Environment monitoring data intelligent analysis and early warning system and method based on AI learning

Abstract

The application discloses an environment monitoring data intelligent analysis and early warning system based on AI learning, and belongs to the technical field of environment monitoring and intelligent analysis. The system comprises a data acquisition and preprocessing module, a multi-dimensional anomaly detection module, a hierarchical early warning module, an intelligent question answering and decision support module, a pollution discharge permit compliance checking module and an intelligent law enforcement route planning module. The system identifies complex anomaly patterns in pollutant data through AI algorithms, realizes intelligent interaction using natural language processing technology, adopts a hierarchical early warning mechanism to give early warning before the pollutant concentration reaches the legal limit, and optimizes law enforcement routes through a geographic information system. When the anomaly detection module identifies an anomaly pattern, auxiliary early warning can be triggered even if the early warning threshold has not been reached, forming a full-process closed loop from data sensing, intelligent analysis to decision support and on-site law enforcement. The application solves the problems of weak anomaly recognition capability, unintelligent data analysis and low law enforcement efficiency of existing environment monitoring systems.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring and intelligent analysis technology, and in particular to an intelligent analysis and early warning system for environmental monitoring data based on AI learning. Background Technology

[0002] With the acceleration of industrialization and urbanization, environmental pollution has become increasingly prominent, and environmental monitoring, as a fundamental aspect of environmental protection, is of paramount importance. Traditional environmental monitoring systems mainly rely on manual sampling and analysis and simple threshold alarm mechanisms. While these systems meet basic monitoring needs to a certain extent, they still have significant shortcomings in terms of intelligence, depth of data analysis, and enforcement efficiency.

[0003] In recent years, with the development of emerging technologies such as artificial intelligence, big data, and the Internet of Things, environmental monitoring systems are moving towards intelligence, automation, and precision. However, existing technologies still have many problems, mainly in the following aspects: First, threshold-based environmental monitoring systems. Traditional environmental monitoring systems typically use fixed thresholds for anomaly detection and early warning. When monitoring data exceeds a preset threshold, the system triggers an alarm. This method is simple and intuitive, but it has obvious drawbacks: First, it can only identify obvious exceedances and cannot capture complex anomaly patterns in the data, such as sudden increases, decreases, or persistent constant values; second, it lacks an early warning mechanism, only generating an alarm after pollutant concentrations exceed legal limits, resulting in a delayed response and inability to detect potential risks in advance; third, fixed thresholds are difficult to adapt to the emission characteristics and trends of different enterprises, easily leading to false alarms or missed alarms. Second, machine learning-based anomaly detection systems. Some researchers and enterprises have attempted to apply machine learning technology to anomaly detection in environmental monitoring data. For example, Chinese patent CN202510784788 discloses an environmental emergency monitoring information management system, including modules for data acquisition, processing, report generation, rapid query, information dissemination, and artificial intelligence consultation. It collects environmental data in real time through sensors and manual input units, and analyzes and processes the data using multi-threading and big data technologies. Although this system introduces artificial intelligence technology, its application depth is limited, mainly remaining at the level of data display and simple consultation, failing to fully leverage the advantages of machine learning in anomaly detection and prediction. Furthermore, the system lacks a multi-level early warning mechanism and intelligent law enforcement support functions, making it difficult to form a complete closed loop from data perception to on-site law enforcement. Three: Environmental monitoring systems based on deep learning. Chinese patent CN121365348A discloses an intelligent ecological and environmental protection monitoring method and system based on artificial intelligence, including integrating and constructing a multi-source data acquisition network, introducing a Q-Learning algorithm to dynamically adjust sampling and cruise frequencies to achieve multi-dimensional data acquisition; identifying and eliminating data anomalies, and standardizing heterogeneous data processing. Although this system uses artificial intelligence technology, it mainly applies the Q-Learning algorithm for sampling frequency adjustment, rather than using AI learning for anomaly detection and data analysis. Furthermore, the system focuses more on data collection and preprocessing, lacking advanced functions such as tiered early warning, compliance verification, and intelligent Q&A, and thus cannot meet the comprehensive needs of actual environmental supervision.

[0004] In summary, existing environmental monitoring technologies have the following main technical problems:

[0005] Weak anomaly detection capability: Traditional systems mainly rely on simple threshold judgment or a single machine learning algorithm, which cannot effectively identify complex anomaly patterns such as sudden increases, decreases, and continuous constant values ​​of data, and are insufficient in identifying potential risks.

[0006] Lack of intelligent analysis capabilities: Existing systems are usually just a display window for data, lacking in-depth data analysis capabilities and unable to provide valuable decision support.

[0007] Response lag: Traditional systems typically only generate alarms after pollutant concentrations exceed legal limits, lacking early warning mechanisms and resulting in a delayed response.

[0008] Poor user experience: It lacks a user-friendly interface, especially natural language interaction, and has a high learning curve.

[0009] Low law enforcement efficiency: There is a lack of complete closed-loop support from data analysis to on-site law enforcement, resulting in slow law enforcement response speed.

[0010] Compliance verification relies on manual labor: The compliance verification of discharge permits is highly dependent on manual comparison, which is inefficient and prone to errors.

[0011] Insufficient system integration: Existing technologies typically only address a specific problem in environmental monitoring, lacking a systematic solution encompassing data collection, intelligent analysis, early warning, and law enforcement support.

[0012] Therefore, there is an urgent need to develop an intelligent analysis and early warning system for environmental monitoring data based on AI learning, which can effectively solve the above-mentioned problems in existing technologies and improve the intelligence level of environmental monitoring and law enforcement efficiency. Summary of the Invention

[0013] To address the shortcomings of existing technologies, such as weak anomaly identification capabilities, inability to effectively capture complex patterns like sudden increases and decreases in data, lack of intelligent data analysis and interactive decision support, delayed response and lack of early warning mechanisms, low enforcement efficiency, and reliance on manual compliance checks, this invention provides an AI-based intelligent analysis and early warning system for environmental monitoring data. Through the deep integration of artificial intelligence, natural language processing, and geographic information systems, it constructs a closed-loop system covering the entire process from data perception and intelligent analysis to decision support and on-site enforcement.

[0014] The technical solution of the present invention is as follows:

[0015] An AI-based intelligent analysis and early warning system for environmental monitoring data includes a data acquisition and preprocessing module, a multi-dimensional anomaly detection module, a hierarchical early warning module, an intelligent question answering and decision support module, a pollutant discharge permit compliance verification module, and an intelligent enforcement route planning module.

[0016] The data acquisition and preprocessing module receives raw monitoring data in real time through a data interface, cleans, formats, and standardizes the raw monitoring data, and then stores it in a real-time / historical database. The multi-dimensional anomaly detection module has a built-in trained AI model to identify sudden increases, sudden decreases, and continuous constant value anomaly patterns in the data sequence and generate structured event records. The hierarchical early warning module dynamically calculates multi-level early warning thresholds and generates early warning events based on the identification results of the multi-dimensional anomaly detection module. The intelligent question answering and decision support module uses natural language processing technology to parse user queries and generate natural language answer feedback. The pollution discharge permit compliance verification module performs automated comparisons according to pollution discharge permit standards. The intelligent enforcement route planning module calculates the optimal enforcement route based on the enterprise's location and real-time traffic conditions.

[0017] Preferably, the AI ​​model of the multi-dimensional anomaly detection module is trained through the following steps: collecting historical environmental monitoring data, including time-series data such as pollutant concentration, flow rate, and pH value, with the data collection covering emission characteristics of different seasons, time periods, and enterprises; labeling the historical environmental monitoring data, labeling normal data and various types of abnormal data, including sudden increases, sudden decreases, continuous constant values, and periodic fluctuations, with a multi-person cross-labeling mechanism introduced during the labeling process, and ensuring labeling quality through consistency checks; dividing the labeled historical environmental monitoring data into training set, validation set, and test set, using a time-series segmentation method to avoid evaluation bias caused by data leakage, with the ratio typically being 80% for the training set, 10% for the validation set, and 10% for the test set; and constructing a time-series neural network model. The model employs a Long Short-Term Memory (LSTM) network or a Gated Recurrent Unit (GRU) as the encoder backbone, and integrates a multi-head attention mechanism to capture dependencies at different time scales. Layer normalization and Dropout techniques are used to prevent overfitting. The temporal neural network model is trained using a training set, and model parameters are optimized through backpropagation and the Adam optimizer. A learning rate decay strategy and an early stopping mechanism are used to monitor validation set performance and avoid overfitting. The trained model is validated using a validation set, and hyperparameters are adjusted using grid search or Bayesian optimization methods to optimize model performance. The anomaly detection performance of the model is evaluated using a test set, including metrics such as accuracy, recall, F1 score, and AUC-ROC. The model is deployed only when the performance metrics meet preset thresholds.

[0018] Preferably, the multi-level early warning threshold calculation method of the graded early warning module includes: acquiring discharge permit limit data as a baseline threshold; setting early warning coefficients α, β, and γ based on the historical statistical characteristics of monitoring data and pollutant toxicity classification, where α < β < γ, and the initial value of the early warning coefficient is determined according to the pollutant type and environmental sensitivity; dynamically adjusting the early warning coefficient through feedback from historical false alarm rate and missed alarm rate; calculating multi-level early warning thresholds: Level 1 early warning threshold = α × baseline threshold, Level 2 early warning threshold = β × baseline threshold, Level 3 early warning threshold = γ × baseline threshold; dynamically monitoring real-time monitoring data, and generating a corresponding early warning event when the real-time monitoring data exceeds any level of early warning threshold; generating early warning signals of different colors and corresponding handling suggestions according to the level of the early warning event; wherein, when the multi-dimensional anomaly detection module identifies an abnormal pattern, the graded early warning module generates an auxiliary early warning signal even if the early warning threshold has not been reached.

[0019] Preferably, the intelligent question answering and decision support module includes: a natural language understanding unit, used to receive natural language queries from users, perform word segmentation, syntactic analysis, and semantic understanding on the natural language queries, and identify the user's query intent; an intent recognition unit, used to map the user's query intent to the functional categories supported by the system, including data query, anomaly analysis, early warning information query, and compliance verification; an entity recognition unit, used to extract key entity information from the user query, including company name, pollutant type, time range, and spatial location; a query construction unit, used to construct a structured query statement based on the identified query intent and the extracted entity information; a result integration unit, used to execute the structured query statement, obtain relevant data from the analysis result database, and integrate multiple query results into a coherent answer; and a natural language generation unit, used to convert the integrated answer into natural language text and provide feedback to the user through the user interface.

[0020] Furthermore, the intelligent question-answering and decision support module employs a hybrid retrieval strategy, allocating weights for semantic retrieval and keyword retrieval according to a preset ratio. This preset ratio can be dynamically adjusted based on the query type: keyword retrieval weights are increased when the query involves precise numerical values ​​or specific companies, while semantic retrieval weights are increased when the query involves trend analysis or attribution analysis. Query results are output based on the retrieval score combined with the information from the highest-scoring query. Specifically, semantic retrieval is used to understand the semantic meaning of the user's query, while keyword retrieval is used to precisely match key entities. The retrieval results from both are weighted and merged according to a preset ratio (e.g., 7:3) to ensure that the query results are both semantically relevant and contain precisely matched information.

[0021] Preferably, the discharge permit compliance verification module includes: a rule base management unit for storing and managing discharge permit compliance verification rules, including rules for self-monitoring data integrity, rules for exceeding the discharge permit limit, rules for exceeding the discharge concentration limit, and rules for failing to meet monitoring frequency standards; a data acquisition unit for acquiring monitoring data of designated enterprises from the real-time / historical database; a rule matching engine for matching the acquired monitoring data with the verification rules in the rule base management unit to identify potential violations; a violation identification unit for determining whether the monitoring data exceeds the discharge permit standards based on the matching results of the rule matching engine; and a report generation unit for generating a compliance verification report, including the violation type, violation time, violation severity, and rectification suggestions. The discharge permit compliance verification module can be triggered by user queries from the intelligent question-and-answer and decision support module, or by warning events from the tiered early warning module. The verification results are stored in the analysis result database for use by the intelligent question-and-answer module.

[0022] Preferably, the intelligent law enforcement route planning module includes: a task analysis unit for receiving law enforcement task information, including the law enforcement location, task type, and priority; a geographic information acquisition unit for acquiring the geographic coordinates, road network information, and real-time traffic conditions of the law enforcement location; a path planning engine for calculating the optimal law enforcement route based on the geographic information and law enforcement task information using an improved A* algorithm or Dijkstra's algorithm; a multi-task optimization unit for optimizing the law enforcement route using a Traveling Salesman Problem (TSP) algorithm or a genetic algorithm when multiple law enforcement tasks exist, minimizing the total distance or total time; and a navigation service unit for sending the planned law enforcement route to the law enforcement personnel's mobile terminal to provide real-time navigation services.

[0023] Furthermore, the system also includes a visualization and report generation module, used to obtain analysis conclusions, early warning information, and raw historical data from the analysis results library, driving the dynamic display of trend charts and distribution maps on the front-end interface, and supporting one-click report generation and export. Specifically, the visualization and report generation module uses data visualization technology to present information such as trend changes, anomaly distributions, and early warning status of monitoring data to users in an intuitive chart format, helping users quickly understand the changing patterns and anomalies in environmental monitoring data. Simultaneously, this module supports automatically generating analysis reports containing details such as company name, monitoring period, anomaly type, and early warning level according to user needs, and supports exporting in Excel, PDF, and other formats for easy archiving and sharing.

[0024] This invention also provides an intelligent analysis method for environmental monitoring data based on AI learning, applied to the above-mentioned system, comprising the following steps:

[0025] S1: Receive raw monitoring data in real time through a data interface, including pollutant concentration, flow rate and pH value data;

[0026] S2: Clean, format and standardize the original monitoring data to obtain processed data, and store the processed data in a real-time / historical database;

[0027] S3: Use a trained AI model to perform multi-dimensional anomaly detection on the processed data, identify sudden increases, sudden decreases and continuous constant value anomaly patterns in the data sequence, generate structured event records and store them in the analysis result library;

[0028] S4: Dynamically calculate multi-level early warning thresholds based on the discharge permit limit data, generate early warning events and store them in the analysis result database; wherein, when the multi-dimensional anomaly detection module identifies an abnormal pattern, even if the early warning threshold has not been reached, the graded early warning module generates an auxiliary early warning signal;

[0029] S5: Based on the natural language query input by the user, use natural language processing technology to analyze the user's query intent, obtain relevant information from the analysis result database, and generate a natural language answer to be fed back to the user;

[0030] S6: Based on the discharge permit standards, perform automated comparison rules on the monitoring data to determine whether the monitoring data exceeds the standards;

[0031] S7: When on-site law enforcement is required, calculate the optimal enforcement route based on the company's location and real-time traffic conditions.

[0032] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described intelligent analysis method for environmental monitoring data based on AI learning.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0034] Strong multi-dimensional anomaly detection capability: By using AI models to identify complex patterns in data sequences that traditional threshold methods cannot capture, such as sudden increases, sudden decreases, or continuous constant values, it can detect early signs of equipment failure or malicious illegal emissions, extending the regulatory reach from "whether it exceeds the standard" to "whether the operation is abnormal", greatly improving the sensitivity and depth of monitoring.

[0035] A robust early warning mechanism has been established: a tiered early warning module is set up to dynamically calculate multiple warning thresholds, such as 60%, 70%, and 80%, to issue early warnings before pollutant concentrations reach legal limits. When the AI ​​model predicts that a data trend may trigger a warning threshold, the system issues an alert in advance, giving regulatory authorities and enterprises more time to respond and overcoming the lag in traditional systems.

[0036] User-friendly intelligent interaction: By introducing natural language processing technology, users can ask questions in natural language. The system can analyze the user's intent, integrate the results of multiple analysis engines, and generate professional and coherent answers, greatly reducing the barrier to entry for using the system.

[0037] Automated Compliance Verification: The discharge permit compliance verification module can automatically compare "self-monitoring data" with rules such as "whether the total discharge exceeds the permit," achieving automation and standardization of compliance verification and improving verification efficiency and accuracy. This module works in conjunction with the intelligent question-and-answer module and the early warning module, and can be automatically triggered based on user queries or early warning events.

[0038] Significantly improved law enforcement efficiency: The intelligent law enforcement route planning module integrates geographic information system services, calculates the optimal route based on enterprise location and real-time traffic conditions, and directly transforms data analysis results into on-site action instructions, improving law enforcement response speed and efficiency, and forming a closed loop of the entire process from data perception and intelligent analysis to decision support and on-site law enforcement.

[0039] Comprehensive system integration: This invention organically integrates multiple technologies such as AI anomaly detection, natural language processing, and geographic information systems, and applies them to the field of environmental monitoring to build a comprehensive and easy-to-use environmental monitoring and early warning system, solving the problem of insufficient system integration in existing technologies. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0041] Figure 1 This is a schematic diagram of the overall architecture of the AI-based intelligent analysis and early warning system for environmental monitoring data provided in this embodiment of the invention.

[0042] Figure 2 This is a schematic diagram of the workflow of the multi-dimensional anomaly detection module provided in an embodiment of the present invention;

[0043] Figure 3This is a schematic diagram of the early warning threshold calculation method of the graded early warning module provided in this embodiment of the invention;

[0044] Figure 4 This is a structural block diagram of the intelligent question answering and decision support module provided in an embodiment of the present invention;

[0045] Figure 5 This is a schematic diagram of the workflow of the discharge permit compliance verification module provided in this embodiment of the invention;

[0046] Figure 6 This is a structural block diagram of the intelligent law enforcement route planning module provided in an embodiment of the present invention;

[0047] Figure 7 This is a flowchart illustrating the intelligent analysis method for environmental monitoring data based on AI learning provided in this embodiment of the invention.

[0048] Figure Label Explanation: 101-Data Acquisition and Preprocessing Module; 102-Multi-dimensional Anomaly Detection Module; 103-Graded Early Warning Module; 104-Intelligent Question Answering and Decision Support Module; 105-Discharge Permit Compliance Verification Module; 106-Intelligent Enforcement Route Planning Module; 107-Visualization and Report Generation Module; 108-Real-time / Historical Database; 109-Analysis Result Library; 201-Data Acquisition Unit; 202-Data Quality Detection Unit; 203-Anomaly Data Processing Unit; 204-Data Standardization Unit; 205-Data Storage Unit; 301-Data Reading Unit; 302-AI Model; 303-Anomaly Identification Unit; 304-Event Log Generation Unit; 401 - Threshold reading unit; 402 - Early warning coefficient setting unit; 403 - Threshold calculation unit; 404 - Data comparison unit; 405 - Early warning event generation unit; 501 - Natural language understanding unit; 502 - Intent recognition unit; 503 - Entity recognition unit; 504 - Query construction unit; 505 - Result integration unit; 506 - Natural language generation unit; 601 - Rule base management unit; 602 - Data acquisition unit; 603 - Rule matching engine; 604 - Violation identification unit; 605 - Report generation unit; 701 - Task analysis unit; 702 - Geographic information acquisition unit; 703 - Path planning engine; 704 - Multi-task optimization unit; 705 - Navigation service unit. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0050] Example 1

[0051] like Figure 1 As shown, this embodiment of the invention provides an intelligent analysis and early warning system for environmental monitoring data based on AI learning. The system includes: a data acquisition and preprocessing module 101, a multi-dimensional anomaly detection module 102, a hierarchical early warning module 103, an intelligent question answering and decision support module 104, a pollution discharge permit compliance verification module 105, an intelligent enforcement route planning module 106, a visualization and report generation module 107, a real-time / historical database 108, and an analysis result database 109.

[0052] The data acquisition and preprocessing module 101 is the foundation of the system, responsible for receiving raw monitoring data in real time through a data interface. This data typically comes from online monitoring equipment deployed at enterprise discharge outlets, including various types of monitoring data such as pollutant concentrations (e.g., COD, ammonia nitrogen, sulfur dioxide, nitrogen oxides), flow rates, and pH values. The system supports multiple data interface protocols, such as Modbus, OPC UA, and MQTT, ensuring compatibility with monitoring equipment from different manufacturers. The data acquisition and preprocessing module 101 cleans, formats, and standardizes the received raw data, including removing outliers, filling missing values, and unifying data formats and units to ensure data quality. The processed high-quality data is stored in the real-time / historical database 108, which uses a high-performance database system capable of supporting high-concurrency read and write operations, providing unified and reliable data support for all upper-level analysis modules.

[0053] The multi-dimensional anomaly detection module 102 is the core analysis module of the system. It incorporates a trained AI model focused on identifying complex patterns in data sequences that traditional thresholding methods cannot capture. This module reads processed data from the real-time / historical database 108, uses the AI ​​model to analyze the characteristics of the data sequence, and identifies anomaly patterns such as sudden increases, sudden decreases, and persistent constant values. Once a pattern is successfully identified, the system generates a structured event record containing the enterprise identifier, timestamp, and anomaly type, and stores it in the analysis results database 109. These structured event records provide crucial information for subsequent early warning, compliance checks, and decision support.

[0054] The tiered early warning module 103 reads discharge permit limit data from the real-time / historical database 108 and dynamically calculates multi-level early warning thresholds based on preset early warning coefficients. For example, the three early warning thresholds are set to 60%, 70%, and 80% of the discharge permit limit, respectively. This module performs real-time comparison of the data, and when the data exceeds any level of the early warning threshold, it generates a corresponding early warning event and stores it in the analysis results database 109. In addition, when the multi-dimensional anomaly detection module 102 identifies an abnormal pattern, even if the early warning threshold has not been reached, this module can generate an auxiliary early warning signal to remind maintenance personnel to pay attention to the equipment's operating status. This tiered early warning mechanism can provide early warnings before pollutant concentrations reach legal limits, giving regulatory authorities and enterprises time to respond and achieving a shift from passive response to proactive prevention.

[0055] The intelligent question answering and decision support module 104 serves as the system's unified interactive interface, utilizing natural language processing technology to parse user queries. When a user asks a natural language question such as "Was Company A's exhaust emissions compliant last week?" or "Which companies have recently shown abnormal COD concentrations?", this module analyzes the user's intent, extracts key entity information (such as company name, pollutant type, and time range), and then intelligently schedules relevant data from the backend resource query analysis result database 109, including anomaly records, early warning information, and compliance verification conclusions. After integration and processing, the query results are converted into coherent and professional natural language answers and fed back to the user. This approach significantly lowers the barrier to entry for the system, enabling non-professionals to easily obtain professional analysis results.

[0056] The discharge permit compliance verification module 105 automatically compares monitoring data against discharge permit standards to determine whether the data exceeds the standards. This module has a rich built-in verification rule library, including rules for self-monitoring data integrity, total emission exceeding the permit, emission concentration exceeding limits, and monitoring frequency not meeting standards. This module can be triggered by user queries from the intelligent question-and-answer and decision support module 104 (e.g., a user asking if a company is compliant) or by warning events from the tiered warning module 103 (e.g., automatically initiating compliance verification when a level 3 warning is triggered). When a user needs to verify a company's discharge permit compliance, this module automatically retrieves the company's monitoring data from the real-time / historical database 108, matches it against the verification rules, quickly determines whether there are any violations, and generates a detailed compliance verification report stored in the analysis results database 109 for use by the intelligent question-and-answer module 104.

[0057] The intelligent law enforcement route planning module 106 is activated when the system confirms the need for on-site law enforcement. Integrating geographic information system services, it calculates the optimal route for law enforcement personnel based on enterprise location and real-time traffic conditions. This module considers various factors, including the geographic coordinates of the enforcement location, road network information, real-time traffic conditions, and task priority, employing an improved A* algorithm or Dijkstra's algorithm to calculate the optimal enforcement route. When multiple enforcement tasks exist, this module can also use the Traveling Salesman Problem (TSP) algorithm or a genetic algorithm to optimize the enforcement route, minimizing the total distance or total time. The planned enforcement route is sent to the law enforcement personnel's mobile terminals, providing real-time navigation services.

[0058] The visualization and report generation module 107 retrieves all analysis conclusions, early warning information, and raw historical data from the analysis results library 109, driving the dynamic display of components such as trend charts and distribution maps on the front-end interface. This helps users intuitively understand the changing trends and anomalies in environmental monitoring data. Furthermore, this module supports one-click generation of reports containing details such as company name, anomaly time, and anomaly type, which can be exported in Excel, PDF, and other formats.

[0059] Example 2

[0060] like Figure 2 As shown in the figure, the workflow of the multi-dimensional anomaly detection module 102 is described in detail in this embodiment of the invention. This module mainly includes a data reading unit 301, an AI model 302, an anomaly recognition unit 303, and an event record generation unit 304.

[0061] The data reading unit 301 is responsible for reading the monitoring data to be analyzed from the real-time / historical database 108. Users can specify parameters such as the enterprise to be analyzed, the type of pollutant, and the time range. The data reading unit 301 will extract the corresponding data from the database based on these parameters. The data is usually presented in the form of time-series data, and each record contains fields such as timestamp, enterprise identifier, pollutant type, and monitoring value.

[0062] AI Model 302 is the core of anomaly detection. It employs a deep learning-based temporal neural network model, such as a Long Short-Term Memory (LSTM) network or a Gated Recurrent Unit (GRU), and integrates a multi-head attention mechanism to capture dependencies at different time scales. This model requires extensive training on historical environmental monitoring data before deployment. The training process includes the following steps:

[0063] Collect historical environmental monitoring data: Obtain a large amount of historical monitoring data from the database, including normal data and various abnormal data. To ensure the model's generalization ability, data collection should cover emission characteristics of different seasons, different time periods, and different enterprises.

[0064] Data annotation: Historical data is annotated by environmental monitoring experts to distinguish between normal data and different types of abnormal data (sudden increases, sudden decreases, continuous constant values, periodic fluctuations, etc.). A multi-person cross-annotation mechanism is introduced during the annotation process, and consistency checks are used to ensure annotation quality.

[0065] Data partitioning: The labeled data is divided into training set, validation set and test set. Time series partitioning is used to avoid evaluation bias caused by data leakage. The ratio is usually 80% for training set, 10% for validation set and 10% for test set.

[0066] Model Construction: A temporal neural network model was constructed, the network structure was designed, and the hyperparameters were determined. Layer normalization and Dropout techniques were used to prevent overfitting.

[0067] Model Training: The model is trained using the training set, and the model parameters are optimized through backpropagation and the Adam optimizer. A learning rate decay strategy and an early stopping mechanism are used to monitor validation set performance and prevent overfitting during training.

[0068] Model validation: The trained model is validated using a validation set. Hyperparameters are adjusted using grid search or Bayesian optimization methods to optimize model performance.

[0069] Model testing: The anomaly detection performance of the model is evaluated using a test set, including metrics such as accuracy, recall, F1 score, and AUC-ROC. The model can only be deployed online when the performance metrics meet the preset thresholds.

[0070] The anomaly identification unit 303 inputs the monitoring data read by the data reading unit 301 into the AI ​​model 302, and the model outputs the anomaly probability and anomaly type at each time point. The anomaly identification unit 303 determines whether an anomaly exists based on a preset threshold and determines the specific type of the anomaly.

[0071] The event record generation unit 304 generates structured event records based on the results of the anomaly identification unit 303, including fields such as enterprise identifier, timestamp, anomaly type, anomaly degree (such as anomaly probability value), and related monitoring values, and stores these records in the analysis result library 109.

[0072] Example 3

[0073] like Figure 3 As shown in the figure, the embodiment of the present invention describes in detail the method for calculating the warning threshold of the graded warning module 103. The module mainly includes a threshold reading unit 401, a warning coefficient setting unit 402, a threshold calculation unit 403, a data comparison unit 404, and a warning event generation unit 405.

[0074] The threshold reading unit 401 reads the discharge permit limit data from the real-time / historical database 108. This data usually comes from the discharge permit management system of the environmental protection department, which stipulates the maximum value of pollutant concentration or the maximum value of total discharge allowed by each enterprise in different time periods.

[0075] The early warning coefficient setting unit 402 sets early warning coefficients based on the historical statistical characteristics of monitoring data and the toxicity classification of pollutants. Early warning coefficients are parameters used to calculate multi-level early warning thresholds, and are typically set to multiple values ​​to form a multi-level early warning mechanism. The initial values ​​of the early warning coefficients are determined based on the pollutant type and environmental sensitivity, and are dynamically adjusted based on feedback from historical false alarm rates and missed alarm rates. Three early warning coefficients, α, β, and γ, are set, where α < β < γ.

[0076] This invention provides the following embodiments illustrating how the warning coefficient is set:

[0077] Example 3-1: Highly Toxic Pollutant Scenario

[0078] For highly toxic pollutants (such as heavy metals and persistent organic pollutants), due to their significant environmental hazards, stricter warning thresholds need to be set. We set α=0.5, β=0.6, and γ=0.7, meaning the first-level warning threshold is 50% of the discharge permit limit, the second-level warning threshold is 60%, and the third-level warning threshold is 70%.

[0079] Example 3-2: Conventional Pollutant Scenario

[0080] For conventional pollutants (such as COD, ammonia nitrogen, sulfur dioxide, etc.), standard warning thresholds are adopted. α=0.6, β=0.7, γ=0.8, meaning the first-level warning threshold is 60% of the discharge permit limit, the second-level warning threshold is 70%, and the third-level warning threshold is 80%.

[0081] Example 3-3: Environmentally Sensitive Area Scenario

[0082] For enterprises located in environmentally sensitive areas such as drinking water source protection areas and nature reserves, stricter early warning thresholds need to be set. Let α=0.4, β=0.5, γ=0.6, meaning the first-level early warning threshold is 40% of the discharge permit limit, the second-level threshold is 50%, and the third-level threshold is 60%.

[0083] Examples 3-4: Industrial Park Scenarios

[0084] For enterprises with high emission compliance rates within industrial parks, the early warning thresholds can be appropriately relaxed to reduce false alarms. α=0.65, β=0.75, γ=0.85, meaning the Level 1 early warning threshold is 65% of the emission permit limit, the Level 2 threshold is 75%, and the Level 3 threshold is 85%.

[0085] In the above embodiments, the warning coefficient α ranges from 0.4 to 0.7, β ranges from 0.5 to 0.8, and γ ranges from 0.6 to 0.9. In practical applications, the warning coefficient can be dynamically adjusted according to the following factors: (1) pollutant toxicity classification, the higher the toxicity, the smaller the coefficient; (2) environmental sensitivity, the higher the sensitivity, the smaller the coefficient; (3) historical compliance records, the higher the compliance rate, the more lenient it can be; (4) seasonal factors, the coefficient can be appropriately reduced when the diffusion conditions are poor in winter. The dynamic adjustment of the warning coefficient is achieved through the feedback mechanism of historical false alarm rate and missed alarm rate: when the false alarm rate is too high, the warning coefficient is appropriately increased; when the missed alarm rate is too high, the warning coefficient is appropriately decreased.

[0086] The threshold calculation unit 403 calculates multi-level early warning thresholds based on the discharge permit limit data and early warning coefficients. The calculation formula is as follows:

[0087] Level 1 warning threshold = α × baseline threshold

[0088] Level 2 warning threshold = β × baseline threshold

[0089] Level 3 warning threshold = γ × baseline threshold

[0090] The data comparison unit 404 obtains real-time monitoring data from the real-time / historical database 108 and compares it with the warning thresholds at all levels.

[0091] The early warning event generation unit 405 generates corresponding early warning events based on the comparison results. Different levels of early warning events correspond to different early warning signals and handling suggestions.

[0092] Example 4

[0093] like Figure 4 As shown in the figure, the structure of the intelligent question answering and decision support module 104 is described in detail in this embodiment of the invention. This module mainly includes a natural language understanding unit 501, an intent recognition unit 502, an entity recognition unit 503, a query construction unit 504, a result integration unit 505, and a natural language generation unit 506.

[0094] The Natural Language Understanding Unit 501 is responsible for receiving natural language queries from users and performing operations such as word segmentation, syntactic analysis, and semantic understanding on the queries.

[0095] The intent recognition unit 502 is responsible for recognizing the user's query intent and mapping the user's query intent to the functional categories supported by the system.

[0096] The entity recognition unit 503 is responsible for extracting key entity information from user queries.

[0097] The query construction unit 504 constructs a structured query statement based on the identified query intent and the extracted entity information.

[0098] The result integration unit 505 executes queries, retrieves relevant data, and integrates multiple query results into a coherent answer.

[0099] The natural language generation unit 506 converts the integrated answer into natural language text and provides feedback to the user through the user interface.

[0100] This module employs a hybrid search strategy, allocating weights for semantic and keyword searches according to a preset ratio. This preset ratio can be dynamically adjusted based on the query type: keyword search weights are increased when the query involves precise numerical values ​​or specific companies, while semantic search weights are increased when the query involves trend analysis or attribution analysis. Search results are weighted and merged according to a preset ratio (e.g., 7:3) to ensure that the query results are both semantically relevant and contain exact matching information.

[0101] Example 5

[0102] like Figure 5 As shown in the figure, the workflow of the discharge permit compliance verification module 105 is described in detail in this embodiment of the invention. This module mainly includes a rule base management unit 601, a data acquisition unit 602, a rule matching engine 603, a violation identification unit 604, and a report generation unit 605.

[0103] The rule base management unit 601 is responsible for storing and managing the rules for verifying compliance with discharge permits. These rules include rules for the integrity of self-monitoring data, rules for exceeding the permit for total emissions, rules for exceeding emission concentration limits, and rules for failing to meet monitoring frequency standards.

[0104] The data acquisition unit 602 is responsible for acquiring monitoring data of the designated enterprise from the real-time / historical database 108.

[0105] The rule matching engine 603 matches monitoring data with verification rules to identify potential violations.

[0106] The violation identification unit 604 determines whether the monitoring data exceeds the discharge permit standards based on the matching results.

[0107] The report generation unit 605 generates a compliance verification report, including the type of violation, the time of violation, the degree of violation, and rectification suggestions.

[0108] This module can be triggered by user queries from the intelligent question-and-answer and decision support module 104 (such as a user asking whether a company is compliant), or by warning events from the tiered warning module 103 (such as automatically initiating a compliance check when a level 3 warning is triggered). The verification results are stored in the analysis results database 109 for use by the intelligent question-and-answer module 104.

[0109] Example 6

[0110] like Figure 6 As shown in the figure, the structure of the intelligent law enforcement route planning module 106 is described in detail in this embodiment of the invention. This module mainly includes a task analysis unit 701, a geographic information acquisition unit 702, a route planning engine 703, a multi-task optimization unit 704, and a navigation service unit 705.

[0111] The task analysis unit 701 is responsible for receiving and analyzing law enforcement task information, including the enforcement location, task type, priority, etc.

[0112] The geographic information acquisition unit 702 is responsible for acquiring the geographic coordinates, road network information, and real-time traffic conditions of the law enforcement location.

[0113] The route planning engine 703 calculates the optimal law enforcement route based on geographic information and law enforcement task information, using an improved A* algorithm or Dijkstra's algorithm.

[0114] When there are multiple law enforcement tasks, the multi-task optimization unit 704 uses a genetic algorithm to optimize the law enforcement route to minimize the total distance or total time.

[0115] The navigation service unit 705 sends the planned enforcement route to the mobile terminal of the law enforcement officer, providing real-time navigation service.

[0116] Example 7

[0117] like Figure 7 As shown in the figure, the embodiment of the present invention describes in detail the intelligent analysis method for environmental monitoring data based on AI learning, which includes steps S1 to S7.

[0118] S1: Receive raw monitoring data in real time via data interface.

[0119] S2: Clean, format and standardize the raw monitoring data, and store the processed data in a real-time / historical database.

[0120] S3: Utilize a trained AI model to perform multi-dimensional anomaly detection on the processed data and generate structured event records.

[0121] S4: Dynamically calculate multi-level early warning thresholds based on the discharge permit limit data and generate early warning events; when the multi-dimensional anomaly detection module identifies an abnormal pattern, it generates an auxiliary early warning signal.

[0122] S5: Based on the natural language query input by the user, use natural language processing technology to parse the user's query intent and generate a natural language answer.

[0123] S6: Based on the discharge permit standards, execute automated comparison rules on the monitoring data to determine whether the monitoring data exceeds the standards.

[0124] S7: Calculates the optimal enforcement route based on the company's location and real-time traffic conditions.

Claims

1. An intelligent analysis and early warning system for environmental monitoring data based on AI learning, characterized in that: The system includes a data acquisition and preprocessing module, a multi-dimensional anomaly detection module, a tiered early warning module, an intelligent question-and-answer and decision support module, a pollution discharge permit compliance verification module, and an intelligent enforcement route planning module. Specifically, the data acquisition and preprocessing module receives raw monitoring data in real time via a data interface, cleans, formats, and standardizes the data before storing it in a real-time / historical database. The multi-dimensional anomaly detection module incorporates a trained AI model to identify sudden increases, sudden decreases, and persistent constant-value anomaly patterns in the data sequence, generating structured event records. The tiered early warning module dynamically calculates multi-level early warning thresholds and generates early warning events based on the identification results from the multi-dimensional anomaly detection module. The intelligent question-and-answer and decision support module uses natural language processing technology to parse user queries and generate natural language responses. The pollution discharge permit compliance verification module performs automated comparisons based on pollution discharge permit standards. The intelligent enforcement route planning module calculates the optimal enforcement route based on the enterprise's location and real-time traffic conditions.

2. The system according to claim 1, characterized in that: The AI ​​model of the multi-dimensional anomaly detection module is trained through the following steps: collecting historical environmental monitoring data; labeling the historical environmental monitoring data; dividing the labeled data into training set, validation set, and test set; constructing a temporal neural network model; training using the training set; validating and tuning hyperparameters using the validation set; and evaluating the anomaly detection performance using the test set.

3. The system according to claim 1, characterized in that: The multi-level early warning threshold calculation method of the graded early warning module includes: acquiring discharge permit limit data as a baseline threshold; setting early warning coefficients α, β, and γ based on the historical statistical characteristics of the monitoring data and the pollutant toxicity classification, where α < β < γ; dynamically adjusting the early warning coefficients through feedback from historical false alarm rate and missed alarm rate; calculating multi-level early warning thresholds: Level 1 early warning threshold = α × baseline threshold, Level 2 early warning threshold = β × baseline threshold, Level 3 early warning threshold = γ × baseline threshold; generating a corresponding early warning event when real-time monitoring data exceeds any level early warning threshold; wherein, when the multi-dimensional anomaly detection module identifies an abnormal pattern, even if the early warning threshold has not been reached, the graded early warning module also generates an auxiliary early warning signal.

4. The system according to claim 1, characterized in that: The intelligent question answering and decision support module includes a natural language understanding unit, an intent recognition unit, an entity recognition unit, a query construction unit, a result integration unit, and a natural language generation unit. The natural language understanding unit is used to perform word segmentation, syntactic analysis, and semantic understanding on user queries. The intent recognition unit is used to map query intents to the functional categories supported by the system. The entity recognition unit is used to extract key entity information. The query construction unit is used to construct structured query statements; the result integration unit is used to integrate multiple query results; and the natural language generation unit is used to convert the answers into natural language text.

5. The system according to claim 4, characterized in that: The intelligent question answering and decision support module adopts a hybrid retrieval strategy, allocating the weights of semantic retrieval and keyword retrieval according to a preset ratio. The preset ratio is dynamically adjusted according to the query type. When the query involves precise values ​​or specific companies, the weight of keyword retrieval is increased. When the query involves trend analysis or attribution analysis, the weight of semantic retrieval is increased. The query results are output based on the retrieval score combined with the information of the highest score.

6. The system according to claim 1, characterized in that: The discharge permit compliance verification module includes a rule base management unit, a data acquisition unit, a rule matching engine, a violation identification unit, and a report generation unit. The rule base management unit stores discharge permit compliance verification rules. The data acquisition unit acquires monitoring data from real-time / historical databases. The rule matching engine matches the monitoring data with the verification rules. The violation identification unit determines whether any violations exist. The report generation unit generates a compliance verification report.

7. The system according to claim 1, characterized in that: The intelligent law enforcement route planning module includes a task analysis unit, a geographic information acquisition unit, a path planning engine, a multi-task optimization unit, and a navigation service unit; the task analysis unit is used to receive law enforcement task information. The geographic information acquisition unit is used to acquire the geographic coordinates and real-time traffic conditions of the law enforcement location; the path planning engine is used to calculate the optimal law enforcement route using an improved A* algorithm or Dijkstra's algorithm. The multi-task optimization unit is used to optimize the routes for multiple law enforcement tasks; the navigation service unit is used to send the planned law enforcement routes to the mobile terminals of law enforcement personnel.

8. The system according to claim 1, characterized in that: The system also includes a visualization and report generation module, which is used to obtain analysis conclusions, early warning information and raw historical data from the analysis results library, drive the dynamic display of trend charts and distribution charts on the front-end interface, and support one-click report generation and export.

9. An intelligent analysis method for environmental monitoring data based on AI learning, applied to the system described in any one of claims 1 to 8, characterized in that: Includes the following steps: Raw monitoring data is received in real time through a data interface; the raw monitoring data is cleaned, formatted, and standardized to obtain processed data which is then stored in a real-time / historical database; a trained AI model is used to perform multi-dimensional anomaly detection on the processed data, identify anomaly patterns, and generate structured event records; multi-level early warning thresholds are dynamically calculated based on the discharge permit limit data to generate early warning events. Based on the user's input natural language query, natural language processing technology is used to analyze the user's query intent and generate natural language answer feedback; automated comparison rules are executed according to the pollution discharge permit standards to determine whether the monitoring data exceeds the standards; when on-site enforcement is required, the optimal enforcement route is calculated based on the enterprise's location and real-time traffic conditions.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the AI-based intelligent analysis method for environmental monitoring data as described in claim 9.

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