Water ecological environment decision response intelligent interaction system based on AI natural language processing

Abstract

The invention discloses a water ecological environment decision response intelligent interaction system based on AI natural language processing, and the system carries out the data collection and preprocessing, and forms a training set. In combination with the training set, data annotation and feature engineering are carried out, and AI model training including data segmentation, a training method and transfer learning is further carried out; on the basis, continuously collecting monitoring data, and inputting the monitoring data into the trained AI model for real-time prediction; data analysis and decision support are carried out; and once an abnormal trend is detected, an early warning mechanism is triggered. Through an AI large model technology in the vertical field, big data and an artificial intelligence algorithm are utilized to simulate and improve intelligence, convenience and precision of water ecological environment supervision, through combination of an AI large model and an intelligent voice interaction technology, based on analysis and processing capability of the AI large model, deep analysis is performed on water ecological environment data, and a water ecological environment monitoring result is obtained. And managers are helped to quickly make scientific decisions.

Description

Technical Field

[0001] The present invention belongs to the technical field of IPC classification G06F16 / 21 for database design, management or maintenance, or G06T17 / 00 for 3D modeling in computer graphics. It relates to the ecological environment protection technology based on AI natural language processing, and particularly to an AI intelligent response water ecological environment decision-making interaction system and its working method. Background Art

[0002] Natural Language Processing (NLP) is an important research direction in the field of artificial intelligence. It integrates knowledge from multiple disciplinary fields such as linguistics, computer science, machine learning, mathematics, and cognitive psychology. It is an interdisciplinary subject that combines computer science, artificial intelligence, and linguistics. It includes two main aspects: natural language understanding and natural language generation. Its research content includes various levels such as characters, words, phrases, sentences, paragraphs, and chapters. It is a bridge for communication between machine language and human language. Its aim is to enable machines to understand, interpret, and generate human language, achieve effective communication between humans and machines, and enable computers to perform tasks such as language translation, sentiment analysis, and text summarization.

[0003] Natural language cognition and understanding is to make the computer turn the input language into meaningful symbols and relationships, and then process according to the purpose. The natural language generation system is to convert computer data into natural language.

[0004] The tasks of natural language processing include developing models representing language capabilities and language applications, establishing computational frameworks to implement and improve language models, designing various practical systems based on language models, and exploring the evaluation techniques of these systems.

[0005] In recent years, with the development of natural language processing technology, large-scale pre-trained language models (LLMs) such as GPT, BERT, and LLaMA have emerged. They have capabilities such as context understanding, generative dialogue, and multi-task learning. Applying large models to the field of water ecological environment helps to mine valuable knowledge from massive unstructured information such as text policies and regulations, expert literature, and forum discussions, which is of great significance for quickly forming domain-specific knowledge graphs and assisting in decision-making. However, the current large model solutions for the ecological field are still in the initial exploration stage. The present invention attempts to combine the specific needs of the water quality sensing and hydroecology fields to vertically customize and transfer learn the large model, so as to achieve high-precision and highly reliable real-time decision-making and intelligent question-answering functions.

[0006] The research and development of AI intelligent technology for the ecological field is developing rapidly, but the public literature is limited.

[0007] Patent Application 202410075614.X relates to a method, apparatus, and electronic device for constructing an ecological flow monitoring system based on an AI intelligent discrimination matrix, including obtaining the morphological information of the river to be detected, obtaining the runoff information of the river to be detected, obtaining the ecological flow monitoring scenario information of the river to be detected, presetting the ecological flow monitoring elements, obtaining the water level information of the river to be detected, presetting the ecological flow monitoring accuracy range, presetting the ecological flow frequency monitoring method, presetting the ecological flow monitoring method, setting the above-mentioned ecological flow index items in the same matrix, and generating an ecological flow monitoring method determination matrix.

[0008] Patent Application 201910812606.8 discloses a water-related ecological environment carrying capacity prediction and regulation system, including: a carrying capacity prediction module and a constraint condition module. The constraint condition module acts on the carrying capacity prediction module, and the water (soil) resources - ecological environment - social economy correlation and interaction module is the key sub-module of the constraint condition module. It can predict and analyze the water-related ecological environment carrying capacity in the study area in the next few years under different ecological environment construction and social economy development scenarios.

[0009] Patent Application 202410622095.4 discloses an ecological protection red line ecological data storage and supervision system, including an infrastructure layer, an information data layer, core functional components, and a business application layer designed with a multi-layer architecture; the business application layer uses plug-in GIS technology to develop GIS services to obtain each module, and the data interaction and information transmission between each module are carried out through a data sharing and exchange system.

[0010] The evaluation and attribution method for changes in the regional ecological security pattern of Patent Application 202410134228.3. It relates to an evaluation and attribution method for changes in the ecological security pattern. To solve the problem that the prior art cannot quantitatively evaluate and attribute the changes in the regional ecological security pattern, the method includes: obtaining various geographical spatial data of the ecological region; determining ecological source areas according to the ecological importance index, ecological sensitivity index, and landscape dominance index of the ecological region; obtaining a comprehensive ecological resistance surface by weighted superposition of each resistance factor; constructing an ecological security pattern; quantitatively evaluating the connectivity of source areas and network connectivity according to the ecological source areas and ecological corridors; and conducting attribution analysis on the relative contributions of climate change and human activities to the changes in the regional ecological security pattern of the ecological region; realizing the quantitative evaluation and attribution analysis of the changes in the regional ecological security pattern. It belongs to the field of evaluation and attribution of changes in the ecological security pattern.

[0011] Patent Application 202410613229.6 discloses an intelligent environmental protection system and method based on cloud monitoring. The system includes: a multi-modal environmental monitoring station unit for positioning and setting environmental monitoring stations in the urban interface according to environmental monitoring thresholds at different locations, and establishing data transmission relationships for environmental monitoring stations with different functional uses; a multi-functional sensing device for using the environmental unit time change data and the preset environmental unit time change to monitor and evaluate the residential safety of urban residents, and obtaining the monitoring and evaluation results; an environmental hazard risk unit for using the monitoring and evaluation results to issue different environmental hazard risk prompt messages, so as to prompt the environmental protection department to carry out environmental maintenance and renovation.

[0012] Patent Application 202410750272.7 discloses an AI digital human platform interaction system and method based on artificial intelligence, belonging to the technical field of AI digital human data analysis. It includes an AI digital human construction module, a data analysis module, a user screening module, and an interaction feedback module; the output end of the AI digital human construction module is connected to the input end of the data analysis module; the output end of the data analysis module is connected to the input end of the user screening module; the output end of the user screening module is connected to the input end of the interaction feedback module.

[0013] Even so, the existing water ecological environment monitoring means and consulting decision-making systems still mainly rely on a large amount of human input, with slow decision-making speed and difficulty in responding to emergencies in real time. Especially when dealing with complex hydrological environments, the efficiency is even lower, and it may even be unable to operate. With the rapid development of artificial intelligence technology, especially the progress in the fields of natural language processing and deep learning, there is an urgent need to innovate an AI-based intelligent system with implementable decision-making response intelligent interaction to meet the above real needs. Summary of the Invention

[0014] The present invention aims to provide a water ecological environment AI intelligent interaction system based on AI natural language processing technology. Through the AI large model technology in the vertical field, it uses big data and artificial intelligence algorithms to simulate human thinking and creativity, focuses on the water ecological environment field, and improves the intelligence, convenience, and accuracy of water ecological environment supervision, aiming to achieve more efficient and intelligent water resource management and ecological protection.

[0015] To achieve the above object, the present invention provides the following technical solutions:

[0016] In order to implement the water ecological environment early warning based on the AI ​​model, the system conducts data collection and preprocessing, including data collection, data cleaning, standardization processing and time alignment; then, a training set is formed; then, combined with the training set, data annotation is performed, including normal data annotation, abnormal data annotation and event classification; then feature engineering is performed, including time series feature extraction, spatial feature extraction and cross-features; further AI model training is performed, including data segmentation, training methods and transfer learning; on this basis, monitoring data is continuously collected and input into the trained AI model for real-time prediction; and data analysis and decision support are performed; once an abnormal trend is detected, the early warning mechanism is triggered.

[0017] The water ecological environment decision-making and response intelligent interactive system based on AI natural language processing provided by the present invention has the following system function layers: data acquisition and perception layer: including distributed water quality monitoring sensors, drone remote sensing observations, ecological monitoring stations, etc., covering parameters such as water temperature, pH value, dissolved oxygen, turbidity, and their transmission modules; data transmission and storage layer: using the Internet of Things (IoT) gateway and cloud database or edge computing node to transmit, cache, distribute and manage the collected multi-source heterogeneous data; data analysis and AI model layer: using deep learning models, large models and transfer learning methods to train and infer time series data, spatial data, and text data such as policies and regulations; intelligent interaction and visualization layer: interacting with users through visual front-ends such as voice recognition, natural language processing (NLP), Web or mobile App, displaying analysis results in the form of charts, GIS maps, real-time monitoring instruments, etc., supporting multiple rounds of dialogue, intelligent question and answer, and decision support; application and decision support layer: based on the prediction results and early warning mechanism of the model, providing managers with a variety of response strategies, scheduling plans, ecological restoration strategies and other decision suggestions; once an abnormal trend is detected, triggering a corresponding early warning or emergency response.

[0018] The present invention is further configured as follows: the data collection and preprocessing: installing multi-parameter water quality sensors at key locations in the watershed to collect water quality data such as water temperature, pH value, dissolved oxygen, turbidity, total phosphorus, total nitrogen, and permanganate index in real time;

[0019] Data cleaning: remove noise, outliers and fill missing values, using interpolation or time series model-based completion;

[0020] Standardization: Normalize or standardize monitoring data of different units and ranges to ensure that the contribution of characteristic values ​​to the model is balanced;

[0021] Time alignment: Align data from different sources by timestamp to form a multi-dimensional, time-continuous feature matrix;

[0022] Data annotation: Based on historical monitoring records and water quality standards, annotate data within the normal range;

[0023] Abnormal data annotation: combining expert experience and historical abnormal event records to annotate different types of abnormal events;

[0024] Event classification: Classify abnormal events into different categories, including biological pollution, chemical pollution and physical pollution, so as to facilitate model classification training;

[0025] Time series feature extraction: Use the sliding window method to extract the time characteristics of monitoring data;

[0026] Spatial feature extraction: Introduce GIS coordinates and spatial proximity relationships to establish the spatial distribution characteristics of data;

[0027] Cross-features: Combine different monitoring indicators to explore potential high-level features;

[0028] Data segmentation: Divide the data into training set, validation set and test set in a ratio of 7:2:1;

[0029] Training method: Use supervised learning algorithm to train the model and optimize the objective function;

[0030] Transfer learning: Use existing water quality prediction models for transfer learning to shorten training time and improve model accuracy in small sample cases.

[0031] Furthermore, the system builds a large AI model through the LLaMA framework, and the model training data includes historical water quality monitoring data, ecological environment data, and relevant policy and regulatory texts.

[0032] Furthermore, the front end of the system uses a voice recognition interface. Users input questions through voice, and the back end of the system converts the voice into text and parses it. The AI ​​large model processes and answers it, and finally feeds back the results to the user through speech synthesis technology.

[0033] Furthermore, the system utilizes artificial intelligence technology, especially natural language processing (NLP) technology, to achieve natural language communication with users.

[0034] Furthermore, the system provides targeted decision-making recommendations or response measures; and displays water ecological environment data and analysis results in the form of charts, maps, etc.

[0035] Furthermore, the system and AI model can construct a two-level optimization scheduling model based on genetic algorithm.

[0036] Furthermore, the system and the AI model conduct aquatic ecological biological monitoring and ecological protection through image recognition technology, quickly identifying and classifying animals and plants in the ecosystem; the AI model analyzes changes in the habitats of aquatic ecological organisms to evaluate the health status of the ecosystem.

[0037] Furthermore, the system and the AI model can simulate the complex interactions of the aquatic ecosystem to provide decision support for ecological restoration, predicting the best ecological restoration plan; the AI model optimizes the planting plan and species selection.

[0038] Furthermore, the system integrates a comprehensive management system that combines Internet of Things, intelligent sensing, cloud computing, big data, and artificial intelligence technologies.

[0039] Furthermore, the system also includes a model online update module, which is used to incrementally train or online learn the AI model based on the labeled data collected in real time when it is detected that the distribution of water quality data has changed significantly or the prediction error continuously exceeds the limit, thereby dynamically correcting the model parameters and improving the prediction accuracy.

[0040] Furthermore, after detecting abnormal data, the system automatically triggers an abnormal handling process, performs secondary annotation and diagnosis on abnormal water quality samples, and at the same time generates a traceable abnormal report and records it in the system log for subsequent model evaluation and traceability analysis.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] Through the combination of the AI large model and intelligent voice interaction technology, the intelligent level of aquatic ecological environment supervision is significantly improved. Based on the analysis and processing capabilities of the AI large model, the system can provide more accurate solutions for specific scenarios, improving the scientific nature of supervision and governance. The system can process a large amount of data in real time and provide intelligent decision support, improving the efficiency of ecological environment supervision. The intelligent voice interaction function enables users to communicate naturally through voice, simplifies the operation process, and improves the user experience. The system is based on big data and neural network technologies, can deeply analyze aquatic ecological environment data, and helps managers make scientific decisions quickly. Through intelligent algorithms, the system can process a large amount of data in real time, and generate analysis reports and visualization results, improving the supervision efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The features and advantages of the present invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as imposing any limitation on the present invention.

[0044] Figure 1 It is a schematic diagram of the system structure and working process in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] In the description of the present application, it should be understood that the orientation or positional relationships indicated by the terms "upper", "lower", "front", "rear", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application. Unless otherwise clearly specified and defined, terms such as "installation", "connection", "fixation", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection.

[0046] The principle of the present invention lies in combining advanced AI technology with the needs of water ecological environment management to provide an efficient, convenient, and accurate intelligent supervision platform. Based on real-time monitoring data, the system can warn of potential water quality problems or environmental anomalies, improving the emergency response speed.

[0047] In the present invention, as shown in the attached Figure 1 figures, for implementing water ecological environment warning based on an AI model, data collection and preprocessing are carried out, including data collection, data cleaning, standardization processing, and time alignment; then, a training set is formed; then, combined with the training set, data annotation is carried out, including normal data annotation, abnormal data annotation, and event classification; then feature engineering is carried out, including time series feature extraction, spatial feature extraction, and cross features; further, AI model training is carried out, including data segmentation, training methods, and transfer learning; on this basis, monitoring data is continuously collected and input into the trained AI model for real-time prediction. And data analysis and decision support are carried out. Once an abnormal trend is detected, the warning mechanism is triggered.

[0048] Among them:

[0049] Data collection and preprocessing: Install multi-parameter water quality sensors at key positions in the basin to collect water quality data such as water temperature, pH value, dissolved oxygen, turbidity, total phosphorus, total nitrogen, and permanganate index in real time.

[0050] Data cleaning: Remove noise, outliers, and fill in missing values, using interpolation or time series model-based completion.

[0051] Standardization processing: Normalize or standardize monitoring data with different units and ranges to ensure the balanced contribution of feature values to the model.

[0052] Time alignment: Align data from different sources according to timestamps to form a multi-dimensional, time-continuous feature matrix.

[0053] Data annotation: Based on historical monitoring records and water quality standards, annotate data within the normal range.

[0054] Abnormal data labeling: combining expert experience and historical abnormal event records to label different types of abnormal events, such as heavy metal pollution and excessive ammonia nitrogen.

[0055] Event classification: Abnormal events are divided into different categories, including biological pollution, chemical pollution, and physical pollution, to facilitate model classification training.

[0056] Time series feature extraction: Use the sliding window method to extract the time characteristics of the monitoring data, including the rate of change, periodicity, etc.

[0057] Spatial feature extraction: Introduce GIS coordinates and spatial proximity relationships to establish the spatial distribution characteristics of data.

[0058] Cross-features: Combine different monitoring indicators to explore potential high-order features, including the coordinated changes in dissolved oxygen and water temperature.

[0059] Data splitting: Divide the data into training set, validation set and test set, usually in a ratio of 7:2:1.

[0060] Training method: The supervised learning algorithm is used to train the model, and the model performance is continuously improved by optimizing the objective function, including cross entropy, mean square error, etc.

[0061] Transfer learning: Use existing water quality prediction models for transfer learning to shorten training time and improve model accuracy in small sample cases.

[0062] In the present invention, further:

[0063] Data collection layer: Through sensors and monitoring equipment deployed in water bodies, real-time data on water quality, water quantity, aquatic life, meteorology, etc. These data include but are not limited to turbidity, pH value, dissolved oxygen, temperature, flow rate, and the types and quantity of phytoplankton.

[0064] To ensure the accuracy and reliability of monitoring data, the system also includes the following data quality control and calibration measures: Sensor calibration: Regular laboratory comparison and field calibration of water quality sensors to correct drift or errors that occur in long-term use of sensors; Redundant sampling: Deploy redundant sensors in key sections or sensitive areas to troubleshoot faults and identify abnormal data points by comparing the real-time readings of multiple sensors; Data confidence assessment: Automatically assign a confidence score to each piece of monitoring data, and conduct a comprehensive assessment based on historical averages, sensor performance indicators, etc.; Abnormal sample learning: Collect identified abnormal samples into the database as new cases, regularly expand the AI ​​model training set, and enhance the model's ability to recognize various types of anomalies.

[0065] Data Processing and Analysis Layer: Utilize big data technologies and machine learning algorithms to perform real-time processing and analysis on the collected data. Through deep learning models, the system can automatically identify water quality change trends, predict water quality changes, and identify potential ecological risks, etc.

[0066] Intelligent Interaction Layer: Provide real-time and accurate water ecological environment information to management personnel and the public through user-friendly interfaces and interaction methods. Users can view water quality monitoring data, ecological risk warnings, treatment suggestions, etc. through terminal devices such as mobile phones and computers at any time and place, and interact with the system.

[0067] In this invention, the application scenarios and advantages include:

[0068] Water Quality Monitoring and Warning: The system can monitor the water body quality in real time and issue warnings in a timely manner when the water quality is abnormal, providing decision-making support for the management department to prevent further deterioration of the water quality.

[0069] Ecological Restoration and Protection: By simulating the complex interactions of the ecosystem, the AI intelligent interaction system can help scientists predict the best ecological restoration plans, optimize planting plans and species selection, and improve the efficiency of ecological restoration.

[0070] Water Resources Management: The system can analyze hydrological data, monitor and predict water flow and water quality in real time, and provide a scientific basis for the rational allocation and efficient utilization of water resources.

[0071] Public Education and Participation: By providing real-time and accurate water ecological environment information, the system can enhance the public's environmental protection awareness and promote public participation in water ecological protection activities.

[0072] In this invention, the system is based on big data and neural network technologies. Through intelligent algorithms, it deeply analyzes ecological environment data, processes a large amount of data in real time, and generates analysis reports and visualization results, helping managers make scientific decisions quickly and improving supervision efficiency.

[0073] In this invention, the system is widely applied in the fields of water ecological environment protection, water resources management, flood control and disaster reduction, etc. For example, in terms of water ecological environment protection, the system can monitor water quality changes in real time, warn of potential pollution sources, and provide ecological restoration suggestions; in terms of water resources management, the system can optimize water resources allocation and improve water resources utilization efficiency; in terms of flood control and disaster reduction, the system can predict the risks and trends of flood occurrence and provide a scientific basis for flood control decision-making.

[0074] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the present invention pertains. In case of conflict, the definitions in this specification shall prevail.

[0075] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0076] Embodiment 1: The system builds an AI large model through the LLaMA framework. The model training data includes historical water quality monitoring data, ecological environment data, and relevant policy and regulation texts. The front end of the system uses a voice recognition interface. Users input questions through voice. The back end of the system converts the voice into text and parses it, which is processed and answered by the AI large model, and finally the result is fed back to the user through voice synthesis technology.

[0077] In the embodiments of the present invention, the system utilizes artificial intelligence technology, especially natural language processing (NLP) technology, to achieve natural language communication with users. Users can input through voice or text, and the system can understand the intentions of users and provide corresponding decision responses for the water ecological environment. The natural language processing technology understands the intentions and context of users to achieve semantic understanding. The voice recognition technology converts the voice commands of users into text.

[0078] In the embodiments of the present invention, since the system integrates a real-time warning function, based on the data stream input of ecological environment monitoring sensors, it can automatically analyze potential environmental risks and issue a voice warning when necessary to remind users to take corresponding measures in a timely manner. Maintain context information, support multi-round conversations, and improve the user experience. Based on the questions or needs of users, the system calls the internal water ecological environment management model for analysis. The system provides targeted decision-making suggestions or response measures. Display water ecological environment data and analysis results in the form of charts, maps, etc. to help users understand the problems more intuitively.

[0079] In the embodiments of the present invention, based on ecological environment data, an AI large model assistant is developed using the LLaMA framework, integrating intelligent voice interaction technology, realizing multi-scenario applications such as water quality and hydrology knowledge Q&A, voice warning, and intelligent agents, and providing a comprehensive and efficient intelligent solution around the three business goals of industry safety, wisdom, and convenience.

[0080] In the embodiments of the present invention, data collection monitors various indicators of water bodies in real time, including turbidity, pH value, dissolved oxygen, etc. Through machine learning algorithms for AI model training, the AI model can perform in-depth data analysis and decision support on continuously collected monitoring data, timely identify the trends and anomalies of water ecological changes, and predict or trigger warnings in real time, greatly improving the efficiency of water ecological management.

[0081] In the embodiments of the present invention, further, the AI model can quickly analyze a large amount of data collected by the water ecological monitoring network, identify potential pollution hotspots, and provide data support for corresponding treatment measures. The water ecological monitoring system driven by the trained AI model can generate pollution warnings based on real-time data, helping the government and enterprises take timely measures to reduce health risks and environmental damage.

[0082] In the embodiments of the present invention, further, the system and the AI model can locate the water leakage point in real time, reduce the waste of urban water, and improve the reliability of the water supply system.

[0083] In the embodiments of the present invention, the system and the AI model can construct a secondary optimization scheduling model based on the genetic algorithm. On the basis of realizing the dynamic prediction of the water ecology, the system and the AI model can optimize the system energy consumption and reduce the operation cost.

[0084] In the embodiments of the present invention, further, the system and the AI model conduct water ecological biological monitoring and ecological protection through image recognition technology, and can quickly identify and classify animals and plants in the ecosystem. The AI analyzes the changes in biological habitats to evaluate the health status of the ecosystem and improve the efficiency of biodiversity protection work.

[0085] In the embodiments of the present invention, further, the system and the AI model can simulate the complex interactions of the water ecological system to support ecological restoration decision-making and predict the best ecological restoration plan. The AI can optimize the planting plan and species selection to achieve higher ecological restoration benefits.

[0086] In the embodiments of the present invention, further, the system is an integrated management system integrating a variety of advanced technologies such as the Internet of Things, intelligent sensing, cloud computing, big data, and artificial intelligence. The system aims to achieve comprehensive, real-time, and intelligent monitoring and management of the water ecological environment, providing strong support for water ecological protection and sustainable development.

[0087] Embodiment 2: On the basis of Embodiment 1, by strengthening and adding necessary technical measures, the real-time acquisition, analysis, and visual display of water ecological environment data are realized to achieve intelligent prediction and scientific decision-making support for water quality problems and environmental anomalies. The specific steps are as follows:

[0088] First, the acquisition of real-time water ecological data, remote sensing image data, meteorological data, and historical pollution event records is realized through multi-source data collection. The acquired data is cleaned, formatted, and standardized by the data preprocessing module to eliminate noise and outliers, and the time and space attributes are uniformly aligned to generate a standardized structured data set. This data set serves as the basic input for subsequent analysis and modeling.

[0089] Second, process the standardized dataset through the in-depth analysis module. This module uses a multi-task learning model based on neural networks, including Long Short-Term Memory (LSTM) and Temporal Transformer models, to predict the time series trends of water quality indicators. Meanwhile, a Graph Neural Network (GNN) is introduced in combination with spatial characteristics to model the spatial associations between water bodies within the basin, enabling precise positioning of pollution sources and detection of abnormal distributions. Key features such as water quality parameters, meteorological factors, and pollutant distributions are extracted through feature engineering, and the hyperparameter configuration of the model is optimized by combining Automated Machine Learning (AutoML) technology to improve the prediction accuracy and robustness.

[0090] Third, implement dynamic analysis and prediction of real-time monitoring data through a stream processing framework (such as Kafka or Flink), and generate abnormal warning signals according to the set rules or model output results. The system can automatically generate an abnormal analysis report, including the abnormal location, the affected range, and recommended treatment measures, and push it to the management terminal in real time. Meanwhile, based on multi-dimensional analysis algorithms, the system supports retrospective analysis of historical data to provide data-driven decision-making support for managers.

[0091] Fourth, present the analysis results in an intuitive way through the visualization module. This module combines Web GIS technology to build a visualization platform for real-time dynamic monitoring and prediction, and displays the water quality changes and abnormal distributions within the basin through 2D maps, 3D models, and multi-dimensional data charts. Users can freely select indicators, query historical trends, or set warning thresholds through the interactive interface, so as to more intuitively grasp the ecological environment status.

[0092] In the specific implementation process, a stream processing framework such as Kafka, Flink, or Spark Streaming can be used to process real-time data streams, receiving and analyzing the time series data continuously input from channels such as water quality sensors and remote sensing observations in a distributed manner. Once a significant drift in data distribution or water quality indicators is detected during the monitoring process, the system will: automatically trigger the model evaluation module to monitor the prediction accuracy or error of the current AI model; when the error exceeds the pre-set threshold, the system will initiate an online training or incremental training process to merge the latest collected labeled data into the training set; and quickly correct the model by leveraging transfer learning or online update online learning techniques including LSTM and Transformer structures to adapt to the new environmental characteristics.

[0093] Through the above online iterative training mechanism, the system can ensure that when sudden events such as a sharp increase in pollutant concentration or long-term changes such as seasonal drought or hydrological feature changes in the rainy season occur in the water ecological environment, the model still has good real-time performance and accuracy, and improves the response ability to abnormal trends.

[0094] In the embodiments of the present invention, further, by combining technologies such as remote sensing and the Internet of Things, a multi-scale ecological flow monitoring data acquisition system is constructed to provide a basis for forming a training set and training an AI model, accurately evaluating the changes in the water ecological environment quality, and evaluating the protection and restoration effectiveness.

[0095] In the embodiments of the present invention, the sharing, extensibility, and reusability of the system can be maximally realized, and a series of functions such as visualization of multi-source information, common map operations, query and analysis of ecological protection red lines, and query of survey and demarcation result data can be realized. Ensure the timeliness of environmental maintenance and renovation, promptly and effectively renovate the environment, and ensure the safety of urban residents. Use a deep learning model to process environmental data to ensure the accuracy of data processing. Improve the interaction ability of the AI model, enhance the personalized analysis of user stickiness, and realize the further development of the AI model.

[0096] In the embodiments of the present invention, to ensure data security and user privacy, the system adopts a hierarchical access control and encrypted storage mechanism: Hierarchical access control: Set corresponding permissions for different roles including ordinary users, environmental protection departments, system administrators, etc., to ensure that sensitive data is only accessible to specific roles; Transmission encryption: Use the TLS / SSL protocol for encrypted transmission between the data acquisition end, cloud storage, and model server to prevent network eavesdropping; Data desensitization and anonymization: For some data that may involve personal privacy or enterprise sensitive information, use desensitization algorithms or anonymization strategies to process it before providing it to the model for analysis; Log auditing: The system records key operation and access logs and audits them regularly to ensure that data operation behaviors are traceable.

[0097] Obviously, the above embodiments are only examples clearly described and not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or variations can be made based on the above description. It is not necessary and impossible to list all the implementation manners here. And the obvious changes or variations derived therefrom are still within the protection scope of the present invention.

Claims

1. The water ecological environment decision-making and response intelligent interactive system based on AI natural language processing is characterized by: In order to implement the AI ​​model-based water ecological environment early warning, the system conducts data collection and preprocessing, including data collection, data cleaning, standardization processing and time alignment; Then, a training set is formed; Then, combined with the training set, data annotation is performed, including normal data annotation, abnormal data annotation and event classification; then feature engineering is performed, including time series feature extraction, spatial feature extraction and cross-feature extraction; Further AI model training is carried out, including data segmentation, training methods and transfer learning. On this basis, monitoring data is continuously collected and input into the trained AI model for real-time prediction. and conduct data analysis and decision support; Once an abnormal trend is detected, the early warning mechanism is triggered.

2. According to the water ecological environment decision-making response intelligent interactive system based on AI natural language processing in claim 1, it is characterized by: The system functional layers include: data collection and perception layer: including distributed water quality monitoring sensors, drone remote sensing observations, ecological monitoring stations, etc., covering parameters such as water temperature, pH value, dissolved oxygen, turbidity and their transmission modules; data transmission and storage layer: using the Internet of Things (IoT) gateway and cloud database or edge computing node to transmit, cache, distribute and manage the collected multi-source heterogeneous data; data analysis and AI model layer: using deep learning models, large models and transfer learning methods to train and infer time series data, spatial data and text data such as policies and regulations; intelligent interaction and visualization layer: interacting with users through visual front-ends such as voice recognition, natural language processing (NLP), Web or mobile App, displaying analysis results in the form of charts, GIS maps, real-time monitoring instruments, etc., supporting multiple rounds of dialogue, intelligent question and answer and decision support; application and decision support layer: based on the prediction results and early warning mechanism of the model, providing managers with a variety of response strategies, scheduling plans and ecological restoration strategies and other decision suggestions; once an abnormal trend is detected, triggering the corresponding early warning or emergency response.

3. The water ecological environment decision-making response intelligent interactive system based on AI natural language processing according to claim 1 is characterized in that: The data collection and preprocessing: installing multi-parameter water quality sensors at key locations in the watershed to collect water quality data such as water temperature, pH value, dissolved oxygen, turbidity, total phosphorus, total nitrogen, and permanganate index in real time; Data cleaning: remove noise, outliers and fill missing values, using interpolation or time series model-based completion; Standardization: Normalize or standardize monitoring data of different units and ranges to ensure that the contribution of characteristic values ​​to the model is balanced; Time alignment: Align data from different sources by timestamp to form a multi-dimensional, time-continuous feature matrix; Data annotation: Based on historical monitoring records and water quality standards, annotate data within the normal range; Abnormal data annotation: combining expert experience and historical abnormal event records to annotate different types of abnormal events; Event classification: Classify abnormal events into different categories, including biological pollution, chemical pollution and physical pollution, so as to facilitate model classification training; Time series feature extraction: Use the sliding window method to extract the time characteristics of monitoring data; Spatial feature extraction: Introduce GIS coordinates and spatial proximity relationships to establish the spatial distribution characteristics of data; Cross-features: Combine different monitoring indicators to explore potential high-level features; Data segmentation: Divide the data into training set, validation set and test set in a ratio of 7:2:1; Training method: Use supervised learning algorithm to train the model and optimize the objective function; Transfer learning: Use existing water quality prediction models for transfer learning to shorten training time and improve model accuracy in small sample cases.

4. The water ecological environment decision-making response intelligent interactive system based on AI natural language processing according to claim 1 is characterized in that: The system builds a large AI model through the LLaMA framework. The model training data includes historical water quality monitoring data, ecological environment data and relevant policy and regulatory texts.

5. The water ecological environment decision-making response intelligent interactive system based on AI natural language processing according to claim 1 is characterized in that: The system front end uses a speech recognition interface. Users input questions through voice. The system back end converts the voice into text and parses it. The AI ​​large model processes and answers the questions, and finally feeds back the results to the user through speech synthesis technology.

6. The water ecological environment decision-making response intelligent interactive system based on AI natural language processing according to claim 1 is characterized in that: The system uses artificial intelligence technology, especially natural language processing (NLP) technology, to achieve natural language communication with users.

7. The water ecological environment decision-making response intelligent interactive system based on AI natural language processing according to claim 1 is characterized in that: The system provides targeted decision-making recommendations or response measures; and displays water ecological environment data and analysis results in the form of charts, maps, etc.

8. The water ecological environment decision-making response intelligent interactive system based on AI natural language processing according to claim 1 is characterized in that: The system and AI model are able to build a two-level optimization scheduling model based on genetic algorithm.

9. The water ecological environment decision-making response intelligent interactive system based on AI natural language processing according to claim 1 is characterized in that: The system and AI model use image recognition technology to monitor and protect aquatic ecosystems, quickly identifying and classifying plants and animals in the ecosystem; the AI ​​model analyzes changes in the habitats of aquatic ecosystems to assess the health of the ecosystem.

10. The water ecological environment decision-making response intelligent interactive system based on AI natural language processing according to claim 1 is characterized in that: The system and AI model can simulate the complex interactions of aquatic ecosystems to provide decision-making support for ecological restoration and predict the best options for ecological restoration; the AI ​​model optimizes planting plans and species selection.

11. The water ecological environment decision-making response intelligent interactive system based on AI natural language processing according to claim 1 is characterized in that: The system is a comprehensive management system that integrates multiple technologies such as the Internet of Things, intelligent sensors, cloud computing, big data and artificial intelligence.

12. The water ecological environment decision-making response intelligent interactive system based on AI natural language processing according to claim 1 is characterized in that: The system also includes a model online update module, which is used to perform incremental training or online learning on the AI ​​model based on the labeled data collected in real time when a significant change in the distribution of water quality data is monitored or the prediction error continues to exceed the limit, thereby dynamically correcting the model parameters and improving the prediction accuracy.

13. The water ecological environment decision-making response intelligent interactive system based on AI natural language processing according to claim 1 is characterized in that: After detecting abnormal data, the system automatically triggers the exception handling process, re-labels and diagnoses the abnormal water quality samples, and generates a traceable exception report and records it in the system log for subsequent model evaluation and traceability analysis.

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