125 results about "Environmental impact assessment" patented technology

The invention discloses a territorial space planning environment influence monitoring method and device. The method comprises the following steps: collecting and preprocessing multi-source environment monitoring data; performing spatio-temporal feature extraction on the preprocessed multi-source environment monitoring data by using a self-supervised spatio-temporal diagram mask transfer attention network to obtain initial spatio-temporal features; performing fusion processing on the initial spatial-temporal characteristics based on a multi-mode diffusion converter to obtain fusion characteristics; calculating an environmental impact evaluation index based on the fusion feature; and based on a preset visualization template and an early warning rule, generating a dynamic monitoring result including a space-time evolution diagram, a trend analysis diagram and early warning information according to the environmental influence evaluation index. Through deep learning and multi-modal data fusion technologies, the precision and efficiency of environmental impact monitoring are improved, and accurate evaluation and timely early warning of environmental impact in the land space planning implementation process are realized.

The invention relates to the technical field of environmental impact assessment, in particular to an intelligent environmental impact assessment method and system, and the method comprises the following steps: collecting environmental data including pollutant discharge, water resource consumption, air quality change and biodiversity, building an impact factor data set, and segmenting the data set to obtain an impact factor data set; and calling a kernel function type to carry out numerical prediction on pollutant emission. According to the method, by integrating data processing and machine learning technologies, the accuracy and efficiency of environmental impact assessment are improved, numerical prediction is performed on environmental data by using a kernel function and a support vector regression model, the adaptability and precision of a prediction model are enhanced, model parameters are optimized through automatic population generation and crossover mutation operation, and the accuracy and efficiency of environmental impact assessment are improved. The model can better learn and adapt in a complex data environment, sensitivity and response speed to environmental changes are further improved through dynamic clustering analysis and time sequence adjustment, and scientific support is provided for formulating timely and effective environmental protection measures.

The invention provides a solid waste recycling full life cycle environmental impact evaluation method, which belongs to the technical field of solid waste recycling, and evaluates environmental impact by constructing a system boundary and time dynamic list including solid waste collection, transportation, pretreatment, recycling and product use stages and applying a time weighted characterization model. A time discount rate is introduced to quantify future environmental influence, a multi-scene influence prediction function is adopted to construct a simulation matrix, and an environmental influence time distribution curve is calculated. A four-dimensional index system of resource recovery efficiency, energy substitution efficiency, pollutant emission reduction and environmental impact mitigation is established, a system dynamics and Envi rPred ictor deep learning model is combined to predict a solid waste long-term environmental behavior, an environmental impact comprehensive evaluation matrix is constructed, characterization factors are dynamically adjusted, an iterative optimization mechanism is formed, and the environmental impact mitigation efficiency is improved. Scientific evaluation on the solid waste recycling full life cycle environment influence is realized.

The invention relates to the technical field of green product analysis, and discloses a green product full life cycle analysis system based on big data. The system comprises a data acquisition module, a preprocessing module, an environmental impact assessment module, a resource consumption optimization module, an association modeling and result generation module and a visual interaction module. The system collects data of all stages of a full life cycle and related element data of environment, resources, carbon emission and the like, after preprocessing of abnormal value elimination, entropy weight method weighting and the like, environment influence is dynamically evaluated through a fuzzy membership function, resource consumption is subjected to multi-objective optimization by utilizing a genetic algorithm, and the resource utilization rate is improved. A key stage is determined by means of grey relational degree, an analysis result is generated in combination with a multi-target decision model, and finally the analysis result is visually presented in the form of a three-dimensional map and a dynamic thermodynamic diagram. The system realizes efficient analysis and comprehensive evaluation of full-life-cycle data of green products, and provides scientific support for product environment performance optimization and decision making.

The invention discloses a green e-commerce platform intelligent decision-making system and method based on multi-objective optimization, and belongs to the field of e-commerce logistics scheduling and operation optimization, and the method comprises the steps: obtaining supply chain data, logistics paths, packaging attributes and user behavior data, and generating an original operation data set; constructing a dynamic environment influence evaluation model to output real-time environment index data; generating a coupled user portrait in combination with user historical behaviors and environmental protection activity data; adjusting the dynamic environment influence evaluation model based on a green strategy and external changes, and generating an environment compensation factor; calculating a cooperative gain factor and outputting a multi-objective decision scheme; collecting feedback data to construct an effect-strategy association map; and a closed loop optimization link is formed through adaptive parameter tuning. According to the method, the dynamic environmental impact assessment model and the real-time cooperative gain factor are adopted, and the coupling user portrait and the environmental compensation factor are combined, so that the improvement of the logistics path optimization rate and the precise decision of green logistics can be realized.

The present invention relates to the technical field of concrete construction, and in particular to a concrete environmental impact assessment method based on a life cycle approach. The method comprises the steps of: determining a concrete type on the basis of a construction environment and design requirements of a project; analyzing each stage of concrete engineering in a functional unit, and determining an evaluation range thereof; establishing a region-specific concrete life cycle database; performing inventory analysis on an evaluation range of a concrete life cycle; after the inventory analysis, using component inventories of full-life-cycle concrete to establish indicators on the basis of environmental policies of a construction region, so as to perform an environmental impact assessment; and finally, analyzing and evaluating various energy indicators of the concrete, and guiding the establishment of a subsequent project construction plan. In the assessment method, a full-life-cycle assessment is performed on concrete, the degrees of environmental impact caused by different construction processes in concrete engineering are objectively and holistically assessed, and evaluation and improvement schemes are proposed for the concrete construction, thereby reducing the energy and material consumption in concrete engineering construction.

The invention relates to an ecological environment inspection and detection information management method and system based on artificial intelligence, and the method comprises the following steps: carrying out the inspection and detection of water quality, air and soil samples collected by an ecological environment monitoring station, and obtaining environment detection data; performing environmental element association calculation based on the data to obtain element association data, and generating an environmental element distribution diagram through spatial-temporal distribution mapping; tracking a pollutant migration rule by using the distribution map, extracting pollutant migration characteristic data, and carrying out environmental impact assessment according to the pollutant migration characteristic data to form an environmental impact assessment report; according to the method, the monitoring area is subjected to risk distribution division according to the report, area environment risk distribution data is obtained, a targeted ecological environment management scheme is generated based on the data, and the technical problems that in the prior art, an effective correlation analysis means is lacked, and migration and transformation rules of pollutants among different media are difficult to reveal are solved.

The invention provides an offshore wind farm electromagnetic environment influence assessment method, and particularly relates to the field of electromagnetic environment influence analysis, and the method comprises the steps: firstly determining a wind farm center point, setting radiation test points in a preset radius, and obtaining electromagnetic environment test data of each test point through an electromagnetic environment test system; according to the method, electromagnetic interference signals overflowing from a wind power plant in electromagnetic environment test data are sorted out, a maritime electromagnetic wave propagation loss model is utilized to perform deduction calculation, the signal receiving strength of the position where existing equipment is located is obtained, and whether the electromagnetic interference signals have interference influence on the existing equipment or not is comprehensively evaluated by combining the technical standard requirements of the existing equipment. And the electromagnetic environment safety of the offshore wind plant is ensured. According to the method, the technical problem that the electromagnetic environment generated in the operation process of the wind power plant and the influence of the electromagnetic environment on peripheral radio frequency equipment are difficult to effectively test and analyze in the special environment of the offshore wind power plant is solved, so that the position and layout of the offshore wind power plant are reasonably planned according to the evaluation result.

The invention discloses a machine room equipment intelligent supervision system and method based on the Internet of Things, and relates to the technical field of the Internet of Things. Acquiring running state data and data interaction records of the equipment; environment data influence evaluation is carried out on each device by constructing a machine room node plane model, environment influence hierarchy division is carried out on each device, and a same-hierarchy environment influence node device chain of a machine room is determined; obtaining an influence environment area corresponding to the corresponding node equipment chain and carrying out environment influence comprehensive analysis; performing collaborative operation risk assessment on the two pieces of equipment with interaction records by combining the equipment interaction records with the environmental influence assessment data of the equipment and the environmental influence comprehensive analysis data of the area where the equipment is located; the environment data and the operation data of the equipment in the machine room are fed back through a visual port, interaction records and collaborative risk assessment data between the equipment are output, and the risk operation equipment is warned.

A present invention embodiment provides a Life Cycle Assessment (LCA) system that addresses the limitations of existing systems. The system offers an intuitive, user-friendly interface that simplifies the complexity of LCA methodologies, making environmental impact assessment accessible to a broader audience. It incorporates advanced algorithms and real-time data integration, ensuring accurate and up-to-date assessments. Additionally, the system is designed with a modular architecture and is highly configurable, allowing seamless integration with various industries and accommodating diverse items including products, services, and processes.

The invention relates to the technical field of data processing, discloses a TBM (Tunnel Boring Machine) jamming risk identification method and system based on multi-source data heterogeneity, and aims to solve the problem of poor accuracy of the existing method. The scheme mainly comprises the following steps: in a TBM construction process, acquiring multi-source heterogeneous data in real time, and preprocessing the multi-source heterogeneous data; obtaining a geological risk probability based on a geological risk identification model; obtaining an equipment risk probability based on the equipment state analysis model; obtaining a process suitability risk probability based on the construction process parameter analysis model; obtaining an environment-induced jamming probability based on the environment influence evaluation model; and carrying out weighted summation on the geological risk probability, the equipment risk probability, the process adaptability risk probability and the environment induced jamming probability, dynamically optimizing the weight of each probability based on a weight distribution module of reinforcement learning, obtaining an integrated jamming risk value, and identifying the jamming risk according to the integrated jamming risk value. According to the invention, the accuracy of TBM card machine risk identification is improved.

The invention relates to the technical field of industrial Internet of Things and supply chain management, and discloses an industrial Internet of Things intelligent supply chain management system. The data acquisition module is used for acquiring physical data, equipment state data and environment data of each link of a supply chain in real time through industrial Internet of Things terminal equipment; edge computing nodes; the cloud server is deployed at an edge node of a factory area and is used for performing historical environment influence evaluation and real-time decision on the collected original data to obtain supply chain key data; a block chain storage module; the encryption module is used for encrypting and chaining supply chain key data to ensure that the data cannot be tampered; a cloud analysis platform; a micro-service architecture is adopted to provide supply chain key data analysis and block chain evidence storage service; a visual interaction terminal; the method is used for providing a visual interaction interface and intelligent decision support. Compared with the prior art, the method has the advantages that the data processing efficiency is improved; the supply chain management efficiency is optimized; the system credibility is enhanced; and the man-machine interaction efficiency is improved.

The invention relates to the technical field of garbage transportation channel ecological restoration, in particular to an urban core mountain garbage transportation channel ecological restoration method which comprises the steps that firstly, aerial photography surveying and mapping are conducted on a transportation channel and a surrounding site to determine an ecological restoration range; geological exploration is carried out, road, soil, runoff and air pollution information is collected and evaluated, and a road lifting and ecological restoration mode is determined; treating garbage and pollution sources of surrounding polluted land parcels according to an environmental assessment conclusion, replacing and filling planting soil, and selecting an ecological restoration variety to form a restoration space; finally, a leisure footpath base is repaired according to geological exploration, a footpath is constructed, and an ecological fitness footpath system is formed by means of a mountain landscape structure. Multiple measures are integrated, the ecological environment is effectively improved, resources are reasonably utilized, social and economic benefits are achieved, the urban image and the resident life quality are improved, and the method has important significance in the field of ecological restoration of the garbage transportation channel.

The invention discloses a road carbon emission built environment influence assessment method and system based on a multi-source trajectory. The method comprises the steps of obtaining and preprocessing multi-source vehicle trajectory data, urban road network data, street view data and interest point data; traffic carbon emission of different pollutants on the road scale is calculated, multi-dimensional representation of the urban built environment is carried out, and built environment indexes on the road scale are calculated; an evaluation model of the influence of the urban built environment on the road carbon emission is constructed, training and prediction are carried out, an importance sequence of built environment indexes is obtained, and correlation analysis is carried out on the road carbon emission and the urban built environment; and evaluating the influence of the road carbon emission construction environment. According to the method, the urban road carbon emission is quantified and evaluated in combination with the multi-source vehicle trajectory data and various built-up environment indexes, the maximum built-up environment index for the road carbon emission is found, the influence degree and the action mechanism of the maximum built-up environment index are researched, and the method has good benefits in the fields of traffic carbon emission reduction, urban planning and the like.

The invention relates to the technical field of suspended sediment dynamic monitoring, in particular to a suspended sediment dynamic change monitoring and deep sea operation scheduling optimization method, which comprises the steps of multi-source fusion data acquisition: synchronously acquiring suspended sediment concentration distribution data, three-dimensional ocean current vector data and seabed microtopography slope data of a target area; simulation of plume dynamic diffusion: inputting the acquired data into a microtopography coupled adaptive plume diffusion model to generate a plume diffusion three-dimensional space-time cloud picture in the future 6 hours; dynamic risk partition generation: performing space-time overlay analysis on the plume diffusion three-dimensional space-time cloud picture and a preset ecological sensitive area dynamic electronic fence to obtain an operation risk thermodynamic diagram; and intelligent partition scheduling execution: dynamically dividing a mining prohibited area, a buffer area and a safe area, and sending an inter-partition rotation scheduling instruction to the mining equipment group. According to the method, the spatial resolution and the aging response capability of sediment diffusion behavior prediction are improved, and a basis is provided for environmental impact assessment and ecological protection.

The invention discloses a life cycle evaluation system and method and a computer program product, and belongs to the technical field of life cycle evaluation, and the life cycle evaluation system comprises a data acquisition module, an artificial intelligence prediction module, an environmental influence calculation module and a feedback and visualization module. The data acquisition module acquires original data in equipment operation; the artificial intelligence prediction module receives the original data and performs prediction to form resource flow data; the environment influence calculation module receives the original data and the resource flow data and converts the original data and the resource flow data into standardized life cycle evaluation indexes; and the feedback and visualization module displays the life cycle evaluation index and generates a parameter adjustment instruction. According to the invention, real-time environmental impact assessment and sustainable optimization in the hybrid manufacturing process are realized.

The invention relates to an ecological risk dynamic simulation evaluation and prediction early warning method based on a material metabolism process, and the method comprises the following steps: 1, ecological risk impact recognition: precisely analyzing the urban metabolism process through dynamic material flow analysis, and constructing an urban metabolism network model; step 2, ecological risk dynamic simulation evaluation: the risk impact of each metabolic subject is incorporated into an input-output model, and an urban ecological risk diffusion model is constructed; step 3, ecological risk prediction and early warning: constructing a prediction model of ecological risk impact indexes and influence factors based on an expandable random environmental influence evaluation model; the method has the advantages that key metabolic risk points are mined through risk impact recognition and quantification of all metabolic subjects, influence linkage conduction process simulation, risk index calculation and metabolic risk prediction and early warning, and stable operation of an urban agglomeration system is ensured; starting from the whole metabolic process of urban source supply and the like, the mechanism of metabolic risk is systematically explored.

The utility model relates to the technical field of environmental monitoring, and discloses a water quality extraction structure for factory environmental assessment, which comprises a sampling barrel, the bottom of the sampling barrel is fixedly connected with a base, the base is internally provided with a rotating mechanism, the outer side of the base is provided with a plurality of circular holes, and the two sides of the sampling barrel are provided with sealing mechanisms. A water outlet pipe is fixedly connected to the outer side of the sampling barrel, a lifting mechanism is arranged in the sampling barrel, a pull rope is fixedly connected to the top of the sampling barrel, the sealing mechanism comprises two sealing doors, sliding grooves are formed in the two sides of each sealing door, and buoyancy blocks are fixedly connected to the outer sides of the sealing doors. During use, the sampling barrel is put into water, when the sampling barrel sinks to a specified depth, the buoyancy block acts to open the sealing door, water flow enters the sampling barrel, drives the buoyancy plate to float upwards and drives the fixed pulley to rotate, and finally the sealing door is automatically closed, so that manual piston operation is omitted, and the working efficiency is improved.

The invention discloses an intelligent auditing method and system for environmental assessment file auditing, and the method comprises the steps: firstly loading a to-be-audited environmental assessment file, and loading an auditing rule according to the type of the environmental assessment file; then extracting a directory of the environmental assessment file, and displaying the content of the environmental assessment file according to a page sequence of the directory of the environmental assessment file; a preset file intelligent auditing model is used for auditing the environmental assessment file; and finally, displaying an audit result through a human-computer interaction interface. Wherein the human-computer interaction interface comprises a basic information display area, an original content display area, a file directory display area, an auditing log display area and an auditing result display area. The application file intelligent auditing model leads or assists the auditing work of the environmental assessment file, so that the auditing efficiency and quality of the environmental assessment file are improved while the auditing content of the environmental assessment file is standardized.

The invention relates to the field of atmospheric pollution diffusion simulation, and particularly discloses an atmospheric pollution diffusion process dynamic simulation and influence evaluation system, which comprises: a) a multi-pollutant collaborative simulation module, which is used for simulating the process of nonlinear interaction between various pollutants through chemical reaction and photochemical reaction, and the diffusion condition of the pollutants in the atmosphere; b) a big data analysis environmental impact assessment module which integrates historical monitoring data and social and economic data information by using a big data analysis technology to construct an assessment model; through the multi-pollutant collaborative simulation module, the system can accurately simulate the nonlinear interaction and diffusion conditions of various pollutants in the atmosphere, the high-precision simulation is beneficial to more accurate understanding of the propagation and conversion process of the atmospheric pollutants, and a solid foundation is provided for subsequent evaluation and strategy formulation.

The invention provides an enterprise environment influence assessment method, device and equipment and a computer storage medium, and relates to the technical field of environment assessment, and the method comprises the steps: collecting environment index original data with a timestamp, and constructing a multi-source environment data set in combination with enterprise resource consumption data and emission factor data; carrying out credibility evaluation and weighted fusion on the data set, inputting the fused data into a pre-training evaluation model, and outputting a comprehensive environmental impact evaluation value; the contribution degree of each input feature is analyzed and calculated through model attribution, key factors are identified based on metadata, and a customized improvement measure list of priority ranking is generated in combination with a measure knowledge base and a multi-objective optimization algorithm; the invention further provides a matched portable detection device, electronic equipment and a storage medium, dynamic quantification, accurate attribution and closed-loop optimization of enterprise environment influences are achieved, and the method is suitable for intelligent environment management of high-emission industrial enterprises.

The invention belongs to the technical field of environmental protection, and particularly discloses an intelligent environmental impact assessment method and system, and the method comprises the following steps: obtaining real-time monitoring data, and constructing a pollutant initial distribution model according to the real-time monitoring data; the real-time monitoring data are environmental parameters including temperature, humidity, wind speed, rainfall and pollutant concentration; correcting the initial distribution model by using meteorological prediction data, wherein the meteorological prediction data comprises a wind speed change trend and a wind direction change trend in a future time period; simulating a pollutant diffusion path based on the corrected distribution model, wherein the simulation of the diffusion path is realized by applying a fluid mechanics principle in combination with a topographic relief effect and a vegetation interception effect; and generating a spatial distribution probability distribution diagram of the environmental influence according to the simulation result, wherein the probability distribution diagram is used for displaying the difference of the threat degrees of environmental pollution to different regions. According to the method, the spatial distribution uncertainty can be reasonably quantified while the prediction precision is improved.

A method and a system for online integrated environmental monitoring, assessment, and impact visualization of projects are disclosed. The method includes receiving project site data representing the geographical boundary of a project in a virtual environment and collecting real-world environmental data from fixed and mobile monitoring devices. Model data, modeling parameters, and digital ground data associated with the project are retrieved and unified through a unified connector interface to form a unified input data. The unified input data is processed through one or more impact modeling entities to assess environmental impacts, including air quality, noise, water quality, and ecological effects. Visualizations of the environmental impact determined are transmitted to an environmental assessment planner interface for display. The method and the system provide for real-time integration, efficient impact modeling, and multi-dimensional visualization, enabling stakeholders to evaluate and monitor environmental effects with enhanced precision and accessibility in a seamless digital environment.

The application discloses a kind of fish suitable flow habitat spatio-temporal distribution calculation method and system, through basic data collection pre-processing, target fish life history and reservoir scheduling spatio-temporal matching, cascade reservoir dynamic backwater length calculation, habitat suitability determination of dehydration river section, spatial superposition between cascades, habitat time dimension accounting and spatio-temporal matrix construction, planning scheme compliance verification and the like steps, realize the spatio-temporal dynamic accurate delineation of the habitat of native fish suitable flow after cascade development.The application fully considers the water level fluctuation and backwater dynamic change of reservoir within a year, the differentiated habitat needs of different life history stages of target native fish, solves the industry pain points of traditional method such as insufficient accuracy, disconnection with protection target and lack of time dimension evaluation, and can provide scientific and accurate technical support for basin hydropower development layout optimization, ecological protection measure formulation and planning scheme environmental impact assessment comparison.

The application discloses a tunnel wall vibration prediction method and system considering line alignment constraint conditions, and is based on a constructed vehicle-line coupling dynamics, train-track-tunnel (soil body) system vibration integrated analysis model, studies the influence degree of different line parameters such as curve radius, line slope and combinations thereof on the system vibration response, obtains tunnel wall vibration acceleration data in the evaluation section, and further calculates the tunnel wall Z vibration level; based on the probability density distribution and numerical fitting method, the influence function relationship of different line parameters on the tunnel wall vibration level is derived. The application fully considers the objective problem that the influence factors of the existing environmental impact assessment prediction method are not comprehensive, quantifies the corresponding coupling relationship between the line parameters and the tunnel wall Z vibration level, can more systematically determine the prediction and evaluation results of the urban rail transit vehicle-induced tunnel wall vibration, and provides a theoretical basis and an engineering applicable method for realizing tunnel structure vibration prediction and line design optimization.

The invention provides a method, a device, equipment and a medium for assessing potential risks of a cavern repository, and relates to the technical field of nuclear radiation environment influence assessment, and the assessment method comprises the steps: obtaining a basic list and project feature information of cavern disposal safety assessment; screening and evaluating the basic list according to the project feature information to generate a corresponding key list; and carrying out network analysis processing on the key list through a complex network analysis algorithm to generate a corresponding relation network, and carrying out scene analysis processing on the relation network based on preset key scene parameters to generate a potential risk scene of the cavern repository. According to the method, efficient identification, scientific classification, accurate assessment screening and deep processing analysis of feature-event-process data are realized, powerful support is provided for potential risk assessment of the cavern repository, and scientificity and reliability of decision making are improved.

The present application relates to the technical field of real-time monitoring, in particular to a real-time monitoring system for road and bridge construction progress, which comprises a dynamic relationship analysis module, an event response decision module, a structure health monitoring module, a risk management and prediction module, a progress prediction and adjustment module, a resource optimization configuration module, an environmental impact assessment module and an overall progress management module.In the present application, the dynamic relationship in construction is captured through the graph neural network, the graph attention network and the dynamic graph convolution network, the construction model is updated in real time, the accuracy of progress analysis is ensured, the event response decision module quickly optimizes the construction plan through the predictive control algorithm, the strategy flexibility is improved, the structure health monitoring and risk management module combines the Markov decision process and the Monte Carlo tree search, effectively prevents risks and ensures safety, the progress prediction, resource optimization and environmental impact assessment module improves resource efficiency and environmental adaptability, and the application of comprehensive evaluation method realizes efficient and high-quality construction management.

The invention belongs to the technical field of cup holders, and discloses a big data analysis-based intelligent dust removal environmental impact assessment method, which comprises the following steps of: acquiring multi-source data, and integrating the multi-source data into a unified data model by using a space-time alignment technology to obtain a comprehensive data set of environment and dust removal system operation; performing data preprocessing on the multi-source data in the comprehensive data set, and performing feature extraction to obtain feature data; establishing an environmental impact assessment model based on the feature data; utilizing the trained model to evaluate the influence of the dust removal measures on the environment; the evaluation result is displayed in a chart form; the time sequence diagram shows the change of the environment indexes before and after the dedusting measures are implemented. According to the invention, through big data fusion and an advanced causal inference model, the defect that the influence of natural meteorological change cannot be eliminated by a traditional front-back comparison method is overcome, and the real contribution of dust removal measures to environment improvement can be quantified more scientifically and accurately.

An automated computer-implemented process can be executed by a monitoring server for real-time environmental impact assessment of a distributed computer system. The process includes receiving power metrics from metrics sources, the power metrics representing power usage by resources of the distributed computer system. The process further includes determining environmental impact metrics that represent amounts of CO2 emissions created by the distributed computer system. The process further includes comparing the environmental impact metrics with a CO2 emissions benchmark. The process further includes determining that the environmental impact metrics exceed the CO2 emissions benchmark. The process further includes outputting for display to a participant of a virtual meeting a prompt with a suggestion for executing a remedial measure that reduces CO2 emissions created by the distributed computer system.

The present application relates to the technical field of suspended sediment dynamic monitoring, and particularly relates to a suspended sediment dynamic change monitoring and deep sea operation scheduling optimization method, comprising multi-source fusion data acquisition: synchronously acquiring suspended sediment concentration distribution data, three-dimensional sea current vector data and seabed microtopography slope data of a target area; plume flow dynamic diffusion simulation: inputting the acquired data into a microtopography coupled adaptive plume flow diffusion model to generate a plume flow diffusion three-dimensional space-time cloud atlas for the next 6 hours; dynamic risk zoning generation: performing space-time superposition analysis on the plume flow diffusion three-dimensional space-time cloud atlas and a preset ecological sensitive area dynamic electronic fence to generate an operation risk heat map; intelligent zoning scheduling execution: dynamically dividing a forbidden mining area, a buffer area and a safe area, and sending zoning interval rotation scheduling instructions to a mining equipment group. The present application improves the spatial resolution and time response capability of sediment diffusion behavior prediction, and provides a basis for environmental impact assessment and ecological protection.