3383 results about "Water environment" patented technology

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 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 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 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 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 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 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 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 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.

The invention relates to the technical field of water quality monitoring, and discloses a water quality multi-parameter real-time monitoring system. The system comprises a monitoring point position configuration module, a water quality parameter acquisition module, a pollution index calculation module, an anomaly detection module, a central control unit and the like. The monitoring point position configuration module sets monitoring point positions and generates acquisition rules; the water quality parameter acquisition module acquires a water quality parameter set according to rules; the pollution index calculation module calculates a pollution index; the anomaly detection module obtains an anomaly detection index; and the central control unit comprehensively analyzes and generates water quality evaluation parameters. In addition, the system is further provided with a pollution source tracking module, a storage unit and an alarm module, pollution sources can be tracked, data can be stored, and an alarm can be given according to evaluation parameters. The system realizes multi-parameter real-time monitoring, accurate analysis and evaluation and pollution source tracking, provides comprehensive support for water quality management, effectively improves the water environment monitoring level, and guarantees the safety of water resources.

The invention discloses a boundary detection and positioning method suitable for trans-boundary water pollution, and relates to the technical field of river water quality monitoring. According to data obtained at the longitudinal and transverse positions of a river channel of each monitoring point of a selected drainage basin, a two-dimensional hydrodynamic model of the selected drainage basin is established; then, combining with the obtained target pollutant concentration to construct a diffusion model of the target pollutant, identifying and integrating a to-be-positioned region set in each time period, and screening out the concentration abrupt change regions which are spatially adjacent and consistent in concentration gradient change trend through clustering analysis by utilizing the spatial distribution positions and concentration gradient values of the concentration abrupt change regions; and the boundary contour of the pollutant is generated by fitting in combination with the direction information of the flow velocity field. According to the method, after concentration abrupt change areas identified in each time period are integrated, clustering analysis is carried out according to spatial distribution and gradient change trends of the concentration abrupt change areas, pollution boundary contour fitting is optimized in combination with flow velocity field direction information, and pollution boundary detection requirements in a complex water area environment are met.

The invention provides an underground water environment automatic monitoring super station state supervision method and system. The method comprises the following steps: collecting a multi-source dynamic time sequence data set and carrying out time-space alignment; generating a dynamic characteristic index set by using a nonlinear dynamic characteristic extraction method; based on the index set, constructing a self-adaptive cooperative measurement network to perform anomaly monitoring, and generating an anomaly detection result and a cooperative control instruction; generating real-time monitoring and early warning information by using a dynamic threshold adjustment algorithm; and generating an adaptive control instruction and a dynamic resource allocation scheme by using a neural network adaptive control strategy and a resource scheduling optimization algorithm. According to the method, a dynamic characteristic index set is generated, a C-C method is adopted to reconstruct a high-dimensional phase space, a Wolf algorithm and a G-P algorithm are combined to calculate related indexes and dimensions, and a multi-scale fractal mode is analyzed through an R / S analysis method and wavelet transform. The methods capture complex dynamic behaviors of the underground water system, and solve the problem that the traditional method is insufficient in non-linear feature extraction capability.

The invention discloses a water environment monitoring system and method based on big data analysis, and the system comprises a data collection and processing module which is responsible for collecting and processing original data related to a water environment, and transmits the processed data to a water flow and pollutant diffusion modeling module; and the water flow and pollutant diffusion modeling module is responsible for constructing a hydrodynamic model by utilizing a CFD simulation method based on the processed data, and predicting a pollutant diffusion trend through a convection-diffusion equation. According to the invention, the data analysis and prediction module deeply excavates potential information in acquired data and simulation results through a K-means clustering algorithm and an LSTM algorithm, and realizes accurate prediction of pollutant diffusion and water quality change time series. And the early warning and emergency response module can discover potential water quality pollution events in advance based on the prediction results and the real-time monitoring data, and give out early warning in time, so that the quick response capability of coping with the water quality pollution events is enhanced.

The invention belongs to the technical field of ship bearings, and particularly relates to a ship bearing radial loading test bench and method for simulating ice load and water immersion working conditions, and the method comprises the steps: installing a to-be-tested intermediate bearing on a test platform, enabling the intermediate bearing to sleeve a test shaft, starting a cooling water circulation system to control the flow, cooling water enters a cooling water coil pipe to establish oil pool cooling circulation; a driving motor is started; a test shaft stably operates through equipment such as a reduction gearbox and the like; a dynamic pressure lubrication state is established, a radial dynamic load or a stable constant load is applied through a hydraulic loading system to simulate an icebreaking load working condition, and a simulated water body is injected into a water tank; the test conditions of the intermediate bearing in a ship immersion environment are constructed through water pressure adjustment, the temperatures of lubricating oil, a bearing bush and cooling water are monitored in real time, the loading force is adjusted through feedback data, and the performance of the intermediate bearing is judged after the test is finished. The problems that ice excitation and soaking environment simulation are insufficient and a loading structure is eccentric in the prior art are solved, and high-precision cooling and thermal stability evaluation are achieved.

The invention discloses a drainage basin water environment intelligent management method and system, and belongs to the technical field of environment monitoring and water resource management. Multi-source heterogeneous water environment data is obtained, and a multi-modal pollution sensing map is constructed through fusion; extracting a time mutation vector and a spatial offset path of a pollution abnormal diffusion source point based on the map, and generating a pollution propagation sensitive weight map; performing cross-space-time matching on the sensitive weight map and historical climate disturbance data, generating a pollution causal chain path map by using a deep causal learning model, identifying a potential hidden pollution source distribution unit, predicting a response trend of the potential hidden pollution source distribution unit to future water quality change, and forming a multi-scene pollution evolution simulation result; and finally, constructing a pollution response priority index map, and generating a treatment resource optimal allocation sequence and an intervention strategy in combination with the drainage basin management database. According to the invention, intelligent identification and dynamic treatment optimization of complex pollution diffusion behaviors can be realized, and scientificity and timeliness of water environment management are improved.

The invention discloses a water environment pollution water treatment and purification device and a purification method thereof, and belongs to the technical field of water treatment, the water environment pollution water treatment and purification device comprises a water inlet tank, a treatment tank is fixedly connected to one side of the water inlet tank, a water outlet tank is fixedly connected to one side of the treatment tank, and water pumps are fixedly connected to the water inlet tank and the water outlet tank; the input end of the water pump is fixedly connected with a water pumping pipe. According to the conveying device, the first motor drives the transmission wheels to rotate, the transmission wheels drive the belt and the other transmission wheel to rotate synchronously, then the connecting shaft rotates, the connecting shaft drives the reciprocating lead screw to move in the fixing plate, the reciprocating lead screw pushes the threaded cap to move, the threaded cap drives the transverse plate to move synchronously, and then the conveying cylinder moves stably in the horizontal direction. Chemical agents can be evenly distributed in the treatment box, the chemical agents and sewage are fully mixed through continuous stirring of the stirring blades, the purification effect is achieved, and finally treated water is sucked into the water outlet box through the water pump and discharged through the water outlet valve.

The invention discloses an AI-driven unattended water environment quality monitoring system, and relates to the technical field of water environment monitoring, and the system comprises a data collection module, a cloud processing module, an edge calculation module, an intelligent analysis and early warning module, and a system management module. Through comprehensive and real-time intelligent monitoring of a water body, monitoring of water quality physical indexes is concerned, aquatic organism behavior monitoring is introduced, so that the health condition of the water body can be reflected more comprehensively, and meanwhile, through the data preprocessing and local model fine tuning functions of the edge calculation module, the water quality monitoring accuracy is improved. The intelligent analysis and early warning module can perform correlation analysis, set an early warning threshold value and generate early warning information based on the AI model after cloud side end collaborative optimization, and provides powerful decision support for water quality management.

The invention belongs to the technical field of pollution source detection, and discloses a pollution source detection method and system for water environment treatment. Comprising the steps that acoustic disturbance data are collected through an underwater acoustic sensor, real-time edge preprocessing is carried out, water environment data of different depths are synchronously collected, water environment gradient data are calculated, and then data synchronization is achieved through a unified timestamp. Fourier transform is adopted to extract frequency domain characteristics of the sound disturbance signals, the main frequency, the energy difference and the frequency spectrum skewness are calculated, and disturbance sound wave indexes are generated according to the main frequency, the energy difference and the frequency spectrum skewness. In combination with disturbance sound wave indexes and water body environment gradient data, a sliding window is adopted to construct a time sequence sample set, a recurrent neural network model for predicting future pollution release risks is trained, differentiated pollution release threshold values are set according to different water body types, and when model output exceeds the threshold values, sediment pollution early warning instructions of corresponding levels are automatically generated. And efficient and graded pollution event intelligent identification and response are realized.

The invention provides an intraday high-frequency automatic monitoring method and system for lake cyanobacterial bloom based on a GOCI-II satellite, and aims to solve the problems that an existing method is susceptible to interference of thin cloud, low in recognition precision, high in false positive rate, low in efficiency and the like, and the processing flow depends on manpower. The system is based on an improved AFAI index, fine cloud detection, cloud expansion processing and classification correction strategies are combined, misrecognition caused by thin clouds and shadows is effectively restrained, and the extraction accuracy and robustness are improved. The system has the whole-process unattended processing capacity from GOCI-II data automatic downloading, preprocessing, algal bloom recognition, thematic map making to report output, can generate a monitoring report within one hour after satellite imaging, and supports intra-day multi-temporal cyanobacterial bloom dynamic monitoring. The method has been successfully applied to lakes such as Taihu Lake, lakes, Chaohu Lake and Hongze Lake, is particularly suitable for monitoring and early warning cyanobacterial bloom in large and medium lakes, and has wide application prospects in the fields of water environment supervision, water quality risk control, ecological assessment and the like.

The invention discloses an aquatic vegetation classification method and device based on a remote sensing image and a storage medium. The aquatic vegetation classification method comprises the following steps: (1) constructing a deep convolutional neural network, namely a CAD-Unet model; (2) generating an aquatic vegetation classification data set including preprocessing, cutting and data enhancement, and dividing the aquatic vegetation classification data set into a training set, a verification set and a test set; (3) training the CAD-Unet model by adopting an end-to-end training strategy, and evaluating and verifying the trained model; and (4) performing block prediction on the to-be-classified remote sensing image, and outputting an aquatic vegetation classification result. According to the method, a deep neural network of a CAD-UNet structure is constructed, the network is composed of an encoder module, a bottleneck module and a decoder module, the classification precision of aquatic vegetation in a complex water area environment can be improved, the detection effect of large and small targets is considered, and powerful technical support is provided for real-time remote sensing image aquatic vegetation monitoring.