6119 results about "Water body" patented technology

The invention relates to a water quality change trend rapid prediction method based on multi-source data fusion and physical constraint, and the method specifically comprises the following steps: 1, synchronously collecting spectral information, DO, COD, temperature, pH and other data at a key monitoring station, constructing a hydrodynamic water quality coupling equation, simulating the spatial-temporal dynamic distribution of water quality parameters, and calculating the water quality change trend; 2, outputting a water quality sensitive area through a hydrodynamic force-water quality model, screening sensor layout point positions in combination with information entropy evaluation and spatial clustering, realizing low-cost water quality sensor network deployment through a multi-objective optimization algorithm, and calculating a water body global water quality distribution diagram by adopting a spatial interpolation method, 3, synchronously collecting spectral information according to key monitoring sites, analyzing main pollution sources, and adopting a principal component analysis and attention mechanism neural network; 4, based on real-time optical characteristic value-DO data, in combination with a spatial topology network, a water quality gradient and a cross-regional covariance, capturing water quality parameter spatial correlation among different sites, and determining the water quality parameter spatial correlation among different sites; an optical characteristic value-DO-COD dynamic prediction model is constructed; a COD predicted value is corrected by combining pollution traceability and spectral characteristics, and multi-source data is assimilated by adopting ensemble Kalman filtering, so that the model precision is improved.

The invention discloses an intelligent water quality regulation and control system and method based on multi-parameter real-time monitoring, and belongs to the technical field of water quality monitoring. According to the intelligent water quality regulation and control system, the water quality data of the water body is obtained in real time through the multi-parameter sensor array, and the monitoring regulation and control server can quickly generate an abnormal report and a water quality regulation and control scheme. The data processing module performs feature extraction on the water quality data to obtain target features; the water quality evaluation module is used for accurately evaluating the water quality by using a pre-trained deep neural network model; the abnormity identification module can timely judge whether the water quality has a pollution risk and generate an abnormity report; and the regulation and control module generates a water quality regulation and control scheme by adopting a multi-objective optimization algorithm. The system realizes real-time performance, accuracy and intelligence of water quality monitoring, can quickly respond to water quality changes, effectively reduces pollution risks, and improves the efficiency and effect of water quality regulation and control.

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention relates to the technical field of water pollution traceability, and particularly discloses a river pollutant traceability system and method based on digital twinning, and the system comprises a water body sampling module which is used for obtaining the current water quality data of a preset region of a target river; the model construction module is used for constructing a digital twinborn model according to the river topographic data and the current water quality data and by fusing the historical hydrological data, the real-time meteorological data and the drain outlet distribution topological graph; the data analysis module is used for performing spatial-temporal feature extraction on a pollutant diffusion path in the digital twinborn model by utilizing a graph convolutional neural network, applying dynamic correction pollution source position probability distribution in combination with Bayesian inference, and generating a traceability path thermodynamic diagram; and the pollution traceability module is used for obtaining a pollutant traceability result according to the confidence coefficient threshold of the traceability path thermodynamic diagram. According to the method, the efficiency and the accuracy of tracing the river pollutants in the complex dynamic environment can be remarkably improved, and technical support is provided for ecological safety and precise treatment of a drainage basin.

The invention relates to the technical field of hydrological monitoring, in particular to a multi-satellite collaborative hydrological monitoring system. The method comprises the following steps that a water level height measurement module obtains radar pulse recovery time delay data through a height measurement satellite and calculates the water level height, abnormal points are removed to generate river water level data, a water remote sensing module collects river water images through a remote sensing satellite to extract water boundary features, water area change data are deduced, and the water level height measurement module calculates the water level height. The meteorological wind shear analysis module obtains river channel meteorological data through a meteorological satellite, microwave scattering measurement and wind speed inversion are carried out, wind-induced shear stress parameters are generated, and the flow estimation module carries out section dynamic analysis and estimates the water flow in combination with river channel water level and water area data. And the flow correction module corrects the river water flow based on the wind-induced shear stress parameter and carries out real-time monitoring, and data are synchronously uploaded to the control terminal, so that comprehensive dynamic monitoring of the hydrological state of the river is realized. According to the invention, a more efficient multi-satellite collaborative hydrological monitoring system is realized.

The invention discloses a water body new pollutant risk assessment method and system. Belongs to the technical field of environmental science. Comprising the steps of collecting multi-source heterogeneous data to generate an initial data set; performing data fusion in the initial data set by using a weighted Bayesian fusion algorithm and a space-time interpolation method to generate a target data set; importing the target data set into a deep learning model for prediction and obtaining a prediction result; and obtaining a preset early warning condition and combining with the prediction result to judge whether risk early warning needs to be carried out. Through constructing a pollutant data acquisition system and data fusion, the monitoring precision of the new pollutants in the water body is improved. The fusion algorithm is used for data preprocessing, and pollutant detection is comprehensive. And the space-time diffusion process of the pollutants can be accurately modeled.

The invention relates to the technical field of water quality detection, and discloses a water quality pollutant detection method, system and equipment based on spectral analysis, and the method comprises the following steps: carrying out real-time spectral data acquisition on a water body sample in a vehicle driving process through a multispectral sensor array in a vehicle-mounted water quality detection device; obtaining a water body spectrum three-dimensional data cube; carrying out anti-vibration wavelength calibration and ambient light source interference elimination processing to obtain a preprocessed spectrum data set; performing standard multivariate decomposition processing and horizontal local constraint optimization of a pollutant target wave band on the preprocessed spectrum data set to obtain enhanced feature fingerprint spectrums for different types of water quality pollutants; and inputting the enhanced feature fingerprint spectrum into a deep belief network model to perform pollutant feature matching analysis to obtain various pollutant types and corresponding concentration values in the water body sample, thereby effectively eliminating ambient light changes and other background interferences, not only determining the pollutant types, but also accurately quantifying the concentration values.

The invention provides a water body monitoring method and equipment based on satellite remote sensing data and a storage medium, and relates to the technical field of water body monitoring, the method comprises the following steps: firstly, acquiring remote sensing image data and water quality measured data (including water quality parameters and geographic positions) of a target area at least covering two spectral bands; performing radiation correction on the remote sensing image data, and extracting target image data corresponding to the water body by using the image segmentation model; matching the water quality parameters with the target remote sensing image data according to the geographic position information, and constructing a training set of time-space alignment; using a multilayer model fusion strategy to train a preset water quality inversion model based on the training set to obtain a target model; and finally, inputting an image to be detected to the target model to obtain water quality parameters, and mapping the water quality parameters to a geographic space to generate a water quality two-dimensional distribution diagram. According to the invention, the water body monitoring precision and monitoring details based on satellite remote sensing data can be effectively improved.

The invention relates to a water quality real-time detection system which comprises the following modules: a multi-source sensing module which is based on a multi-parameter sensing array, adopts a self-adaptive sampling strategy, realizes sensor time sequence synchronization through a state estimation algorithm, completes water body multi-dimensional parameter acquisition in combination with a micro-fluidic chip, generates a multi-modal sensing data set, and transmits the multi-modal sensing data set to a data processing module; the multi-source sensing module comprises a multi-source sensing sub-module, a signal conditioning sub-module, a time sequence synchronization sub-module and an anomaly capture sub-module. The method has the advantages that through the synergistic effect of the adaptive sampling strategy and the state estimation algorithm, the multi-sensor time sequence synchronization precision is remarkably improved, the phase deviation problem caused by traditional fixed frequency sampling is effectively eliminated, the sliding window polynomial fitting is combined with the wavelet threshold de-noising technology, and the multi-sensor time sequence synchronization precision is improved. High-frequency noise interference is greatly suppressed on the premise that effective components of the signals are reserved, and meanwhile, the abnormal value detection accuracy is improved through a dynamic threshold mechanism.

The invention relates to the technical field of error correction, in particular to a water conservancy facility environment interference error correction method and device based on multi-dimensional data. The method comprises the following steps: collecting historical monitoring logs of water conservancy facilities, carrying out environmental disturbance event identification and multi-scale disturbance relevance mining, and constructing an environmental disturbance space-time relevance map; monitoring surrounding environment water monitoring parameters of the water conservancy facility, performing sequential response trend change analysis and dynamic response characteristic modeling, and constructing a facility environment personalized change response portrait; identifying a plurality of water body position monitoring data according to the surrounding environment water body monitoring parameters, and constructing a hydrodynamic full-field sensing graph; and performing nonlinear correlation analysis based on the hydrodynamic full-field perception graph and the personalized change response portrait of the facility environment, and constructing a digital twinborn model of the water conservancy facility under the water flowing condition. According to the invention, through dynamic error trend analysis and correction, the monitoring precision and monitoring stability of the water conservancy facility are improved.

The invention provides a hydraulic engineering information management method and system based on big data. The method comprises the following steps: firstly, uploading flood flow velocity dynamic data, terrain elevation data and spectral feature data to a cloud server, then carrying out multi-dimensional superposition on the flood flow velocity dynamic data, associating with a historical inundation path, and generating a superposition association result; constructing a three-dimensional geographic model based on terrain elevation data and spectral feature data, generating a spatial superposition map according to boundary features of a water body and a terrain abrupt change area and a flood routing path, performing position association with a material reserve position, and performing dynamic priority ranking according to a submerging probability gradient, and finally, according to the resource scheduling instruction sequence, correcting the flight path of the unmanned aerial vehicle cluster and the data acquisition area to form a closed-loop feedback mechanism. According to the technical scheme provided by the invention, the accuracy and timeliness of real-time data updating are ensured.

The invention discloses a water quality prediction method based on a Transform-LSTM fusion model, and belongs to the technical field of water quality time series data prediction and artificial intelligence. Comprising the following steps: (1) acquiring water quality data from a water quality monitoring station; (2) carrying out pretreatment; (3) screening out water quality characteristic data; (4) dividing into a training set, a verification set and a test set; (5) inputting the data into a Transform-LSTM (Long Short Term Memory) fusion model; (6) embedding water quality data time sequence information by a Transformer encoder through position coding, extracting a global dependency relationship among features by utilizing a multi-head attention mechanism, and optimizing gradient propagation by combining residual connection and layer normalization; (7) the LSTM layer receives the high-order features after Transform coding, and captures a local time sequence dynamic mode; and (8) mapping the extracted water quality time sequence characteristics to a specific prediction result by a regression output layer by adopting a linear activation function, and calculating an evaluation index. According to the method, the water quality change trend of the surface water body can be effectively predicted, and powerful support is provided for water ecological protection and sustainable development.

The invention relates to the technical field of automatic control, in particular to an intelligent aquaculture automatic control system which comprises a feeding feedback control module, a feeding rhythm allocation module, a water body disturbance partition module, a control unit recognition module and a control path construction module. According to the method, the bait residue density is quantified through image recognition, a feeding state dynamic judgment mechanism is constructed, the accuracy and timeliness of feeding response are improved, the feeding speed is calculated through linkage of time difference and density change, a rhythm allocation strategy based on behavior feedback is formulated, the matching degree of the feeding rhythm to the actual feeding ability is enhanced, and the feeding speed is increased. A main flow path continuous block is constructed by combining flow velocity and flow direction, disturbance levels are marked in a grading manner, a key equipment action area is judged according to cross of fish school active density and dissolved oxygen trend, and an equipment linkage path is constructed by connectivity of a physical channel and an inhabitation area. And an intelligent control scheme of cooperative linkage of ingestion behavior driving, disturbance partition recognition and space layout optimization is realized.

The invention discloses a lake terrain prediction method based on machine learning fused multi-source satellite data, and relates to the technical field of machine learning, and the method comprises the steps: extracting the optical features of a target lake from an optical satellite image; extracting water features of each water body pixel in the target lake from the SWOT satellite data; performing space-time alignment and fusion on the optical features and the water features to obtain a fusion feature vector; the actually measured water depth point of the real water depth is matched to the geographic position of the corresponding pixel, and then the real water depth and the fusion feature vector of the corresponding pixel form a training sample; selecting a machine learning model and initializing the structure or parameters of the machine learning model; training the initialized machine learning model by using the training sample; utilizing the trained machine learning model to predict the water depth value of the corresponding pixel according to the fusion characteristic vector of each water body pixel; and determining the terrain of the target lake according to each water depth value. According to the method, the terrain of a large-scale lake can be efficiently predicted at low cost.

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.

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 provides a lake water chemical oxygen demand internal and external source pollution quantitative tracing method and system, and relates to the technical field of pollution quantitative tracing. The method comprises the following steps: performing three-dimensional fluorescence spectrum analysis on water samples at different point positions of a lake to obtain fluorescence excitation / emission matrix spectrum EEMS data; performing parallel factor analysis on the EEMS data, identifying and classifying the fluorescent components of the dissolved organic matter DOM, and determining the type characteristics of each component; based on the maximum fluorescence intensity and the corresponding COD value of the fluorescence component, establishing a standardized multiple linear regression equation by adopting ridge regression analysis, and calculating the contribution percentage of each fluorescence component to the COD of the lake water body; analyzing influence paths of potential variables on DOM migration and transformation, and quantifying direct contributions and indirect contributions of different pollution sources to each fluorescent component through path coefficients; and calculating the total contribution of lake-entering rivers, bottom mud release and phytoplankton to the COD of the lake water body.

The invention relates to the technical field of underwater navigation, and discloses a real-time attitude estimation method of an underwater inertial navigation system. The method comprises the steps that the underwater vehicle collects multi-axis angular velocity and linear acceleration data through an inertial measurement unit and uploads the data to a central processing module, and the central processing module conducts kinematics modeling on the data to generate attitude change initial feature vectors; receiving water flow velocity data of a Doppler velocimeter and pressure data of a depth sensor, and performing flow field interference compensation optimization on the attitude change initial feature vector based on the data to generate an anti-interference attitude feature vector; fusing the anti-interference attitude feature vector and a historical attitude estimation result to generate space-time fusion attitude state representation, and then resolving a real-time Euler angle through a nonlinear filter; and matching a corresponding attitude stability control instruction from the navigation strategy library according to the real-time Euler angle, and issuing the attitude stability control instruction to a propeller execution unit. The method can effectively cope with underwater complex environment interference and guarantee stable operation of the underwater vehicle.

The invention discloses a microplastic transport simulation and risk identification method based on multi-factor coupling, which comprises the following steps: by coupling a hydrodynamic model and a microplastic transport model, comprehensively considering multiple environmental factors such as tide, water level, runoff, wind speed, wind direction and the like, constructing a wave flow coupling microplastic migration and diffusion model suitable for complex hydrodynamic conditions of a Pearl River estuary; the model is calibrated and verified through measured data, and the precision of the model is evaluated by using indexes such as a Nash efficiency coefficient and a root-mean-square error; in combination with the simulation result and the spatial distribution characteristics of the typical sensitive area, representative sections and stations are selected, and annual-scale micro-plastic concentration change analysis is carried out; and further introducing an ecological risk index to carry out regional ecological risk grade identification, and determining a micro-plastic high-risk area and influence main control factors. The method has high regional adaptability and expansibility, and scientific support and technical reference can be provided for prevention and control of microplastic pollution of estuary and coastal water.

An unmanned ocean vehicle apparatus having a hull, solar array, hydrofoil, and electric thruster is operable on a surface of a body of water to travel from one point to another with long range capabilities. The unmanned ocean vehicle can further be placed in a more compact configuration, incorporating a mechanism to alternate between operational mode and collapsed storage mode to facilitate physical storage or shipment of the vehicle. Methods are described for optimizing travel and determining favorable movements of the unmanned ocean vehicle by calculating current and predicted conditions at certain waypoints using a combination of a sensor, a computer, and a control system. The physical support of hydrofoils, a drag reducing system, capsize-prevention technique, and a three-dimensional arrangement of solar panels provides efficient power management, optimal route strategy, and sustainability of the vehicle's high speeds in wildly varying ocean conditions.

The invention discloses a remote sensing image fishpond extraction method based on an adaptive edge enhanced neural network. The method comprises the following steps: selecting a sentinel No.2 satellite multispectral band to synthesize a false color image; constructing a fishpond region labeling data set; establishing an adaptive edge enhanced neural network architecture, wherein the architecture fuses an edge detection module capable of learning a threshold and semantic feature extraction; an edge perception double attention module and a pyramid pooling module are integrated, and the multi-scale feature representation capability is enhanced; optimizing a training process by adopting a multi-level depth supervision and prediction fusion strategy; and finally, extracting a fishpond area through the training model and generating a standardized result. According to the method, the fishpond boundary segmentation precision is remarkably improved, the adaptability to fishponds with different scales is enhanced, the detection rate of small-scale fishponds is particularly improved, the model convergence process is accelerated through the depth supervision strategy, misclassification of similar water bodies such as rivers and ditches is effectively reduced, and the accuracy of fishpond boundary segmentation is improved. The technical problems that in the prior art, edges are fuzzy, multi-scale adaptability is poor, complex background interference is sensitive, and the omission ratio of a small-scale fishpond is high are solved.

The invention discloses an intelligent environment monitoring and regulation method for a marine ranch, and belongs to the field of environment monitoring, and the method comprises the steps: collecting water body parameters, employing a data fusion algorithm to generate a comprehensive environment state index, and solving the business problems of data dispersion, response lag and inaccurate adjustment in water body environment monitoring and regulation. A time sequence analysis model and an optimization algorithm are combined, potential anomalies are analyzed, a risk change trend is predicted, environment adjustment requirements are dynamically calculated, a precise regulation and control instruction set is generated and transmitted to equipment to be executed, and meanwhile, through deviation comparison between real-time feedback data and a preset threshold value, comprehensive indexes and regulation and control parameters are iteratively updated, so that environment stability is ensured. Through multi-level data processing and a self-adaptive adjustment mechanism, the timeliness and accuracy of water body environment regulation and control are remarkably improved, closed-loop management from monitoring to optimization is achieved, and efficient support is provided for intelligent decision making in a complex environment.

The invention discloses a high-precision flow online monitoring device and method for a siltation section, and belongs to the technical field of hydrological monitoring. The device comprises a sensing terminal, an edge terminal and a mounting mechanism, the sensing terminal is integrated with an upper and lower section layered speed measurement module, an underwater distance measurement module, a first absolute pressure type water level measurement module and an attitude and temperature sensor, and vertical section flow velocity data is obtained by receiving water body reflection signals in a time-sharing manner; and the dynamic deposition section area is corrected in real time in combination with ultrasonic ranging. And the edge terminal compensates water level measurement data by using atmospheric pressure, and calculates the flow through fusion of a telemetering module. The installation mechanism supports fixed or telescopic installation, and the height of the installation mechanism is automatically adjusted according to the distance between a deposition layer and equipment. The method comprises the steps of dynamically correcting the section, calculating the flow through layered integration and compensating errors of multiple sensors, the problems of large area deviation, high operation and maintenance cost and the like caused by sedimentation in a traditional flow measurement technology are solved, and high-precision online monitoring under the high-sand-content complex flow state is achieved.

The invention provides a dynamic change monitoring method and system based on a multi-temporal river channel image, and relates to the technical field of image monitoring, and the method comprises the steps: obtaining a multi-temporal multispectral remote sensing image and an unmanned plane oblique photogrammetry image; identifying a riverway water body boundary and constructing a water area mask; dense point cloud data are generated and cut; generating a balanced riverbed point cloud through density compensation sampling and local curvature constraint; constructing a terrain model to generate a section sequence; calculating hydrodynamic correlation strength to construct an evolution propagation sequence; and recursively calculating the erosion and deposition change rate and performing early warning. According to the invention, high-precision monitoring and early warning of river form changes are realized.

The invention provides a freezing circle watershed hydrological prediction method and system based on multi-source data fusion, and relates to the technical field of hydrological monitoring and sediment monitoring, and the method comprises the steps: obtaining a vibration signal of the interaction of a water flow and a riverbed and meteorological data in a region, and carrying out the preprocessing; the preprocessed meteorological data and vibration signals are input into a multi-source data fusion model, sequential feature extraction is conducted on the meteorological data and the vibration signals through a multi-layer perceptron in the multi-source data fusion model, the extracted sequential features are fused through a gating circulation unit, and multi-source data fusion features are obtained; and carrying out empirical mode decomposition and Hilbert spectrum analysis on the preprocessed vibration signal to obtain a plurality of signal characteristics such as instantaneous frequency and instantaneous amplitude of the vibration signal, inputting the signal characteristics and the multi-source data fusion characteristics into a hydrological parameter prediction model, and outputting corresponding predicted values of the water flow velocity, the flow and the sediment transport capacity.

The invention discloses a multi-source data-combined pit-pond water depth calculation and storage capacity calculation method, which comprises the following steps of: acquiring actually measured water depth data, performing data preprocessing, and dividing the data into a training sample point set and a test sample point set; acquiring a multispectral remote sensing image of the reckoning area, and executing image preprocessing; unifying to the same coordinate system; fractal dimensions of each wave band and the combined wave band are calculated, and a water body region is segmented; establishing a mapping relation between the water depth values of the training sample points and the standardized multispectral apparent reflectance; establishing a water depth inversion model, and verifying the water depth inversion model by using the test sample point set; calculating discrete water depth points of the pit pond by using the optimal water depth inversion model; and establishing a minimum energy curved surface to obtain the water storage amount of a single pit pond, and accumulating to generate the total water storage amount. According to the method, actual measurement and remote sensing data are fused, the energy minimization water depth inversion model is constructed, accurate calculation of the water storage capacity of the pit pond is achieved, and the method has the advantages of being high in precision and high in applicability.

The invention relates to the technical field of exploration, and discloses a water depth inversion method and system based on a multispectral remote sensing image, which utilizes an independent verification sample set to carry out hierarchical precision evaluation, and analyzes model performance differences according to dimensions such as a water depth range and a substrate type. Error propagation of links such as atmospheric correction and water level correction is quantified through full-chain uncertainty analysis, an uncertainty quantification model of ensemble learning is constructed, and a pixel-level precision distribution diagram is generated. Therefore, according to the technical scheme, a closed-loop dynamic adaptation framework is formed by systematically integrating multi-temporal data dynamic modeling and an uncertainty quantification mechanism, a spatio-temporal variation compensation mechanism is embedded in the whole process from data acquisition to result verification, the interference of spatial-temporal heterogeneity of water optical characteristics on water depth inversion is effectively dealt with, and the accuracy of water depth inversion is improved. The problems of parameter mismatch and precision reduction of the water depth inversion model caused by dynamic change of optical characteristics of a coastal water body in seasonal and tidal scales are solved.

The invention discloses an aquaculture environment intelligent regulation and control method and system based on an AI model, and relates to the technical field of aquaculture management and control. The AI model-based aquaculture environment intelligent regulation and control method comprises the steps of S1, collecting water quality state data, feeding behavior data and meteorological data in an aquaculture environment, preprocessing and storing the data, and then constructing an aquaculture regulation and control management database; s2, constructing a water body state time sequence causal model based on the multi-source time sequence data, and outputting a causal connection structure; s3, performing prediction and risk assessment on water quality evolution under different feeding schemes through multivariable time series and causal coupling; s4, analyzing a feeding scheme target based on the feeding decision amount, the water quality risk and the growth income; and S5, continuously correcting the causal relationship and the feeding strategy through feeding execution feedback. The problems that the bidirectional coupling relation between the feeding behavior and the water quality feedback is not clear, and the feeding decision and the water quality risk are difficult to cooperatively regulate and control are solved.

The invention discloses a karst underground river system multi-source pollution comprehensive quantitative traceability method, which comprises the following steps: S1, multi-dimensional sample collection and new pollutant screening: carrying out multi-period sampling on a water body, measuring conventional water quality indexes and nitrogen and oxygen isotopes, screening new pollutants, and carrying out targeted quantification on characteristic new pollutants; s2, principal component analysis dimension reduction and pollution factor identification: carrying out principal component analysis on conventional indexes and new pollutant data, extracting principal components of which the cumulative contribution rate exceeds 85%, identifying composite pollution factors, and eliminating secondary interference factors; s3, nitrogen and oxygen isotope auxiliary verification and denitrification elimination: eliminating denitrification interference by utilizing the ratio of delta 15N-NO3 <-> to delta 18O-NO3 <-> in combination with underground water burial depth and runoff velocity, and distinguishing the nitrate source as a chemical fertilizer, soil nitrogen or sewage and wastewater; and S4, PMF model optimization and pollution source quantification: inputting the conventional indexes and the new pollutant concentration matrix into a PMF model, and analyzing the contribution rate of each pollution source.

The invention relates to the technical field of earthquakes, in particular to an optical remote sensing image feature landslide information extraction method. A double-time-phase NDVI time sequence analysis strategy is adopted, and landslide area recognition is achieved by constructing a vegetation index difference chart before and after an earthquake. A landslide mass area before an earthquake presents a high NDVI value due to complete vegetation coverage, and a vegetation index of the area in an image after a disaster is significantly attenuated due to surface disturbance, so that an obvious change response is formed. If the normalized vegetation index does not change, a non-landslide area can be determined, and the greater the change of the normalized vegetation index is, the greater the possibility of landslide occurrence is, and a landslide preselection area is determined; then Otsu threshold segmentation is carried out by combining cloud layer features and water body features, cloud layer and water body change parts are effectively eliminated, object-oriented geometric shape rule fine recognition is carried out on a preselected area, a DEM is adopted to calculate a slope value to constrain low-lying or flat earth surface changes, and efficient and accurate landslide information extraction is achieved.