10131 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 relates to the technical field of hydraulic engineering monitoring control, and discloses an automatic monitoring method and system for ecological hydraulic engineering. The method comprises the steps that water body parameters are collected through a sensor and preprocessed to obtain standard monitoring data; performing noise reduction alignment and correlation analysis on the monitoring data to obtain an optimized data set and a feature library; evaluating the water environment state according to the data set and the feature library to obtain an evaluation index and an early warning signal; analyzing the treatment scheme according to the index and the signal to obtain a regulation and control instruction; adjusting equipment operation parameters according to the instruction to obtain operation state data; and performing cycle analysis and strategy optimization based on the state data to obtain regulation and control parameters. Multi-dimensional correlation analysis of water quality parameters is realized, an accurate water environment regulation and control scheme is formulated on the basis, and the pertinence and efficiency of water quality improvement are improved.

The invention 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 computers, and discloses a cultivated land resource quality grade evaluation management method and system, and the method comprises the steps: carrying out the spectrum residual error normalization processing of a multi-temporal remote sensing image, generating a corrected image sequence, effectively inhibiting the reflectivity difference caused by the alternation of wetting and drying of a water body, and achieving the evaluation of the quality grade of cultivated land. A first boundary result and a second boundary result are respectively extracted by using depth segmentation and progressive edge detection, the adaptability and robustness of boundary extraction are enhanced, a difference graph is further generated based on a complex phase difference matrix, the second boundary is corrected, a coherent boundary result is obtained, the boundary time sequence consistency is remarkably improved, and the robustness of boundary extraction is improved. And finally, the cultivated land parcel units are constructed based on the coherent boundaries, and the random forest model is input to complete scoring and grading, so that the stability and traceability of the cultivated land quality evaluation result under multiple temporal conditions are ensured, and the technical targets of stably extracting the boundaries and accurately completing grading in a dynamic change scene are achieved.

The invention relates to the technical field of marine biological diversity assessment, and particularly discloses a marine biological diversity geographic information system and an ecological diversity risk assessment method.The marine biological diversity geographic information system comprises a water quality diversity risk assessment module, a diversity and water quality assessment comprehensive model module and an RDA analysis method screening module; according to the system and the method, marine organism data and water quality data of a sea area for many years are integrated, an interactive visual system is developed, data interconnection and intercommunication are realized, and the sea area can be evaluated through diversity investigation of multiple organisms in the sea area, water environment physical and chemical factor analysis, correlation mining and evaluation model construction. The model can assess the sea water environment condition, the water body environment quality and the biodiversity risk, provides basic data and methods for related research, powerfully promotes ocean monitoring to be converted from manual work to intelligence, promotes biodiversity data integration and ecological environment monitoring in a bay area, and tracks the biological activity and survival state.

A method for inversing a depth of a subsurface chlorophyll-a maxima (SCM) of an oceanic water body based on remote sensing reflectance is provided. The method includes: band selection, R value calculation, data collection, model construction, and ZSCM calculation. This method can not only reduce the dependence of machine learning on sea surface chlorophyll-a concentrations, sea surface temperature data and sea surface height data, and reduce the calculation resource consumption of running machine learning, but also effectively improve the inversion accuracy, and effectively reduce the calculation resource consumption and inversion difficulty of inversing the SCM.

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 provides an air-ground integrated mining area environment monitoring method and system based on hyperspectral imaging, and relates to the technical field of mining area hyperspectral imaging determination, and the method comprises the steps: obtaining hyperspectral image data and spectral data of atmosphere, water, soil and landform of a mining area, and carrying out the preprocessing; interpreting the preprocessed hyperspectral image data and spectral data of the atmosphere, the water body, the soil and the landform respectively to obtain hotspot feature vectors of the atmosphere, the water body, the soil and the landform respectively; and inputting the hotspot feature vectors of the atmosphere, the water body, the soil and the landform into an anomaly monitoring model to obtain an environment anomaly monitoring result, performing data processing and model prediction identification on the atmosphere, the water body, the soil and the landform of the mining area by adopting an artificial intelligence means, and accelerating the network training speed by utilizing a customized transfer learning strategy to obtain an environment anomaly monitoring result. The model generalization is improved; and finally, mining area prediction is carried out by using on-site imaging spectrum data, so that intelligent monitoring of the mining area environment is realized.

The invention provides a self-adaptive underwater image enhancement method and system based on a Retinex theory and Mama, and relates to the technical field of underwater image enhancement, and the method comprises the steps: taking an original underwater image and a corresponding illumination priori image as input, obtaining illumination factor mapping through feature splicing, a first convolution layer, a multi-scale channel aggregation module layer and a second convolution layer, and obtaining an illumination priori image; performing Hadamard product operation on the illumination factor mapping and an original image to obtain a basic enhanced image; the multi-scale degradation features of the basic enhanced image are extracted step by step through an encoder, the spatial resolution is recovered through jump connection of a decoder, the illumination-texture relevance is enhanced through selective state space modeling of a WLMama bottleneck module, and finally a high-quality underwater image enhancement result is generated. According to the method, the mathematical representation system of the optical characteristics of the water body is established, the model can adaptively adjust the chromaticity compensation intensity, and image restoration under the constraint of the physical characteristics of the underwater scene is realized.

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 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 discloses a virtual water gauge water level identification method based on a multi-mask matching segmentation network. The method comprises the steps that S1, a real-time image of a target water body area is acquired through a preset position camera; S2, a virtual water gauge geometric model is constructed; s3, extracting a region with high confidence as a candidate region of the segmentation model; s4, designing a new training data structure optimization method to help the model better understand and utilize information of different levels; s5, respectively carrying out maximum matching on the rows and the columns, finding out an optimized matching path with the minimum total cost, and obtaining a finer segmentation result; s6, identifying a water body boundary in the high-precision water body segmentation map through a Canny edge detection algorithm, and extracting a waterline position; and S7, calculating an actual water level value, and for the water body with dynamic movement, smoothing the water level reading by adopting an interval time sampling moving average method. The defects of a traditional physical water gauge and an existing image processing technology are overcome, and an innovative solution is provided for water level monitoring.

The invention relates to the technical field of image recognition processing, in particular to a black and odorous water body recognition method and system based on image recognition processing. The method comprises the following steps: based on black and odorous water body image data, adjusting a pixel value range through sampling, comparing color channel distribution, extracting gray value distribution characteristics, analyzing partition brightness values, and generating a basic characteristic data set; according to the method, the pixel value range is adjusted, the gray value distribution characteristics are extracted, the partition brightness value analysis is carried out, the characteristics of the water body pollution area are refined, the texture gradient variation and the gray value distribution are analyzed, the pollution area boundary is divided in combination with accumulation analysis and edge strength, and the pollution types are classified in combination with geometric parameters, gray values and color channel changes. The diffusion direction and radius are analyzed through vectorization expansion, the coverage range is constructed in combination with density and path information, the accumulated pollution value is measured and calculated, the pollution identification accuracy and the dynamic tracking capability are enhanced, and the evaluation systematicness is comprehensively improved.

The invention relates to the technical field of three-dimensional imaging, and discloses a high-precision underwater structured light three-dimensional point cloud imaging method and system, and the method comprises the following steps: deploying a plurality of sensor nodes, monitoring the temperature, salinity and turbidity key parameters of a water body in real time, selecting proper light source wavelength and power according to the collected data, and carrying out the three-dimensional point cloud imaging of the underwater structured light. It is ensured that the optimal signal-to-noise ratio is obtained under the current condition, and meanwhile the exposure time and gain setting of the camera are initialized. According to the method, the optimal signal-to-noise ratio is obtained by monitoring water body conditions in real time and dynamically adjusting light source characteristics, the optimal structured light pattern is predicted by using a machine learning model, scattering noise is removed, advanced registration and fusion algorithms and an accurate phase unwrapping method are applied, and a high-quality comprehensive structured light pattern is generated. Point cloud gaps are filled through a geometric correction algorithm and an interpolation / filtering algorithm, so that a reconstruction model is closer to the surface of a real object.

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.

Disclosed are an intelligent simulation device for bottom sediment pollution process and control as well as an experimental method. The device includes an experimental flume to which a water inlet tank and a water return tank are connected; a wave-making system, an illumination system, a dosing system and an aeration system; an environmental condition parameter online monitor capable of determining water environmental parameters online; an online water quality index analyzer for monitoring water quality indexes in real time; a water automatic sampling device for automatically collecting water samples in a single or cycle mode; an offline analyzer capable of determining water quality indexes of the collected water samples or physical-chemical indexes of sediments; and an automatic control module for automatically controlling various devices. Further disclosed is an experimental method based on the intelligent simulation device for bottom sediment pollution process and control.

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 provides a lake ecological water level calculation method, device and system and a storage medium, and relates to the technical field of water resource regulation and control. The method comprises the following steps: obtaining the water turbidity of a target lake and the biomass of all submerged plants existing in the target lake; the water depth required by growth is calculated; dividing the target lake into habitat units; calculating the actual water depth of each habitat unit; calculating the plant coverage by using the water depth required for growth and the actual water depth; and obtaining an ecological water level range and an annual water level regulation and control range based on the plant coverage and taking the ranges as lake ecological water level results. By constructing the growth-illumination model and the underwater illumination prediction model of the submerged plants, the monthly water level accurate regulation and control based on biological requirements are realized, and by subdividing habitat units and analyzing ecological requirements, the growth conditions of the plants can be optimized, the water quality and biological diversity of a lake ecosystem can be improved, and the water quality and the biological diversity of the lake ecosystem can be improved. And the scientificity and systematicness of lake management are enhanced.

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

The invention discloses a water area surface flow velocity area monitoring method based on intelligent visual analysis, and relates to the technical field of intelligent visual analysis. According to the specific implementation scheme, continuous image collection is conducted on a target water area, dynamic images flowing on the surface of the water area are obtained, and image preprocessing is conducted on video frames of the dynamic images; directly comparing global feature similarities through feature extraction, feature matching and optical flow optimization by using an optical flow estimation algorithm of a convolutional neural network, and outputting optical flow field data of the water area surface flow velocity; after the optical flow field data are obtained, pixel displacement information of optical flow estimation is converted into a flow velocity field on an actual physical unit, and meanwhile regional distribution of the flow velocity on the surface of the water body is analyzed. According to the invention, the optical flow estimation algorithm of the convolutional neural network is utilized to directly compare global feature similarities and output water area surface flow velocity data.

The invention relates to a river water ecological space global monitoring method and system, and the method comprises the steps: recognizing and segmenting a water body image from an RGB image photographed by an unmanned plane based on a U-Net deep learning model, and forming a water body recognition image; converting and reconstructing the RGB image aerially photographed by the unmanned aerial vehicle into a water body hyperspectral image based on a hyperspectral information continuation reconstruction model; the U-Net deep learning model extracts a multispectral segment sequence of pixel points of a water body in the water body hyperspectral image and sends the multispectral segment sequence to the classification regression model; and calculating the multispectral segment sequence based on a classification regression model so as to label a concentration classification result of chlorophyll a in a water body hyperspectral image, thereby realizing visualization of chlorophyll concentration. The method gets rid of limitation of technical means and time acquisition of hyperspectral data acquisition, and realizes the effect of acquiring river global images at any time and at any moment.

The invention relates to the technical field of wetland damage identification, in particular to a coastal wetland damage dynamic identification method and system based on a remote sensing technology, and provides the following scheme: calling multispectral remote sensing data through a cloud computing platform, performing cloud masking, atmospheric correction and resolution resampling, and generating a high-quality remote sensing image data set; collecting ground feature samples, and constructing a random forest classification model based on spectrums, vegetation indexes, water body indexes and texture features; applying the classification model to a remote sensing image, generating a coastal wetland classification chart, and distinguishing a natural wetland from a damaged area; through time sequence analysis, ground feature changes of the same spatial position are identified, and dynamic evolution characteristics, including change amplitude, rate and conversion relation, of the damaged area of the wetland are extracted; and evaluating the precision of the classification model by using the confusion matrix, and optimizing the model according to a verification result. According to the invention, automation and precision of dynamic monitoring of wetland damage are improved, and scientific support is provided for wetland protection and management.

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

The invention 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 discloses a long and narrow river cyanobacterial bloom information identification method and system based on remote sensing data, and the method comprises the following steps: selecting a remote sensing image in a research region range through a CEE cloud platform, and carrying out the preprocessing of the remote sensing image; a Sentinel-2 satellite image and a Landsat satellite image are respectively utilized to detect a cloud layer in the preprocessed remote sensing image within a cloud cover threshold value; based on a detection result, removing cloud pixels from the preprocessed remote sensing image to obtain a cloud-removed image; analyzing the cloud-removed image by using the improved normalized difference water body index, and determining the threshold change range of the water body in each image of the cloud-removed image so as to realize the recognition of the water body area; identifying cyanobacterial bloom information in the water body area by using a floating algae index method, and determining a threshold change range of cyanobacterial bloom in each image so as to realize identification of distribution characteristics of cyanobacterial bloom; according to the method, the accuracy and flexibility of narrow and long river cyanobacterial bloom information identification are remarkably 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.