4229 results about "Water environment" patented technology

The invention relates to the technical field of hydraulic engineering monitoring control, and discloses an automatic monitoring method and system for ecological hydraulic engineering. The method comprises the steps that water body parameters are collected through a sensor and preprocessed to obtain standard monitoring data; performing noise reduction alignment and correlation analysis on the monitoring data to obtain an optimized data set and a feature library; evaluating the water environment state according to the data set and the feature library to obtain an evaluation index and an early warning signal; analyzing the treatment scheme according to the index and the signal to obtain a regulation and control instruction; adjusting equipment operation parameters according to the instruction to obtain operation state data; and performing cycle analysis and strategy optimization based on the state data to obtain regulation and control parameters. Multi-dimensional correlation analysis of water quality parameters is realized, an accurate water environment regulation and control scheme is formulated on the basis, and the pertinence and efficiency of water quality improvement are improved.

The invention relates to the technical field of environment monitoring, and discloses a water ecological pollution diffusion prediction method and system based on big data, and the method comprises the steps: constructing a sensing network of a water environment, obtaining real-time water environment data, carrying out the preprocessing of the data through an edge calculation node, and synchronously transmitting the data to a central platform; based on historical data and real-time water body environment data, establishing a dynamic prediction model of pollutant boundary condition parameters; according to the dynamic boundary condition parameters, constructing a pollutant propagation atlas, analyzing the spatial and temporal distribution trend of pollutant concentration and identifying the position of a pollution source; and according to a pollution source position identification result, executing aging prediction, and triggering a pollution early warning response in combination with a set threshold value. The space coverage rate and timeliness of pollution monitoring are improved, the intelligence and credibility of pollution tracking are enhanced, and the intelligent and scientific level of water ecological environment pollution prevention and control is improved.

The invention discloses a water environment pollution monitoring management method and system based on big data, and particularly relates to the technical field of water environment monitoring. A drainage basin is divided into a pollution influence main control area, a pollution diffusion boundary area and a pollution interaction transition area; extracting high-frequency pollution factors by using a big data clustering algorithm, constructing a high-frequency pollution factor incidence matrix, establishing a pollution diffusion path prediction model in combination with historical flow direction data and a time sequence analysis technology, and outputting a potential pollution propagation area and intensity; the current pollution diffusion state is analyzed through dynamic matching of real-time monitoring data and the prediction model, and a pollution diffusion real-time prediction result is generated; according to the method, the pollution risk level of each area in the drainage basin is evaluated based on the prediction result, the partition pollution early warning information is generated, the historical treatment case data is combined, pollution prevention and control and treatment suggestions are provided, precision and intelligence of water environment pollution monitoring are achieved, support is provided for water pollution treatment, and the method has remarkable environmental protection benefits and practical application value.

The invention relates to the technical field of water quality monitoring, in particular to a mining area groundwater environment monitoring system and method, and the system comprises a mining area groundwater partition recognition module, a groundwater pollution risk assessment module, a groundwater monitoring point optimization module, a groundwater pollution path analysis module and a groundwater dynamic monitoring planning module. According to the invention, through data-driven zoning and risk assessment processes, fine and dynamic management is realized in underground water environment monitoring, functional zone zoning boundaries are accurately calibrated, understanding of an underground water flowing mode is enhanced, pollution source control is more targeted, diffusion risk intensity is assessed in combination with flow velocity and pollutant release intensity, and the risk assessment accuracy is improved. The risk management strategy is optimized, the pollution diffusion range is predicted and identified more efficiently, the emergency response accuracy is improved, monitoring point arrangement is optimized through risk data, reasonable monitoring resource allocation is ensured, the sampling task weight and the time interval are dynamically adjusted, and resource optimization and monitoring efficiency improvement are achieved.

The invention discloses an amphibious unmanned aerial vehicle riverway sediment concentration real-time monitoring method based on underwater light field imaging and cross-modal fusion, and relates to the technical field of riverway sediment monitoring. Comprising the following steps: acquiring aerial multispectral data and preprocessing the aerial multispectral data; an integrated polarized light field camera is carried on an amphibious unmanned aerial vehicle to capture underwater light field data, and underwater three-dimensional light field information is reconstructed and sediment scattering characteristics are extracted through an optical model and a lightweight deep learning network; the air multispectral data and sediment scattering characteristics are fused through space-time alignment and an attention mechanism, a sediment concentration prediction model is generated, and then a spatial distribution diagram of the river sediment concentration is output; and carrying out real-time monitoring and early warning on the river sediment concentration according to the spatial distribution diagram of the river sediment concentration through edge calculation and cloud collaboration architecture. The method is combined with an advanced deep learning algorithm, high-resolution silt concentration inversion can be realized, and the method has real-time performance and adapts to a complex water body environment.

The invention discloses a method and system for improving satellite remote sensing inversion precision by fusing hyperspectral proximity, and belongs to the technical field of water environment and water ecology monitoring. Acquiring a water body hyperspectral near-sensing reflectivity spectral data set, a satellite remote sensing surface reflectance data set and a water environment actual measurement data set, and performing preprocessing; a synchronous data set containing # imgabs0 # and water quality and a synchronous data set containing # imgabs1 # and water quality are constructed; based on the synchronous data of the # imgabs2 # and the water quality, synchronous data of the # imgabs3 # and the water quality are generated, and synchronous data of the # imgabs4 # and the # imgabs5 # are obtained; the # imgabs6 # is normalized to obtain # imgabs7 #, and the quantitative conversion relation between the # imgabs7 # and the # imgabs8 # is constructed band by band to eliminate errors; generating a fused satellite-ground data set, and constructing a training water quality parameter inversion model; and updating corresponding methods and data sets in real time by repeating specified steps, and optimizing the model. According to the method, the inversion result is displayed and downloaded according to the query instruction, the satellite spectrum precision and the satellite-ground synchronous data set scale are improved, and the method is used for high-precision water environment monitoring.

The invention relates to the technical field of water environment risk monitoring, in particular to a method for evaluating the algal bloom risk of a water body. The method comprises the following steps: collecting historical monitoring data of a to-be-evaluated water body, wherein the historical monitoring data comprises blue-green algae abundance data, water quality data and hydrological data; analyzing the correlation between the cyanobacteria abundance or chlorophyll a concentration and the water quality and hydrological data of the to-be-evaluated water body; hydrological and water quality parameters with the highest correlation with the cyanobacteria abundance or chlorophyll a concentration are screened out; hydrology and water quality parameters of a water body to be evaluated are taken as predictive variables, and cyanobacteria abundance or chlorophyll a concentration is taken as a response variable to construct a Bayesian network model; the weight of each parameter in the Bayesian network model is calculated, and the algal bloom risk probability that the cyanobacteria abundance exceeds a specific threshold value under the given parameter condition is calculated according to the weights. According to the invention, the scene-based probability deduction of the stable period and the dynamic period is realized through the double-branch Bayesian network model, so that the accuracy and timeliness of algal bloom risk assessment are improved.

The invention discloses a pollution research and water quality prediction method based on river segmented water environment capacity analysis. The method comprises the following steps: accurately dividing a drainage basin into water environment capacity accounting units; obtaining a river water body image through satellite remote sensing; identifying and quantifying the positions and the intensities of point source and non-point source pollution sources in the drainage basin by utilizing the water quality parameters of the river obtained by the remote sensing image; establishing a river water quality prediction model; calculating the water environment capacity of each river reach under different surface water environment quality standards; comparing the estimated pollutant discharge amount with the water environment capacity under each water quality standard, and judging whether the pollutant discharge amount exceeds the capacity or not; and predicting future water quality change by using the model. According to the method, remote sensing, GIS and machine learning technologies are utilized, rapid analysis of the river segmented water environment capacity and accurate prediction of the water quality are achieved, dependence on a large amount of field sampling and complex software simulation is reduced, and the working efficiency is greatly improved.

The invention belongs to the field of deep sea corrosion experiment equipment, and particularly relates to a multivariable coupling control deep sea corrosion environment simulation and monitoring device, which comprises an environment control unit, a deep sea corrosion environment simulation unit, a deep sea corrosion environment simulation unit, a deep sea corrosion environment simulation unit and a deep sea corrosion environment monitoring unit, and is characterized in that the environment control unit comprises a seawater environment cabin, a test sample is vertically arranged in the seawater environment cabin, and two ends of the test sample penetrate through and extend to the outer side of the seawater environment cabin; the pressure control system and the temperature control system are connected with the seawater environment cabin through sealing flanges and are respectively used for simulating deep sea hydrostatic pressure and a low-temperature environment; the mechanical loading system is used for bearing the seawater environment cabin and clamping the two ends of the test sample through the sample clamping system so as to apply a multi-axis dynamic mechanical load; the gas regulation and control unit is used for controlling the concentration of dissolved oxygen in the seawater environment cabin; and the multi-source monitoring module is used for collecting detection data and analyzing the detection data to obtain the corrosion rate of the test sample. The single-variable control limitation of a traditional device is broken through, and a high-fidelity experiment platform is provided for deep sea material corrosion mechanism research and equipment life evaluation.

The invention discloses a marine water environment abnormal state secondary and derivation probability modeling and estimating method, which belongs to the field of state modeling estimation, and comprises the following steps: acquiring real-time data flow of marine environment parameters, performing segmentation processing by adopting a sliding window mechanism, and reducing data dimension by utilizing principal component analysis. Then, constructing a graph structure model based on a time sequence, and capturing correlation and a space-time dependency relationship among parameters; and performing nonlinear mapping on the graph structure model by adopting a radial basis function core, and capturing complex nonlinear characteristics among the marine environment parameters. And finally, classifying the abnormal states by using a multi-class support vector machine algorithm, and outputting secondary and derivative probability estimation results of the abnormal states in real time. According to the method, abnormal events in the marine environment can be found in time, dynamic modeling support can be provided for marine environment monitoring, and the accuracy and real-time performance of marine environment monitoring are remarkably improved.

The invention relates to the technical field of information, and discloses a multi-factor space-time coupling water environment prediction method, which comprises the following steps: acquiring water environment internal data and water environment external data; preprocessing the water environment internal data and the water environment external data to obtain preprocessed multi-source data; the preprocessed multi-source data serve as a training set to be input into a random forest regression model, importance selection is conducted on the multi-source data, an influence factor set with the minimum error of the random forest regression model is selected, and the influence factor set serves as an optimal feature selection set; and taking the optimal feature selection set as a training set, inputting the training set into a least square support vector machine model, training the least square support vector machine model, obtaining a multi-factor coupled water quality prediction model, and obtaining a water environment prediction result based on the multi-factor coupled water quality prediction model. According to the method, the dimensionality of the least square support vector machine training model is reduced, and the prediction precision is improved.

The invention belongs to the technical field of environmental water quality pollution monitoring, and particularly relates to a drainage basin water environment monitoring and emergency pollution rapid tracing method and system based on water quality pollution detection, and the method comprises the following steps: constructing a multi-parameter cooperative monitoring model, and carrying out the training and deployment; through distributed sampling points and sampling stations, real-time data with verification marks and time-sharing data of watershed water environment water quality pollution detection are collected; mLP and LSTM / GRU models are adopted, EEM spectrum data and environment characteristics are fused, and multi-source heterogeneous data are fused and analyzed; whether an emergency pollution event occurs or not is automatically identified according to a preset condition, the pollution types and traceability results of water environment monitoring and emergency pollution are automatically output, manual further checking is carried out, and environmental law enforcement checking is carried out. According to the invention, drainage basin water environment pollution condition monitoring and emergency pollution rapid tracking and tracing can be completed in a large-range, low-cost and high-efficiency manner so as to support environment law enforcement.

The invention relates to the technical field of ecological restoration prevention and control, in particular to a sea-entering river pollution source targeted blocking and ecological restoration integrated prevention and control system which comprises a pollution source recognition unit, a targeted blocking unit, an ecological restoration unit and a cooperative control platform. The recognition unit combines a multispectral unmanned aerial vehicle with a water quality sensor and positions a pollution source through artificial intelligence traceability; the targeted blocking unit implements physical-chemical combined blocking according to pollution types; the ecological restoration unit purifies water and reconstructs ecology through a modular constructed wetland and the like; the cooperative control platform optimizes each unit strategy based on hydrological data; each unit correlation formula accurately calculates parameters, such as a recognition algorithm, a gate dam opening control formula and the like. According to the invention, multi-technology fusion and system collaboration are realized, pollution events can be quickly responded, measures are accurately applied, the sea-entering river pollution prevention and control efficiency is effectively improved, the treatment cost is reduced, and drainage basin water environment ecological restoration and sustainable development are promoted.

The invention relates to a water quality prediction method and system based on gating residual enhancement and feature fusion, and belongs to the technical field of water environment intelligent analysis and deep learning. Taking each water quality index as a node of the graph, and constructing two complementary variable relation graph structures by utilizing a Pearson's correlation coefficient and mutual information; respectively inputting the two graph structures into a graph convolutional network, extracting deep dependency features among indexes, and splicing and fusing the deep dependency features. A multi-head attention mechanism is used as a trunk to extract global time dependence, a GRU network is introduced to extract local time sequence features, GRU output is used as an adjustable residual term to be injected into the attention trunk through a residual gating mechanism, self-adaptive enhancement of local dynamic features is achieved, and finally a self-adaptive fusion mechanism is introduced to generate comprehensive representation. According to the method, the complex dependency relationship between the water quality indexes and the time dynamic evolution process can be modeled in a collaborative manner, the response capability to key local change and sudden change events is remarkably enhanced, and the accuracy and robustness of water quality prediction are improved.

The invention discloses an underwater propeller control method and system based on water body environment analysis. The method comprises the following steps: acquiring image data and laser point cloud data of a target water body, and constructing a three-dimensional environment model; initial position information and a target position of the underwater propeller are obtained, and a working path is generated based on environment model analysis; doppler sonar data on a path are collected, water flow information is analyzed, and a deviation correction control instruction is generated; and the instruction transmission quality is analyzed, and an optimal transmission scheme is determined. According to the method, dynamic modeling and analysis of a complex water body environment are realized by fusing multi-source environment sensing data, the path planning precision and autonomous control capability of the underwater propeller are improved, and the method is suitable for precise navigation and dynamic obstacle avoidance scenes in complex underwater operation tasks.

The invention relates to the technical field of remote sensing, and discloses a remote sensing ecological index assessment method based on deep learning, which comprises the following steps: acquiring Landsat remote sensing image data and land utilization data of a research area, and extracting remote sensing ecological index factors of the land utilization data and the preprocessed Landsat remote sensing image data; normalizing the remote sensing ecological index factors and performing dimension reduction analysis to obtain dimension-reduced remote sensing ecological index factors, inputting the dimension-reduced remote sensing ecological index factors into a time sequence convolutional network model, and generating a comprehensive feature vector through causal convolution, extended convolution and global average pooling operations; performing linear combination on the comprehensive feature vector and the principal component analysis feature value, and constructing a time sequence convolutional neural network remote sensing ecological index model for evaluating the ecological environment quality condition of the research area; according to the method, the accuracy, the reliability and the practicability of remote sensing ecological index evaluation are improved by adding the remote sensing ecological landmark factors reflecting human disturbance and water environment change and constructing the time sequence convolutional neural network remote sensing ecological index model.

The invention discloses a water quality monitoring method and system based on artificial intelligence. The method comprises the following steps: acquiring a comprehensive data set composed of sensor data, satellite images and meteorological parameters; according to the water flow velocity and pollution concentration gradient in the comprehensive data set, adopting a dynamic sampling algorithm to adjust the sampling frequency and position, and outputting adjustment data; performing space-time interpolation processing on the adjusted data to obtain a preprocessed data set with high-density space-time coverage; key features of sensor values, image textures and meteorological parameters are extracted from the preprocessed data set by adopting a principal component analysis method, a weighted feature matrix is constructed, and a fusion feature set is obtained; and judging whether the dimension of the fusion feature set exceeds a preset threshold value, if the dimension of the fusion feature set exceeds the preset threshold value, performing dimension reduction and classification on the fusion features by adopting a random forest algorithm, and optimizing model parameters through cross validation to obtain a pollution concentration prediction result. Effective technical support is provided for water environment protection, and important ecological and social benefits are achieved.

A water pollution risk early warning and tracing method based on multi-source data fusion belongs to the technical field of water pollution monitoring and early warning, and comprises the following steps: step 1, constructing a multi-source heterogeneous data acquisition network and realizing real-time data transmission; 2, performing multi-source data fusion and feature enhancement processing based on space-time reference; 3, constructing a water pollution risk dynamic early warning system based on a WOA-LSSVM model; fourthly, reverse positioning of the pollution source is completed on the basis of a CNN-GRU-SE Attention model; and 5, carrying out development and emergency response on a multi-dimensional visual decision support system, and positioning a pollution source. Multi-source information such as water quality sensor data, unmanned aerial vehicle image data and geographic information data is fused, and an intelligent monitoring network is constructed; through multi-source data fusion and an intelligent algorithm, water pollution risk early warning accuracy and traceability efficiency are effectively improved, and the method has the advantages of high response speed, wide monitoring range, accurate positioning and the like, can be widely applied to the fields of urban water supply, drainage basin management and the like, and meets the requirements of water environment safety guarantee.

The invention provides an optical complex water body suspended sediment monitoring method based on man-machine cooperation, and belongs to the technical field of remote sensing water environment monitoring. The method comprises the following steps: acquiring a multispectral remote sensing image of a target area, preprocessing the multispectral remote sensing image, and performing space-time fusion to obtain a spectral data set with high space-time resolution; carrying out manual labeling on the spectrum data set, constructing a suspended sediment concentration classification data set, and carrying out synthesis expansion on a minority class of samples to generate an equalization training set; training a suspended sediment concentration inversion model by using the equalized training set to obtain an initial suspended sediment concentration predicted value, and dynamically optimizing the spectral feature weight and the classification boundary of the suspended sediment concentration inversion model based on expert knowledge; and locally correcting the initial suspended sediment concentration predicted value and diffusing the initial suspended sediment concentration predicted value to a global range based on a hydrodynamic law and a space consistency constraint to obtain a global suspended sediment concentration predicted value. The method combines remote sensing image analysis, machine learning and expert knowledge to realize high-precision inversion of SSC.

The invention discloses a water environment dynamic pollution monitoring and atmospheric diffusion prediction method based on deep learning, and the method comprises the following steps: S1, collecting multi-source monitoring data of a water environment and an atmospheric environment, and constructing a pollution time series data set and an atmospheric meteorological data set; s2, constructing a variational auto-encoder model; s3, jointly optimizing a variational auto-encoder model structure and hyper-parameters by using a hosting crab optimization algorithm; s4, the optimized variational auto-encoder model is used for training, and potential variables of the pollution state are extracted; s5, constructing a diffusion modeling network, and outputting a diffusion prediction result; and S6, comparing a diffusion prediction result with a pollution threshold value, and triggering early warning information output when the diffusion prediction result exceeds the standard. According to the invention, intelligent monitoring of the water environment pollution state and accurate prediction of the diffusion behavior of pollutants to the atmosphere are realized, and efficient decision support is provided for environment early warning and treatment.

The invention relates to the technical field of water ecological environment remote sensing monitoring, and provides a water body turbidity remote sensing inversion method and system, a medium, equipment and a program. The water body turbidity remote sensing inversion method comprises the following steps: converting actually measured hyperspectral remote sensing reflectivity into satellite equivalent remote sensing reflectivity, and combining each wave band of the satellite equivalent remote sensing reflectivity to obtain a wave band combination; calculating a Pearson correlation coefficient between each single wave band and each wave band combination and the turbidity data, and screening the wave band combination meeting a set threshold value; encoding the wave band combination and the water environment data meeting a set threshold value, respectively obtaining wave band features, time features and water body type features, and constructing feature vectors; and by taking the feature vector as input and the turbidity data as output, constructing an objective function, and training a turbidity remote sensing inversion model, so as to minimize the objective function and select an optimal hyper-parameter combination in each round of iteration. The method can improve the prediction precision and reliability.

The invention discloses a multi-wave detection and imaging system for fish schools, which relates to the field of detection and imaging of fish schools and comprises a metamaterial acoustic emission module, a self-adaptive signal receiving module, a three-dimensional point cloud generation module, an image enhancement and generation module and an intelligent decision control module. According to the multi-wave detection and imaging system for the fish school, clustering parameters can be dynamically adjusted based on the real-time fish school density, the neighborhood radius is automatically expanded in a high-density area to avoid segmentation errors, and the core point judgment threshold is reduced in a low-density area to reduce missing detection; an acoustic attenuation model is constructed in combination with water body environment parameters, emission parameters are calibrated in real time, images can be enhanced, space and time dimension features are fused, the image resolution and definition are improved, accurate recognition of multiple fish species is achieved, the fish species can be better distinguished, meanwhile, a generative network is adopted to directly convert acoustic data into visual images, and the recognition accuracy is improved. Therefore, the generated image can reflect the actual distribution condition of the fish school more truly and accurately.

The invention relates to the technical field of water quality monitoring, in particular to a surface water environment online monitoring method which comprises the following steps: acquiring water quality monitoring data of urban pipe network nodes, establishing a pipe network topological structure, adjusting the topological structure in combination with a water flow direction, determining the water flow direction, performing time segmentation on the topological structure, and calculating topological change frequency. And establishing a pipe network topology evolution structure. According to the method, the topological change frequency is calculated in combination with time segments, pipe segments with obvious changes are screened, and the continuous diffusion condition of pollutants is judged by removing trend influence, extracting stable water quality characteristic information, screening continuous abnormal points and analyzing the pollution diffusion trend in combination with the topological relation in the screening of abnormal fluctuation pipeline connection. The pollution influence range is combined with concentration changes, key diffusion channels are marked, pollution duration is combined, and pollution source candidate points are screened, so that water quality monitoring can adapt to pipe network changes, and the real-time performance and the intelligent level of an urban pipe network water quality monitoring system are enhanced.

The invention discloses a water body cleaning equipment path optimization method based on deep reinforcement learning, and the method comprises the following steps: S1, collecting water body environment data through a sensor, and carrying out the preprocessing; s2, establishing a state space and an action space for path optimization, and constructing a reward mechanism; s3, carrying out feature extraction on the preprocessed data by adopting a Transform network; s4, training the strategy network and the value network by using an improved trust region strategy optimization algorithm, optimizing an update step length of the strategy network based on KL divergence constraint, and optimizing a path planning strategy based on a strategy gradient method; s5, performing path planning by using the trained strategy network and value network; and S6, calculating the energy consumption of the water body cleaning equipment, and adjusting the operation mode or operation path of the water body cleaning equipment. According to the method, the path planning of the water body cleaning equipment is optimized by combining Transform and an improved trust region strategy optimization algorithm, and the method has the advantages of being high in environmental adaptability, low in energy consumption, high in cleaning efficiency and high in operation stability.

The invention relates to a lake water quality real-time monitoring method and system based on a sensor array. The method comprises the steps that the sensor array is arranged in a target lake, and data acquisition frequency parameters are set; acquiring working parameters of the sensor array in real time, analyzing the working parameters, determining health states of the sensors, and performing adaptive adjustment on the sensors based on the health states of the sensors; collecting water quality monitoring data in real time based on the adjusted sensor array, performing preprocessing and quality control verification on the water quality monitoring data, and constructing a monitoring data set with qualified quality control; and comparing each water quality index in the monitoring data set with qualified quality control with a preset environmental standard limit value, marking a limit value exceeding event, and performing early warning, thereby completing real-time monitoring of the water quality of the target lake. According to the invention, intelligent real-time monitoring, abnormity identification, data quality control and early warning response of lake water quality are realized, and comprehensive technical support is provided for water environment management.

The invention discloses a water environment management system based on cloud computing, and relates to the technical field of environment monitoring. In order to solve the problems that in the prior art, water environment emergencies cannot be responded in real time, the requirement for rapid early warning cannot be met, regular manual data monitoring is limited by manpower, material resources and environmental conditions, high-frequency and large-range coverage cannot be achieved, and therefore the continuity and integrity of monitoring data are affected. Monitoring data are collected and transmitted to the cloud computing platform in real time, continuous and high-frequency monitoring of the water environment is achieved, the water quality change can be rapidly recognized through the instant analysis capacity of the cloud computing platform, abnormal key monitoring points are accurately recognized, the data collection and transmission time interval is dynamically adjusted according to the abnormity severity degree and the influence range, and the monitoring accuracy is improved. Therefore, key information can be acquired in time, unnecessary data transmission is avoided, resources are saved, abnormal conditions are tracked and managed, and problems can be effectively solved.

The invention discloses a three-dimensional visual management system and method for groundwater resources, and relates to the field of groundwater resource management and pollution control, and the system comprises a groundwater data obtaining module which is used for obtaining groundwater dynamic monitoring data of a target area in real time; the underground water environment digital twinborn model construction module is used for constructing an underground water environment digital twinborn model based on the underground water dynamic monitoring data; the underground water change and pollution control simulation module is used for carrying out numerical simulation on underground water resource and pollutant migration based on the underground water environment digital twinborn model, carrying out pollution control area division and formulating a corresponding underground water remediation scheme; and the decision support and knowledge management module is used for integrating groundwater dynamic monitoring data, the groundwater environment digital twinborn model, the divided pollution control area and the corresponding groundwater remediation scheme, and realizing data storage, three-dimensional display, data evaluation and user interaction. According to the invention, efficient management and pollution control of underground water resources can be realized.

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

The invention relates to the technical field of drainage basin water environment management, in particular to a construction method of a water pollution source emission list. The method comprises the following steps: performing classification and account subdivision on different types of pollution sources based on pollution source sub-categories, pollution production modes, treatment technologies and pollution discharge paths to obtain a pollution source classification and classification system; a multi-source data fusion strategy and a sparse data processing strategy are utilized to carry out parameter fine value processing on data in the pollution source grading and classification system, and calculation parameters of different types of pollution sources are determined; performing modeling characterization on the calculation parameters of the different types of pollution sources according to a pollutant generation and emission mechanism to obtain the river entering quantity of different types of pollutants of the different types of pollution sources, and constructing a water pollution source emission list based on the river entering quantity. Therefore, the problems of difficulty in full-aperture refined estimation of the pollution source emission list, relatively low space-time precision of the list and the like are solved, and refined calculation of pollution source emission is realized.

The invention provides a water environment monitoring system and method, and the method comprises the steps: preliminarily screening an abnormal region with an abnormal water environment from all local monitoring regions through the information difference degree of each piece of water spectral data, and extracting the water spectral features of each sensor node in the abnormal region; performing spectrum fusion on all the water body spectrum characteristics according to the topological incidence relation among the sensor nodes in the abnormal area, and further determining the fusion state quantity of water quality pollution in the abnormal area; interference characteristics of the spectrum sensor in the external environment in the abnormal area are obtained, correlation analysis is further carried out on the interference characteristics of the external environment and the fusion state quantity of the water quality pollution, and then the abnormal credibility of the water quality pollution in the abnormal area is determined according to an analysis result. And whether water quality abnormity exists in the water environment monitoring area is judged through the abnormity credibility. By adopting the scheme of the invention, multi-level anomaly analysis of the water environment monitoring area can be realized, so that the reliability of a monitoring result is improved.