433 results about "Environment management" patented technology

The invention discloses an intelligent monitoring and early warning system and method for agricultural non-point source pollution, and relates to the technical field of environmental monitoring, accurate prediction of water pollutant concentration is realized through multi-source heterogeneous data acquisition and fusion, a dynamic attention mechanism and a PINN-Transformer coupling model, the system extracts data spatial and temporal characteristics by using an optimized Transformer model, and the method is applied to the intelligent monitoring and early warning of agricultural non-point source pollution. A water pollution diffusion physical constraint is embedded, it is ensured that a prediction result conforms to an actual hydrodynamic law, and based on high-precision spatial-temporal distribution data, an intelligent algorithm is adopted to track a pollution diffusion path and rapidly lock a pollution source; meanwhile, the cellular automaton model simulates pollution risk dynamic diffusion and assists regional risk assessment, the system also combines a block chain technology to carry out credible evidence storage on key monitoring data, and real-time data processing and early warning pushing are realized through a cloud edge collaborative architecture. And an efficient and reliable technical solution is provided for agricultural water environment management and pollution prevention and control.

The invention relates to the technical field of environment management, in particular to a marine environment risk assessment method and system based on multi-source data fusion, and the method comprises the following steps: carrying out the assessment based on the multi-source marine environment monitoring data through the data integrity, consistency and timeliness of a plurality of data sources, and calculating efficiency scores of the plurality of data sources and adjusting the contribution proportion of the target data source to generate a multi-source data fusion model. According to the method, by analyzing the efficiency of multiple data sources, adjustment of data source contribution is achieved, dynamic adaptability and flexibility of data fusion are optimized, the accuracy and timeliness of marine environment risk assessment are improved, and detection and recognition of abnormal data of multiple environment indexes are achieved by combining analysis of water quality related indexes and temperature data; through analysis of marine development activity events, a decision basis is provided for adjustment of marine management policies, and accurate assessment of marine environment risks is realized in combination with prediction of marine biological diversity.

The invention is suitable for the technical field of machine rooms, and provides a machine room environment management and control method based on a digital twinning technology, which comprises the following steps: constructing a digital twinning machine room model based on a three-dimensional structure of a machine room and a machine room equipment layout; real-time load data of each IT device, real-time power data of each cooling device and environmental parameter data of each type of sensor are obtained, thermal load distribution of the machine room is predicted, a preset environmental control strategy is combined, corresponding working conditions are simulated and generated for the machine room in a digital twin machine room model, PUE values of the machine room under the working conditions are obtained through calculation, and the PUE values of the machine room under the working conditions are obtained. Dynamically adjusting an environment control strategy; running state parameters of the IT equipment and video image data in the machine room are obtained in real time, the running state parameters and the video image data are analyzed, and potential abnormal equipment and abnormal areas are determined in the digital twin machine room model. According to the invention, comprehensive optimization of machine room energy efficiency, operation and maintenance efficiency and environment resource configuration is realized, and the intelligent level of machine room management and control is improved.

The invention discloses a forestry ecological environment real-time monitoring and management method based on big data, and belongs to the technical field of forestry ecological environment management, and the method comprises the following steps: S1, multi-source heterogeneous data collection and sensor network deployment; s2, multi-modal data fusion and intelligent transmission: developing an adaptive communication protocol dynamic switching module, and automatically switching to a satellite communication link in a 4G / 5G network blind area; s3, mass data storage and distributed calculation: constructing a hybrid cloud storage architecture, and storing real-time monitoring data into a Redis cache queue; s4, intelligently extracting ecological environment indexes; s5, dynamically evaluating the ecological bearing capacity; s6, performing multi-level early warning and emergency response; s7, making a precise forestry management decision; and S8, carrying out system self-optimization and closed-loop management. According to the method, the air-space-ground integrated monitoring network is constructed, so that the comprehensiveness and the real-time performance of forestry ecological environment perception are remarkably improved; multi-source data of the unmanned aerial vehicle, the satellite remote sensing and the ground sensor are complementary.

The invention discloses an ecological environment inspection and detection information management system based on an environmental AI large model, and relates to the technical field of data management. A data acquisition module is used for collecting extreme weather and pollutant data in real time; the feature extraction module converts the data into a new feature set, the convolutional neural network training module trains the features and historical data, the generalization ability of the model under extreme conditions is evaluated through the A / B test module, and then the model is divided into a high generalization ability level and a low generalization ability level; the model with high generalization ability is retrained regularly through real-time data feedback, and the model with low generalization ability generates an early warning signal on the basis of prediction accuracy analysis and takes corresponding emergency measures; the technical problem that historical data cannot comprehensively cover future environment changes is effectively solved, the response speed and accuracy of an environment management department to extreme environment changes and sudden pollution events are improved, and potential ecological and public health risks are reduced.

The invention relates to the technical field of environment management, in particular to a land coverage change monitoring method based on fusion of a remote sensing image and a geographic information system, which comprises the following steps: S1, data acquisition: acquiring multi-temporal remote sensing and auxiliary data; s2, data preprocessing: preprocessing the collected remote sensing and auxiliary data; s3, land feature classification and land cover change identification: classifying land cover types, and performing land cover change identification in combination with the spatial texture features of the remote sensing image data; and S4, result output and analysis: performing fusion and analysis on the terrain classification and land cover change identification result and the existing land cover data in the geographic information system, and evaluating the change trend of land utilization. According to the method, different types of land coverage can be identified carefully, and particularly, when complex remote sensing data is processed, the classification precision and the accuracy of change identification are remarkably improved.

The invention discloses an electricity room environment regulation and control method and device based on an edge gateway. An intelligent sensing network is constructed by deploying at least one type of environment sensor in an electricity room; an edge intelligent fusion platform is constructed by customizing edge gateway hardware design and establishing a software platform; setting an environment parameter intelligent regulation and control strategy and a self-adaptive regulation and control strategy; and constructing a cloud-side collaborative architecture and augmented reality remote operation and maintenance system, and performing electricity room environment regulation and control in combination with the intelligent sensing network and the edge intelligent fusion platform. According to the technical scheme of the invention, through highly refined design, intelligence, precision and foresight of electricity room environment regulation and control are realized. By integrating the latest edge calculation, big data analysis, machine learning and Internet of Things technologies, an electricity room environment management mode capable of real-time sensing, intelligent analysis, autonomous regulation and control and continuous optimization is constructed.

The invention discloses a non-point source pollution and waterlogging collaborative four-pre management and control system based on short temporary rainfall forecast, which relates to the technical field of urban water environment management and comprises a short temporary rainfall forecast module, a non-point source pollution control module, a waterlogging management module, a four-pre management and control platform, an intelligent decision support system, a real-time monitoring and feedback system and a water ecological management module. By integrating meteorological monitoring, hydrological monitoring and environment monitoring data, high-precision short and temporary rainfall forecast is realized by using a machine learning algorithm, the operation strategies of the intelligent catch basin and the initial rain storage pond are dynamically adjusted to control non-point source pollution, and meanwhile, the waterlogging risk is predicted and preventive measures are taken in combination with real-time hydrological data. According to the technology, integrated management of forecasting, early warning, rehearsal and plans is achieved through a four-pre-management and control platform, an intelligent decision support system, a real-time monitoring and feedback system and a water ecological management module under the sponge city design concept are used in an auxiliary mode, and the efficiency and effect of urban water environment management are comprehensively improved.

The invention relates to the field of environmental protection, and discloses an emergency pollution diffusion model construction method, which comprises the following steps: constructing a pollutant diffusion basic model, and describing the diffusion process of pollutants in a water body along with time and space; dynamically optimizing control parameters of the pollution diffusion model, adjusting control variables in real time, and optimizing a pollution diffusion path through an adaptive optimization control method; based on the real-time monitoring data and the prediction result of the pollution diffusion model, performing state estimation and correction on the pollution diffusion path by using a Kalman filtering algorithm; in combination with real-time data from different monitoring sources, optimizing a prediction result of the pollution diffusion model by using a spatio-temporal data fusion technology; and according to the optimized pollution diffusion result, an emergency response scheme is provided for an environment management department through a decision support system. According to the method, a spatio-temporal data fusion technology is adopted, and the prediction result of the pollution diffusion model is optimized through weighted averaging of multi-source monitoring data.

The invention discloses a pigsty environment regulation and control optimization method and system, and belongs to the technical field of breeding environment management. The system comprises an intelligent sensing module, an environment analysis module, a parameter optimization module and a control management module. The intelligent sensing module is used for collecting pig house environment parameters and live pig physiological parameters through a multi-mode sensor; the environment analysis module is used for constructing a behavior recognition model, analyzing behavior characteristics based on physiological parameters and dividing abnormal regions; the parameter optimization module is used for establishing a physiological environment control model and setting an optimal optimization scheme for each abnormal region; and the control management module is used for generating an environment control instruction according to the optimal optimization scheme and controlling equipment execution. The pig house environment and the pig physiological parameters are collected and intelligently analyzed through the multi-mode sensor, automatic and precise environment optimization management is achieved, and the comfort level and the health level of the pigs are improved.

The invention relates to the technical field of soil environment monitoring, and discloses a soil remediation construction site environment monitoring method based on edge computing cloud collaboration. The method comprises the following steps: deploying an edge computing node at a restoration construction site, and collecting real-time data of soil humidity, heavy metal concentration, volatile organic compound content and meteorological parameters through a multi-source sensor; inputting the data into a spatio-temporal feature extraction network, and generating a spatio-temporal fusion feature matrix by means of hierarchical convolution and a cross-channel attention mechanism; on the basis of the matrix, the index anomaly probability is calculated by using a depth probability network, and a risk feature sequence with uncertainty measurement is generated; constructing a dynamic risk field model in combination with repair process parameters, and predicting a pollution diffusion path through a space-time propagation algorithm; and in combination with an equipment operation state, an edge side local regulation and control instruction and a cloud global optimization strategy are generated by a collaborative decision engine, and efficient and accurate environment management requirements of a construction site are met.

The invention discloses a factory network river-lake integrated scheduling method and system based on a butterfly optimization algorithm, and solves the problems that resource scheduling is not systematic and the optimization effect is poor in urban water environment management. The method comprises the following steps: firstly, collecting and sorting data information such as a rain sewage pipe network, pump gate characteristics and a water system in a research area, constructing one-dimensional and two-dimensional hydrodynamic models, and simulating dynamic behaviors of a drainage system; on the basis, a meta-heuristic butterfly optimization algorithm is adopted to construct an optimization scheduling model, and an optimization scheme conforming to a scheduling target is generated through the steps of population initialization, fitness calculation, iterative search of an optimal solution and the like. In the optimization process, scheduling parameters are adjusted in combination with real-time monitoring data, and the effectiveness and practicability of the scheme are ensured.

The invention relates to the technical field of air quality monitoring, in particular to an air quality monitoring station site selection optimization method, medium and equipment, and the method comprises the steps: S1, obtaining remote sensing monitoring data and ground station monitoring data of pollutant concentration of a certain research area; s2, correcting the remote sensing monitoring data based on a random forest model to obtain corrected monitoring data; s3, evaluating the credibility of the corrected monitoring data based on the consistency measurement index, and if the consistency measurement index of the corrected monitoring data is higher than the consistency measurement index of the remote sensing monitoring data, entering S4; otherwise, returning to S2; and S4, according to the corrected monitoring data, optimizing the monitoring station at the pollution view angle, and obtaining a monitoring station site selection result after the pollution view angle optimization. According to the invention, overall consideration of monitoring efficiency and health protection is realized, future development change is considered, and more accurate and comprehensive data support can be provided for air quality monitoring and environment management.

The invention provides a carbon emission optimization method and system based on green supply chain management, and relates to the technical field of data processing, M key links are positioned based on historical carbon emission data and called to obtain M local carbon emission data, and the M local carbon emission data are adopted to construct a carbon emission prediction model; environment data acquisition is carried out on the M key links, acquisition results are synchronized to a carbon emission prediction model to carry out carbon emission trend prediction, M pieces of carbon emission trend information are obtained, and then a real-time emission reduction strategy is output to execute carbon emission optimization management. The technical problem that in the prior art, targeted emission reduction management cannot be carried out on a supply chain, so that the execution effectiveness of environment management measures is weak is solved. The technical effects of accurately identifying the key carbon emission link in the supply chain and predicting the carbon emission trend of the key carbon emission link with high precision are achieved.

The invention relates to the technical field of environment management, in particular to a carbon emission data management method and device based on a block chain, and the method comprises the following steps: screening missing information data based on the block chain, eliminating abnormal calculation efficiency, extracting user behavior analysis stability, adjusting authority mapping to generate a credible index, screening illegal nodes, and writing the illegal nodes into a tracking block. And comparing policy marking abnormity to obtain a compliance state. According to the method and the system, the data with missing identity labels and timestamps are screened out, the data validity ratio is improved, the timeliness and the credibility of written information are ensured, the uploading action is identified, the frequency and the operation interval are modified, the node behavior stability is described, the compliance degree is reflected, the access permission is dynamically adjusted according to the stability, and the hyper-permission operation records are screened out; high-frequency violation writing behaviors are detected and recorded, the real-time monitoring capability is enhanced, abnormity is dynamically marked, compliance evaluation and response of carbon emission data are achieved, and the credibility, precision and intelligent level of data management are comprehensively improved.

The invention belongs to the technical field of environmental science, and provides an ozone forecasting method based on multi-dimensional meteorological factors and variables, which comprises the following steps: S1, collecting and sorting the multi-dimensional meteorological factors, and calculating change variables of each element; s2, taking all processed multi-dimensional meteorological factors as forecast factors, and carrying out standardization processing; s3, screening all processed forecasting factors by adopting a stepwise regression method to obtain an optimal forecasting factor combination; s4, performing model training on historical data by adopting a multi-layer perceptron machine learning method according to the screened forecast factors; s5, checking and evaluating the trained perceptron learning model, and comparing and evaluating the trained perceptron learning model with a linear regression model and a radial basis function model; s6, predicting the ozone pollution process in different time periods and different weather backgrounds; according to the invention, the accuracy and timeliness of ozone forecasting can be obviously improved, and more reliable data support is provided for weather and environment management departments.

The invention relates to the technical field of environment management, in particular to a carbon neutralization management method and system based on digital asset circulation, and the method comprises the following steps: screening associated data of energy consumption and carbon emission based on carbon emission data, energy consumption, a total production value and an industrial production value, and eliminating abnormal values and redundant information. According to the invention, through combination of trend weight and time sequence fitting, the influence range of trend change is determined, the prediction capability of dynamic association of energy consumption and carbon emission is effectively enhanced, and through analysis of quota distribution records and time span parameters, quota adjustment in a transaction path is optimized, the quota distribution efficiency and flexibility are improved, and the prediction efficiency of the dynamic association of energy consumption and carbon emission is improved. Supply and demand fluctuation analysis is combined with quota price and distribution adjustment, a supply and demand matching mechanism is dynamically optimized, distribution accuracy and response efficiency are improved, fluctuation intervals and risk association weights are dynamically calculated, real-time assessment and regulation of risk changes are realized, and adverse effects of risk accumulation on carbon transaction management are reduced.

The invention discloses a carbon right-green certificate collaborative transaction method based on a block chain, and belongs to the field of block chain technology and environment management. The method specifically comprises the following steps: 1) deploying an intelligent contract based on an Ethereum platform, and integrating power transaction, carbon quota delivery and green certificate issuing process chains to realize automatic execution of transaction rules and non-tampering of data; (2) a Carbon Token excitation mechanism is innovatively designed, a DPoS-CT consensus algorithm considering the right of the Carbon Token is proposed, and a market subject is excited to continuously reduce emission; 3) a dynamic reputation model and a reward and punishment mechanism are used to quantify subject performance behaviors and distribute on-chain rights and interests; and 4) resetting the transaction cycle every 24 hours, and optimizing quota allocation and consensus parameters based on historical data. According to the mechanism provided by the invention, renewable energy transaction can be effectively stimulated, and after the block chain carbon pass system is introduced, the quota completion rate, the green electricity utilization rate, the carbon emission, the carbon right-green certificate transaction volume and the economic benefit are optimized to a certain extent.

The invention provides a computing power center resource centralized self-adaptive control scheduling system and method, and the method comprises the steps: S1, a user submits a computing power training task through an API, and sends the task to a DAG task composer, and forms a task list to be scheduled; s2, the intelligent monitoring analysis module is used for continuously collecting node data and calculating real-time CS and HS scores of each node; s3, analyzing the dependency relationship type data by a task composer, and calling a dynamic programming algorithm in an adaptive scheduler to make a decision; s4, the adaptive scheduler performs dynamic task distribution according to the task list of the DAG task orchestrator and the intelligent monitoring analysis module, and S5, the distributed runtime and environment management module is used for uploading the prepared Docker mirror image to the distributed storage; and S6, through the unified communication adaptation layer, the mirror image is pulled from the distributed storage on the target node and the container is started, the user does not need to concern the difference of the operating system in the whole process, and efficient and intelligent adaptive scheduling is realized.

The invention belongs to the technical field of ecology, geographic information system (GIS) and environment management, and discloses a preferential restoration method for an ecological degradation region, which comprises the following steps: acquiring change characteristics of each land utilization type in the region; identifying an ecologically degraded land urgently to be subjected to ecological restoration; estimating the habitat quality of the ecological system in the region, the carbon immobilization service, the grain yield and income in the region, and the animal husbandry yield and income; the economic loss caused by ecological restoration evaluation is obtained; setting a plurality of recovery scenes; and obtaining a restoration strategy of the ecological degradation area based on setting of a plurality of different restoration scenarios, and the like. Compared with a traditional restoration method depending on expert experience and field investigation, the preferential restoration method can scientifically and accurately identify the ecological degradation area to be subjected to ecological restoration through systematic analysis and evaluation, and provides diversified restoration strategy suggestions for decision makers.

The invention provides a flying dust online detection method and system based on laser, and the method comprises the following steps: obtaining laser scattering signal intensity, particle concentration, wind speed, wind direction, humidity and other environmental meteorological data before and after the abnormality when the flying dust concentration of a flying dust monitoring region is monitored to be abnormal; calculating laser scattering energy and a particulate matter concentration change rate to obtain a flying dust concentration change coefficient; and judging an abnormal type according to the change coefficient and an abnormal identification criterion, and if the flying dust concentration in the region exceeds the standard, starting an early warning action to realize management of the flying dust monitoring region. According to the invention, the abnormal type of dust raising can be accurately identified, early warning is started in time, and the environment management and control capability of a dust raising monitoring area is improved.

The invention relates to a pollution source strong dynamic inversion method based on an air quality machine learning rolling prediction model. The method comprises the following steps: S1, obtaining and processing an air quality modeling data set; s2, constructing an air quality machine learning prediction model; s3, air quality meteorological standardization evaluation; and S4, carrying out air quality rolling prediction and meteorological standardization iterative calculation. According to the method, the problem that the existing meteorological normalization technology is limited by timeliness and is mainly applied to post analysis of policy intervention is solved, biased estimation caused by random meteorological normalization is reduced, the result accuracy is improved, and the method is particularly suitable for environment management application scenes with high timeliness requirements, such as heavy pollution weather emergency assessment and the like; the important application value is realized.

The invention provides an ecological system quality quantitative analysis method and system, and the method comprises the steps: carrying out the standardization processing of a multi-dimensional data factor, and obtaining the evaluation results of three dimensions, i.e., an ecological system background state, an ecological system landscape structure, and an ecological system service function, based on the processed data factor. And performing weight distribution and comprehensive calculation on evaluation results of the background state, the landscape structure and the service function of the ecological system to obtain an ecological system quality index. Based on this, regression analysis is performed on the ecological system quality index and a plurality of data factors, and then the core driving factor is accurately identified. According to the scheme, multiple dimensions such as the background, the function and the structure of the ecological system are coupled to perform ecological system quality evaluation, the defects of single dimension, evaluation framework limitation and the like in the prior art are avoided, and an effective basis can be provided for regional ecological environment management, ecological restoration project layout and protection decision by determining the core driving factor.

The invention discloses a thermal comfort monitoring robot system and method based on multi-modal body language feature recognition and fusion, and aims to realize individual thermal comfort monitoring, regulation and control in a dynamic indoor environment. The intelligent mobile platform carries multiple types of environment sensors and visual acquisition equipment, and performs autonomous navigation and acquires environment parameters and human body image data; the multi-modal feature recognition module performs human body detection, posture recognition and identity maintenance on a video stream frame by frame based on a deep learning network, and extracts multi-modal human body features; the data processing and fusion module realizes cross-frame identity consistency through an algorithm and fuses multi-modal human body features and environmental parameters; the thermal comfort calculation module dynamically adjusts a metabolic rate parameter based on a predicted average voting model, incorporates the metabolic rate parameter into a psychological correction factor and calculates an individual thermal comfort index; the three-dimensional visualization and regulation and control module generates a comfort distribution map, is in butt joint with a building environment automatic control system to achieve dynamic regulation and control, and provides support for intelligent building environment management.

The invention discloses a vegetable greenhouse environment management method and system, a storage medium and a program product, and the method comprises the steps: obtaining historical environment parameters of a vegetable greenhouse, vegetable growth data and the growth state of vegetables in the vegetable greenhouse at the current moment; establishing a multistable growth model according to the historical environmental parameters and the vegetable growth data, wherein the multistable growth model comprises a plurality of stable growth points; positioning a growth state in the multistable growth model, and determining a dominant stable growth point closest to the growth state; establishing an attractor potential field, setting an advantage stable growth point as an attractor center, and calculating a potential energy gradient from a growth state to the attractor center; based on the potential energy gradient, a progressive regulation and control path of vegetable greenhouse environment parameters is constructed, and a staged environment regulation and control instruction is generated; and the staged environment regulation and control instruction is sent to the environment control equipment, so that the environment control equipment carries out environment regulation according to the staged environment regulation and control instruction and the progressive regulation and control path, and the timeliness and the accuracy of farming operation and environment regulation are improved.

The invention discloses a soil and groundwater environment in-situ real-time monitoring system which is installed in a monitoring device and comprises a soil monitoring platform and a groundwater monitoring platform. The soil monitoring platform comprises a soil monitoring module, a basic data module, a first monitoring evaluation module and a collaborative management and control module, and mainly realizes the functions of real-time monitoring analysis, evaluation, environment management and the like of soil related indexes; the underground water monitoring platform comprises a monitoring content module, a monitoring operation module, a monitoring display module and a second monitoring evaluation module, and mainly realizes the functions of real-time monitoring, analysis, alarm, evaluation and the like of underground water related indexes. According to the invention, in-situ real-time monitoring of soil and underground water can be provided at the same time, the monitoring system is constructed by taking construction land and agricultural land soil and underground water environment management as a main line, and monitoring and management of related soil and underground water environments by a plurality of departments such as natural resources, ecological environment, agriculture and rural areas, housing and urban and rural construction can be realized.

The invention relates to the field of environment management, and discloses a watershed digital twinning environment real-time simulation and interaction platform fusing cloud edge collaboration and intelligent modeling, and the platform comprises the steps: carrying out the dynamic collection of the Yangtze River and Yellow River watershed, and generating environment state information organized according to the temporal-spatial resolution; associating the generated environment state information with multi-source environment data to form a multi-source knowledge basis of the drainage basin pollution field; establishing a rule knowledge base covering pollution identification, early warning and prevention and control business processes by utilizing a generative knowledge representation method, and generating a pollution event candidate identification result; carrying out fusion calculation on the pollution event candidate recognition result and multi-source heterogeneous data, constructing a digital twin simulation model, and eliminating data islands between modules and data sources; and executing linkage calculation on the pollution area and the risk node calibrated in the digital twinborn simulation model, generating environment state interaction information and synchronously updating the environment state interaction information to the digital twinborn environment. The method has the advantage that the regional water environment risk prevention and control capability is improved.

The invention relates to the technical field of environment monitoring, and discloses an intelligent environment monitoring and GIS (Geographic Information System) data fusion system, which comprises the following steps: a data fusion algorithm module, performing deep fusion on preprocessed environment data and GIS information through space-time correlation analysis, a machine learning or deep learning algorithm and a weighted average method or a maximum value method to obtain a GIS data fusion module; according to the intelligent environment monitoring and GIS data fusion system, space-time correlation analysis, a machine learning or deep learning algorithm and a weighted average method or a maximum value method mathematical method are adopted, and the preprocessed environment data and GIS information are deeply fused. The environmental data set with spatial distribution characteristics is generated, a scientific basis is provided for environmental condition evaluation, trend prediction and anomaly detection, the environmental condition and the association between the environmental condition and the geographic position can be more comprehensively understood through data fusion, and the efficiency and accuracy of environmental management are improved.

The invention relates to the technical field of environment management information systems, in particular to a method and a system for extracting an ecological environment access list rule of an intelligent agent. The method comprises the steps of collecting ecological environment policy and regulation data from a plurality of data sources, and performing data cleaning and labeling to obtain a standardized text data set; training and optimizing the data set to obtain an ecological environment professional knowledge large model; designing a prompt project template including input, instructions, examples and output requirements; inputting the cleaned new policy data into the large model, and automatically extracting a structured access list rule according to a prompt template; and finally outputting and storing the rule. According to the method, automatic conversion from an unstructured policy text to a structured access rule is realized, the problems of low efficiency, poor accuracy and difficulty in updating and maintenance of a traditional manual extraction mode are effectively solved, and the intelligent level and decision support capability of ecological environment partition management and control are remarkably improved.

The invention relates to the technical field of environmental supervision, in particular to a tailing pond classification and grading method based on environmental supervision, which comprises the following steps: identifying leaching rules and environmental influences of specific pollutants of differentiated tailing types, capturing geographical and surrounding environmental data around a tailing pond through geographical information, calculating environmental sensitivity indexes, and classifying the tailing pond according to the environmental sensitivity indexes. Analyzing the potential influence degree of the tailings pond, and generating an environment supervision level framework. According to the method, geography and surrounding environment data around the tailings pond are captured through remote sensing of geographic information and the like, a machine learning algorithm data mining technology is utilized, the data are preprocessed, key indexes are screened, the weight of each evaluation index factor is optimized, and a qualitative and quantitative tailings pond environment risk evaluation index system is constructed. The algorithm calculates the comprehensive score of the environmental supervision risk of each tailing pond, analyzes the potential influence degree of the tailing pond, improves the pertinence and accuracy of data utilization, improves the environmental management efficiency of the tailing pond, and prevents the environmental risk.