dimensional dynamic capacity calculation mechanism based on pollutant factors, control units, and time slices. After cleaning, standardization, and vectorization, the training samples are mapped to high-dimensional feature vectors centered on (p,u,τ) ternary units.

[0043] The ECP-Predict model employs a hierarchical ensemble structure: the first branch receives and processes spatial sensitivity factors after principal component decomposition; the second branch receives the capacity occupancy vector ηp,u,τ and related dynamic thresholds; the third branch receives discrete features such as industry category, emission method, and emission structure. Each branch trains a sub-model (improved gradient boosting tree) in the intermediate layer, and a unified judgment result is obtained through weighted ensemble in the fusion layer. The model output not only includes a permit feasibility probability score and a final judgment conclusion, but also further divides it into multiple permit suggestion levels such as "permitted," "conditionally permitted," "capacity critical," and "overload warning," and provides contribution rate explanations in the (p,u,τ) dimension.

[0044] Through this step, the proprietary supervised learning model not only integrates spatial risk, three-dimensional capacity, and industry characteristics, but also has hierarchical learning and unit-level interpretation capabilities. Compared with traditional machine learning models, it has stronger targeting, transparency, and practicality, providing transferable and traceable technical support for intelligent assessment in project pre-review.

[0045] It should be noted that the method introduces the following innovative mechanisms: 1. Three-dimensional capacity embedding layer: The three-dimensional mapping relationship (pollutant factor, control unit, time slice) is embedded into sparse matrix features for direct use by the model; 2. Separate models are built for spatial factors, capacity factors, and industry factors, and soft voting is performed in the fusion layer; 3. Multi-level licensing determination mechanism: Unlike the traditional two-level classification, the output includes four licensing levels, which is suitable for actual approval scenarios; 4. Enhanced interpretability at the unit level: Based on the Shapley value, the contribution rate is further decomposed to specific (p,u,τ) units, supporting regulators to trace the specific sources of risk.

[0046] Taking Lincang City as an example, in the preliminary review of the pharmaceutical factory project in Fengqing County Industrial Park, Lincang City, the system assessed the project's spatial sensitivity score (0.42) and capacity occupancy rate (COD occupancy 62.5%, ammonia nitrogen 40%). 38%, The system inputs feature vectors such as 44% (indicating a certain percentage of emissions), industry category (pharmaceutical manufacturing), and emission method (elevated exhaust emissions + river wastewater discharge) into the ECP-Predict model. Under the combined effect of the three-branch sub-model, the model outputs a permit feasibility probability value of 0.87 for the project. The final judgment is "permittable," and it is classified as "permittable" under the multi-level permitting mechanism, rather than "conditionally permitted" or "capacity critical." In the interpretability results, the system calculates and outputs the contribution rate of (p,u,τ) units: COD has the highest contribution rate in the annual unit of the Fengqing River Basin (weight 0.31), followed by... In the quarterly unit of Fengqing urban area (weight 0.27), the spatial sensitivity factor (distance from the secondary protection zone of drinking water sources) contributed 0.22, while the industry category factor contributed 0.20. Based on this, regulators can trace the main risks to COD emissions and urban areas. Emissions can be monitored to allow for targeted subsequent control measures.

[0047] Intelligent pre-screening report generation and platform scheduling and control mechanism: It integrates core results such as spatial overlay analysis, sensitivity scoring, capacity comparison and model prediction to generate structured intelligent pre-screening reports and complete archiving, while providing a visual task scheduling and multi-role permission control mechanism.

[0048] First, the outputs of each module are uniformly summarized to form a standardized report structure covering project information summaries, spatial conflict determination, ecological risk scoring, capacity comparison conclusions, and permit prediction results. The report supports the combined presentation of tables, graphs, and figures, and automatically annotates data sources, model versions, and timestamps, enabling full-process traceability. The system can export the report to Word, PDF, and other formats, or push it to the government approval platform via an interface, and archive it by project code and time, supporting subsequent retrieval, reuse, and horizontal comparison.

[0049] The platform further provides task scheduling and access control mechanisms. Users can configure, reuse templates, and manage versions of pre-review process nodes (such as spatial analysis, capacity comparison, model invocation, and report generation) through a graphical interface. The platform implements access and operation permission control based on role hierarchy (such as administrators, reviewers, data entry personnel, and report reviewers), and logs and audits key behaviors such as login, task execution, and result export, forming a complete chain of responsibility.

[0050] This step enables the standardized output and unified archiving of preliminary review conclusions, while providing flexible task scheduling and secure access management support to ensure that the results are interpretable, the process is controllable, and the operation is traceable, thus meeting the compliance and efficiency requirements of multi-departmental collaboration and government review scenarios.

[0051] The systematic pre-approval method proposed in this application, focusing on the actual needs of environmental management in construction projects and the collaborative challenges of spatial control and capacity assessment, is divided into eight key steps: project data access, spatial overlay analysis, capacity comparison and assessment, intelligent auxiliary judgment, result report output, and system scheduling management. It covers the entire process from project information import to pre-approval conclusion output. By constructing a multi-layered ecological control element fusion mechanism and a principal component analysis scoring model, it achieves the quantitative expression of ecological spatial risks and the scientific ranking of project site selection. By establishing a three-dimensional capacity accounting mechanism of pollutants, control units, and time periods, it effectively improves the accuracy of judging the feasibility of discharge permits. The platform system built based on this invention integrates GIS map services, a model running engine, result visualization components, and structured report generation functions, streamlining the entire process between pre-approval data, analysis logic, and output conclusions. Users can independently construct differentiated pre-approval process templates based on spatial layer configuration, emission parameter settings, and business rule matching, achieving site-specific intelligent judgment and efficient output. The system also supports historical data backtracking, approval experience modeling, and continuous iteration of supervised learning models, possessing strong knowledge accumulation capabilities and model evolution potential. In addition, the platform provides a visual task scheduling mechanism and role-based access control strategy to meet the requirements of access security and accountability tracking in government scenarios involving multiple users, processes, and departments. It has good scalability, reusability, and maintainability, and can adapt to project approval systems and ecological management requirements in different regions, significantly improving the standardization, scientification, and intelligence of environmental pre-approval.

[0052] The specific steps involved in constructing a spatial overlay analysis process for multi-source ecological environment layers are as follows: This document outlines the steps for loading and calling ecological and environmental management layer data for spatial overlay analysis. It involves integrating and dynamically loading multiple types of ecological and environmental spatial layers, including: ecological protection red lines, water source protection areas, nature reserves, land use status, atmospheric functional zoning, water environment control units, ecological red line buffer zones, and ecological restoration priority areas. Each layer's data is stored in a layer database using standard spatial formats (such as Shapefile, GeoJSON, and PostGIS tables). The database can be PostGIS, GeoServer, or a compatible spatial database management system. Layer data is accessed through a layer service interface, supporting spatial data service protocols such as WMS (Web Map Service) and WMTS (Web Map Tile Service) to achieve remote loading and on-demand rendering of layer data.

[0053] The layer data version synchronization mechanism regularly synchronizes layer updates from the ecological and environmental authorities or business systems to ensure that the loaded spatial data has temporal validity, spatial accuracy consistency, and management authority, providing a stable and reliable data foundation for subsequent overlay analysis.

[0054] Establish a mechanism for determining the spatial overlay relationship between project boundaries and ecological environment control layers. Utilize a spatial analysis engine (such as GEOS, JTS, or ArcPy) to perform spatial calculations on the imported project boundary graphics and various layer data. These spatial calculations include the following relationship calculation methods: Intersection: Determines whether the project boundary overlaps with the layer object; Containment: Determines whether the project boundary is completely contained within a certain type of ecologically sensitive area, or vice versa; Buffer Analysis: Constructs a buffer within a specified distance range and analyzes whether it intersects with the project range; Distance Calculation: Calculates the shortest distance between the project boundary and the nearest layer boundary.

[0055] Based on the above spatial calculation results, perform logical judgment operations, such as determining whether there is a condition that "intersects with the ecological protection red line in an area greater than 0 square meters" as a spatial conflict.

[0056] Configure spatial judgment rule parameters. The platform has built-in default thresholds for commonly used judgment rules and allows users to customize and modify conflict identification thresholds (such as buffer radius, minimum allowed cross area, etc.) and sensitivity level classification standards in specific analysis tasks, enabling flexible configuration and scenario adaptation of spatial relationship analysis parameters.

[0057] Set layer conflict judgment weights and classification rules: Set conflict judgment weights and classification criteria for various ecological environment layers to enhance the discrimination of ecological sensitivity assessment and the expression accuracy of project ranking results. Set conflict impact levels (such as high, medium, and low risks) and corresponding weight coefficients for each type of ecological environment layer. The determination basis of the above levels and weights includes the previous spatial overlay results, combined with the classification criteria of core areas, buffer zones, and general control zones in national and local ecological control policies, as well as the actual impact degrees of different conflict factors in historical approval cases. The weight values can be corrected by the variance contribution rate of each conflict factor through principal component analysis, and optimized with the assistance of expert assignment or data fitting results. The above weight values will be used as weighted parameters in the subsequent ecological risk scoring and license assistance judgment model for the layer conflict results, to strengthen the influence of ecological element differences on the scoring and model results. To ensure the regional adaptability and configuration flexibility of the method, users are allowed to manually fine-tune the layer weight values. This operation can be adjusted based on the ecological control requirements of the specific management area or the differential characteristics of the actual project, to avoid the default configuration misleading or deviating from the actual evaluation results. The weight adjustment process supports being completed through configuration files or graphical interfaces, and the platform automatically records all adjustment histories, including the states before and after parameter modification, operator information, and timestamps, etc., for supporting result reproduction and audit traceability.

[0058] Through this step, a flexible and controllable weighting mechanism is introduced based on the expression of ecological layer conflict results, constructing a quantitative modeling foundation for ecological risks with configurability, interpretability, and traceability, providing a unified input specification and differential expression ability for subsequent intelligent scoring and decision-making.

[0059] Generate spatial conflict analysis results and layer records: Structurally extract and record the spatial overlay analysis results of the construction project boundary and ecological environment layers. Based on spatial relationship judgment and layer attribute analysis, generate structured data results that can describe core contents such as conflict status, spatial overlap degree, layer feature information, and conflict level, and store and manage them in a unified format. Support item-by-item recording of multi-layer overlay results. Each recording unit corresponds to the spatial relationship analysis result between a project and a single layer, and the generated data can directly serve subsequent modules such as ecological risk scoring, model input construction, and report generation.

[0060] Present the spatial conflict analysis results in the map visualization interface. The system can highlight the conflict areas graphically in the geographic information interface, supporting operations such as layer classification viewing, conflict level screening, layer visibility control, and multi-layer linkage preview as required by users, thereby improving the expression effect of spatial information and the interpretability of analysis results.

[0061] This step enables the quantitative expression, structured recording, and visual presentation of spatial conflicts in the ecological layer, providing an intuitive, clear, and traceable data support foundation for the preliminary environmental review of projects.

[0062] Constructing a layer conflict factor matrix: The spatial conflict analysis results of each layer are transformed into a modeling input form, specifically by constructing a two-dimensional feature matrix of "project × layer factor".

[0063] First, the extracted spatial conflict elements (conflict area, conflict ratio, risk level, etc.) are standardized, including field unification, unit normalization, and numerical correction. Standardization methods may include Z-score standardization, Min-Max Scaling, or custom threshold mapping. A two-dimensional matrix structure is constructed by using conflict indicators from multiple layers as columns (i.e., layer factor dimensions) and different items as rows (i.e., sample dimensions). This matrix can be defined as follows: Row index: Unique identifier for the project (e.g., project code); Column fields: Conflict factor fields (such as intersection area, risk weight, conflict ratio, etc.); Cell value: The standardized factor value of the item in the corresponding layer.

[0064] Outlier removal and field filtering are performed to eliminate noise factors or null values ​​that could interfere with subsequent analysis. The final factor matrix is ​​written to a cache or intermediate database table in real time and bound to the project number, serving as the input data source for the principal component analysis model and providing quantitative basis for ecological sensitivity scoring and site selection ranking.

[0065] Establish a version control and process backtracking mechanism for analysis results: Construct a version management and process backtracking mechanism for spatial overlay analysis results. This mechanism supports the recording and archiving of the execution process of each spatial overlay analysis task, covering elements such as task identifier, executor information, timestamp, version identifier of input layer, and corresponding analysis result snapshot.

[0066] Through the aforementioned spatial data management mechanism, a closed loop was completed for the entire process of spatial analysis of the project, from boundary overlay, conflict extraction, weight setting to matrix construction. This resulted in standardized, structured, and quantifiable spatial input data, providing a complete support chain for subsequent ecological sensitivity scoring, environmental capacity comparison, and permit determination.

[0067] Unlike the static logic of "layer hit judgment" in traditional environmental pre-screening processes, the method of this invention supports parallel overlay analysis of multiple layers, quantitative expression of conflict intensity, and flexible setting of layer influence weights, significantly improving the expressiveness, interpretability, and adaptability to regional differences in the spatial analysis process. Simultaneously, the spatial factor matrix generated by this method possesses excellent structured input characteristics, laying a modeling foundation for the subsequent introduction of principal component analysis models and machine learning algorithms, and enhancing the data-driven capability and intelligence level of the overall pre-screening method.

[0068] The specific steps involved in using principal component analysis (PCA) to score risk factors are as follows: Principal Component Analysis (PCA) Dimensionality Reduction: PCA is performed on the constructed layer conflict factor matrix to achieve compressed representation and dominant feature extraction of multi-source spatial overlay indicators. First, the layer conflict factor matrix is ​​standardized, converting all numerical fields to a standard format with zero mean and unit variance. Then, the covariance matrix between variables is calculated, and its eigenvalues ​​and corresponding eigenvectors are extracted, thereby constructing the directions of each principal component axis in the multidimensional space. Through this process, several representative and independent principal component indicators are obtained to replace the original high-dimensional conflict factor input, thus improving computational efficiency and model robustness while maintaining information validity.

[0069] Based on the cumulative contribution rate, a dimensional truncation threshold is set. When the cumulative explained variance exceeds the set proportion, the required number of principal components is automatically determined to form the best approximate expression of the original conflict factor combination under a lower dimension.

[0070] Principal component scores are calculated by projecting the projected coordinates of the project onto the principal component axis. They reflect the comprehensive performance of each project in the spatial ecological sensitivity dimension and serve as the basic input data for subsequent sensitivity scoring and risk level determination.

[0071] Further record the core parameters of the principal component model, including the feature loading matrix used in modeling, the original factor contribution of each principal component, the layer field information used in the analysis, and the model generation timestamp, to ensure that the scoring process has good traceability and interpretability.

[0072] Finally, the principal component score of each project is bound to the project identifier and output in a structured format for use by subsequent ecological risk level classification, visual representation and licensing model calling modules.

[0073] This step effectively reduces the redundancy and collinearity interference of conflict factors in high-dimensional layers, constructs a low-dimensional core indicator system for sensitivity quantitative modeling, and enhances the stability, modeling efficiency, and model adaptability of risk assessment.

[0074] Constructing an Ecological Sensitivity Risk Scoring Model: An ecological sensitivity risk scoring model is constructed to quantify the spatial environmental risk of each project. Weights are automatically assigned based on the variance contribution rate of each principal component, and a linear combination method is used to weight the scores of all principal components to form a single comprehensive risk score. The scoring model format is as follows:

[0075] in, Indicates project Sensitivity score, For the project in the Scores on principal components The weights of the principal components (determined by their variance contribution rate).

[0076] The obtained scores are normalized to a fixed range (e.g., [0,1]), where higher values ​​indicate higher ecological risks in the project's location area, reflecting the project's comprehensive sensitivity in terms of spatial conflict. To enhance the method's regional adaptability, users can adjust the principal component weights according to local ecological management requirements. This adjustment can be achieved through a configuration interface or API call, and modification logs are automatically recorded to ensure traceability. The final scores are associated with metadata information such as project identifier, analysis time, and principal component model version, outputting structured data for subsequent use by modules for ecological risk level classification, permit-assisted judgment, and report generation.

[0077] The scoring model has a simple and transparent structure with traceable logic, making it suitable for assisting in the risk interpretation needs during administrative review processes. It also has good compatibility and can be used for feature fusion and composite judgment with supervised learning models to improve the intelligence and expressiveness of the overall evaluation method.

[0078] Establish risk level grading and classification rules: Define risk level grading rules and classify projects. The aim is to transform continuous scoring results into clearly defined and easily manageable ecological risk level labels, providing a structured basis for preliminary review recommendations and management decisions. Multiple level classification strategies are supported, including static segmentation based on fixed thresholds and dynamic clustering based on data distribution characteristics. The static segmentation strategy divides levels according to preset scoring intervals, facilitating standardization and interpretability of results; the dynamic clustering strategy uses an automatic classification algorithm to generate risk level boundaries based on historical project scoring distribution, improving the objectivity and adaptability of the grading.

[0079] The classification results include fields such as score, corresponding risk level label, level number and classification method type used, and are associated with project identifier. These are used to generate final environmental pre-assessment recommendations in conjunction with capacity comparison results and model judgment conclusions.

[0080] This step establishes a complete mapping chain from scoring to grading to spatial representation, enabling the conversion of continuous ecological risk values ​​into policy judgment levels and enhancing the enforceability, standard consistency, and visualization capabilities of the environmental pre-assessment process.

[0081] Outputting Ecological Sensitivity Scores and Layer Highlighting Results: The system outputs ecological sensitivity scores and displays them in conjunction with map layer visualization. Ecological score values ​​and risk levels for each project are loaded into the map rendering engine, and layer color styles are set according to the score range to achieve a level-based highlighting visualization effect. Project boundary layers can be displayed using heatmaps or hierarchical rendering methods, intuitively reflecting the ecological sensitivity of the project's location area. It supports the overlay of conflict information with the original layers, combining principal component analysis results to display the weight contribution of each layer factor to the final score, helping users understand the source and composition of project ecological risks. Users can view project details through interactive operations, including score values, corresponding levels, principal component scores, and spatial overlay characteristics.

[0082] A unified mapping mechanism was established between the ecological sensitivity scoring results and their graphical display, numerical structure, and report application, enabling multi-perspective linkage output of "graph-data-table" and improving the expressiveness, perceptibility, and practicality of the intelligent assessment results.

[0083] Establishing a mechanism for extracting emissions information and mapping it to capacity control units includes the following steps: Extracting pollutant information and establishing an emission inventory: Based on the fields in the declaration data regarding pollutant type, emission scale, emission facilities, and emission cycles, extract the emission factor parameters involved in the project and establish a standardized emission inventory. The extracted emission factors include... , This method records common water and air pollutants such as COD and ammonia nitrogen, along with their corresponding emission source types (e.g., point source, area source), emission intensity (in tons / year or mg / L), emission mode (continuous or intermittent), and accounting units. Referring to the national "Pollutant Emission Standards" and industry emission guidelines, the entered fields undergo semantic parsing and standard field completion, and a field mapping logic is used to form structured inventory data. Each emission record is uniquely indexed by "project number + pollutant factor" and written into a pre-defined emission information table. Fields include pollutant code, name, type, emission cycle, emission load, name of the associated facility, and whether it is a key monitoring target. To improve unit consistency and numerical comparability, the input emission intensity is converted to ensure all emission data participates in subsequent capacity calculations under the same standard. If any field is missing, unit is incorrect, or numerical anomalies are found, this method will automatically mark it as pending verification and prompt manual correction.

[0084] In addition, a mapping relationship between pollutants and emission type classifications is established. The pollutant factor dictionary table automatically labels the category (such as "water pollutants", "air pollutants", "toxic and hazardous substances") and emission control attributes (such as "limit control" and "total quantity control"), and binds it with the spatial boundary information of the project land, thus opening up a data channel for subsequent three-dimensional capacity comparison of pollutants, regions and time.

[0085] Matching emission factors with control unit capacity data: Based on the spatial location of the project site, the ecological and environmental control units where the project is located are identified, and the capacity data of various pollutants involved in the project are mapped to the corresponding control units. Control units include water environment control units (such as river sections, water function zones, and lake / reservoir areas) and atmospheric environment function zones (such as grid units and atmospheric zones). Matching is based on the spatial relationship between the project boundary and the control unit boundary, such as logical judgment methods like inclusion, intersection, maximum area superposition, or center point inclusion. Capacity data uses pollutant-regional unit as the primary key and stores fields including capacity upper limit, occupied capacity, remaining capacity, capacity accounting year, data source, and update date. This information is retrieved from the regional environmental capacity database, and the pollutants in the emission inventory are matched with similar factors in the control units. The matching process can automatically filter the control unit type based on the medium (water or air) to which the pollutant belongs, and supports matching multiple control units simultaneously (e.g., the project spans multiple water bodies). The matching results are represented by a triplet structure of "project number - pollutant factor - control unit number", and metadata such as matching method (automatic / manual confirmation), spatial coverage ratio, and capacity data integrity status are recorded and written into the capacity mapping result table to provide clear boundaries and data basis for the next comparison and judgment.

[0086] Compared to the crude matching logic of "allocating capacity according to administrative regions or estimated proportions" in traditional methods, this method is based on precise matching of control units according to spatial relationships, ensuring that the capacity assessment basis of emission factors is more accurate and scientific, which helps to improve the regional adaptability of permit decisions and the targeting of emission control.

[0087] Establishing a time-based emission-capacity ternary mapping relationship: By introducing the emission time dimension, a ternary mapping structure of "pollutant-control unit-emission time period" is established between pollutant emissions and regional environmental capacity. This structure is used to reflect the dynamic occupancy of capacity by emission intensity in different time periods, improving the accuracy and timeliness of capacity assessment.

[0088] Identify information fields related to emission cycles in project application materials, including annual average emissions, quarterly emissions, emissions in specific months, and emissions varying between wet and dry seasons. For projects with periodic or seasonal emission characteristics, break down their total emissions by time period to generate time-period emission factor data.

[0089] At the same time, the time-sharing capacity data of the control unit is retrieved from the regional capacity database, such as the remaining capacity values ​​divided by year, quarter, heating season / non-heating season, flood season / dry season, etc., and corresponding time index fields are established to ensure the consistency between the emission time and the capacity time dimension.

[0090] A capacity comparison unit with a four-element structure of "project number - pollutant factor - control unit number - emission time period" is constructed, and the following information is clearly recorded in this structure: unit emission amount, matching time period, corresponding capacity value, occupancy ratio, and time deviation risk level. This structure is written into the capacity matching table and serves as an input for subsequent overload judgment and dynamic permit determination.

[0091] The implementation of this step breaks through the static logic of traditional methods that only perform "annual total comparison" or "fixed period comparison". It can identify the phased capacity shortage caused by concentrated emissions, seasonal overlap and other reasons, and provides an important foundation for dynamic management and time period control of capacity constraints.

[0092] Establish a comparative judgment mechanism between pollutant intensity and remaining capacity: quantitatively compare the declared emission intensity of each pollutant with the remaining environmental capacity of the corresponding control unit to form a capacity occupancy rate index, and judge the feasibility of project emissions and whether there is an overload risk based on this index.

[0093] The following capacity comparison calculation formula is used:

[0094] in, This indicates the pollutant emission intensity of the project at a specific control unit and during a specific time period, expressed in tons per year or... ; This indicates the remaining environmental capacity of the unit during that time period.

[0095] The comparison results are divided into different prediction levels according to the occupancy rate. The specific division rules are as follows: occupancy rate ≤ 0.6: sufficient capacity, recommended to pass; 0.6 < occupancy rate ≤ 0.9: capacity is close to the critical point, recommended to review; occupancy rate > 0.9: capacity is tight or overloaded, recommended to issue a warning.

[0096] Furthermore, this method supports setting capacity sensitivity weights based on pollutant category or industry type, allowing users to adjust capacity comparison rules for different scenarios. For example, stricter threshold standards or additional restrictions can be set for heavily polluting industries or specific high-emission areas.

[0097] The comparison output is stored in a structured format with fields such as "project number - pollution factor - time period - control unit - occupancy rate - judgment level," which is used to generate subsequent permit feasibility recommendations and can be overlaid on a map to display the capacity pressure status graphically. This ensures a quantitative connection between the intensity of pollutant declarations and the regional environmental carrying capacity, providing a transparent, quantifiable, and verifiable basis for intelligent permit determination, and significantly enhancing the scientific rigor, interpretability, and standardization of the pre-approval method.

[0098] Output capacity comparison suggestions and boundary judgment labels: Based on preset judgment rules, generate permission judgment conclusions and spatial boundary judgment labels, which serve as the basis for subsequent environmental approval and layer display.

[0099] First, based on the system's set grading thresholds, the occupancy rate results are categorized into different levels of permissible recommendation categories. For example, when the occupancy rate does not exceed 0.6, it is judged as "recommendation approved"; results between 0.6 and 0.9 correspond to the "capacity critical" state; and results exceeding 0.9 are classified as "overload warning". The above judgment results are output in a structured format, with fields such as pollutant type, control unit name, analysis period, occupancy rate value, judgment level, and judgment basis, and are uniformly stored in the table t_proj_capacity_result.

[0100] Secondly, the judgment results are bound to the project boundary graphic objects, and graphic display attribute values ​​are assigned to each type of result to achieve spatial representation. The system generates boundary labels for map visualization by converting the judgment level into layer rendering parameters (such as color labels and transparency levels), making it easier for approvers to quickly identify high-risk areas in the layer view.

[0101] In addition, to enhance the ability to identify boundary conditions, control units in the critical range (such as those with an occupancy rate between 0.85 and 0.95) are marked as "sensitive boundary areas" as important reference sections for subsequent environmental management and optimization design.

[0102] Through the above mechanism, the quantitative expression, structured recording and graphical output of capacity assessment results are realized, improving the transparency, compliance and practicality of the capacity comparison module, and providing support for the traceability and interpretability of project environmental pre-assessment conclusions.

[0103] The execution capacity comparison logic and license feasibility assessment specifically include the following steps: Obtaining pollutant capacity occupancy calculation results: Based on the established ternary matching relationship of "project-pollutant-control unit-emission period", a data index structure of pollutant emissions and remaining capacity of control units is constructed, and the defined capacity comparison formula is called to complete the occupancy calculation. Specifically, the preliminary review method calls the pollutant emission intensity Eproj in the project pollution inventory data table t_proj_pollutant_info and establishes a one-to-one mapping with the remaining capacity data Cremain of the corresponding control unit in the regional capacity database t_ctrlunit_capacity_remain. The capacity occupancy rate of pollutants in the corresponding control unit is calculated according to the formula, and the result is written into the structured capacity occupancy calculation result table t_proj_capacity_ratio.

[0104] This results table supports parallel calculations for multiple pollutant types and can call different capacity standards for comparison for point source and area source pollutants. Each record includes fields such as project number, pollutant code, control unit number, emission period, emission intensity, remaining capacity, and occupancy rate. In the event of missing capacity data or incomplete project emission parameters, the method automatically marks the corresponding record as "pending confirmation," excludes it from the comparison analysis process, and marks it with the field is_valid=0 to ensure the robustness of the calculation logic and the reliability of the results.

[0105] Through the above process, the capacity consumption of project pollutants in the space control unit is quantitatively expressed, forming the basis for subsequent permit judgment logic and capacity ratio classification. Compared with traditional judgment methods based on experience estimation, this step has higher accuracy, consistency and traceability, improving the objectivity and data support capability of the project's pollution discharge permit assessment results.

[0106] Set permit judgment rules and tiered thresholds: Based on the obtained pollutant capacity occupancy rate calculation results, construct a set of permit judgment rules and set multi-level judgment thresholds to support the feasibility assessment of projects under environmental capacity constraints.

[0107] The permit determination rules are set based on relevant environmental management regulations and regional pollutant control policies, and a tiered approach is used to classify thresholds. Taking the emission occupancy rate (r) of a certain pollutant as an example, the permit determination levels are defined as follows: If 𝜂 < 0.6, it is judged as "recommended to pass", indicating that the project has little impact on the control unit and is feasible; If 0.6≤A<0.9, it is judged as “capacity critical”, indicating that the project is close to the regional capacity limit, and it is recommended to further optimize the emission intensity or conduct a risk assessment. If α ≥ 0.9, it is judged as an "overload warning", indicating that the project has significantly exceeded the capacity safety threshold and direct approval is not recommended.

[0108] The platform supports sorting by pollutant type (e.g.) , The system allows for independent judgment rules for pollutants such as COD, while also enabling managers to manually adjust threshold parameters based on regional policies. To enhance the flexibility of the assessment, it also supports the introduction of weighted coefficients to perform weighted overlay analysis of the environmental sensitivity of different pollutants.

[0109] In addition, the platform has a special handling mechanism. When a project involves multiple pollutants and there is a risk of cross-contamination, the system adopts a "risk-first" strategy, using the assessment level of the most unfavorable factor as the reference result for the overall permit assessment, to ensure that the environmental safety bottom line is not breached.

[0110] The above method realizes the logical mapping from pollutant occupancy rate indicators to administrative approval recommendations, supports the scientific judgment of project site selection and emission schemes, and provides classification label basis for subsequent intelligent model-assisted judgment and report generation.

[0111] Generate capacity comparison conclusions and result labels: Based on the occupancy status and judgment results of each pollutant under different control units, output a comprehensive capacity comparison conclusion for the project, and simultaneously generate information records with permission assessment labels. This conclusion comprehensively considers pollutant types, environmental media properties, the matched capacity control units and their code names, the project's actual emissions and remaining capacity values, occupancy rate calculation results, judgment level, applicable judgment rule version number, and execution batch information.

[0112] While registering information, the entire capacity comparison process will be incorporated into a log auditing mechanism. This includes automatically recording the rule version used, personnel information, comparison parameter configurations, and model task identifiers, forming operation log entries corresponding to this comparison action. Relevant logs will be written to the capacity comparison log database table in a unified format, enabling full-process operation tracking, comparison result backtracking, and version control support.

[0113] By deeply integrating capacity data with comparison logic, a closed-loop mechanism has been built, from emission intensity determination to permit label generation and operational auditing. This significantly enhances the intelligent management capabilities, interpretability of results, and transparency of system compliance in the environmental pre-approval process, laying the foundation for the stable operation and continuous evolution of the intelligent pre-approval system.

[0114] The specific steps for implementing license prediction and judgment using a supervised learning model include the following: The training dataset for constructing the permit determination model is built by backtracking historical data such as approval files, pollution discharge permit records, and environmental impact assessment conclusions of past construction projects to extract structured feature information highly correlated with the permit results. The extracted fields include: the project's spatial location code, spatial relationship indicators with ecologically sensitive layers (such as ecological red lines, water source protection areas, and atmospheric functional zones), major pollutant categories and emission intensity parameters, calculated environmental capacity occupancy rate, ecological sensitivity risk score results, industry classification codes, and project scale indicators.

[0115] Early warning methods for resource and environmental carrying capacity: This application also proposes a method for early warning of resource and environmental carrying capacity. For example... Figure 3 As shown, this method establishes a dynamic early warning mechanism for regional environmental carrying capacity through capacity calculation and hierarchical determination of multiple resource and environmental elements. Specifically, the method includes: First, loading a multi-source resource and environmental element layer. Water environmental elements include indicators such as chemical oxygen demand (COD), ammonia nitrogen, total nitrogen, and total phosphorus; atmospheric environmental elements include sulfur dioxide, nitrogen oxides, and particulate matter; and simultaneously incorporating information on the spatial distribution and utilization status of water and land resources. Then, taking the regional control unit as the analysis object, the ratio between the developed and utilized amount of various resource and environmental elements and the total carrying capacity is calculated to obtain the carrying capacity utilization rate. Based on this, a regional and hierarchical early warning result is formed: when the utilization rate is ≤60%, it is judged as a green channel, indicating sufficient capacity; when 60% < utilization rate ≤95%, it is judged as a yellow warning, indicating approaching the critical point; when the utilization rate is >95%, it is judged as a red warning, indicating a significant risk of overload. For priority protection zones and prohibited discharge zones, rigid constraints are directly given, and a blue pollution warning is triggered when environmental risks exist. Finally, the tiered early warning results are linked to the corresponding regions to form a tiered identifier for resource and environmental carrying capacity, which reflects the carrying pressure status of regional water environment, atmospheric environment, water resources and land resources.

[0116] Through such Figure 3 The carrying capacity early warning method shown in this invention realizes the dynamic determination and hierarchical expression of multi-element carrying capacity with method steps as the core, providing a scientific basis for regional environmental planning and differentiated management.

[0117] Training a supervised learning model to achieve the license prediction function: The training phase of the license prediction model adopts the proprietary supervised learning model ECP-Predict proposed in this application as the core algorithm framework.

[0118] Training sample preparation: The platform first cleans and standardizes historical approval data, extracting multi-dimensional features such as ecological space conflict factors, ecological sensitivity scores, pollutant emission intensity and capacity occupancy rates, industry categories, and regional codes to form an input feature vector. The input feature vector for projects to be approved is as follows:

[0119] in, Indicates the spatial overlay analysis factor. Indicates the ecological sensitivity score. Indicators representing pollutant emissions and capacity occupancy rate Indicates industry category characteristics, This indicates the region coding characteristics.

[0120] Approval Result Label As an output variable, it is related to the input feature vector. Constructing standard supervised learning training sample pairs All samples are used to construct a unified training data table to ensure field consistency and data integrity.

[0121] Model Structure: The ECP-Predict model employs a multi-layer ensemble discriminant structure, consisting of the following core modules: Input Feature Layer: This layer receives input features from multiple sources, including spatial overlay analysis, sensitivity scoring, capacity comparison, and industry categories. Through feature normalization and embedding mapping mechanisms, this layer unifies heterogeneous inputs into a single feature space, generating standardized input vectors.

[0122] Where Norm(•) represents the normalization and embedding mapping function.

[0123] Risk Feature Fusion Layer: Based on the input vector, risk features generated by Principal Component Analysis (PCA) are introduced separately and then concatenated with the original features at multiple scales. The concatenation process is as follows:

[0124] in, This represents vector concatenation. This represents the dynamic fusion coefficient.

[0125] This layer uses a weighted fusion mechanism to highlight the contribution of spatial ecological risks to permit determination, ensuring that highly relevant factors are prioritized for capture in subsequent learning.

[0126] The regional sub-model layer automatically divides projects into several sub-models based on their regional capacity level and industry category. Each sub-model is trained independently and adapted to its corresponding scenario (e.g., high-capacity / low-capacity areas, industrial / agricultural projects). Within each sub-model, a gradient-boosted swarm of weak classifiers is used for residual fitting to learn the nonlinear discrimination rules for that specific scenario.

[0127]

[0128] in, Indicates the area With the industry Sub-model, For the first A weak classifier, For the corresponding weights.

[0129] Dynamic Feature Weight Layer: During training, the contribution of each input factor is calculated in real time, and the splitting strategy of the weak classifier in subsequent iterations is adjusted through a weight control mechanism. This mechanism enables the model to adaptively improve its sensitivity to key features when faced with changes in feature distribution, avoiding the underestimation of certain factors in different regional scenarios.

[0130] Fusion Discriminant Layer: This layer performs weighted soft-voting fusion of the outputs from all sub-models. The fusion weights are dynamically updated by the model's internal performance evaluator, resulting in higher robustness and generalization ability of the overall prediction results across different regional and industry combinations.

[0131] Explanation and Feedback Layer: Before the final output, the built-in Shapley value interpretation module is invoked to generate feature contribution ranking and decision path backtracking for the judgment results. It also supports a feedback labeling mechanism, allowing user-corrected samples to be reintroduced into the training pool, providing a basis for subsequent incremental updates.

[0132] Through the above structure, ECP-Predict has the ability to integrate multi-source features, distinguish between partitioned sub-models, dynamically adjust feature weights, and provide interpretable output. It no longer relies on traditional ensemble learning frameworks, but constitutes a complete new type of supervised learning model.

[0133] Training and validation process: The platform adopts The cross-validation mechanism divides the training samples into training and validation sets, and iterates through the training multiple times to test the stability and generalization performance of the model on different subsets of data.

[0134] Results archiving and model finalization: After training is completed, the system archives and stores model version information, hyperparameter configuration, training logs, feature importance results, etc., forming a traceable model evaluation record table.

[0135] The trained model is invoked to predict the feasibility of licensing for the projects to be approved: The proprietary supervised learning model ECP-Predict proposed in this application is invoked to predict the feasibility of licensing for the projects to be approved.

[0136] When a new construction project enters the pre-approval process, the system automatically extracts key features such as spatial overlay indicators, ecological sensitivity scores, pollutant emission intensity and capacity occupancy rates, principal component risk scores, and industry categories, encodes them into standard input vectors, and inputs them into the trained ECP-Predict model.

[0137] An integration mechanism is established between model prediction results and the pre-audit system: After completing the ECP-Predict call, this step standardizes the model output results (permission recommendations, confidence levels, key factor contributions) and writes them into the project evaluation results table. This maintains a consistent structure with modules such as layer conflict analysis, capacity comparison, and sensitivity scoring, facilitating process scheduling and reuse. Model judgment results are automatically linked to the main pre-audit process node for report generation and visualization. The system supports triggering manual review or rule base verification by combining confidence levels and factor ranking, forming a multi-level judgment mechanism of "model judgment—rule correction—manual confirmation," improving compliance and interpretability. Simultaneously, the system records core log information of model calls (model version, call time, executor, etc.) and archives it in the audit table, achieving full-process traceability and auditability.

[0138] This step enables seamless integration of ECP-Predict's prediction results with the pre-review process, ensuring both the structured representation and traceability of the model's output and strengthening stable application and institutional support across multiple departments and roles.

[0139] Build a continuous learning and updating mechanism for the permit prediction model: In order to ensure that the permit judgment model maintains good predictive performance and adaptability under different regional, temporal and policy change backgrounds, a continuous learning and dynamic updating mechanism for the model is built, so that the pre-approval method has the intelligent evolution capability of "learning while running".

[0140] First, after the permit prediction results are generated, the method continuously monitors the final approval conclusion of the project in the actual approval process, and collects the final permit opinions issued by the approval department (such as whether it has actually been approved, whether there is a time limit for rectification, additional conditions for approval, etc.) through interface or database synchronization mechanism. It uses these opinions as real labels to compare with the model output results, automatically labels whether the prediction is accurate and records the model error.

[0141] Secondly, the method introduces an incremental sample collection strategy, periodically adding new project samples that have undergone real-world approval and verification to the training sample set. Data batching is managed through a sample data version control mechanism (such as sample timestamps, spatial labels, and industry characteristics) to ensure the representativeness, completeness, and timeliness of the sample data. The system also supports an anomaly sample screening mechanism; cases with a high probability of misjudgment are automatically submitted to expert review or a manual feedback pool, further enhancing the authenticity and credibility of the training data.

[0142] Regarding the update strategy, the method supports two model evolution methods: (1) a full retraining mechanism, which schedules all sample data for model retraining on a quarterly or annual basis; and (2) an incremental fine-tuning mechanism, which uses new samples to learn or fine-tune the parameters of the original model online through transfer learning or local parameter tuning. The updated model must undergo validation set accuracy evaluation, cross-validation accuracy test and error analysis before it can replace the original model and be put into use.

[0143] To ensure the controllability and stability of model updates, a model version management system is designed to record information such as the structural parameters, training sample composition, training time, key performance indicators, and deployment scope of each version of the model, and to support version rollback and switching mechanisms. The system synchronously records the model evolution path, forming an evolutionary knowledge graph of the licensed prediction model, which is used to support the intelligent and continuous optimization of the pre-approval system.

[0144] By employing the above methods, the permission determination model is endowed with the ability to continuously evolve, realizing the transformation from "static model determination" to "dynamic knowledge accumulation". This significantly improves the robustness, transferability, and long-term applicability of the environmental pre-approval method in complex scenarios, and provides the core algorithmic foundation for building an adaptive, sustainable, and data-driven intelligent environmental pre-approval platform.

[0145] The intelligent pre-screening report generation and platform scheduling and control mechanism specifically includes the following steps: Summarize the pre-approval results of each module and construct a structured data form: After completing spatial overlay, sensitivity scoring, capacity comparison and permit prediction, the results are uniformly summarized into a structured data form, covering key fields such as conflict information, risk score, capacity occupancy rate and permit prediction conclusion, and binding data sources and calculation basis to ensure interpretability and traceability.

[0146] Generate pre-approval reports and visualizations: Based on form data, the system automatically generates standardized pre-approval reports (Word / PDF), covering project overview, conflict summary, sensitivity score, capacity comparison and permit recommendations, and integrates WebGIS rendering components to output intuitive spatial maps.

[0147] Establish a traceability and version control mechanism: During report generation, the system synchronously records the source of fields, model version, and parameter configuration, supporting result traceability and verification. Simultaneously, a version control and archiving mechanism is set up to achieve historical version retrieval, tamper prevention, and long-term retention.

[0148] Provides scheduling, permission and log auditing support: The method is equipped with task scheduling, permission level and log auditing functions, supports module process configuration, user permission boundary control and full process operation traceability, to ensure the security and supervision of collaborative applications.

[0149] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

Claims

1. An intelligent pre-assessment method for ecological environment based on zoning management and capacity control, characterized in that, Includes the following steps: Project data access and spatial boundary import: Access project land boundary graphic data and project attribute information, and unify coordinate transformation and layer binding; Multi-layer ecological environment control element overlay analysis: Load multiple types of ecological environment sensitive layers, perform spatial overlay calculations with project boundaries, identify conflict areas, and form a structured output of layer conflicts; Principal component analysis model construction and ecological risk scoring: A factor matrix is ​​constructed based on the layer conflict index data. Principal component analysis is used to compress dimensions and weight factors to form a comprehensive ecological sensitivity scoring index for the project. Pollutant Emissions and Regional Capacity Mapping: Construct and continuously update the environmental capacity database of control units. Based on the main pollutants, emission intensity and emission cycle information declared in the project, establish a three-dimensional dynamic capacity calculation mechanism of pollutant factors-control units-time slices; Regional capacity comparison and preliminary feasibility assessment: The total emissions of the project are compared with the remaining environmental capacity of the target control unit, the permissible analysis level is output, and the comparison status, degree of exceedance and spatial unit of each pollutant are recorded. Supervised learning model training and permit prediction: Construct a pollution discharge permit prediction model based on a supervised learning mechanism, train it using historical approval data, extract the relationship between feature variables and permit determination, complete the classification judgment and probability prediction of the feasibility of new project permits, and assist manual evaluation; Results: Automatically generated structured project pre-screening report.

2. The intelligent ecological environment pre-assessment method based on zoning management and capacity control according to claim 1, characterized in that, The multi-layer ecological environment management element overlay analysis includes: Load and call ecological and environmental control layer data for spatial overlay analysis: integrate and dynamically load multiple types of ecological and environmental spatial layers; call layer data through layer service interfaces; Layer data version synchronization mechanism: Regularly synchronize layer updates from the ecological and environmental authorities or business systems; Establish a spatial overlay relationship judgment mechanism between project boundaries and ecological environment control layers: call the spatial analysis engine to perform spatial calculations on the imported project boundary graphics and various layer data; Based on the spatial calculation results, a logical judgment operation is performed to determine whether there is a condition that "the area intersecting with the ecological protection red line is greater than 0 square meters" which is considered a spatial conflict. Set spatial judgment rule parameters: The platform has built-in default thresholds for commonly used judgment rules, and allows users to customize and modify conflict identification thresholds and sensitivity level classification standards in specific analysis tasks; Set layer conflict judgment weights and grading rules: Set conflict judgment weights and grading standards for various types of ecological environment layers, and set conflict impact levels and corresponding weight coefficients for each type of ecological environment layer; users can manually fine-tune the layer weight values ​​based on the ecological control requirements of specific management areas or the differences in the characteristics of actual projects; Generate spatial conflict analysis results and layer records: The spatial overlay analysis results of the construction project boundary and the ecological environment layer are extracted and recorded in a structured manner. Based on spatial relationship judgment and layer attribute parsing, structured data results that can describe the core content of conflict status, spatial overlap degree, layer feature information and conflict level are generated and stored and managed in a unified format. The spatial conflict analysis results are presented in the map visualization interface. Conflict areas can be highlighted in the geographic information interface, allowing users to view layers by category, filter conflict levels, control the visibility of layers, and perform multi-layer linked preview operations as needed. Constructing a layer conflict factor matrix: The spatial conflict analysis results of each layer are transformed into a modeling input form, specifically by constructing a two-dimensional feature matrix of "project × layer factor"; Outlier removal and field filtering are performed to eliminate noise factors or null values ​​that may interfere with subsequent analysis; the final factor matrix is ​​written to the cache or intermediate database table in real time and bound to the project number. Establish a version control and process backtracking mechanism for analysis results: Construct a version management and process backtracking mechanism for spatial overlay analysis results. This mechanism records and archives the execution process of each spatial overlay analysis task, covering task identifier, executor information, timestamp, version identifier of input layer, and corresponding snapshot elements of analysis results.

3. The intelligent ecological environment pre-assessment method based on zoning management and capacity control according to claim 1, characterized in that, The principal component analysis model construction and ecological risk scoring include: Perform principal component analysis for dimensionality reduction: Perform principal component analysis on the constructed layer conflict factor matrix: Standardize the layer conflict factor matrix to convert all numerical fields into a standard format with zero mean and unit variance; calculate the covariance matrix between variables and extract its eigenvalues ​​and corresponding eigenvectors, thereby constructing the directions of each principal component axis in the multidimensional space; obtain the principal component indices. The dimensional truncation threshold is set based on the cumulative contribution rate. When the cumulative explained variance exceeds the set proportion, the required number of principal components is automatically determined to form the best approximate expression of the original combination of conflicting factors in a lower dimension. The principal component score is calculated by projecting the item onto the principal component axis. Record the core parameters of the principal component model, including the feature loading matrix used in modeling, the original factor contribution of each principal component, the layer field information used in analysis, and the model generation timestamp content; The principal component score for each item is bound to the item identifier and output in a structured format; An ecological sensitivity risk scoring model is constructed to quantify the spatial environmental risk of each project. Weights are automatically assigned based on the variance contribution rate of each principal component, and a linear combination method is used to weight the scores of all principal components to form a single comprehensive risk score. The scoring model is as follows: ， in, Indicates project Sensitivity score, For the project in the Scores on principal components The weight of the principal component; The scoring results are normalized and standardized to a fixed range, where higher values ​​indicate higher ecological risks in the project's location area, reflecting the project's comprehensive sensitivity in terms of spatial conflict. The principal component weights can be adjusted according to local ecological management requirements. This adjustment can be achieved through a configuration interface or API call, and modification logs are automatically recorded to ensure traceability. The final scoring results will be associated with project identification, analysis time, principal component model version metadata information, and output as structured data. Set risk level classification and grading rules: Set risk level classification rules and classify projects; multiple classification strategies can be used, including static segmentation based on fixed thresholds and dynamic clustering based on data distribution characteristics; among them, the static segmentation strategy can divide levels according to preset scoring intervals, which facilitates standardization and interpretability of results; the dynamic clustering strategy uses an automatic classification algorithm to generate risk level boundaries based on historical project scoring distribution. Output ecological sensitivity scores and layer highlighting results: Load the ecological score values ​​and risk level results corresponding to each project into the map rendering engine, and set the layer color style according to the score range to form a highlighting visualization effect based on the level division.

4. The intelligent ecological environment pre-assessment method based on zoning management and capacity control according to claim 1, characterized in that, The mapping of pollutant emissions to regional capacity includes: Extract pollutant information from the project and establish an emissions inventory: Based on the data submitted regarding pollutant type, emission scale, emission facilities, and emission cycle, emission factor parameters related to the project were extracted, and a standardized emission inventory was established. The extracted emission factors include... , Common water and air pollutants such as COD and ammonia nitrogen are recorded, along with their corresponding emission source type, emission intensity, emission method, and accounting unit. The entered fields are semantically parsed and standard fields are completed, and a structured list of data is formed using field mapping logic. Each emission information is uniquely indexed by "project number + pollutant factor" and written into a preset emission information table. Establish a mapping relationship between pollutants and emission type classifications, automatically label the category and emission control attributes through a pollutant factor dictionary, and bind it to the spatial boundary information of the project land use; Matching emission factor and control unit capacity data: Based on the spatial location of the project site, the ecological and environmental control unit where it is located is identified, and the capacity data of the various pollutants involved in the project are mapped to the corresponding control unit. The capacity data uses pollutant-regional unit as the primary key and stores fields including capacity limit, occupied capacity, remaining capacity, capacity accounting year, data source and update date. This information is retrieved from the regional environmental capacity database, and the pollutants in the emission inventory are matched with the same factors in the control unit. The matching process can automatically filter the control unit type according to the medium to which the pollutant belongs and supports matching multiple control units simultaneously. The matching result is represented by a triple structure of "project number-pollutant factor-control unit number", and the matching method, spatial coverage ratio, and capacity data integrity status metadata are recorded and written into the capacity mapping result table. Establish a three-dimensional mapping relationship between emissions and capacity across different time periods: By introducing the emission time dimension, a three-element mapping structure of "pollutant—control unit—emission period" is established between pollutant emissions and regional environmental capacity: The method identifies information fields related to emission cycles in project application materials, including annual average emissions, quarterly emissions, emissions in specific months, and emissions differences between wet and dry seasons. For projects with periodic or seasonal emission characteristics, the method breaks down their total emissions by time period to form time-period emission factor data. Retrieve time-of-use capacity data of the control unit from the regional capacity database and establish a corresponding time index field to ensure consistency between emission time and capacity time dimension; Construct a mechanism for comparing and determining pollutant intensity versus remaining capacity: The declared emission intensity of each pollutant is quantitatively compared with the remaining environmental capacity of the corresponding control unit to form a capacity occupancy rate index, and the feasibility of project emissions and whether there is an overload risk are judged accordingly. The capacity comparison calculation formula is based on the following: ， in, This indicates the pollutant emission intensity of the project at a specific control unit and during a specific time period, expressed in tons per year or... ; This indicates the remaining environmental capacity of the unit during that time period; The comparison results are divided into different prediction levels according to the occupancy rate. The specific division rules adopt the following intervals: Utilization rate ≤ 0.6%: Sufficient capacity, approval recommended; 0.6 < occupancy rate ≤ 0.9: Capacity is approaching the critical level; a review is recommended. Utilization rate > 0.9: Capacity is tight or overloaded; warning is recommended. The comparison output results are stored in a structured format with fields such as "project number - pollution factor - time period - control unit - occupancy rate - judgment level" for the generation of subsequent permit feasibility recommendations, and can be overlaid on a map to display the capacity pressure status in a graphical form; Output capacity alignment suggestion results and boundary judgment labels: Based on the grading thresholds set by the system, the occupancy rate results are divided into different levels of permission recommendation categories; The judgment results are bound to the project boundary graphic object, and graphic display attribute values ​​are assigned to each type of result to achieve spatial expression; boundary labels for map visualization are generated by converting the judgment level into layer rendering parameters. Control units in the critical range are marked as "sensitive boundary areas" as important reference sections for subsequent environmental management and optimization design.

5. The intelligent ecological environment pre-assessment method based on zoning management and capacity control according to claim 1, characterized in that, The regional capacity comparison and preliminary assessment of permit feasibility include: Obtain the pollutant capacity occupancy rate calculation results: Based on the established ternary matching relationship of "project-pollutant-control unit-emission period", a data index structure of pollutant emission and remaining capacity of control unit is constructed, and the capacity comparison formula is called to complete the occupancy rate calculation. The pollutant emission intensity Eproj in the project pollution inventory data table t_proj_pollutant_info is called and a one-to-one mapping is established with the remaining capacity data Cremain of the corresponding control unit in the regional capacity database t_ctrlunit_capacity_remain. The capacity occupancy rate of pollutants in the corresponding control unit is calculated, and the result is written into the structured capacity occupancy rate calculation result table t_proj_capacity_ratio. Set permission determination rules and tiered thresholds: Construct a set of permission judgment rules and set multi-level judgment thresholds to support the feasibility assessment of projects under environmental capacity constraints; The permit determination rules are set based on relevant environmental management regulations and regional pollutant control policies, and thresholds are divided using a tiered and graded approach; the pollutant emission occupancy rate (U) is set at the following permit determination levels: If 𝜂 < 0.6, it is judged as "recommended to pass", indicating that the project has little impact on the control unit and is feasible; If 0.6≤A<0.9, it is judged as "critical capacity", indicating that the project is close to the regional capacity limit, and it is recommended to further optimize the emission intensity or conduct a risk assessment. If α ≥ 0.9, it is judged as an "overload warning", indicating that the project has significantly exceeded the capacity safety threshold and direct approval is not recommended; Generate capacity comparison conclusions and result labels: Based on the occupancy status and judgment results of each pollutant under different control units, the project's capacity comparison comprehensive conclusion is output, and information records with permission assessment labels are generated simultaneously.

6. The intelligent ecological environment pre-assessment method based on zoning management and capacity control according to claim 1, characterized in that, The supervised learning model training and license prediction determination include: Training dataset for building the permission determination model: By tracing back the approval files, pollutant discharge permit records, and historical data of environmental impact assessment approval conclusions of past construction projects, structured feature information that is highly correlated with the permit results is extracted; Training a supervised learning model to achieve license prediction functionality: The supervised learning model ECP-Predict is adopted as the core algorithm framework; Training sample preparation: Historical approval data is cleaned and standardized to extract multi-dimensional features such as ecological space conflict factors, ecological sensitivity scores, pollutant emission intensity and capacity occupancy rates, industry categories, and regional codes, forming an input feature vector; the input feature vector for projects to be approved is as follows: ， in, Indicates the spatial overlay analysis factor. Indicates the ecological sensitivity score. Indicators representing pollutant emissions and capacity occupancy rate Indicates industry category characteristics, Indicates region coding features; Approval Result Label As an output variable, it is related to the input feature vector. Constructing standard supervised learning training sample pairs All samples are used to construct a unified training data table to ensure field consistency and data integrity. Model structure settings: The ECP-Predict model employs a multi-layered ensemble discriminant structure, consisting of the following core modules: Input Feature Layer: This layer receives multi-source input features from spatial overlay analysis, sensitivity scoring, capacity comparison, and industry categories. Through feature normalization and embedding mapping mechanisms, this layer unifies heterogeneous inputs into a single feature space, generating standardized input vectors. ， Where Norm(•) represents the normalization and embedding mapping function; Risk Feature Fusion Layer: Based on the input vector, risk features generated by principal component analysis are introduced separately and then concatenated with the original features at multiple scales. The concatenation process is as follows: ， in, This represents vector concatenation. The dynamic fusion coefficient; Partitioned Sub-model Layer: Based on the regional capacity level and industry category of the project, it is automatically divided into several sub-models. Each sub-model is trained independently and adapted to the corresponding scenario. Within each sub-model, a group of weak classifiers based on gradient boosting is used for residual fitting to learn the nonlinear discrimination rules for that scenario. ， in, Indicates the area With the industry Sub-model, For the first A weak classifier, For the corresponding weights; Dynamic feature weight layer: The contribution of each input factor is calculated in real time during training, and the splitting strategy of the weak classifier in subsequent iterations is adjusted through the weight adjustment mechanism; Fusion discriminant layer: The output results of all sub-models are weighted and soft-voted for fusion; the fusion weights are dynamically updated by the performance evaluator inside the model, so that the overall prediction results have higher robustness and generalization ability in different regional and industry combination scenarios. Explanation and Feedback Layer: Before the final output, the built-in Shapley value explanation module is called to generate feature contribution ranking and decision path backtracking for the judgment results; Training and validation process: The platform uses a k-fold cross-validation mechanism to divide the training samples into training and validation sets, and performs multiple iterations of training to test the stability and generalization performance of the model on different data subsets. Results archiving and model finalization: After training is completed, the system archives and stores the model version information, hyperparameter configuration, training logs, and feature importance results, forming a traceable model evaluation record table; The supervised learning model ECP-Predict is invoked to predict the feasibility of obtaining a permit for projects awaiting approval. When a new construction project enters the pre-approval process, the system automatically extracts its spatial overlay indicators, ecological sensitivity score, pollutant emission intensity and capacity occupancy rate, principal component risk score, and key features of industry category, encodes them into a standard input vector, and inputs them into the trained ECP-Predict model; Establish a mechanism for integrating model prediction results with the pre-screening system: The model output results are standardized and written into the project evaluation result table, and the same structure is maintained with the layer conflict analysis, capacity comparison and sensitivity scoring modules; the model judgment results are automatically linked to the pre-review main process node for report generation and visualization. Continuous learning and updating mechanism for building permission prediction models: The continuous learning and dynamic update mechanism of the model enables the pre-screening method to have the intelligent evolution capability of "learning while running"; Continuously monitor the final approval conclusion of the project in the actual approval process, and collect the final permission opinions issued by the approval department through interface or database synchronization mechanism. Use these opinions as real labels to compare with the model output results, automatically label whether the prediction is accurate and record the model error. An incremental sample collection strategy is introduced to periodically include new project samples that have been verified through real approval into the training sample set. Data batching is managed through a sample data version control mechanism to ensure the representativeness, completeness and timeliness of the sample data.

7. The intelligent ecological environment pre-assessment method based on zoning management and capacity control according to claim 1, characterized in that, The results are presented as follows: Summarize the preliminary review results from each module and construct a structured data form: After completing spatial overlay, sensitivity scoring, capacity comparison and permit prediction, the results are summarized into a structured data form, covering key fields such as conflict information, risk score, capacity occupancy rate and permit prediction conclusion, and the data source and calculation basis are bound to ensure interpretability and traceability. Generate preliminary review reports and visualizations: Based on the form data, the system automatically generates a standardized pre-approval report, covering project overview, conflict summary, sensitivity score, capacity comparison and permit recommendations, and integrates WebGIS rendering components to output intuitive spatial maps; Establish a source tracing and version control mechanism: During the report generation process, the system synchronously records the field source, model version, and parameter configuration; at the same time, it sets up version control and archiving mechanisms to achieve historical version retrieval, tamper prevention, and long-term retention. Provides support for scheduling, permissions, and log auditing: It is equipped with supporting task scheduling, hierarchical permission and log auditing functions, supports module process configuration, user permission boundary control and full-process operation traceability, and ensures the security and supervision of collaborative applications.

8. The intelligent ecological environment pre-assessment method based on zoning management and capacity control according to claim 2, characterized in that, The construction of the two-dimensional feature matrix of "project × layer factor" includes: Standardize the extracted spatial conflict elements; Construct a two-dimensional matrix structure by using conflict indicators corresponding to multiple layers as columns and different items as rows; the matrix is ​​as follows: Row index: A unique identifier for the item; Column field: Conflict factor field; Cell value: The standardized factor value of the item in the corresponding layer.

9. The intelligent ecological environment pre-assessment method based on zoning management and capacity control according to claim 2, characterized in that, The spatial operations include the following types of relational calculation methods: Intersection: Determines whether the project boundary overlaps with the layer object in space; Includes: Determine whether the project boundary is completely contained within a certain type of ecologically sensitive area, or vice versa; Buffer analysis: Constructs a buffer within a specified distance range and analyzes whether it intersects with the project range; Distance Calculation: Calculates the shortest distance between the project boundary and the nearest layer boundary.

10. The intelligent ecological environment pre-assessment method based on zoning management and capacity control according to claim 1, characterized in that, The project data access and spatial boundary import include: Construct a multi-source graphic data import interface mechanism: upload project boundary space data files through the interface; call spatial data decoding operations to automatically extract graphic boundary coordinate point sets, attribute fields and projection information; convert multiple common coordinate systems into a unified standard coordinate system; identify and handle duplicate points, dangling points and non-closed boundary structural problems in graphic data through spatial verification functions; Perform spatial boundary and project master data binding operations: Based on project code, uploaded file name, or spatial overlap logic matching method, identify and establish the mapping relationship between graphic objects and corresponding project information; receive parameter files and extract project information fields; automatically identify and parse core field information in the file; match uploaded fields with platform standard fields through a preset field mapping table; Perform graphic attribute verification and structured conversion: Perform integrity and consistency verification on the uploaded graphic data and attribute data, convert the graphic data and attribute data into a standardized structure, and write it into an intermediate database table; Online drawing and manual data entry: Users can manually draw project boundary graphics on an interactive map interface, generating corresponding spatial layers in real time, and providing graphic editing and saving functions; project information can be entered through web forms, allowing users to manually fill in basic project information fields when standard drawings or attribute files are lacking; Visualization and multi-layer preview of import results: The imported project boundary graphics are loaded into the map view for visualization; by calling the GIS map service module, the project land boundary is presented in the interactive map interface, and overlaid with the preset ecological and environmental sensitive layer in the platform; users can adjust the transparency of each layer, adjust the layer order, and switch between multiple layers to check whether the project boundary has spatial overlap, occlusion, or offset with the sensitive area layer; project attribute fields are displayed in the interface in the form of structured tables, which users can browse, check, and manually edit; for detected missing fields or data anomalies, they are automatically marked and highlighted to assist users in completing information correction and completion during the import stage; Establish a full-process log recording and data version management mechanism: During the process of project data access and import, log information on various operation behaviors and data status is recorded.

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