Intelligent auditing system for environmental assessment report based on dynamic classification and knowledge mapping

Abstract

The invention relates to the technical field of environmental assessment text processing, and discloses an intelligent auditing system for an environmental assessment report based on dynamic classification and knowledge mapping, which enables a classification system to automatically identify a novel pollution type and dynamically adjust a type boundary through multi-source data fusion and an incremental learning algorithm. The method ensures that the classification result always meets the newest environmental policy requirement, and ensures that the knowledge base can synchronize the newest standard specification content in real time by establishing a classification system and a trigger updating mechanism of the standard specification, thereby achieving the effect of comprehensively improving the professionalism and timeliness of the environmental assessment system. The system can automatically adapt to environmental policy changes and technical standard updating, can maintain scientificity of a classification system and integrity of a knowledge base without manual intervention, ensures that newest and most relevant standard specifications can be called for professional judgment when various environmental assessment reports are audited, and remarkably reduces audit deviation caused by knowledge lag.

Description

Technical Field

[0001] This invention relates to the field of environmental impact assessment (EIA) text processing technology, and in particular to an intelligent review system for EIA reports based on dynamic classification and knowledge mapping. Background Technology

[0002] An environmental impact assessment (EIA) system is used to comprehensively analyze, predict, and evaluate the potential environmental impacts of construction projects or plans. This system typically integrates environmental monitoring data, standards and regulations, and policies and laws. Through big data analysis and artificial intelligence models, it automatically reviews, deconstructs, and checks the compliance of EIA reports, thereby assisting environmental management departments or enterprises in quickly identifying pollution types (such as air, water, and solid waste), assessing environmental risks, and generating review opinions. Its core objective is to improve the accuracy and efficiency of EIA reports through automation, reduce reliance on manual labor, and ensure the scientific and standardized nature of environmental management.

[0003] Existing environmental impact assessment (EIA) systems are typically built upon environmental monitoring data and predefined rules, integrating data acquisition, preprocessing, and basic analysis modules. For example, these systems collect historical environmental data such as air quality, water quality, and noise through sensors or databases, and utilize basic machine learning models to assess environmental quality levels. Some systems also incorporate intelligent query and fuzzy matching functions, supporting rapid retrieval and comparison of pollutant information to aid in the initial assessment of environmental impacts.

[0004] Existing environmental impact assessment systems have the following problems in terms of intelligent processing:

[0005] In the review of environmental impact assessment reports, the system's structured splitting and classification of text data is not precise enough, and it cannot dynamically adapt to diverse report formats and pollution types. This results in unstable clustering results, and the classification system lacks adaptive optimization, affecting the accuracy and consistency of the review.

[0006] Model training relies on historical data, but environmental data is often unevenly distributed, resulting in poor model generalization ability. At the same time, the training process does not fully incorporate real-time updated standards and specifications, and the knowledge base partitioning is unclear, making the model prone to bias and overfitting when reviewing new types of reports.

[0007] The existing technical announcement (CN115146062A) describes an intelligent event analysis method and system that integrates expert recommendation and text clustering. While this method achieves intelligent classification of monitored events through text clustering, keyword extraction, and attention mechanisms, its classification system is essentially a static model built on historical data. The core of this method lies in learning fixed patterns from known events and classifying them using similarity thresholds. However, the environmental impact assessment (EIA) field exhibits significant dynamic evolution: new pollutant types constantly emerge, and environmental policies and emission standards are frequently updated. Static classification models cannot proactively identify these emerging pollution concepts; their category boundaries are fixed, lacking the ability to adaptively optimize and dynamically expand based on new policy semantics or new data distributions. This leads to unstable and inaccurate classification results when the system reviews reports involving new pollution types or new standards, heavily relying on manual correction and model retraining.

[0008] For example, a rapid analysis and management system for typical pollutants, with announcement number CN115114352B, uses a MySQL database to build a pollutant database, employs data mining and web crawling to capture pollutant source information, and achieves rapid query and location through fuzzy retrieval and feature vector comparison. Although this solves the problem of pollutant data retrieval efficiency, it cannot better address the issues of inflexible report classification systems and lack of cluster visualization, because it focuses on database query and matching and lacks cluster analysis and model training optimization for environmental impact assessment texts. Summary of the Invention

[0009] The technical problem to be solved by this invention is that the existing technology has the disadvantages of low accuracy in the structured and dynamic classification of environmental impact assessment reports and poor performance in model training and optimization. To address this, we propose an intelligent review system for environmental impact assessment reports based on dynamic classification and knowledge mapping.

[0010] To achieve the above objectives, this application adopts the following technical solution: an intelligent review system for environmental impact assessment reports based on dynamic classification and knowledge mapping, comprising:

[0011] The classification system construction module is used to build a dynamically updated pollution type classification system. By integrating the semantic features of environmental policy documents and the numerical features of historical pollution data, and combining clustering algorithms to identify pollution types, it establishes a multi-level category structure and a two-way mapping relationship between categories and standards. The system is dynamically optimized based on incremental learning algorithms.

[0012] The knowledge base and sample processing module automatically collects standard specification documents according to the classification system, establishes a system update trigger mechanism, extracts key information through text parsing, and constructs a standardized training sample dataset.

[0013] The model training and optimization module performs domain-adaptive pre-training based on the sample dataset, optimizes model parameters using a hybrid loss function that combines knowledge distillation and online hard example mining, and strengthens the mapping and association between categories and knowledge base partitions through regularization techniques and domain knowledge adaptation layers.

[0014] The report review and results integration module is used to parse the report to be reviewed and dynamically split it into semantic modules. Based on the classification system, it locates the exclusive standard specification partition through the mapping relationship, calls the large model for professional review, and outputs the final review report in the form of interactive annotations after verifying the results through the rule engine.

[0015] Furthermore, the classification system construction module specifically includes:

[0016] The pollution type dynamic identification module receives environmental policy documents and multi-source historical pollution data, automatically parses the document content using natural language processing technology, fuses the semantic features extracted from the policy documents with the numerical features of the historical pollution data, dynamically identifies pollution types using clustering algorithms, calculates category similarity based on data distribution to optimize type definitions, and outputs an initial pollution type set.

[0017] Category hierarchy construction module: Receives the initial set of pollution types, constructs a multi-level category structure through hierarchical clustering algorithm, analyzes the relationship between categories based on graph theory method, synchronously associates the knowledge base with the standard specification keywords pre-collected by the sample processing module, establishes a bidirectional mapping relationship between categories and specifications, and outputs a category hierarchy system;

[0018] Real-time update module: Receives data from the category hierarchy system, new environmental data streams, and user feedback information. It monitors the consistency between the data and the current system in real time through stream processing technology. For text description data of new pollution types, it extracts semantic features through NLP technology and fuses them with numerical features. Then, it applies incremental learning algorithms to compare and fuse the new data features with the existing category system, dynamically adjusts the structure and boundaries of the classification system, and outputs the updated classification system.

[0019] Validation module: It receives the classification system, evaluates the system's performance through cross-validation algorithm and consistency check, optimizes the class splitting threshold based on the test dataset, and outputs the validated classification system.

[0020] Furthermore, the knowledge base and sample processing module specifically includes:

[0021] Standards and Specifications Collection Module: This module receives the verified classification system, automatically collects relevant standards and specifications documents through web crawlers and API interfaces, establishes a classification system update trigger mechanism, and uses text parsing technology to extract key information and output the original specification data.

[0022] Sample processing module: It receives raw standardized data, cleans it, identifies key entities and transforms it into vectors, uses the categories in the classification system as sample labels, constructs a training sample dataset with features, labels and specifications, and applies balanced sampling techniques to handle the imbalance of data distribution, and outputs the sample dataset.

[0023] Furthermore, the model training and optimization module specifically includes:

[0024] Model initialization module: It is used to receive sample datasets, perform domain-adaptive pre-training based on environmental impact assessment domain corpus through pre-trained model, optimize basic weights, configure hyperparameters, and output the initial model;

[0025] Training execution module: It receives the initial model and sample dataset, executes the training process through the deep learning framework, optimizes the model parameters using the loss function and gradient descent algorithm, monitors the training error and accuracy, and outputs the model in training.

[0026] The optimization and adjustment module receives the model during training, prevents overfitting through regularization techniques and early stopping strategies, adds a domain knowledge adaptation layer, strengthens the mapping association between category and knowledge base partitions, adjusts the model structure based on the validation set, and outputs the optimized model.

[0027] Furthermore, the report review and result integration module specifically includes:

[0028] Report parsing module: It receives reports to be reviewed, performs in-depth parsing of the report text using natural language processing technology, performs targeted identification by combining pollution type keywords in the classification system, improves the structural parsing accuracy of non-standard format reports, identifies report structural elements, and outputs the parsed report structure data;

[0029] Structure splitting module: It receives the parsed report structure data, dynamically splits the report into semantic modules according to the Environmental Impact Report Preparation Guidelines, synchronously calls the real-time updated classification system, dynamically adjusts the splitting dimensions received, and adjusts the splitting scale based on dynamic thresholds to ensure that the module size is suitable for large model review, and outputs a set of split report modules.

[0030] The review execution module is used to receive the set of split report modules and the trained large model of the optimization and adjustment module. It calls the large model API to input the modules as background information for knowledge question answering. Based on the classification system categories corresponding to the split modules, it accurately locates the exclusive standard specification partition through the category-knowledge base partition mapping relationship, puts forward specific review requirements and prompts the relevant standard specifications to guide the model to quickly locate the exclusive knowledge partition, and outputs the preliminary review results.

[0031] The result verification module is used to receive preliminary review results. The rule base of the rule engine is synchronized with the dynamically updated classification system and standard specifications in real time. It automatically adds / modifies the corresponding verification rules, verifies the logical rationality of the results through the rule engine and consistency check algorithm, marks abnormal or conflicting items based on historical review data, and outputs the verified review results.

[0032] The annotation insertion module receives the verified audit results and the original report content. It uses a text matching algorithm to accurately locate the corresponding position in the report content. For tables and charts in the report, it uses table structure parsing and chart data extraction technology to locate the key data positions, find the parent directory structure of the current content, and insert the audit results in the form of interactive annotations, outputting the final audit report with annotations.

[0033] The technical effects and advantages of this invention are as follows:

[0034] In this invention, through multi-source data fusion and incremental learning algorithms, the classification system can automatically identify new types of pollution and dynamically adjust category boundaries, ensuring that the classification results always meet the latest environmental policy requirements. By establishing a trigger-based update mechanism for the classification system and standards and specifications, the knowledge base can be synchronized with the latest standards and specifications in real time, thereby comprehensively improving the professionalism and timeliness of the environmental impact assessment system. The system can automatically adapt to changes in environmental policies and updates in technical standards, maintaining the scientific nature of the classification system and the integrity of the knowledge base without manual intervention. This ensures that the latest and most relevant standards and specifications can be used for professional judgment when reviewing various environmental impact assessment reports, significantly reducing review deviations caused by knowledge lag, and keeping environmental impact assessment review work at the forefront of the industry, providing continuous and reliable technical support for environmental management decisions.

[0035] This invention employs a hybrid training strategy combining knowledge distillation and hard example mining to deepen the model's understanding of professional knowledge while maintaining its generalization ability. A domain knowledge adaptation layer strengthens the mapping relationship between categories and knowledge partitions, ensuring the model can accurately invoke specific standard specifications. Dynamic splitting and precise positioning mechanisms enable intelligent association between report content and regulatory clauses. This significantly improves review efficiency and accuracy—the system can automatically complete the entire process of report parsing, professional judgment, and result verification, freeing reviewers from tedious review and comparison work. Simultaneously, a multi-level verification mechanism ensures the reliability and consistency of review conclusions, effectively avoiding oversights and misjudgments caused by human factors. This standardizes, regulates, and automates environmental impact assessment (EIA) review work, greatly enhancing the effectiveness and credibility of environmental supervision. Attached Figure Description

[0036] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts:

[0037] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0038] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0039] Reference Figure 1 As shown, this invention provides a technical solution: an intelligent review system for environmental impact assessment reports based on dynamic classification and knowledge mapping, comprising:

[0040] The classification system construction module is used to build a dynamically updated pollution type classification system. By integrating the semantic features of environmental policy documents and the numerical features of historical pollution data, and combining clustering algorithms to identify pollution types, it establishes a multi-level category structure and a two-way mapping relationship between categories and standards. The system is dynamically optimized based on incremental learning algorithms.

[0041] The knowledge base and sample processing module automatically collects standard specification documents based on the classification system, establishes a system update trigger mechanism, extracts key information through text parsing, and constructs a standardized training sample dataset.

[0042] The model training and optimization module performs domain-adaptive pre-training based on the sample dataset, optimizes model parameters using a hybrid loss function that combines knowledge distillation and online hard example mining, and strengthens the mapping relationship between categories and knowledge base partitions through regularization techniques and domain knowledge adaptation layers.

[0043] The report review and results integration module is used to parse the report to be reviewed and dynamically split it into semantic modules. Based on the classification system, it locates the exclusive standard specification partition through the mapping relationship, calls the large model for professional review, and outputs the final review report in the form of interactive annotations after verifying the results through the rule engine.

[0044] Furthermore, as a preferred embodiment, the classification system construction module specifically includes:

[0045] The pollution type dynamic identification module receives environmental policy documents and multi-source historical pollution data. It automatically parses the document content using natural language processing technology, performs feature fusion between the semantic features extracted from the policy documents and the numerical features of the historical pollution data (such as attention-based weighted fusion), and dynamically identifies pollution types (including air, water, and solid waste) using clustering algorithms. It also calculates category similarity based on data distribution to optimize type definitions and outputs an initial pollution type set. Through dynamic clustering and semantic analysis, it addresses the problem of low accuracy in structured and dynamic classification of environmental impact assessment reports, thereby improving the accuracy and adaptability of type identification.

[0046] Category hierarchy construction module: This module receives the initial set of pollution types, constructs a multi-level category structure through hierarchical clustering algorithm, analyzes the relationships between categories based on graph theory, synchronously links the knowledge base with standard and specification keywords pre-collected by the sample processing module, such as pollution type labels corresponding to pollutant limits, establishes a two-way mapping relationship between categories and specifications, and outputs a category hierarchy system. Through hierarchical clustering and graph theory analysis, it addresses the problem of low accuracy in the structured and dynamic classification of environmental impact assessment reports, thereby enhancing the system's hierarchy and scalability.

[0047] The real-time update module receives data from the category hierarchy, externally inputted new environmental data streams, and user feedback. New environmental data streams are automatically collected by IoT sensors deployed at monitoring points, such as real-time data from air quality monitoring stations. User feedback is obtained through the manual annotation function of the system's interactive interface, such as auditors' correction instructions for classification results. Stream processing technology monitors the consistency of data with the current system in real time. For textual descriptions of new pollution types, semantic features are extracted using NLP technology and fused with numerical features before being input into an incremental learning algorithm. This algorithm compares and fuses the new data features with the existing category system, dynamically adjusting the structure and boundaries of the classification system to integrate new pollution types or correct existing category boundaries, outputting an updated classification system. This real-time feedback-based system evolution mechanism addresses the low accuracy of structured and dynamic classification in environmental impact assessment reports, achieving the effect of maintaining the system's real-time performance and adaptability.

[0048] The validation module receives the classification system, evaluates its performance through cross-validation and consistency checks, optimizes the class division threshold based on the test dataset, and outputs the validated classification system. By using cross-validation and threshold optimization, it addresses the issue of low accuracy in structured and dynamic classification of environmental impact assessment reports, thereby improving the robustness and accuracy of the system.

[0049] Furthermore, as an optimal implementation, the incremental learning algorithm is applied to compare and fuse new data features with the existing category system, dynamically adjusting the structure and boundaries of the classification system, and outputting an updated classification system. Specifically, the following operations are performed:

[0050] Based on similarity thresholds and category distance metrics, the matching degree between the new data feature vector and the existing category cluster centers is calculated. Data points and outliers that conform to the existing category boundaries are identified. For data points with high matching degree, the feature centers and boundary ranges of the corresponding categories are updated using a weighted average method. For outliers, a new category generation mechanism is initiated. When the outlier density reaches a set threshold, a new pollution type category is created, and an initial mapping relationship between it and relevant standards and specifications is established. The overall clustering quality after adjustment is evaluated based on the silhouette coefficient, the hierarchical relationship between categories is optimized, and an updated classification system with complete structure and clear boundaries is output.

[0051] The outlier density is obtained using the following formula:

[0052] ,

[0053] in: and Represents the feature vector of outliers. It is the number of outliers. It is an adjustable temperature parameter used to control the distance scaling effect. Outlier The Euclidean distance between the nearest neighbor outlier and the new category generation mechanism is activated when the ODS is greater than a set threshold.

[0054] Furthermore, as an optimal implementation, the system performance is evaluated through cross-validation and consistency checks, and the class splitting threshold is optimized based on the test dataset to output the validated classification system. Specifically, the following operations are performed:

[0055] The test dataset is divided into multiple training-validation subsets. The generalization ability of the system is evaluated by running the classification system on different subsets and comparing the stability of the results. Multi-dimensional indicators such as precision, recall and silhouette coefficient of each pollution type are calculated to identify the boundary regions of classification ambiguity. Based on the indicator analysis results, the category division threshold is dynamically adjusted to optimize the classification boundary definition and ensure that the system can maintain stable and reliable classification performance under different data distributions. The output is a fully validated classification system.

[0056] Furthermore, as a preferred embodiment, the knowledge base and sample processing module specifically includes:

[0057] Standards and Specifications Collection Module: This module receives the validated classification system and automatically collects relevant standard and specification documents, including PDF and TXT formats, through web crawlers and API interfaces. It also establishes a classification system update trigger mechanism—when a new or modified category is added to the classification system, it automatically redirects the crawler to retrieve the latest standard and specification documents for that category, uses text parsing technology to extract key information, and outputs the original specification data. Through automated collection and parsing, it addresses the issues of insufficient model knowledge base updates and weak generalization ability, achieving comprehensive and timely specification data.

[0058] The sample processing module receives raw, standardized data and, through data cleaning, key entity identification, and vectorization transformation, uses the categories in the classification system as sample labels to construct a training sample dataset with features, labels, and specifications. It also applies balanced sampling techniques to address data imbalance and outputs a sample dataset. By standardizing and balancing the samples, it solves the problems of insufficient model knowledge base updates and weak generalization ability, thereby improving data quality and model generalization ability.

[0059] Furthermore, as a preferred embodiment, the model training and optimization module specifically includes:

[0060] Model initialization module: This module receives the sample dataset, performs domain-adaptive pre-training based on the environmental impact assessment corpus using a pre-trained model, optimizes the basic weights, configures hyperparameters, and outputs the initial model. The preferred pre-trained model is DeepSeek. Initialization using the pre-trained model addresses the issues of insufficient model knowledge base updates and weak generalization ability, achieving faster model convergence and stability.

[0061] Training Execution Module: This module receives the initial model and sample dataset, executes the training process using a deep learning framework, optimizes model parameters using loss functions and gradient descent algorithms, monitors training error and accuracy, and outputs the model under training. Through deep learning training and parameter optimization, it addresses the issues of insufficient model knowledge base updates and weak generalization ability, achieving the effects of model knowledge learning and performance improvement.

[0062] The optimization and adjustment module receives the model during training, prevents overfitting through regularization and early stopping strategies, adds a domain knowledge adaptation layer, strengthens the mapping association between category and knowledge base partitions, and adjusts the model structure based on the validation set to output the optimized model. Through regularization and structural adjustment, it addresses the problems of insufficient model knowledge base updates and weak generalization ability, thereby enhancing the model's generalization ability and robustness.

[0063] Furthermore, as an optimal implementation, the training process is performed using a deep learning framework, optimizing model parameters using a loss function and gradient descent algorithm, monitoring training error and accuracy, and outputting the model during training. Specifically, the following operations are performed:

[0064] The sample dataset is input into the model for forward propagation to calculate the prediction results. The loss value between the prediction results and the true labels is calculated, and the gradient is calculated using a hybrid loss function that combines knowledge distillation and online hard example mining. The overall expression is as follows:

[0065]

[0066] in, The main task loss function (e.g., cross-entropy loss) is used to measure the difference between the model's predictions and the true labels; To account for knowledge distillation loss, temperature parameters are used. The output distribution of the softened teacher model is expressed as follows:

[0067]

[0068] here, This is a temperature scaling parameter used to adjust the smoothness of the probability distribution. The Kullback-Leibler divergence is used to measure the difference in distributions. and These represent the teacher model and the student model, respectively, for the samples. The output probability distribution, The total number of samples; The dynamic hard example mining loss adjusts the weights dynamically based on the sample difficulty, and its expression is:

[0069]

[0070] in, For the sample The difficulty weights are dynamically calculated by the online mining algorithm based on the loss value or gradient information. This represents the number of difficult cases in the current batch. Indicates sample Task loss value; hyperparameters These are used to balance the contributions of the three loss terms. Finally, the model parameters are updated based on the calculated gradients, and the optimized trained model is output.

[0071] Furthermore, as an optimal implementation, overfitting is prevented through regularization techniques and early stopping strategies. A new domain knowledge adaptation layer is added to strengthen the mapping and association reception of categories and knowledge base partitions. Specifically, the following operations are performed:

[0072] By integrating L1 / L2 regularization and randomized Dropout techniques, a parameter norm penalty term is added to the loss function, and some neuron connections are randomly masked to constrain model complexity. A dynamic early stopping strategy is implemented, and the loss curve on the validation set is monitored in real time. When the loss curve no longer decreases significantly for several consecutive training cycles, training is automatically stopped to avoid ineffective learning. A dedicated domain knowledge adaptation layer is introduced. Specifically, a learnable projection matrix is ​​used to align the model's internal representation with the category vectors in the classification system. A custom mapping reinforcement function is used to explicitly maximize the mutual information of category-knowledge base partition association pairs, thereby ensuring the accurate injection of professional knowledge.

[0073] The specific formula for the mapping enhancement function is as follows:

[0074]

[0075] Parameter description:

[0076] The number of samples in a batch represents the number of environmental impact assessment report samples input for one training session. The total number of categories in the classification system, output by the classification system construction module, includes, for example, air pollution, water pollution, solid waste pollution, and their subcategories. : Indicator function, when the first The true label of each sample Equal to the current category When the value is 1, the function evaluates to 1; otherwise, it evaluates to 0. This ensures that only the loss for a sample and its correct class is calculated. The model is a sample The generated deep feature vectors are generated by a model (such as DeepSeek) for the first... The deep semantic feature representation generated by each report module encodes the text content of that module. and They are the same concept, both representing category prototype vectors. Affinity function: calculates the similarity between feature vectors and class vectors. Temperature hyperparameter, used to control the smoothness of the probability distribution. Category vector matrix The projection after normalization. The graph Laplacian matrix, constructed based on the hierarchical structure of the classification system, is generated by the category hierarchy construction submodule using graph theory methods. It encodes the hierarchical relationships between categories in the classification system (e.g., air pollution is the parent category of VOCs pollution, and water pollution and air pollution are sibling categories). The trace of a matrix. : A hyperparameter that controls the strength of the structure regularization term, used to balance the importance of the first term (classification accuracy) and the second term (structure preservation) in the loss function.

[0077] Furthermore, as a preferred embodiment, the report review and results integration module specifically includes:

[0078] Report parsing module: This module receives reports awaiting review. These reports are submitted directly by staff through the system's file upload interface. The module uses natural language processing (NLP) technology to deeply analyze the report text, combining it with pollution type keywords from the classification system for targeted identification. This improves the accuracy of structural parsing for non-standard format reports. The module also identifies structural elements of the reports and outputs the parsed report structure data. Through deep NLP analysis, it addresses the issue of low accuracy in the structured and dynamic classification of environmental impact assessment reports, achieving a precise understanding of the report content.

[0079] The structure splitting module receives the parsed report structure data, dynamically splits the report into semantic modules according to the Environmental Impact Report Preparation Guidelines, synchronously calls the real-time updated classification system, dynamically adjusts the received splitting dimensions, and adjusts the splitting scale based on dynamic thresholds to ensure that the module size is suitable for large model review. It outputs a set of split report modules. Through dynamic semantic splitting and threshold adjustment, it solves the problem of low accuracy in the structured and dynamic classification of environmental impact assessment reports, and achieves the effect of high efficiency and accuracy in modular processing.

[0080] The audit execution module receives the set of split report modules and the trained large model of the optimization and adjustment module. It calls the large model API to input the modules as background information for knowledge-based question answering. Based on the classification system categories corresponding to the split modules, it accurately locates the exclusive standard and specification partitions through the category-knowledge base partition mapping relationship, proposes specific audit requirements and prompts the relevant standards and specifications to guide the model to quickly locate the exclusive knowledge partitions, and outputs preliminary audit results. Through knowledge partition guidance and question-and-answer interaction, it solves the problem of low accuracy in the structured and dynamic classification of environmental impact assessment reports, and achieves a professional and targeted audit process.

[0081] The result verification module receives preliminary review results. The rule base of the rule engine is synchronized in real time with the dynamically updated classification system and standard specifications. It automatically adds / modifies the corresponding verification rules, verifies the logical rationality of the results through the rule engine and consistency check algorithm, marks abnormal or conflicting items based on historical review data, and outputs the verified review results. The rule engine and consistency check are used to solve the problem of low accuracy in the structured and dynamic classification of environmental impact assessment reports, so as to achieve the effect of reliable and consistent results.

[0082] The annotation insertion module receives the verified audit results and the original report content. The original report refers to the initial electronic document submitted by the user through the system upload interface without any structured processing, maintaining the original format and content integrity. This document is stored separately within the system as baseline data for reference in each processing stage. It uses text matching algorithms to accurately locate the corresponding positions in the report content. For tables and charts in the report, it uses table structure parsing and chart data extraction technology to locate key data positions, find the parent directory structure of the current content, and insert audit results in an interactive annotation format, outputting a final audit report with annotations. This text matching and interactive annotation approach addresses the low accuracy issues of structured and dynamic classification in environmental impact assessment reports, achieving integrated visualization of results and improved user-friendliness.

[0083] Furthermore, as an optimal implementation, based on the classification system categories corresponding to the split modules, the specific standard specification partition is accurately located through the category-knowledge base partition mapping relationship, specifically by performing the following operations:

[0084] The semantic encoder transforms the split report module content into high-dimensional feature vectors and extracts their corresponding classification system category embedding representations. Using a learnable heterogeneous mapping matrix, a multi-dimensional attention score is calculated between the report features and the prototype vectors of all knowledge base partitions. The semantic relevance of the report features with each standard specification partition is dynamically evaluated. A selector based on a gating routing mechanism is introduced. By combining the attention score, historical access frequency, and rule confidence, the final knowledge partition location probability distribution is calculated. Only the most relevant Top-K exclusive standard specification partitions are activated as background knowledge for review.

[0085] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. An intelligent review system for environmental impact assessment reports based on dynamic classification and knowledge mapping, characterized in that, include: The classification system construction module is used to build a dynamically updated pollution type classification system. By integrating the semantic features of environmental policy documents and the numerical features of historical pollution data, and combining clustering algorithms to identify pollution types, it establishes a multi-level category structure and a two-way mapping relationship between categories and standards. The system is dynamically optimized based on incremental learning algorithms. The knowledge base and sample processing module automatically collects standard specification documents according to the classification system, establishes a system update trigger mechanism, extracts key information through text parsing, and constructs a standardized training sample dataset. The model training and optimization module performs domain-adaptive pre-training based on the sample dataset, optimizes model parameters using a hybrid loss function that combines knowledge distillation and online hard example mining, and strengthens the mapping and association between categories and knowledge base partitions through regularization techniques and domain knowledge adaptation layers. The report review and results integration module is used to parse the report to be reviewed and dynamically split it into semantic modules. Based on the classification system, it locates the exclusive standard specification partition through the mapping relationship, calls the large model for professional review, and outputs the final review report in the form of interactive annotations after verifying the results through the rule engine.

2. The intelligent environmental impact assessment report review system based on dynamic classification and knowledge mapping according to claim 1, characterized in that: The classification system construction module specifically includes: The pollution type dynamic identification module receives environmental policy documents and multi-source historical pollution data, automatically parses the document content using natural language processing technology, fuses the semantic features extracted from the policy documents with the numerical features of the historical pollution data, dynamically identifies pollution types using clustering algorithms, calculates category similarity based on data distribution to optimize type definitions, and outputs an initial pollution type set. Category hierarchy construction module: Receives the initial set of pollution types, constructs a multi-level category structure through hierarchical clustering algorithm, analyzes the relationship between categories based on graph theory method, synchronously associates the knowledge base with the standard specification keywords pre-collected by the sample processing module, establishes a bidirectional mapping relationship between categories and specifications, and outputs a category hierarchy system; Real-time update module: Receives data from the category hierarchy system, new environmental data streams, and user feedback information. It monitors the consistency between the data and the current system in real time through stream processing technology. For text description data of new pollution types, it extracts semantic features through NLP technology and fuses them with numerical features. Then, it applies incremental learning algorithms to compare and fuse the new data features with the existing category system, dynamically adjusts the structure and boundaries of the classification system, and outputs the updated classification system. Validation module: It receives the classification system, evaluates the system's performance through cross-validation algorithm and consistency check, optimizes the class splitting threshold based on the test dataset, and outputs the validated classification system.

3. The intelligent environmental impact assessment report review system based on dynamic classification and knowledge mapping according to claim 2, characterized in that: The incremental learning algorithm compares and fuses new data features with the existing classification system, dynamically adjusts the structure and boundaries of the classification system, and outputs an updated classification system. Specifically, it performs the following operations: Based on similarity thresholds and category distance metrics, the matching degree between the new data feature vector and the existing category cluster centers is calculated. Data points and outliers that conform to the existing category boundaries are identified. For data points with high matching degrees, the feature centers and boundary ranges of the corresponding categories are updated using a weighted average method. For outliers, a new category generation mechanism is initiated. When the outlier density reaches a set threshold, a new pollution type category is created, and an initial mapping relationship between it and relevant standards and specifications is established. The overall clustering quality after adjustment is evaluated based on the silhouette coefficient, the hierarchical relationship between categories is optimized, and an updated classification system with complete structure and clear boundaries is output. The outlier density is specifically obtained by the following formula: ,in: and Represents the feature vector of outliers. It is the number of outliers. It is an adjustable temperature parameter. Outlier The Euclidean distance between the nearest neighbor outlier and the new category generation mechanism is activated when the ODS is greater than a set threshold.

4. The intelligent environmental impact assessment report review system based on dynamic classification and knowledge mapping according to claim 2, characterized in that: The system performance is evaluated using cross-validation and consistency checks, and the class splitting threshold is optimized based on the test dataset to output the validated classification system. Specifically, the following operations are performed: The test dataset is divided into multiple training-validation subsets. The generalization ability of the system is evaluated by running the classification system on different subsets and comparing the stability of the results. Multi-dimensional indicators for each pollution type are calculated, and the boundary regions of classification ambiguity are identified. The classification threshold is dynamically adjusted based on the indicator analysis results, the classification boundary definition is optimized, and a fully validated classification system is output.

5. The intelligent environmental impact assessment report review system based on dynamic classification and knowledge mapping according to claim 1 or 4, characterized in that: The knowledge base and sample processing module specifically includes: Standards and Specifications Collection Module: This module receives the verified classification system, automatically collects relevant standards and specifications documents through web crawlers and API interfaces, establishes a classification system update trigger mechanism, and uses text parsing technology to extract key information and output the original specification data. Sample processing module: It receives raw standardized data, cleans it, identifies key entities and transforms it into vectors, uses the categories in the classification system as sample labels, constructs a training sample dataset with features, labels and specifications, and applies balanced sampling techniques to handle the imbalance of data distribution, and outputs the sample dataset.

6. The intelligent environmental impact assessment report review system based on dynamic classification and knowledge mapping according to claim 1, characterized in that: The model training and optimization module specifically includes: Model initialization module: This module receives the sample dataset, performs domain-adaptive pre-training based on the environmental impact assessment domain corpus using the pre-trained model, optimizes the basic weights, configures the hyperparameters, and outputs the initial model. Training execution module: It receives the initial model and sample dataset, executes the training process through the deep learning framework, optimizes the model parameters using the loss function and gradient descent algorithm, monitors the training error and accuracy, and outputs the model in training. The optimization and adjustment module receives the model during training, prevents overfitting through regularization techniques and early stopping strategies, adds a domain knowledge adaptation layer, strengthens the mapping association between category and knowledge base partitions, adjusts the model structure based on the validation set, and outputs the optimized model.

7. The intelligent environmental impact assessment report review system based on dynamic classification and knowledge mapping according to claim 6, characterized in that: The process involves executing the training using a deep learning framework, optimizing model parameters using a loss function and gradient descent algorithm, monitoring training error and accuracy, and outputting the model during training. Specifically, the following operations are performed: The sample dataset is input into the model for forward propagation to calculate the prediction results. The loss value between the prediction results and the true labels is calculated, and the gradient is calculated using a hybrid loss function that combines knowledge distillation and online hard example mining. The expression is as follows: in, The main task loss function is used to measure the difference between the model's prediction and the true label. To account for knowledge distillation loss, temperature parameters are used. The output distribution of the softened teacher model is expressed as follows: , For temperature scaling parameters, The Kullback-Leibler divergence is used to measure the difference in distributions. and These represent the teacher model and the student model, respectively, for the samples. The output probability distribution, The total number of samples. The dynamic hard example mining loss adjusts the weights dynamically based on the sample difficulty, and its expression is: , For the sample Difficulty weighting This represents the number of difficult cases in the current batch. Indicates sample Task loss value, hyperparameters These are used to balance the contributions of the three losses, update the model parameters based on the calculated gradients, and output the optimized model from the training process.

8. The intelligent environmental impact assessment report review system based on dynamic classification and knowledge mapping according to claim 6, characterized in that: The method prevents overfitting through regularization techniques and early stopping strategies, adds a domain knowledge adaptation layer, and strengthens the mapping and association reception of categories and knowledge base partitions. Specifically, the following operations are performed: By integrating L1 / L2 regularization and randomized Dropout techniques, a parameter norm penalty term is added to the loss function, and some neuron connections are randomly masked to constrain model complexity. A dynamic early stopping strategy is implemented, and the loss curve on the validation set is monitored in real time. A domain knowledge adaptation layer is introduced, specifically by using a projection matrix to align the model's internal representation with the category vectors in the classification system. Furthermore, a mapping enhancement function is used to explicitly maximize the mutual information of category-knowledge base partition association pairs, thereby ensuring the accurate injection of professional knowledge. The specific formula for the mapping enhancement function is as follows: , The number of samples in the batch. The total number of categories in the classification system. Indicator functions, The model is a sample The generated deep feature vectors, and They are the same concept, both representing category prototype vectors. Affinity function Temperature exceeding parameters Category vector matrix Normalized projection A graph Laplace matrix constructed based on the hierarchical structure of the classification system. The trace of a matrix. : Hyperparameters that control the strength of the structure regularization term.

9. The intelligent environmental impact assessment report review system based on dynamic classification and knowledge mapping according to claim 1, characterized in that: The report review and result integration module specifically includes: Report parsing module: It receives reports to be reviewed, performs in-depth parsing of the report text using natural language processing technology, performs targeted identification by combining pollution type keywords in the classification system, improves the structural parsing accuracy of non-standard format reports, receives reports, identifies report structural elements, and outputs the parsed report structure data; Structure splitting module: It receives the parsed report structure data, dynamically splits the report into semantic modules according to the Environmental Impact Report Preparation Guidelines, synchronously calls the real-time updated classification system, dynamically adjusts the splitting dimensions received, and adjusts the splitting scale based on dynamic thresholds to ensure that the module size is suitable for large model review, and outputs a set of split report modules. The review execution module is used to receive the set of split report modules and the trained large model of the optimization and adjustment module. It calls the large model API to input the modules as background information for knowledge question answering. Based on the classification system categories corresponding to the split modules, it accurately locates the exclusive standard specification partition through the category-knowledge base partition mapping relationship, puts forward specific review requirements and prompts the relevant standard specifications to guide the model to quickly locate the exclusive knowledge partition, and outputs the preliminary review results. The result verification module is used to receive preliminary review results. The rule base of the rule engine is synchronized with the dynamically updated classification system and standard specifications in real time. It automatically adds / modifies the corresponding verification rules, verifies the logical rationality of the results through the rule engine and consistency check algorithm, marks abnormal or conflicting items based on historical review data, and outputs the verified review results. The annotation insertion module receives the verified audit results and the original report content. It uses a text matching algorithm to accurately locate the corresponding position in the report content. For tables and charts in the report, it uses table structure parsing and chart data extraction technology to locate the key data positions, find the parent directory structure of the current content, and insert the audit results in the form of interactive annotations, outputting the final audit report with annotations.

10. The intelligent environmental impact assessment report review system based on dynamic classification and knowledge mapping according to claim 9, characterized in that: The classification system categories corresponding to the splitting modules are used to accurately locate the exclusive standard specification partitions through the category-knowledge base partition mapping relationship, and the following operations are performed: The semantic encoder transforms the content of the split report modules into high-dimensional feature vectors and extracts their corresponding classification system category embedding representations. Using a learnable heterogeneous mapping matrix, a multi-dimensional attention score is calculated between the report features and the prototype vectors of all knowledge base partitions. The semantic relevance of the report features with each standard specification partition is dynamically evaluated. A selector based on a gating routing mechanism is introduced. By combining the attention score, historical access frequency, and rule confidence, the final knowledge partition location probability distribution is calculated. Only the most relevant Top-K exclusive standard specification partitions are activated as background knowledge for review.

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