3478 results about "Water body" patented technology

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 relates to the field of river pollution treatment, and discloses a water quality treatment method, system, equipment and medium based on a remote sensing inversion technology, and the method comprises the following steps: S1, collecting a hyperspectral remote sensing image of a to-be-monitored water body, the hyperspectral remote sensing image comprising chlorophyll concentration, suspended matter concentration and dissolved oxygen; s2, based on the hyperspectral remote sensing image, preprocessing the hyperspectral remote sensing image, including geometric correction, atmospheric correction, noise removal and waveband fusion, to obtain a preprocessed image meeting an inversion precision requirement; and S3, inputting the preprocessed image into a water quality inversion model, and obtaining pixel-level chlorophyll, suspended solids and dissolved oxygen concentration data based on physical radiation transmission. By combining the hyperspectral remote sensing image and the water quality inversion model, large-range, real-time and high-precision water quality monitoring and pollution zoning are realized, model parameters are dynamically updated, and the timeliness and precision of water quality treatment are improved.

The invention belongs to the crossing field of environmental engineering and hydrology and hydrodynamics, and particularly relates to a traceability method based on coupling hydrodynamics and a pollutant degradation equation, which comprises the following steps: firstly, acquiring and preprocessing multi-source heterogeneous monitoring data, then selecting a one-dimensional Saint-Venant equation or a two-dimensional shallow water equation according to a water body form to solve a hydrodynamic field, and finally, determining the hydrodynamic field. A multi-component convection-diffusion-reaction equation is coupled to simulate pollutant migration and transformation; an LSTM module is introduced to identify suspected pollution events, pollution source parameters are inverted through a two-channel framework of PDE constraint optimization and Bayesian inference, uncertainty is quantified, model parameters are updated online in combination with data assimilation, and finally the uncertainty is quantified and verified. The method considers traceability precision, efficiency and compliance, supports multiple water bodies and multiple data sources, and is suitable for complex water body pollution traceability.

The invention discloses a river organic carbon flux inversion method based on multi-source data and related equipment. The method comprises the following steps: acquiring multi-source data of a river basin range in a target research area; obtaining a normalized difference index based on remote sensing image conversion; river parameters of the target research area are obtained based on the normalized difference water body indexes and multi-source data processing, and river runoff is obtained through conversion of a hydraulic geometrical relationship; on the basis of the remote sensing image, the normalized difference chlorophyll index and the normalized difference turbidity index, the particle organic carbon concentration is obtained through inversion by means of a first inversion model; based on the population number and the rainfall capacity, a second inversion model is used for inversion to obtain the concentration of dissolved organic carbon; and converting the river runoff, the particle organic carbon concentration and the dissolved organic carbon concentration to obtain the river organic carbon flux. The method can significantly improve the accuracy and reliability of monitoring on a high-dynamic river, accurately realizes the inversion of the organic carbon flux of the river, and can be widely applied to the technical field of data processing.

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.

The invention discloses a river sediment multi-parameter sensing self-adaptive sampling method and system, and relates to the technical field of environment monitoring, and the method comprises the following steps: S1, synchronously collecting the pH value, electrochemical potential, heavy metal binding state proportion, water body flow velocity and hydrodynamic parameters of sediment through a sensing device, and establishing a multi-parameter response baseline; and S2, based on the multi-parameter response baseline, collecting monitoring data in real time and executing differential analysis, and identifying a high-risk time period when the pH value change rate exceeds a threshold value and the heavy metal binding state proportion fluctuates. By constructing a multi-parameter response baseline and a linkage recognition mechanism, early sensing and accurate sampling of heavy metal release are realized, a closed-loop response system is constructed by combining chemical form analysis, self-correction and data feedback optimization recognition model, and the accuracy and intelligent level of pollution monitoring are improved.

The invention relates to the technical field of underwater detection, and discloses an underwater comprehensive detection system. The system collects multi-dimensional acoustic data including sound wave reflection intensity, propagation time delay and frequency dispersion characteristics of a target water area through an underwater sonar array; dividing the detection area into a plurality of layered units according to a water attenuation coefficient and a substrate reflection characteristic of the acoustic data, and extracting an environment intrinsic parameter of each layered unit; constructing an acoustic propagation path model based on the environmental intrinsic parameters, and generating adaptive filtering parameters in combination with a preset noise suppression strategy; calling sonar array hardware configuration data, matching filtering parameters with an array beam forming mode, and calculating a detection sensitivity threshold value of each layered unit; and integrating the sensitivity threshold and the acoustic data, generating a three-dimensional acoustic characteristic spectrum of the target water area, and marking the space coordinates and confidence level of the abnormal area. The system can improve the comprehensiveness and accuracy of underwater detection, and meets the requirement of high-precision detection.

The invention discloses a drainage basin nitrogen and phosphorus pollution prediction method based on rainfall runoff migration, and relates to the technical field of water quality pollution prediction, and the method comprises the steps: accurately obtaining land utilization and water body distribution through a remote sensing technology, carrying out the space division of a pollution source through combining a nitrogen and phosphorus load coefficient, and constructing a rainfall runoff model. The runoff volume and time and space distribution under different rainfall events are reflected, and the spatial dynamic change of pollutants is captured through a hydrodynamic model, in combination with a convection diffusion equation and by introducing a conversion model of nitrogen and phosphorus in various forms. By integrating remote sensing data, rainfall runoff simulation, hydrodynamic force and nitrogen and phosphorus form transformation, the space-time migration and transformation process of nitrogen and phosphorus pollution in a drainage basin is described, so that the coupling relation between pollution source distribution and runoff power is revealed, the conveying and diffusion rule of pollutants in a river network is dynamically reflected, and the drainage effect is improved. And refining to transformation evolution of different nitrogen and phosphorus forms, and generating intuitive concentration distribution and thermodynamic diagrams through spatial interpolation.

The invention belongs to the field of environment monitoring and computer vision, and particularly relates to a water body color recognition regression method and system based on space-time causality and manifold learning, and the method mainly comprises the steps: carrying out the detection of a current target water body video sequence, extracting a water body region, carrying out the high-dimensional feature dimension reduction of the water body region, and obtaining a water body color recognition result; and performing feature extraction on the water body region through a space-time causal feature learning model, fusing the flow shape learning features and the space-time causal features to obtain fused features, and outputting a finally predicted water body color value. According to the method, end-to-end assembly line design of preprocessing-segmentation-feature modeling-regression is adopted, manual intervention is not needed from video input to color prediction, and through cascade cooperation of five core modules (video preprocessing, water body segmentation, manifold learning, time sequence causal modeling and color recognition), the real-time performance of the system is improved. Full-link automation from environmental interference suppression, feature extraction to result output is realized, information loss of intermediate links is avoided, and recognition efficiency and robustness are improved.

The invention discloses a water heavy metal concentration simulation and prediction method, which comprises the following steps: integrating original monitoring data, hydrodynamic data, total suspended solids, image remote sensing data and human activity data, and generating a multi-source cleaning sequence data packet; executing cross-modal adsorption capacity estimation by using image remote sensing data in the data packet, inferring particle chemical composition and adsorption isotherm parameters from image textures, and generating a capacity feature packet containing an adsorption capacity upper bound; time-varying travel time is calculated based on the hydrodynamic data and the human activity data, causal alignment is performed on the capacity feature packet and the upstream signal, and a travel time alignment feature packet is generated; and in combination with metal fingerprint parameters, applying an adsorption capacity upper bound as a physical constraint on a form distribution constraint head, explicitly decoupling and predicting the form, and generating a prediction result packet. According to the method, the hydrodynamic physical mechanism and the particle adsorption chemical mechanism are deeply coupled, and the prediction precision and the physical consistency of the model under the unsteady state condition are improved.

The invention discloses a tuna pond culture strategy optimization method and system based on meteorological influence analysis, and the method comprises the steps: collecting regional meteorological data and pond culture monitoring data, and constructing a multi-modal pond ecological feature vector; establishing a weather-water body dynamic coupling model by using a space-time diagram neural network, and generating water body situation response prediction information; a breeding regulation and control strategy is made based on the prediction information, and strategy self-adaptive optimization is achieved through real-time monitoring data; evaluating the anti-disaster capability of the tuna after the meteorological influence is finished, screening high-quality germplasm resources by combining genetic analysis, and generating an anti-disaster breeding scheme. According to the method, full-chain optimization from meteorological early warning to culture regulation and control to genetic breeding is achieved, and the climate toughness and economic benefits of tuna pond culture are comprehensively improved.

The invention discloses a method for completing an autonomous deep-sea exploration task by a deep-sea cross-domain unmanned aerial vehicle. The method comprises the following steps: determining a target area; taking off to a specified height from the sea level, receiving a task execution command and then flying to a target area; when the relative distance between the current position and the coordinates of the target area reaches a preset value, descending; when it is detected that the device is contacted with seawater, the device dives through gravity; when it is detected that the set depth is reached, the propeller is restarted, and the detection task is executed; after the detection task is completed, the standby battery is rejected and ascends; after leaving the water surface, rising to a specified height and switching to a homeward flight mode; and when the deep-sea cross-domain unmanned aerial vehicle is within the sight distance range, a manual landing task is executed. The invention further discloses the deep-sea cross-domain unmanned aerial vehicle. According to the unmanned aerial vehicle and the method, autonomous switching operation between an air medium and an underwater medium can be realized, and the unmanned aerial vehicle has the capability of autonomously completing detection and water body sampling in an extreme deep sea environment.

A system is for cooling at least one computing device using water. The system has a body of water defining a first waterline and a structure at least partially submerged in the body of water. An internal volume of water defines a second waterline. At least one outlet drains water from the internal volume of water to the body of water. A container is at least partially submerged in the internal volume of water and has at least one computing device that generates heat in use. An inlet pipe has a pump to move water from the body of water to the internal volume of water. A flow path of water is provided across the container towards the at least one outlet. The second waterline is at a height above the first waterline to drive the flow path to provide cooling of the container and computing device therein.

The invention relates to a water depth estimation method of a KAN network based on self-adaption and physical constraint, and belongs to the technical field of remote sensing data processing. Grades are divided according to different water body turbidity, different KAN expert networks are adopted for different grades to learn the complex nonlinear relation between B3 and B4 wave band reflectivity and water depth, and KAN initial model parameters corresponding to different grades of water bodies are obtained through independent self-adaptive training; taking the B3 and B4 wave band reflectivity after normalization processing as input, selecting a corresponding KAN network model according to the water turbidity grade, and outputting predicted water depth; and training the KAN network by using the physical constraint loss function L, and finally extracting an interpretable mathematical inversion formula of the wave band reflectivity and the water depth after the training is completed. Through deep fusion of B3 / B4 waveband spectral characteristics of the KAN, a remote sensing technical solution with high precision, low cost and strong adaptation is provided for dynamic monitoring of the water depth of the shallow water area.

The invention discloses a lightweight optical flow neural network method for river velocity measurement, and the method comprises the steps: collecting a dynamic monitoring image of a water area surface flow field, calculating an approximate optical flow field through combining with a DeepFlow optical flow algorithm, and generating an optical flow label; an improved lightweight ConvFFN-Flow optical flow estimation model is built, and the model comprises a feature extraction module, a 4D related structure body module, a GRU optical flow residual error iteration module and an up-sampling module; by introducing a ConvFFN module, the model parameter scale and the calculation amount are reduced, and the optical flow prediction precision is kept; and based on pixel coordinates of front and rear frames of images of the optical flow field data, obtaining corresponding positions in a real three-dimensional space, calculating an actual displacement amount, and deducing to obtain a real flow velocity vector size and a flow direction of the water body surface. The method still has stable and high-precision optical flow estimation capability when being used for processing scenes such as fine river water surface texture and complex flow state change, and meanwhile, the calculation overhead and the storage demand are remarkably reduced.

The invention discloses a comprehensive remote sensing recognition system for hidden danger of ice lake outburst. The system comprises a data acquisition and preprocessing module, an ice lake extraction module, an ice dam stability evaluation module, a risk calculation module and a visualization and early warning module. According to the system, optical images, SAR images, LiDAR point cloud and meteorological data are utilized, firstly, a glacial lake boundary is extracted through a water body index, the area and the volume are calculated, then the water level rising rate is monitored in combination with multi-temporal data, the dam body stability is evaluated by utilizing three-dimensional terrain and deformation parameters, and the dam body stability is evaluated by combining the temperature melting corrosion rate and triggering factors such as rainfall, earthquakes and inflow. And constructing a multi-factor outburst risk index model, and finally carrying out visualization and graded early warning on a result in a GIS platform. The method can achieve the quick, comprehensive and intelligent recognition of the hidden danger of ice lake outburst, has the advantages of being high in monitoring precision, high in automation degree, high in early warning timeliness and the like compared with a traditional single data or experience judgment method, and can be widely applied to the disaster prevention and control work of dense ice lake areas such as the Qinghai-Tibet Plateau and the Himalaya mountain.

The invention discloses a cultured seaweed identification method and system based on time sequence remote sensing and morphological constraint, and the method comprises the steps: obtaining and preprocessing an original multispectral remote sensing image, forming a multispectral remote sensing image set, and generating a water body range mask; under the constraint of a water body range mask, synthesizing each time phase image, enhancing the texture through local maximum filtering, calculating a seaweed light absorption index, and generating a feature enhanced image; performing threshold segmentation on the feature enhanced image, extracting and vectorizing a potential seaweed pattern spot raster image, and calculating morphological indexes such as area and firmness of each vector pattern spot; and screening the vector pattern spots based on a preset area and a firmness threshold, and outputting a cultured seaweed distribution result. According to the method, the self-defined index and the morphological constraint are combined, the recognition precision of regular form targets such as culture rafts is effectively improved, area time sequence change analysis can be generated, and an automatic and high-precision technical scheme is provided for dynamic monitoring of culture seaweed resources.

The invention provides a water body water level monitoring method of an image segmentation model, and the method comprises the steps: obtaining multi-modal water flow data of a target water area, carrying out the feature extraction of the multi-modal water flow data, and obtaining a turbulence feature map, the multi-modal water flow data comprising water flow motion data and water body image data; performing physical constraint segmentation processing on the turbulence feature map and the water body image data to obtain a water body region segmentation mask and a corresponding pixel-level confidence map; carrying out water gauge geometric correction processing on the water body region segmentation mask to obtain a corrected water line; and performing water level value conversion processing on the corrected water level line and the pixel-level confidence map to obtain a water level measurement value and uncertainty data. By adopting the method, the real water line and the dynamic water flow artifact can be effectively distinguished, and the monitoring precision under the complex hydrological condition is ensured.

The invention provides a coastal wetland ecological corridor identification and optimization method and system, and relates to the technical field of ecological environment monitoring. The method comprises the following steps: integrating optical, radar, terrain, night light and road data on an earth engine cloud platform, and unifying time and space references to form an initial data set; extracting a normalized vegetation index and a vegetation coverage degree, and combining with a water body frequency to identify an ecological source land; generating a comprehensive resistance surface according to the land utilization and coverage type and the road distance; and performing minimum cost path and circuit connectivity analysis in a geographic information system to obtain potential galleries, key nodes and barrier regions, and outputting gallery grades and optimization suggestions in a grading manner according to a cost threshold, thereby realizing high-precision identification and optimization of the coastal wetland ecological network.

The invention belongs to the technical field of water pollutant identification, and particularly relates to a water pollutant distinguishing method and system based on multi-source spectrum fusion. According to the method, ultraviolet visible spectrum data and three-dimensional fluorescence spectrum data are effectively integrated in a multi-source spectrum data fusion mode, so that the characteristic information of water pollutants is captured, stably appearing characteristic absorption wavebands can be positioned by utilizing the ultraviolet visible spectrum data, and the water pollutants can be accurately detected. The method comprises the following steps: identifying a characteristic sub-region capable of representing the fluorescence response characteristic of a pollutant through three-dimensional fluorescence spectrum data, in a characteristic extraction stage, performing first-order derivative operation on an ultraviolet visible spectrum to position an absorption peak position, calculating curvature distribution of a three-dimensional fluorescence spectrum to identify the characteristic sub-region, and when a fusion characteristic is generated, performing first-order derivative operation on the three-dimensional fluorescence spectrum to identify the characteristic sub-region; according to the method, the key operation parameters are subjected to grid search optimization, and the optimal key operation parameter combination is screened out by performing multiple rounds of iterative training and evaluation on a preset training set and a verification set, so that the recognition performance of the real-time collected water sample is improved.

The invention provides a water chlorophyll a concentration inversion method and system based on multi-modal data and a lightweight model, and relates to the technical field of water environment remote sensing evaluation. The method comprises the following steps: firstly, acquiring a Gaofeng No.5 satellite remote sensing image, a sentinel No.3 satellite image and ground actual measurement data, and completing image preprocessing and water body pixel extraction; constructing a hyperspectral index and an aquatic vegetation index, and fusing the hyperspectral index and the aquatic vegetation index with the water body temperature, the pH environmental factors and the spectral reflectivity to form a multi-dimensional feature sample set; a core feature subset is obtained through random forest and XGBoost coupling feature selection, and a lightweight student model is trained based on knowledge distillation; and constructing a to-be-predicted feature sample for the to-be-predicted time phase image and the environment factor, inputting the to-be-predicted feature sample into the lightweight student model to obtain a chlorophyll a concentration predicted value, and generating a spatial distribution map and a quality control map layer. According to the invention, high-precision, low-redundancy and efficient deployment chlorophyll a concentration inversion is realized.

The utility model discloses a multi-filtering-form multifunctional sewage blocking treatment system for a riverway, which comprises a group of soft enclosures which are sequentially arranged along the riverway to form a plurality of independent water purification areas, floating plants and ecological plant floating beds are arranged in the soft enclosures, and a vertical bank slope automatic liftable river water body filtering device is arranged between the soft enclosures. According to the system disclosed by the utility model, through the innovative design of the filter cloth and the application of a plurality of soft enclosures, step-by-step filtration and regional division of a water body are realized, and the filtration efficiency is improved. Meanwhile, the system adopts a hydrophilic high polymer material and a super-hydrophobic high polymer material, and combines biological filtration and physical filtration, so that the water quality is effectively purified. The system is further provided with an in-situ functional treatment area, and the water treatment efficiency and effect are improved by planting aquatic plants and adding functional filler.

The invention relates to a tide level obtaining method and system based on an OWC wave energy conversion device. The method comprises the following steps: constructing an energy loss prediction model in the air chamber; acquiring field incident wave energy, reflected wave energy, water potential energy increment, kinetic energy increment and pneumatic output energy at the current time period; inputting incident wave energy and reflected wave energy into the model to obtain predicted energy loss; and the tide level variation is solved based on the energy conservation relational expression, and the current tide level is calculated. The system comprises an energy loss prediction module, a wave energy acquisition module, a water body energy acquisition module, a pneumatic output acquisition module, an energy loss calculation module, a tide energy solving module and a tide level calculation module, which cooperatively realize the method. According to the scheme, an external tide gauge station is not needed, the local sea area tide level can be inversed in situ, at high frequency and with high precision, the method is suitable for an OWC-breakwater fusion scene, and cooperative operation of a port and ocean energy is supported.

The invention discloses a surface water storage amount monitoring method and system based on space-air-ground water multi-source collaboration, and relates to the technical field of water resource investigation, and the method comprises the steps: obtaining multi-source collaboration collection data of a target water body, and generating a multi-source data set of a unified space-time reference; carrying out automatic extraction and change detection on a surface water area range by using a water body index method and deep learning to obtain a water area boundary; taking a water area boundary as a spatial constraint, extracting ashore terrain and underwater terrain data, and inputting the ashore terrain and underwater terrain data into a deep learning fusion model based on an encoder-decoder structure to generate a water-land integrated digital elevation model; and according to the water body types, based on the model, respectively adopting corresponding deep learning models to carry out storage volume monitoring, and calculating the water storage volumes and dynamic changes of different water body types. Efficient fusion and intelligent processing of multi-source data are achieved, the precision and efficiency of surface water storage capacity monitoring are remarkably improved, and the method is suitable for refined dynamic monitoring of various water bodies such as rivers, reservoirs and ponds.

The invention discloses a system and a method for measuring the release flux of methane in a water body, and belongs to the technical field of measurement of the release flux of methane gas in the water body. When the diffusion flux is measured, the box body is placed on the liquid level of the water body in a floating manner, and the partition plate is arranged below the first opening of the box body at the interval of the first preset distance, so that methane gas released in the form of bubbles in the water body below the partition plate can be prevented from entering the accommodating cavity, and the interference of bubble release on the measurement result can be reduced; the determination accuracy of the diffusion flux is improved. When the bubble flux is measured, the cover body covers the sediment at the bottom of the water body below the box body, and the methane gas released by the sediment is collected by the gas collection container below the liquid level, so that the probability that bubbles are dissolved in the water body in the floating process can be reduced, and the measurement accuracy of the bubble flux is improved.

The invention discloses a water body health assessment system based on big data, relates to the technical field of water body health assessment, and realizes time synchronization, space mapping and quality assessment and restoration of multi-source data, calculation of water quality semantic indexes, data fusion, health level output and sampling work order generation. According to the method, deep fusion and standardized processing of multi-source heterogeneous monitoring data are realized, data islands are broken through, through high-precision space-time alignment and intelligent quality control, data reliability is ensured, human activity events are creatively fused for causal analysis, result interpretation is enhanced, multi-scale dynamic evaluation and adaptive threshold determination are supported, accuracy is improved, and the method is suitable for popularization and application. A closed-loop mechanism of sampling verification is evaluated, the unmanned ship is used for actively checking a suspicious area, secondary judgment is triggered, the response speed and result reliability of emergencies are remarkably improved, the whole-process compliance recording and standardized interface guarantee process is traceable, results are easy to share, and key technical support is provided for fine management of a water body.

The invention discloses a sea-land collaborative coastal wetland ecological quality remote sensing evaluation method under a spartina alterniflora expansion background, and the method comprises the steps: obtaining multi-source remote sensing data of a research region, and carrying out the preprocessing of the data, and obtaining a preprocessed remote sensing image; according to the preprocessed remote sensing image, obtaining a salt marsh vegetation space-time distribution diagram of the research area; remote sensing evaluation is conducted on coastal wetland land and water body ecological quality, land ecological quality remote sensing evaluation indexes integrate NDVI, WET, NDSI, LST and DHI, and water body ecological quality remote sensing evaluation indexes integrate TSM, WST, CDOM and Chl-a; and finally, quickly obtaining the ecological quality change of the affected area of the spartina alterniflora through a spartina alterniflora change condition mask evaluation result. The ecological quality remote sensing evaluation method provided by the invention is more suitable for the coastal wetland under the background of spartina alterniflora expansion, and a scientific basis is provided for making coastal wetland ecological management and protection strategies.

The invention discloses a polluted river water nitrogen and phosphorus removal efficiency evaluation system and method based on element analysis, and relates to the technical field of sewage treatment evaluation. An algae growth dynamic model is trained, and the future growth dynamic state of algae is obtained according to future water body data and future environment driving data; the method comprises the following steps: analyzing the interference influence of interference ion concentration on sensor measurement according to future water body data, dynamically simulating the attachment rate and biofilm thickness of algae on the surface of a sensor probe according to future growth of the algae, analyzing the attachment influence of the algae on sensor measurement, and correcting the measurement precision of the sensor according to the interference influence and the attachment influence. According to the corrected sensor data, whether the nitrogen and phosphorus removal efficiency of the polluted river water meets the removal standard or not is evaluated, real-time correction of the sensor data is achieved through interference ion concentration analysis and dynamic simulation of biological adhesion influence, and the accuracy of monitoring data and the scientificity of pollution treatment efficiency evaluation are effectively improved.

The invention discloses a reservoir desilting method and device using fixed jet flow, and relates to the technical field of water conservancy and hydropower engineering desilting. According to the method, the flow speed, pressure, mud layer thickness and gradient parameters of water around a nozzle are collected in real time; calculating a jet flow angle compensation coefficient, and identifying a nozzle direction stable state; on the basis of a nozzle set with stable direction, a nozzle group is constructed and a time sequence differential control table is established by combining a topological relation between nodes and disturbance propagation characteristics, so that ordered migration of turbulent flow disturbance is realized; sediment concentration, flow velocity and flow data are collected in a flow guide plate and a sediment discharge port area, and a desilting efficiency index is calculated and used for evaluating sediment transportation continuity and flow guide performance; and a comprehensive operation optimization coefficient is generated through a multivariable adaptive weighted fusion algorithm, and the system automatically executes adaptive regulation or sends out a manual intervention prompt. According to the method, self-monitoring, self-correction and global optimization of the fixed jet system can be achieved, and the reservoir dredging efficiency and the operation stability are remarkably improved.