150 results about "Environmental risk assessment" patented technology

The invention provides a CO2 sequestration simulation method based on transfer learning and a physical information neural network, which aims at a geological carbon dioxide sequestration process by combining the transfer learning and the physical information neural network, and comprises the following steps of: simulating and generating data by using a Matlab oil reservoir simulation tool box, and constructing a neural network basic model; the physical law of carbon dioxide migration in the porous medium is trained and learned through coarse resolution, then fine tuning is performed on the generated actual geological model data by adopting a migration learning strategy to adapt to different geological structures and injection conditions, and a neural network loss function is embedded in combination with a Darwest equation and a mass conservation equation to obtain a carbon dioxide migration model. Compared with a traditional numerical simulation method, the method has the advantages that the calculation efficiency is remarkably improved while the prediction precision is kept, and the method is suitable for carbon dioxide sequestration optimization and environmental risk assessment under complex geological conditions and has good industrial popularization value and environmental benefits.

The invention relates to the technical field of artificial intelligence, in particular to a driver fatigue state monitoring system and method based on multi-modal biological feature fusion, and the system comprises a sensing module, a data processing and analysis module, an environment risk assessment module, a personalized model module, a self-adaptive decision fusion module, and a vehicle interaction and vehicle control module. Compared with the prior art in which fatigue judgment is performed by adopting a fixed threshold value, the fatigue judgment cannot adapt to a complicated and changeable driving environment and may cause excessive alarm or missing alarm under a monotonous road condition in a dangerous road section, an environment perception and dynamic threshold value adjustment algorithm is introduced, so that the system can intelligently evaluate the environmental risk and monotonicity, and the fatigue judgment accuracy is improved. The alarm sensitivity is automatically adjusted, and the early warning accuracy and the scene adaptability are both improved.

The invention discloses a distributed intelligent control method for unmanned aerial vehicle cluster cooperative path planning, and relates to the technical field of unmanned aerial vehicle cluster cooperative control, and the method comprises the steps: collecting a dynamic feature set of an unmanned aerial vehicle in real time, and carrying out the time synchronization, noise filtering and formatting integration, and forming a local state data set; performing neighborhood collaborative analysis and temporary link planning on the local state data set to obtain relay chain layout data; performing environmental risk assessment and path feasibility analysis on the relay chain layout data to generate a candidate path set; adjusting the candidate path set through local obstacle avoidance and dynamic constraint to generate an executable path sequence, and generating a collaborative execution path through neighborhood communication and collaborative adjustment; performing task execution result integration and state updating on the collaborative execution path to generate group state data; and performing path feedback and loop optimization on the group state data to obtain a group collaboration result. And the task completion rate, the cooperation efficiency and the environment adaptability are improved.

The invention provides a transformer environmental protection risk assessment method and system based on a dynamic threshold value. The method comprises the steps of obtaining operation state parameters and environmental parameters of an abnormal transformer in a construction site; obtaining a current composite risk degree of the abnormal transformer based on the operation state parameters, the environmental parameters and a pre-constructed composite environmental protection risk calculation model; obtaining a dynamic early warning threshold based on the environmental parameters and a pre-constructed early warning threshold dynamic adjustment model; performing risk early warning based on the current composite risk degree and the dynamic early warning threshold value; wherein the early warning threshold dynamic adjustment model is constructed by using the obtained historical composite risk degree and historical environmental parameters of the abnormal transformer and the obtained time attenuation coefficient. And a dynamic threshold adjustment mechanism is adopted, so that the system can automatically optimize early warning triggering conditions according to the change of the construction environment, the flexibility and adaptability of risk assessment are improved, and the situation of false alarm or missing alarm does not exist.

The invention relates to the technical field of glacier research, and discloses a drainage basin glacier material balance analysis method and system based on big data, and the method comprises the steps: collecting remote sensing images, ground observation and meteorological data of a glacier drainage basin, carrying out the standardization processing and fusion of multi-source data, constructing a random forest model, analyzing the relevance between glacier changes and climate factors, and carrying out the analysis of the relevance. And determining a key driving factor. The dynamic response of glacier substances is further predicted by using a long-short-term memory network, high-sensitivity climate factors are determined through sensitivity analysis, and the prediction model is optimized. A dynamic response database is also established for long-term monitoring, and an automatic early warning mechanism is set to evaluate the abnormal change risk. According to the method, accurate analysis and prediction of glacier change and climate factor interaction are realized, and an effective tool is provided for glacier change research and environmental risk assessment.

The invention discloses a semantic analysis-based user abnormal intention recognition method and system, and belongs to the technical field of computer data processing, and the method comprises the steps of analyzing logic contradiction word combinations in a user session text to generate abnormal semantic marks, and collecting a user operation behavior sequence and environmental parameters to construct a spatio-temporal behavior trajectory map. And matching the abnormal semantic mark with a preset extensible abnormal mode library, if the generated primary abnormal confidence exceeds a threshold value, further comparing the behavior trajectory map to generate a behavior abnormal index, associating the abnormal semantic mark with the primary abnormal confidence to form a comprehensive abnormal feature vector, and calculating an environmental risk coefficient in combination with environmental parameters. And finally, judging by a triple decision arbiter, and if the abnormity is suspicious abnormity, activating a secondary verification process and updating an abnormal mode library. According to the method, semantic logic contradiction analysis, behavior trajectory map analysis and environmental risk assessment are fused, deep and camouflage abnormal intentions can be accurately recognized, and the method has adaptive learning ability.

The invention discloses an escalator operation environment monitoring and risk assessment method, and relates to the related field of escalator safety monitoring, and the method comprises the following steps: cooperatively carrying out escalator monitoring data acquisition; real-time receiving and transmission are carried out based on the Internet of Things; performing data association mining on the time sequence environment parameters and the time sequence operation parameters, and outputting environment risk association data; loading to an environment risk assessment model for operation risk assessment, and constructing an integrated assessment system from the aspects of structural stability, durability, safety protection and the like; and escalator operation and maintenance decision making is carried out according to the risk assessment result, multi-dimensional risk prevention and control and response are achieved, performance assessment, risk grade division and early warning are included, and preventive maintenance, environmental adaptability transformation and emergency response mechanisms are established. The technical problems that existing escalator risk assessment is incomplete in monitoring, not real-time, inaccurate in risk identification and insufficient in assessment result reliability are solved, and the technical effect of improving the risk identification accuracy and the assessment result reliability is achieved.

The invention discloses an ecological environment big data processing method based on mining area pollutant migration behavior modeling, and relates to the technical field of big data processing. Comprising the steps that protection points in a target range and geodetic coordinate values of a mining area are obtained, the protection points comprise a mass residential area, an ecological protection area and a water source protection area, and the geodetic coordinate values are used for representing latitude and longitude coordinates and the altitude; and according to the first group of geodetic coordinate values and the geodetic coordinate values of the mining area, the maximum diffusion range interval of the mining area pollutants is determined, and the first group of geodetic coordinate values specifically refer to the geodetic coordinate values taken by any protection point type. According to the method, the three-dimensional geodetic coordinates of the protection point and the mining area are obtained, the pollutant diffusion range is scientifically defined, it is ensured that the space coverage of pollution monitoring is comprehensive and effective, potential threats of mining area pollution to surrounding people residential areas, ecological protection areas and water source protection areas can be accurately recognized, and the risk of mining area pollution to the surrounding people residential areas, ecological protection areas and water source protection areas is reduced. And the accuracy and pertinence of environmental risk assessment are improved.

The invention discloses an environmental risk step-by-step assessment method in a multi-medium and multi-scale complex scene, and belongs to the technical field of environmental risk assessment. The method comprises the following steps: obtaining the heavy metal concentration of the waste residue through a leaching experiment, and determining the toxicity grade by using a single pollution index method and a potential ecological risk index method; dividing volume grades based on quantiles, dividing local diffusion risk grades by using an inverse distance weighted interpolation method and the like, dividing remote receptor sensitivity grades according to a surrounding land and water source construction model, and generating priority control grades by integrating the grades; and finally, fusing multi-source data to construct a spatio-temporal conjoint analysis model verification result. Therefore, by integrating pollution source toxicity, volume, diffusion risk and receptor sensitivity to carry out risk step-by-step assessment, a multi-source data fusion and spatio-temporal conjoint analysis method is proposed for the first time, and the problems of single assessment dimension, insufficient data fusion, lack of dynamic verification and the like of an existing assessment method in a multi-medium and multi-scale scene are effectively solved.

The invention discloses an environmental event dynamic risk assessment method and system based on a Bayesian network, belongs to the technical field of artificial intelligence, and aims to solve the technical problems that in existing environmental risk assessment, dynamic data adaptability is poor, multi-source heterogeneous data fusion is difficult, and real-time performance is insufficient. Comprising the following steps: acquiring environment data through a sensor cluster deployed in an environment, and uploading the environment data to an edge computing node; performing data preprocessing on the environmental data through the edge computing node; extracting time series data fragments from the standardized data stream based on a predefined sliding time window; constructing a risk prediction model based on the dynamic Bayesian network; and taking the extracted time series data fragments as input, performing risk level analysis through a risk prediction model in combination with particle filter reasoning, predicting and outputting a risk level, a risk level probability value and a risk conduction path as prediction results, and constructing a visual risk conduction map based on the prediction results.

The invention discloses a distribution box room environment parameter integrated measurement method, and particularly relates to the field of multi-parameter measurement, and the method comprises the steps: firstly constructing a machine room three-dimensional model and a sensor correlation degree matrix, then building a heat balance, humidity diffusion and airflow motion model, and calibrating parameters; deploying a measurement system and calibrating through dual synchronization and cross check; establishing a sensor confidence evaluation system and an adaptive threshold based on 72-hour reference data; fusing calibration data by adopting a three-level correlation calibration mechanism; four types of measurement modes and conversion logics are designed, and measurement resources are dynamically allocated through environmental risk assessment; multi-dimensional state parameters are extracted, a comprehensive evaluation value is calculated through normalization, dynamic weighting and combinatorial algorithms, and five-level early warning response is achieved; according to the method, multiple models and an intelligent algorithm are integrated, the parameter measurement precision and the environment risk identification efficiency are improved, the response delay is reduced, the fault diagnosis accuracy is improved, and reliable technical support is provided for safe operation and maintenance of the distribution box room.

The invention relates to the technical field of computer simulation, and discloses a coal mine underground reservoir environment risk assessment method and system, and the method comprises the steps: constructing a digital twinborn body of a coal mine underground reservoir; establishing a risk assessment index system; collecting monitoring data corresponding to the index system in real time; calculating risk coupling factors among different dimension indexes; according to the risk coupling factor and the real-time numerical value of each index, dynamically adjusting the weight of the index system, and generating a dynamic weight set; calculating a current comprehensive risk level; and extracting physical field parameter values in the real-time monitoring data, inputting the physical field parameter values into a multi-physical field coupling simulation engine for simulation, and outputting a prediction report. According to the invention, dynamic assessment and advanced early warning of the environmental risk of the coal mine underground reservoir can be realized, the risk identification precision and the emergency response capability are improved, and the method is suitable for safety management and control of the whole life cycle of the underground reservoir under complex geological conditions.

The invention provides a transformer construction period environmental protection risk assessment method and system, and the method comprises the steps: inputting transformer image data of a construction site in a construction period into an image recognition model, and recognizing the current abnormal coordinate information of a transformer; obtaining current abnormal state data based on the current abnormal coordinate information; inputting the current abnormal state data, the obtained current operation state parameters of the transformer and the obtained current environment parameters into a pre-constructed dynamic evaluation model for evaluation to obtain the current risk degree of the transformer; based on the current risk degree, outputting an environmental protection risk assessment result and an equipment safety monitoring result of the transformer; wherein the dynamic evaluation model is obtained by training an adaptive neural network by adopting historical abnormal state data, historical operation state parameters, historical environment parameters and historical risk degrees of the transformer. The current environmental protection risk degree of the transformer can be output in real time, and the dynamic monitoring requirement of the transformer in the construction period is met.

The invention discloses a rare and endangered organism toxicity prediction method based on a machine learning algorithm and a quantitative structure-activity relationship, which constructs a toxicity prediction model through the machine learning algorithm, can effectively assess the toxicity influence of environmental pollutants on rare and endangered organisms, and provides technical support for rare and endangered organism protection and ecological environment risk assessment. Comprising the following steps: 1, collecting data related to rare and endangered organisms from a public database, and establishing a rare and endangered organism toxicity prediction database based on machine learning; 2, generating molecular descriptors for the chemical substances; step 3, data preprocessing; 4, development of acute and chronic toxicity prediction models of rare and endangered organisms based on multiple machine learning is carried out, and performance evaluation is carried out; 5, performing internal and external verification on the rare and endangered biotoxicity prediction model; step 6, analyzing the importance of the features by using the valuable, rare and endangered biotoxicity prediction model, and finding out the most influential features; and 7, predicting the toxicity value of the pollutants in combination with the optimal machine learning model.

The invention provides a park environment risk priority management and control dynamic sorting system based on a Bayesian network. The system comprises a data acquisition layer, a data analysis layer and an output layer, the data acquisition layer is the basis of park environment risk assessment and is used for acquiring real-time and historical data from different sources; the data analysis layer constructs a dynamic index weight matrix by using a Bayesian network and is used for calculating a dynamic risk score RSI of an enterprise; the output layer is used for calculating the comprehensive risk score of the enterprise through the weight generated by the Bayesian network, dynamic weight adjustment and multi-source data fusion are utilized, an original static major / larger risk enterprise list is upgraded to a real-time dynamic sorting list, accurate resource allocation is achieved, and the risk assessment efficiency is improved. Through dynamic index fusion and Bayesian network weight adjustment, traditional static classification is upgraded to a real-time sorting priority management and control list, and an intelligent solution is provided for park environment risk management.

The invention relates to the technical field of environment monitoring, in particular to an environment parameter real-time early warning method and system for a digital chicken house, and the method comprises the following steps: collecting ammonia gas concentration, temperature and humidity and illumination intensity data in a chicken house, uniformly integrating and generating a monitoring record, extracting a safety interval, counting the exposure time of ammonia gas, and judging a multi-factor deviation state; and carrying out conjoint analysis on environment distribution change, and outputting a risk state and early warning information. According to the method, multi-source environment data are integrated through a unified time reference, the time sequence consistency among parameters is improved, the identification capability of low-concentration long-term exposure is enhanced by screening an ammonia gas safety interval and counting the exposure duration, and a multi-factor time sequence offset feature is constructed by collecting the deviation state of temperature and humidity and illumination intensity; the conjoint analysis of exposure and deviation distribution is combined, the continuity and accuracy of environmental risk judgment are improved, high-risk time periods and monitoring data thereof are extracted, and the positioning and response efficiency of abnormal states is enhanced.

The invention discloses a solid fuel environmental protection project online intelligent early warning system based on big data, relates to the technical field of data processing, and solves the problem that it is difficult to establish a solid fuel environmental protection project database and store monitoring data; a temperature pollutant diffusion correlation model is difficult to establish, and the pollutant discharge rate and the pollutant diffusion influence range are analyzed; the influence degree of the solid fuel on environment sensitive areas, soil and underground water is difficult to evaluate; and an environmental protection risk assessment index is difficult to comprehensively assess and three-level early warning is difficult to establish according to the above technical problems. According to the invention, the monitoring and early warning of the solid fuel environmental protection project are realized by comprehensively evaluating the environmental protection risk evaluation indexes of the solid fuel to atmosphere, soil, underground water and environmental sensitive areas in each link.

The invention belongs to the technical field of environmental risk assessment, and particularly discloses a BIM-based fabricated steel structure assembly scheme comparison and selection method and system, and the method comprises the steps: building a three-dimensional building model based on BIM software, and extracting component information and GIS data; establishing national and local standard libraries, matching applicable standards through building positioning information, and generating parameterized assembly rules; generating a plurality of assembly type combination schemes according to rules and component logic, and interactively binding with the BIM model; calculating an assembly rate and a standard score of each scheme, and screening candidate schemes meeting structural safety and a cost threshold value; and constructing an incremental cost library, completing cost difference analysis of the candidate schemes, and outputting an optimal assembly type steel structure combination scheme. And data driving and automatic comparison and selection in the whole process are supported, the method can be applied to prefabricated building scheme design and optimization links, and the design efficiency and the scheme economy are improved.

The invention belongs to the technical field of data security, and discloses a cloud data security management method and device, and the method comprises the steps: carrying out the environment risk assessment of the current cloud health file access, and then adaptively adjusting the security parameter of a dynamic verification code through an environment risk assessment value. And according to the adjusted security parameter, generating a dynamic verification code for granting the cloud health record access permission to the user of the session, thereby effectively ensuring the security of the cloud personal health record data, simplifying the user operation process, reducing the hardware equipment cost, and improving the public service efficiency.

The invention discloses a museum cultural relic environment monitoring method and system based on a fusion technology. The method comprises the following steps: monitoring a cultural relic circuit in real time by using a graph convolutional neural network, extracting node features to construct a logic circuit directed graph, determining fault nodes, and obtaining a fault diagnosis result; obtaining fixed reference objects and environment perception data in the museum to form environment information, inputting the environment information into a multi-reference object system analysis model, and analyzing the influence of environment factors on fault nodes to obtain an environment analysis result; combining the two results to position a cultural relic associated equipment power device which needs to be optimized emphatically; carrying out refined segmentation, simulation and optimization adjustment on the devices by utilizing a simulation technology, and generating a targeted power regulation and control strategy; and outputting the strategy so as to carry out environment adjustment and equipment maintenance treatment according to the strategy. By implementing the method provided by the invention, collaborative linkage of cultural relic state monitoring, environmental risk assessment and equipment power regulation and control can be realized, and comprehensive guarantee is provided for safety of cultural relics in a museum.

The invention discloses a new energy distributed photovoltaic intelligent distribution recommendation and dynamic monitoring system, and relates to the technical field of photovoltaic layout, and the system comprises a plot function prediction module which is configured to output the function attribute of a surrounding plot and a future construction behavior prediction result; an external impact assessment module configured to generate an external environmental efficiency attenuation factor; a photovoltaic efficiency core module configured to output a photoelectric conversion dynamic efficiency value; the power transmission loss optimization module is configured to calculate the comprehensive line loss rate from the site selection of the target photovoltaic power station to the grid-connected access point; the comprehensive evaluation decision engine is configured to generate a stationing feasibility score and output a recommended site selection map; and the full-life-cycle monitoring platform is configured to monitor the running state of the built new energy photovoltaic facility. The method has the advantages that the shielding pollution hidden danger in the site selection stage is avoided through plot function prediction and dynamic environment risk assessment; and the photovoltaic conversion efficiency and the power grid loss collaborative optimization model are fused, so that the power generation efficiency and the grid-connected economy are improved.

The invention discloses a bulk industrial solid waste storage yard environment risk assessment method, and relates to the technical field of solid waste risk assessment. Comprising the steps of screening and establishing an environment risk assessment index system based on natural environment characteristics and influence factors of a solid waste storage yard; determining the index weight of each level of the environmental risk assessment index system, and performing grading quantification processing on part of non-quantitative indexes; solid waste storage yard environment risk evaluation index grading and scoring standards are formulated; and on the basis of the determined index weight of each level, comparing with the formulated environmental risk evaluation index grading and scoring standard, evaluating the environmental risk of the solid waste storage yard by using a sampling fuzzy evaluation method, calculating to obtain a risk evaluation score, and determining the environmental risk level. The method is high in universality, makes up for the deficiency of an industrial solid waste storage yard environment risk assessment method, and provides reference for subsequent research and formulation of a comprehensive and comprehensive bulk industrial solid waste storage yard environment pollution risk grading evaluation and management method.

The invention provides a commercial vehicle mountainous road driving assistance system, method and device and a medium, and belongs to the technical field of commercial vehicle intelligent driving assistance. The system comprises a data acquisition sensing module used for acquiring vehicle state, environment and driver information; the data processing module is used for performing fusion analysis, environmental risk assessment, vehicle stability calculation and intelligent vehicle speed prediction based on the multi-source data; the AR-HUD dynamic display module is used for fusing and displaying the generated early warning and guiding information and a road scene in an augmented reality mode; and the data interaction module is used for realizing real-time data transmission among the modules. According to the method, specific modeling is carried out on the special road condition of the mountainous area, and key safety information is visually presented in combination with AR-HUD, so that the driving safety of the commercial vehicle under the complex terrain is effectively improved.

The invention relates to the technical field of environmental risk assessment, and discloses a regional cumulative environmental risk assessment system and method, and the method comprises the steps: constructing a collection network, and dynamically fusing monitoring data and generated data, and obtaining restoration monitoring data; analyzing and identifying emerging pollutants; a collaborative migration network of emerging pollutants and traditional pollutants is constructed, and the migration probability is calculated; establishing a dynamic conversion kinetic equation, and predicting a newly-emerging pollutant conversion product; in combination with an intergenerational accumulation model, simulating cross-generation accumulation of emerging pollutants in the biocenosis; nonlinear exposure-response mapping is constructed, short-term monitoring data and long-term repair monitoring data are fused, a sudden change early warning index is designed, and a risk critical point is recognized in advance; performing inversion updating on the emerging pollution migration parameters, and optimizing the emerging pollution migration parameters; fusing multi-source evidences, and judging whether the risk of the emerging pollutants exceeds the standard or not; and constructing a full-life-cycle decision-making model, designing a reinforcement learning dynamic strategy library updating strategy, and shortening the measure response time.

An apparatus, a method, and a computer program product are provided that combine policy compliance with vulnerability management to provide a more accurate risk assessment of an environment. The method includes training a policy machine learning model using a first training dataset to generate a policy machine learning model to produce mitigation technique classifications and training a vulnerability machine learning model using a second training dataset to generate a vulnerability machine learning model to produce weakness type classifications. The method also includes mapping the mitigation technique classifications to attack techniques to produce a policy mapping and mapping the weakness type classifications to the attack techniques to produce a vulnerability mapping. The method further includes producing a risk assessment of a vulnerability based on the policy mapping and the vulnerability mapping.

The invention relates to a mineral green prediction method and device based on cross-domain self-adaption and multi-modal fusion, and belongs to the technical field of mineral resource exploration and remote sensing. The method comprises the following steps: acquiring multi-source data, inputting the multi-source data into a transfer learning module to learn transferable model parameters, fusing the acquired data through a modal gating network, then constructing a line map through a map construction model, and enhancing the line map by using a map convolutional network; the enhanced multi-modal data is input into a Transformer encoder of an embedded graph convolutional network, a mining area space topological structure and spectral features are extracted, and an ore forming body potential probability graph is obtained through prediction of a prediction head; and an environmental risk assessment model is constructed, ore body prediction accuracy maximization and environmental risk minimization are realized through multi-objective optimization, and the accuracy of ore body prediction is verified through drilling activities. The method not only improves the regional generalization ability of the model, but also realizes the interpretability of green mineral exploration and results, and improves the precision, efficiency and sustainability of mineral exploration.

The invention relates to the technical field of oil and gas field environment monitoring, and particularly discloses a digital intelligent risk assessment method, platform and equipment for an oil and gas field environment. The method comprises the following steps: acquiring environmental monitoring data of an oil and gas field, the environmental monitoring data comprising pollutant concentration data and meteorological data; the environment monitoring data is preprocessed; constructing an environmental protection regulation knowledge graph; matching the preprocessed environmental monitoring data with the environmental protection regulation knowledge graph to generate a matching result; evaluating the environmental risk level of the oil and gas field according to the matching result; and outputting an environmental risk assessment result according to the environmental risk level. According to the method, through data preprocessing and knowledge graph matching, automatic compliance assessment and risk quantification of environmental indexes are realized, the timeliness and accuracy of risk identification are improved, preventive intervention is supported, and the method is suitable for green development and management of oil and gas fields.

The invention provides a sedimentary strata three-dimensional geology construction method and system integrating carbon footprint evaluation and intelligent block modeling, and relates to the field of energy and geology. According to the method, carbon footprint evaluation and sedimentary stratum three-dimensional geological construction are organically fused, so that full-process integration of data acquisition, dynamic partitioning, spatial reconstruction and low-carbon scheduling is realized, and the bottlenecks that data and carbon emission information are separated and model precision and energy consumption control are difficult to balance in the traditional technology are broken through; the real-time performance of fine expression of geological structures and environmental risk assessment is greatly improved, and more quantitative and intelligent technical support is provided for carbon capture site selection and green exploration decision.

The invention provides an intelligent warehouse management method based on a WMS, and relates to the technical field of intelligent warehouse, and the method comprises the following steps: 1, environment risk quantitative management: firstly, collecting warehouse environment data through an Internet of Things sensor network integrated by the WMS; then establishing an environmental risk evaluation model, and calculating the risk coefficient of each storage location; according to the invention, an end-to-end smart warehouse management closed loop is constructed through an environmental risk quantification model, a dynamic scheduling decision, multi-party collaborative auditing and dominant cost control; wherein discrete monitoring data is converted into a linkage scheduling instruction, and the splitting state of risk perception and disposal in a traditional WMS is broken; the problem of quality loss caused by single-dimensional scheduling is solved through a two-dimensional decision rule fusing the environmental risk and the service emergency degree; multi-party data mutual trust is realized based on an alliance chain, and a cross-mechanism collaboration barrier is eliminated; and through a risk quantification cost model, the implicit disposal cost is visualized.

This invention discloses an adaptive zoning management method for water environment pollutants based on differences in ecosystem service functions, belonging to the field of environmental risk assessment and pollutant management technology. The method includes the following steps: S1, ecosystem service function zone division; S2, establishment of a candidate pollutant inventory and concentration measurement; S3, construction of a differentiated multi-criteria evaluation system; S4, comprehensive subjective and objective weighting, determining the weights of each evaluation indicator for each ecosystem service function zone using a comprehensive subjective and objective weighting method; S5, pollutant priority ranking and inventory generation; S6, implementation of differentiated management measures. This invention, by constructing a zoning assessment framework and integrating multi-criteria decision analysis and a mixed subjective and objective weighting model, achieves accurate identification and scientific ranking of the environmental and health risks of pollutants in different functional zones. By generating a differentiated priority management inventory for each zone, it achieves the technical effect of transforming from unified supervision to precise adaptive management.