128234 results about "Environmental engineering" patented technology

The invention relates to the technical field of port ecological construction, and particularly discloses a port ecological construction evaluation method and system, and the method comprises the following steps: collecting dynamic ecological environment data in port construction operation through a multi-source sensor array; performing space-time coupling analysis on the data, calculating a dynamic attenuation rate # imgabs0 # of each dimension environment index by adopting a self-adaptive sliding time window algorithm, adjusting an ecological response time constant theta in combination with hydrodynamic conditions of a sea area around a port, performing synchronous integral calculation on a historical cumulative effect # imgabs1 #, and generating an environment health degree evolution curved surface; constructing a comprehensive evaluation model; and predicting an environmental risk value based on the hybrid prediction model. According to the invention, an intelligent, automatic, safe, reliable, green and low-carbon evaluation solution is provided for ecological construction of the port, and the ecological protection and sustainable development level of the port is remarkably improved.

The invention provides a mine ecological risk prediction method and system based on artificial intelligence, and the method comprises the steps: collecting multi-source ecological monitoring data through a distributed sensor network disposed in a mine region, carrying out the time-space alignment processing to generate a time-space correlation feature set, calling a pre-trained ecological risk prediction model to predict the features, and carrying out the prediction of the features. Generating a risk conduction mode set including a risk propagation path, an initial node, a propagation direction and strength, constructing an ecological risk evolution network based on the risk conduction mode set, and displaying a topological structure and a time sequence dependency relationship of risk nodes, and according to the ecological risk evolution network, generating a risk early warning instruction set containing a risk grade division result and an ecological restoration strategy, and sending the risk early warning instruction set to an ecological management platform, thereby comprehensively and accurately predicting the ecological risk of the mine, providing an effective risk early warning and ecological restoration strategy, and ensuring the ecological safety of the mine.

The invention provides an intelligent ecological restoration system, method and device for a high and steep slope of a strip mine in an arid region. Comprising a data acquisition layer which realizes real-time acquisition of multi-dimensional environmental data through InSAR satellite remote sensing, a ground sensor network and unmanned aerial vehicle multispectral imaging; the transmission layer adopts LoRa and 5G hybrid networking; the platform layer is used for constructing a slope stability prediction and restoration scheme optimization platform based on a digital twinborn model and a deep learning algorithm; and the application layer is used for remotely controlling the repairing device through a mobile terminal and a Web terminal and monitoring the repairing progress in real time. The problems that a traditional restoration technology is poor in adaptability, low in vegetation survival rate, high in ecological restoration cost, insufficient in monitoring technology application, insufficient in monitoring feedback mechanism, insufficient in intelligence, long in ecological restoration period and the like are solved.

The invention relates to the technical field of river pollution identification, in particular to a pollutant identification and early warning method and system for a river patrol pollution source, and the method comprises the steps: constructing a three-dimensional monitoring network system, so as to build a three-dimensional data collection architecture; establishing a space-time reference unified framework to realize synchronous time service of different monitoring nodes; deploying an edge computing node, and implementing data preprocessing at an equipment end of the monitoring node; constructing a pollutant feature library which comprises spectral features, water quality parameter correlation features and visual morphological features of river pollutants, and establishing a water quality parameter correlation model of organic pollutants and inorganic pollutants, which comprises a plurality of feature dimensions; and a self-adaptive threshold early warning mechanism is established, a space-time composite early warning model is constructed, and accurate positioning and hazard degree grading early warning of pollution events are realized. According to the invention, through the hierarchical fusion model of technology fusion, the precision and speed of identifying the river pollutants are improved.

A scheduling method and system for the operation of reservoirs to recharge freshwater for repelling saltwater intrusion under changing conditions. Research data of a research area is collected, the research area is generalized, and the river network topology of the area is constructed; the research data acquire the seawater flow data of each station in the research area for N time periods, analyze the correlation between the flow of a predetermined station and the saltwater intrusion and build a seawater flow spatial-temporal evolution model; rainfall data and freshwater runoff of each station are acquired to analyze the variation trends of freshwater flow and hydrological regime of each station and a first ending point and build a freshwater flow spatial-temporal evolution model; and a scheduling model for recharging freshwater for repelling saltwater intrusion is built for simulation, a scheduling method is provided, and a scheduling scheme set is formed.

The invention relates to the technical field of carbon sink prediction, in particular to an ecological restoration area carbon sink dynamic prediction method based on time sequence remote sensing, which comprises the following steps: acquiring a multi-source time sequence data set of a target area, and extracting spatial features and a time sequence fluctuation mode of the multi-source time sequence data set; dividing the ecological restoration region in the target region into a plurality of dynamic aggregation units based on the spatial features and the time sequence fluctuation mode, and combining the dynamic aggregation units with similar features; according to the ecosystem type of each dynamic aggregation unit after combination, constructing a differentiated carbon sink accounting model, extracting multi-scale time sequence features and screening key influence factors; based on the multi-scale time sequence characteristics and the screened key influence factors, constructing a carbon sink dynamic prediction model and outputting a carbon sink dynamic prediction result; and solving a repair decision scheme according to a carbon sink dynamic prediction result by taking maximization of the carbon sink increment as a target. The objective of the invention is to realize carbon sink dynamic prediction of a large-scale ecological restoration area and improve carbon sink prediction precision and calculation efficiency.

The invention relates to the technical field of intelligent agriculture and ecological internet of things, in particular to an ecological irrigation decision dynamic optimization method and related equipment. Comprising the following steps: acquiring multi-source environment data, acquiring a soil profile humidity gradient in real time through a soil humidity sensor array, acquiring future rainfall probability distribution, a temperature change rate and a wind speed predicted value in combination with a weather forecast interface, and synchronously accessing a geographic information system to acquire terrain elevation and crop distribution data. The three technical bottlenecks of model dimension collapse, parameter estimation instability and optimization response lag in a traditional irrigation decision-making system are systematically solved by constructing a space-time coupling analysis framework and a closed-loop optimization mechanism of multi-source heterogeneous data.

The invention relates to the technical field of data processing, and discloses a hazardous chemical substance leakage and diffusion prediction method and system based on an unmanned ship, and a storage medium. The method comprises the following steps: acquiring water quality, weather and geographic data through an unmanned ship sensor, and processing to obtain an initial data set; calculating a diffusion range according to the data, optimizing a monitoring area, and generating a cooperative monitoring track; performing exception elimination and smoothing processing on the data, and constructing spatial distribution; solving a convection diffusion equation, and establishing a three-dimensional flow field model; analyzing a hazardous chemical substance diffusion path, and predicting pollutant distribution; and establishing a risk assessment system, and generating emergency disposal suggestions and decision-making schemes. According to the application, rapid and accurate monitoring of the polluted area is realized through the multi-parameter sensor array carried by the unmanned ship, and accurate prediction of the hazardous chemical substance pollutant diffusion process is realized in combination with multi-source data fusion processing and a three-dimensional flow field simulation technology.

A method and a system for constructing a soil water movement model based on the physical information neural networks are provided. The soil water movement model is constructed by fusing the physical information neural network and a control equation of the soil water movement model, and the model adopts automatic differentiation to replace difference operation of grid scale, so that the calculation error caused by equation discretization in the solving process of numerical differentiation is avoided, and the calculation accuracy is improved; and the automatic differentiation mode is mainly carried out aiming at the output of a neural network, so that the numerical value of gradient calculation is more accurate.

The embodiment of the invention discloses an industrial production monitoring method and monitoring device applied to pollution reduction and carbon reduction of an ecological environment. The method comprises the following steps: firstly, obtaining an operation parameter of target industrial production equipment and an environment parameter set of an associated region; secondly, processing the operation parameters and the environment parameter set through a pre-trained pollution and carbon reduction prediction model, and outputting a pollution emission feature vector and a dynamic emission reduction strategy of the target industrial production equipment; then generating a multi-level control instruction set (an equipment operation parameter adjustment threshold, an energy switching priority and a pollution processing node start-stop rule) for the target industrial production equipment according to the pollution emission feature vector and a dynamic emission reduction strategy; and finally, the multi-level control instruction set is transmitted to a control terminal of the target industrial production equipment through an Internet of Things interface, and the execution weight of the multi-level control instruction set is dynamically adjusted based on the environment parameter updating set fed back in real time. In this way, pollution reduction and carbon reduction of industrial production can be achieved in a high-quality and intelligent mode.

The invention relates to the technical field of data processing, and discloses a water quality and water quantity accurate simulation method and system based on an SWAT model and a storage medium. The method comprises the following steps: analyzing rainfall and evapotranspiration data through a two-parameter trend test, and generating standard hydrological data by adopting a triple configuration algorithm; dividing sub-basins by combining topographic information, and calculating a hydrological response unit; performing water balance operation and water conservation evaluation; pollutant diffusion is analyzed, and a diffusion equation is solved; running state monitoring is implemented, and an early warning system is designed; and comparing and correcting measured data, and constructing a hierarchical monitoring mechanism. According to the invention, the whole-process precise management from hydrological data acquisition, water quality evaluation to early warning monitoring is realized, and the accuracy and reliability of water quality and water quantity monitoring are remarkably improved.

The invention relates to a high and cold wetland dynamic monitoring and health evaluation method and system based on multi-source remote sensing. The method comprises the following steps: step 1, data acquisition and preprocessing; step 2, multi-modal data feature extraction; step 3, performing multi-modal data fusion; step 4, dynamic monitoring; step 5, evaluating wetland health; according to the invention, the calculation method of the SAR polarization decomposition model and the optical wetland index is optimized due to the unique seasonal change and earth surface complexity of the high and cold wetland, a scientific basis is provided for the management of the wetland protection area, and the ecological restoration efficiency is improved; alpine region water resource management and ecological protection policy making are supported; and data support is provided for researching the wetland carbon sink function and the climate change influence.

The invention discloses an intelligent water and fertilizer integrated precise irrigation control method. The method comprises the following steps: step 1, establishing a multi-modal data acquisition network; step 2, inputting the data obtained in the step 1 into a trained LSTM neural network, and predicting future crop transpiration water demand; 3, according to nutrient concentration distribution fed back by a soil conductivity sensor in real time, combining with a water and fertilizer coupling migration model, dynamically calculating a nitrogen-phosphorus-potassium ratio of each irrigation zone, realizing precise mixing of a fertilizer mother solution and irrigation water through a three-channel proportioning pump, and implementing a variable irrigation decision; 4, dynamically adjusting the pulse irrigation duration of each branch through an MPC algorithm; and step 5, recovering the residual liquid at the tail end of the pipe network to a buffer tank, detecting the EC value of the recovered liquid through an electrochemical sensor, and taking the EC value as a reference parameter automatic correction proportioning algorithm of the next irrigation period. Accurate control over the irrigation process can be achieved, so that the water and fertilizer utilization efficiency is improved, and resource waste and environmental pollution are reduced.

The invention relates to the field of water quality pollution traceability, in particular to a water quality pollution traceability analysis method and system based on an intelligent unmanned ship. The method comprises the following steps: acquiring a multi-mode network monitoring parameter of a real-time position based on a multi-mode communication module, performing a dynamic multi-mode network intelligent switching decision, and constructing an intelligent multi-mode network switching strategy; real-time water area multi-dimensional water quality monitoring parameters of the intelligent unmanned ship are obtained according to the multi-dimensional water quality sensor, abnormal pollution parameter change detection is carried out, and abnormal change pollution parameters are marked; performing reverse water quality spatial change analysis and gradient descent iterative traceability calculation on the abnormal change pollution parameters to generate a potential pollution diffusion path; and performing intelligent traceability cruise according to the potential pollution diffusion path, and tracking and positioning the polluted water source to obtain a polluted water source point. According to the invention, efficient and accurate water quality pollution traceability is realized.

The invention provides an intelligent management method for natural resource engineering data, and relates to the technical field of data processing, and the method comprises the steps: inputting a time-space coupling model into real-time weather forecast data, simulating water distribution schemes under different rainfall scenes, automatically recognizing a river reach position of which the ecological flow is lower than a design threshold value, and carrying out the calculation of the river reach position. In combination with a historical water replenishing strategy case library, generating a multi-objective optimization scheduling scheme including a water replenishing time sequence, a water replenishing path and a water replenishing amount; three monitoring points are arranged at a downstream section of an ecological protection area, a flood control section and a canal head of an irrigation area in a drainage basin, and ecological flow, flood control section water level and canal water level data are monitored in real time respectively; and dynamically correcting the water supplementing time sequence, the water supplementing path and the water supplementing amount in the multi-objective optimization scheduling scheme to obtain a corrected scheduling scheme. According to the method, refinement and toughness of drainage basin water resource management can be improved.

The invention discloses a land degradation supervision method based on remote sensing monitoring, and relates to the technical field of land degradation monitoring. The method is used for solving the problems of hidden degradation risk identification and dynamic propagation simulation. According to a multi-temporal thermal infrared remote sensing image, daily variation characteristics of surface temperature are extracted through spectral analysis, a recessive salinization risk distribution map is constructed by combining measured data of soil salinity, and interference of vegetation coverage is overcome. And multi-source remote sensing and geographic data are fused, dominant driving factors significantly associated with the salting risk are screened, and the degeneration cause is clarified. And in combination with ecological restoration potential and treatment resource constraints, calculating an antagonism index of a driving factor and the restoration potential, and dividing degradation threat levels. And a natural hydrology and human activity dual-path propagation model is constructed, a risk space-time evolution process is simulated, and a future hotspot migration path and an optimal blocking window are predicted. According to the method, accurate early warning and active prevention and control of land degradation are realized through a technical chain of risk identification, driving analysis, threat grading and dynamic simulation.

The invention relates to the technical field of recoverable material sorting, and discloses an efficient intelligent sorting system for life source low-value recoverable materials, which comprises an acquisition unit used for monitoring environmental parameters of an urban low-value recoverable material sorting center and the speed of a conveyor belt in real time, synchronously acquiring voiceprint data, three-dimensional point cloud data and hyperspectral data of the recoverable materials, and sending the voiceprint data, the three-dimensional point cloud data and the hyperspectral data to a cloud server; through close cooperation and data interaction among all the functional units, rapid and comprehensive analysis and accurate classification of low-value recoverable materials are achieved, under the complex working condition of a large-scale solid waste treatment scene, the system can recognize and judge the recoverable materials conveyed at a high speed and then classify and sort the recoverable materials, and in the process, the recoverable materials are classified and sorted, so that the recovery efficiency of the recoverable materials is improved. According to the method, the interference of external conveyor belt speed change and environmental factor fluctuation on identification can be reduced, so that the accuracy of overall classification is ensured, and the overall identification effect can completely meet the requirements of mixing and sorting of recyclable materials under complex working conditions.

The invention discloses a garden landscape plant health assessment method, and belongs to the technical field of resource assessment. Comprising the following steps: step 1, constructing a time sequence database of environmental factors and plant growth states; step 2, according to different plant species and growth stages thereof, dynamically calculating and generating a health reference value adapted to the current environmental condition; step 3, in the optimal evaluation time window, performing preliminary evaluation on the plant health condition, extracting original health indexes, and performing correction in combination with an environment disturbance compensation factor; step 4, performing double comparative analysis on the corrected health assessment result, eliminating false anomalies caused by environmental fluctuation, and calculating an anomaly confirmation credibility index; step 5, grading the plant health conditions, visually displaying the plant health conditions in a thermodynamic diagram form, and marking the environmental interference degree; and step 6, based on an evaluation result, automatically generating a targeted maintenance suggestion, and designing a corresponding maintenance effect verification scheme.

The invention discloses an intelligent agricultural condition monitoring system and method for grain and oil crops, and relates to the technical field of agricultural condition monitoring. Farmland multi-source data are acquired and preprocessed in real time through a multi-source data acquisition module; multi-factor step-by-step fusion of farmland multi-source data is realized by using a D-S evidence theory through a multi-source data fusion module, and a multi-factor fusion evaluation index is formed; constructing a grain and oil crop environment-growth state comprehensive model based on LSTM through an environment state fusion module, and fusing the grain and oil crop environment-growth state comprehensive model with farmland multi-source data and multi-factor fusion evaluation indexes to obtain a farmland digital twin system; and a growth deviation index is analyzed and generated through the decision feedback optimization module and is fed back to the optimization system. The problems that in the prior art, data acquisition hysteresis is remarkable, multi-source heterogeneous information is isolated and difficult to fuse, and early warning fails due to lack of correlation between environment mutation and crop response are solved, and accurate monitoring and risk active intervention of grain and oil crops in the whole growth period are achieved.

The invention provides an agricultural source solid waste pollution reduction and carbon reduction synergistic effect calculation method and system. According to the agricultural solid waste pollution reduction and carbon reduction synergistic effect calculation method, a livestock and poultry manure and straw mixture serves as a treatment object, an environmental parameter set of microbial metabolism intensity is obtained, and then a microbial agent feeding acceleration rate and a flora activity attenuation curve are dynamically matched. In the aerobic fermentation stage, through linkage of pile turning operation and a microbial inoculum adding strategy, activity balance of surface and deep flora is maintained, and a microbial inoculum adding time window is corrected in combination with historical data. And establishing a quantitative model with a carbon-nitrogen ratio imbalance threshold value as a constraint, iteratively optimizing a nonlinear association relationship between the inoculant feeding acceleration rate and the pile turning operation frequency, and outputting a full-period synergistic effect evaluation result. According to the technical scheme, the pollution reduction and carbon reduction synergistic effect of agricultural source solid waste treatment is improved, and the dual purposes of resource utilization and environmental protection are achieved.

The invention discloses a machine learning-based soil pollution risk assessment method and a machine learning-based soil pollution risk assessment device, and belongs to the technical field of soil pollution risk assessment. The split biological effect and chemical indexes in traditional evaluation are converted into unified risk driving force measurement. A nonlinear interaction entropy calculation mechanism can adaptively capture an antagonism-collaborative balance relationship between microbial communities and pollutants in a soil environment, so that the degradation potential of functional flora on specific pollutants is reflected, and the dynamic regulation effect of environmental factor fluctuation on bioavailability is quantified. According to the cross-scale feature fusion technology, the evaluation model not only can identify the current pollution space distribution, but also can pre-judge the morphological transformation trend of pollutants in biogeochemical circulation, and provides a quantitative basis with ecological interpretation for risk early warning.

The invention relates to the technical field of drainage basin water scheduling, and discloses a drainage basin water regulation and control optimization method based on ecological element changes. The method comprises the following steps: deploying a drainage basin monitoring system, and collecting ecological element real-time data such as a hydrological parameter sequence and a remote sensing image; after the data is cleaned and converted, hydrological trend features and spatial distribution features are extracted by adopting a feature learning model, and the hydrological trend features and the spatial distribution features are fused into unified ecological representation through a cross-modal alignment mechanism; inputting the unified ecological representation into a physically constrained neural network prediction model, and outputting a water regimen dynamic prediction value; and finally, based on the predicted value, a water resource regulation and control instruction is generated and executed by using a multi-objective decision algorithm so as to optimize the watershed water circulation process. According to the method, feature extraction comprehensiveness is improved through multi-source data fusion and cross-modal analysis, prediction reliability is enhanced in combination with physical constraints, reasonable allocation of water resources is achieved by means of multi-target decision, the ecological condition of a drainage basin can be improved, and the water utilization efficiency is improved.

The invention discloses a traceable road surface runoff pollutant monitoring method and system, and relates to the technical field of water pollution monitoring. The method comprises the following steps: capturing environmental factors of precipitation, air temperature and traffic flow according to road surface basic monitoring data, extracting pollutant concentration data of sampling points, analyzing relevance of the environmental factors, identifying and classifying pollutant concentrations, and obtaining pollutant concentration change trend characteristics. According to the method, the relationship between the environmental factors and the pollutants is accurately analyzed by capturing the environmental factors and the pollutant concentration data, the understanding of the change trend of the pollutant concentration is improved, the pre-judgment precision of future changes is effectively improved, in real-time data comparison, error analysis helps to identify deviations and adjust model weights, the prediction accuracy is improved, and the prediction accuracy is improved. Through monitoring resource demand prediction, optimization of monitoring point layout and resource configuration and dynamic adjustment of a monitoring strategy, pollutant concentration monitoring is more flexible and accurate, and monitoring efficiency and response speed are improved.

A pyrolysis method and system are provided that enhances the production of C3 and C4 alkanes in the resulting pyrolysis effluent. More particularly, the disclosed pyrolysis method and system may be configured to enhance the production of C3 and C4 alkanes due to the use of certain pyrolysis catalysts and more intense pyrolysis conditions.

The invention relates to a coastal wetland ecological restoration effect evaluation method and system, and belongs to the technical field of ecological monitoring and restoration. Aiming at the problems of static evaluation index lag, insufficient data fragmentation integration and lack of causal diagnosis mechanism in the prior art, multi-source time sequence ecological data are acquired by constructing a three-dimensional monitoring network consisting of satellite remote sensing, an unmanned aerial vehicle, an Internet of Things sensor and an underwater robot; a fusion data set is generated based on space-time alignment and tidal phase marking, dynamic evolution characteristics are extracted by using an LSTM network, and a spatial distribution difference chart of ecological parameters before and after restoration is generated in combination with a GAN; and furthermore, through hot area clustering and historical data association mining, a degradation driving factor is positioned, and an optimization suggestion is generated. The method breaks through the space-time limitation of a traditional evaluation method, remarkably improves the degradation diagnosis precision and the regulation and control efficiency of restoration engineering, and is suitable for various ecological restoration scenes such as estuary delta and coral reefs.

The invention discloses an intelligent building energy-saving optimization platform and method based on carbon footprint tracking, and relates to the technical field of building energy saving and carbon emission management. The method is used for solving the problems of extensive carbon emission evaluation, rigid quota distribution and insufficient energy-carbon collaboration. A three-dimensional carbon density map is constructed by collecting people flow, equipment energy consumption and environment data in real time, and carbon emission hotspots are dynamically identified. And analyzing the association between the power grid and the renewable energy source through a carbon flow tracking model, and correcting a weight output contribution matrix. The characteristics of equipment energy efficiency, building material hidden carbon emission and the like are fused to construct a carbon emission gene entropy, a quota migration strategy is generated in combination with a game algorithm, and oriented transfer from high carbon to low carbon buildings is promoted. A double-ring collaborative framework is constructed, an inner ring chaos search optimization device starts and stops to suppress carbon density fluctuation, an outer ring carbon price mapping adjusts energy storage scheduling, accurate carbon emission tracing, quota dynamic allocation and energy-carbon deep collaboration are achieved, building low-carbon transformation is supported, and the building cluster carbon emission reduction efficiency is improved.

The invention provides a dynamic self-optimization and adaptive learning method for a large model of a water conservancy system, and the method comprises the steps: data collection and preprocessing, model construction and initialization, real-time monitoring and model evaluation, difference analysis and adaptive adjustment strategy generation, model updating and optimization, continuous learning and knowledge accumulation, etc. According to the method, through dynamic data driving, intelligent difference analysis and self-adaptive optimization, conversion of a water conservancy system model from static passive to dynamic active is realized. Compared with the prior art, the method has accuracy, real-time performance, economical efficiency and sustainability, provides a landing technical path for intelligent upgrading of a water conservancy system, and has wide application potential in the fields of flood control and disaster reduction, agricultural irrigation, clean energy production and the like.

The water tank assembly comprises a water tank body and a sealing side wall, the interior of the water tank body is hollow, a containing cavity is formed in the water tank body, the two ends of the water tank body are in a transverse semi-barrel shape, the middle of the water tank body is in a square column shape, and the middle of the upper portion of the water tank body is in a notch shape capable of containing a hinge head assembly to rotate. A water inlet is formed in the top wall of one end of the water tank body; a water outlet is formed in the middle of the lower part of the water tank body; the hinge head clamping cover body of the cleaning head can clamp the hinge assembly between the cleaning head shell body and the water tank body and between the cleaning head shell body and the hinge head clamping cover body through the connecting mechanism. The cleaning head comprises a cleaning head shell assembly, a cleaning roller, an extrusion roller and a water tank assembly, a driving motor is installed on the cleaning head shell assembly to drive the cleaning roller to rotate to wipe the floor, and a clean water outlet in the cleaning head shell assembly can spray out clean water to directly wash the surface of the cleaning roller.

The invention provides a river water quality parameter supervision method and system based on deep learning. The method comprises the steps of self-calibration multi-source data acquisition, diffusive water quality prediction, extreme water quality parameter simulation, reverse diffusion pollution positioning and water quality parameter intelligent supervision. The invention relates to the technical field of river water quality supervision, in particular to a river water quality parameter supervision method and system based on deep learning. By introducing a graph convolutional neural network and a physical diffusion constraint model, space-time diffusion trend modeling of pollutants in a river channel is realized; constructing an extreme pollution event simulation and attribution mechanism by combining a generative adversarial network and physical verification; further adopting a multi-modal Bayesian inversion model and a graph deconvolution structure to realize accurate source tracing of the pollution source; the system can dynamically sense hydrological changes, construct an adaptive threshold judgment mechanism, realize prediction, tracking and response to pollution risks, and provide efficient and intelligent technical support for river ecological safety management.

The invention discloses an underground water infiltration replenishment evaluation method and equipment based on digital twinning, and relates to the technical field of underground water digital twinning evaluation, and the method comprises the steps: traversing a target area to carry out multi-source sensing collection; performing underground water infiltration simulation to generate an infiltration full-process data set, and obtaining an infiltration coupling model; geologic structure parameters of the target area are extracted, and a three-dimensional geologic structure model is constructed; performing association mapping on the infiltration coupling model and the three-dimensional geological structure model to obtain a dynamic simulation result; deviation analysis is carried out, automatic correction optimization is carried out, and an underground water infiltration supply evaluation report is generated. According to the method, the technical problems of insufficient multi-source data fusion, low space-time simulation precision and weak model dynamic correction capability in underground water infiltration supply evaluation in the prior art are solved, and underground water infiltration supply dynamic simulation and digital twinborn automatic correction of multi-source data coupling are realized; and the technical effect of improving the space-time precision of groundwater infiltration supply evaluation is achieved.