184 results about "Chlorophyll a" patented technology

The invention provides a multi-fusion seaweed field ecosystem observation method, system, device and medium, and belongs to the technical field of ecological monitoring, the method comprises the following steps: obtaining chlorophyll a concentration, seaweed canopy spectrum and three-dimensional biomass point cloud; aligning the chlorophyll a concentration of the target sea area with the seaweed canopy spectrum, and fusing the three-dimensional biomass point cloud to generate a three-dimensional biomass model; constructing an in-situ sampling network to monitor water quality parameters, benthic organism video streams and eDNA metagenome sequencing data, calibrating a three-dimensional biomass model, executing anomaly detection through a lightweight LSTM model, and identifying benthic organism species in real time through an improved YOLOv5 model; constructing a graph neural network, outputting a carbon sink prediction value, generating a brown tide early warning signal when the carbon sink prediction value is lower than a dynamic threshold value, optimizing a patrol path of the unmanned aerial vehicle based on reinforcement learning, and improving the sampling frequency of the water quality sensor. According to the invention, multi-fusion monitoring of the seaweed field is realized, the ecological condition is accurately evaluated, and abnormity is warned in advance.

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

The invention 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 water ecological environment remote sensing monitoring, and discloses a vertical pre-classification and deep learning-based total algae amount remote sensing estimation method and system, and the method comprises the steps: obtaining a chlorophyll a concentration inversion result of a water surface layer; obtaining a chlorophyll a profile distribution type based on the chlorophyll a concentration inversion result of the water body surface layer and the water environment information; and based on the chlorophyll a profile distribution type and the surface chlorophyll a concentration inversion result, performing vertical multi-layer continuous chlorophyll a concentration and total algae amount inversion to obtain an estimation result of the total algae amount. According to the method, the advantages of floating algae profile monitoring and a deep learning algorithm are fused, and stable and high-precision total algae amount estimation can be carried out in a water body which is greatly influenced by a terrestrial source and has complex vertical distribution and optical characteristics of floating algae. According to the method, more effective technical support is provided for remote sensing estimation research of the total amount of algae, occurrence of harmful algae blooms is found and warned in time, and the influence of algae bloom disasters is reduced.

The invention relates to the field of chlorophyll a concentration prediction, and discloses a 1D-DSCAM-based subsurface chlorophyll a concentration inversion method, which comprises the following steps of: obtaining sea surface temperature and sea surface chlorophyll a concentration data of satellite remote sensing and subsurface chlorophyll a concentration, subsurface temperature and salinity profile data of a biogeochemical buoy; carrying out preprocessing, constructing a data set, and inputting a training set into a 1D-DSCAM-GRU deep learning model to carry out model training; the model comprises an Embedding module, a 1D-DSCAM module and a multi-layer GRU module; and preprocessing sea surface temperature and sea surface chlorophyll a concentration data obtained by satellite remote sensing of a sea area to be predicted and subsurface layer temperature and salinity profile data obtained by a biogeochemical buoy, and inputting the data into the qualified model for subsurface layer chlorophyll a concentration prediction. According to the method disclosed by the invention, the prediction precision can be improved.

The invention provides a cyanobacterial bloom monitoring method and system. The cyanobacterial bloom monitoring method comprises the steps that a target water body remote sensing image of a target water body area of a monitoring area is acquired; screening a chlorophyll a sensitive wave band of the target water body remote sensing image, and obtaining chlorophyll a inversion concentration based on a preset chlorophyll a concentration inversion model; calculating a normalized vegetation index according to the multi-band remote sensing reflectivity value of the target water body remote sensing image; according to the chlorophyll a inversion concentration and the normalized vegetation index, constructing a cyanobacterial bloom grading threshold dynamic calibration model, and performing cyanobacterial bloom risk grading on the target water body area; and according to the cyanobacterial bloom risk grading result of the target water body area, generating an early warning instruction corresponding to the risk grade. The cyanobacterial bloom grading threshold value can be dynamically adjusted, so that the subjective deviation of manual weighting can be eliminated, and the real-time performance, accuracy and efficiency of cyanobacterial bloom monitoring and early warning are effectively improved.

The invention discloses a sea surface chlorophyll concentration prediction method and system based on remote sensing and deep learning, and relates to the technical field of marine ecological environment, and the method comprises the steps: inputting the marine chlorophyll a data of a target region into a trained marine chlorophyll concentration prediction model, and obtaining the marine chlorophyll concentration information of the target region; wherein the prediction model is an improved marine chlorophyll a concentration prediction model based on a convolutional long short-term memory network; the periodic feature extraction module is used for extracting historical synchronous related periodic features based on historical synchronous chlorophyll concentration data, and the noise removal and fusion module is used for eliminating noise output by the recent spatial-temporal feature extraction module and the periodic feature extraction module and fusing output results to obtain a marine chlorophyll a concentration data prediction result. According to the invention, the sea surface chlorophyll concentration can be predicted more accurately.

The invention discloses a chlorophyll calculation method based on a double-branch multi-scale fusion network model, which belongs to the technical field of chlorophyll calculation, is used for calculating a chlorophyll a concentration value, and comprises the following steps: taking the chlorophyll a concentration value of a measurement site as a label of a water body characteristic sequence data set, dividing the data set into a training set and a verification set, inputting the training set into the double-branch multi-scale fusion network model to obtain a trained double-branch multi-scale fusion network model; inputting the verification set into the double-branch multi-scale fusion network model to obtain a verified double-branch multi-scale fusion network model; and taking the neighborhood spectral feature tensor and the neighborhood inherent optical feature tensor as the input of the verified double-branch multi-scale fusion network model to obtain the chlorophyll a concentration value of the pixel at the corresponding position. According to the method, the calculation result of the chlorophyll a with relatively high precision is obtained through neural network processing, and the complex relationship among the concentration change of the chlorophyll a in the water body, the spectrum and other optical parameters can be captured.

The invention discloses a water source chlorophyll a concentration prediction model design method based on machine learning. The method comprises the following steps: acquiring chlorophyll a concentration data in a to-be-predicted region for a continuous period of time; carrying out data preprocessing on the chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing a concentration prediction model, carrying out data preprocessing on chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing different concentration prediction models, and inputting the processed chlorophyll a concentration data and physicochemical parameters into the prediction models to obtain a chlorophyll a concentration data prediction result; and comparing prediction results of different prediction models, and determining the prediction model. According to the prediction model design method, the WT-GRU model is adopted to preprocess the data through wavelet transform, the wavelet transform effectively extracts key time scale characteristics through signal decomposition, and the accuracy of chlorophyll a concentration prediction is remarkably improved.

A photon counting laser radar-based open water chlorophyll a concentration profile detection method is used for accurately acquiring profile concentration distribution of chlorophyll a in a water body. According to the method, ICESat-2 photon counting laser radar data and MODIS satellite remote sensing images are combined, a photon propagation process is modeled by using a Monte Carlo simulation technology, energy attenuation and scattering characteristics of photons in a water body are calculated, an extinction coefficient and a backscattering coefficient are further derived, and chlorophyll a concentration distribution is inversed according to the parameters. High-precision water chlorophyll a concentration profile data can be provided by accurately simulating the propagation process of photons in a water body and considering the optical characteristics and environmental factors of the water body. MODIS remote sensing data is used for assistance, the accuracy and reliability of an inversion result are further improved, and accurate chlorophyll concentration detection can be achieved in different open water body areas. The method is suitable for monitoring the chlorophyll concentration of water bodies such as oceans, lakes and rivers, and provides a scientific basis for water body ecological environment evaluation.

The invention provides a regional ecological environment quality evaluation method and system based on remote sensing data, and relates to the technical field of environment remote sensing and ecological monitoring, and the method comprises the steps: extracting red light and near-infrared reflectivity at the peak of a growing season by using Landsat8 satellite data, and calculating a mixed vegetation index; synchronously measuring vegetation indexes of pure vegetation and bare soil on site to obtain a vegetation coverage rate, and obtaining water transparency, suspended solid concentration and chlorophyll a concentration through on-site measurement on the basis of green light, red light, blue light and near-infrared reflectivity in a heavy rainfall period and a dry season; the method comprises the following steps: constructing a linear regression model of reflectivity and water quality parameters through a least square method, calculating a water quality index, constructing an extreme weather influence factor based on ten-year extreme weather data, and finally fusing a vegetation coverage rate, water quality, the extreme weather influence factor, annual precipitation, annual average temperature, optimal regional vegetation temperature and historical rainfall extremum. And constructing an ecological quality index, and dividing ecological environment quality grades.

The invention discloses an apple anthracnose leaf blight recognition five-dimensional data fusion method based on a patrol robot, relates to the technical field of anthracnose leaf blight recognition, and solves the problems of limited sensing dimension, large illumination interference and inaccurate spatial positioning in the prior art. Comprising the following steps: S1, acquiring surface curvature characteristics of apple leaves, collecting RGB-D images of the leaves, constructing a three-dimensional topological structure of the leaves, and positioning scab space distribution; s2, acquiring spectral reflection information of apple leaves, quantifying biochemical components of chlorophyll a and carotenoid, and constructing a vegetation index feature set sensitive to diseases; s3, apple tree canopy temperature information is collected, a transpiration anomaly detection model is constructed, and multi-modal fusion of thermal infrared and point cloud data is realized; and S4, integrating spatial distribution, spectral reflection and thermal radiation information, constructing a five-dimensional phenotypic characteristic matrix model, and comprehensively analyzing apple phenotypic characteristics. According to the method, the recognition accuracy under the conditions of blade shielding, uneven illumination, blade posture change and environment interference can be remarkably improved.

The invention provides a water chlorophyll a concentration inversion method based on a hyperspectral remote sensing image, which relates to the technical field of remote sensing inversion and comprises the steps of image preprocessing, water pixel extraction, actually measured sample space registration, spectral feature construction, model score optimization, concentration prediction and the like. According to the method, an optimal scheme is screened in a feature-model combination through cross validation and a unified scoring function, the optimal scheme is applied to whole image calculation, and a chlorophyll a concentration spatial distribution map and a matched quality control map layer are output. The method has the characteristics of high precision, self-adaption and high engineering practicability, and is suitable for a water quality remote sensing inversion scene driven by multi-source hyperspectral data.

The invention provides a lake and reservoir algal bloom prediction and early warning system and method based on a PCA-RBF neural network and time-space fusion. Multi-dimensional water quality parameters are monitored and obtained in real time through a water quality sensor array device carried on an unmanned ship; key water quality parameters are dynamically screened by calculating Pearson's correlation coefficients of the water quality parameters, and PCC gt is reserved; a time sequence data set is generated according to the index of 0.22; inputting the time sequence data set into a PCA-RBF neural network prediction model for dynamic prediction, outputting a chlorophyll a concentration prediction value, and generating a prediction sequence; calculating the water bloom outbreak probability of the prediction sequence of the chlorophyll a concentration by adopting a time sequence decomposition and ARIMA combined algorithm; and carrying out risk grade division on the algal bloom outbreak probability according to a grading early warning rule. According to the method, dimension reduction is carried out on multi-dimensional water quality parameters through the PCA-RBF neural network, the concentration of chlorophyll a is predicted, a concentration time sequence rule is analyzed by adopting an STL-ARIMA combined algorithm, and graded early warning signals are output by fusing spatio-temporal characteristics, so that accurate prevention and control of water bloom risks are realized.

The invention provides a seawater chlorophyll a concentration three-dimensional remote sensing detection method, medium, equipment and product, and relates to the technical field of ocean remote sensing. The method comprises the following steps: acquiring satellite remote sensing data and photon counting laser radar data of a to-be-detected area; obtaining a seawater chlorophyll a concentration profile based on the diffusion attenuation coefficient; the method comprises the following steps: constructing an improved VGGNet model which comprises a multi-branch VGGNet, replacing standard convolution in the VGGNet with depth separable convolution, and introducing an SE attention mechanism into a backbone network; the time and position variables, the satellite remote sensing data, the photon density, the backscattering coefficient, the diffusion attenuation coefficient and the water depth serve as input variables of the model, the seawater chlorophyll a concentration profile serves as a target variable, and the model is trained; and inputting new satellite remote sensing data into the trained model to generate three-dimensional distribution prediction of the seawater chlorophyll a concentration. The method can better extract the features in the data, improves the prediction precision of the model, and is suitable for a complex water body environment.

The invention discloses a Chla monitoring method based on visible-near infrared spectrum and machine learning, which is a modeling method for performing chlorophyll a concentration parameter inversion by using visible-near infrared hyperspectral data, and combines primary screening of spectral characteristic wave bands, training sample expansion based on GAN, spectral characteristic wave band fine screening based on CARS and a regression modeling technology. The problems of high dimension of hyperspectral data and insufficient samples are solved, the overall Chl-a modeling precision is improved, and the method is suitable for water eutrophication monitoring, marine ecological assessment and environment remote sensing application.

The invention belongs to the technical field of remote sensing image data processing, and relates to a multi-feature fusion coastal zone culture pond extraction method, which comprises the following steps: obtaining spectral features and polarization features based on an obtained Sentinel-1 image and an obtained Sentinel-2 image; calculating and evaluating an NDWI time sequence based on the NDWI to generate a time sequence synthesis NDWI image; a water body main body is obtained through the hierarchical feature fusion decision tree; obtaining morphological characteristics based on the water body object; obtaining the chlorophyll a concentration and the dynamic characteristic factor of the chlorophyll a concentration based on the Sentinel-2 image; and extracting a culture pond through a random forest classifier, and generating a culture pond spatial distribution diagram. According to the method, the spectral features, the polarization features, the morphological features, the chlorophyll a concentration and the chlorophyll a concentration dynamic feature factors are fused, and the decision tree and the random forest classifier are fused through the hierarchical features, so that the problems of low accuracy and poor stability of existing culture pond extraction are solved.

A method for early warning of algal bloom levels based on an Ordinal Forests model includes the following steps: S1, preprocessing water quality data from a system for online monitoring of water quality and water ecology; S2, determining an algal bloom level according to a chlorophyll a value of the pre-processed water quality data; S3, using a resampling method to solve the problem of imbalanced algal bloom level data, and synthesizing a dataset of balanced algal bloom levels; and S4, taking the newly synthesized dataset in the S3 as an input variable, constructing a model for early warning of algal bloom levels based on the Ordinal Forests model, and performing early warning of algal bloom levels through the trained model for early warning of algal bloom levels.

The invention belongs to the technical field of water ecology risk early warning, and provides an algal bloom risk remote sensing intelligent identification method and system, and the method comprises the steps: obtaining remote sensing image data, meteorological data and water quality data of a target region, and carrying out the preprocessing; performing frequency domain feature extraction to generate a three-dimensional frequency domain feature vector; fusing the global feature representation obtained by modeling and the generated three-dimensional frequency domain feature vector by using a multi-task deep learning network to obtain a feature map; obtaining an algae bloom binary segmentation probability graph and a continuous value distribution graph of chlorophyll a concentration based on the characteristic graph; generating an image semantic embedding vector by utilizing a semantic embedding head, and performing semantic alignment on the image semantic embedding vector by adopting a pre-trained knowledge graph to generate a research and judgment information text; and performing cross validation on the study and judgment information text, the algae bloom binary segmentation probability graph and the continuous value distribution graph of the chlorophyll a concentration to obtain a final algae bloom risk judgment result. According to the invention, the algal bloom risk remote sensing intelligent identification is realized.

The invention discloses an intelligent algae control method and device based on target recognition, and relates to the technical field of wastewater biological treatment. According to the method, algal bloom severity identification is carried out based on the average concentration of chlorophyll a, algae identification is carried out according to images at different positions, algae identification results are obtained, the algae identification results of the images at different positions are counted, the number of different types of algae in a target algae control water area is obtained, and the algal bloom severity is determined. According to the method, the hydration severity, the number of the different types of algae and the growth rate can be automatically monitored, different algae control schemes are set according to the different hydration severity, the number of the different types of algae and the growth rate, and the algae control efficiency is improved. The functions of all-weather fixed-point water quality monitoring, water quality data analysis and early warning, algae outbreak early growth inhibition and the like of an unmanned ship algae control system are achieved, and efficient targeted algae control is completed.

The invention provides a remote sensing estimation method for the chlorophyll a concentration of a river, and belongs to the technical field of hydrological remote sensing, and the method comprises the following steps: during a satellite transit period, sampling at a plurality of point locations of the river to obtain measured data, obtaining satellite data, carrying out Rayleigh scattering correction on the satellite data to obtain reflectivity after Rayleigh correction, and calculating the chlorophyll a concentration of the river; the method comprises the following steps: screening measured data, establishing an XGBoost model, training the model data, and calculating the chlorophyll a concentration of the river by using the trained XGBoost model. The invention relates to a river chlorophyll a concentration remote sensing estimation method by using Landsat and machine learning. According to the method, based on actually measured chlorophyll a concentration data of the Pearl River basin, a machine learning model capable of estimating the river chlorophyll a concentration is established by using Landsat data after Rayleigh scattering correction, a new thought is provided for monitoring river water quality, and powerful technical support is provided for river eutrophication monitoring and water environment protection and treatment.

The invention belongs to the technical field of ocean prediction, and discloses a chlorophyll a prediction method and system based on heterogeneity perception and equation embedding, and the method comprises the steps: 1, obtaining the related input data of chlorophyll a concentration prediction: 2, carrying out the spatial heterogeneity measurement: carrying out the sub-region division of an original input region, and obtaining the spatial heterogeneity level of the sub-region; step 3, heterogeneity perception space-time evolution modeling: constructing a multilayer adaptive heterogeneity perception network, and outputting heterogeneity perception space-time evolution characteristics; 4, designing a physical constraint guide prediction network embedded by the fluid motion equation, and calculating a space-time evolution characteristic sequence of future chlorophyll a; and step 5, carrying out remodeling and aggregation decoding operation on the space-time evolution feature sequence, and merging the sub-region prediction sequences in corresponding time periods to obtain a final prediction result. By means of the method, space-time evolution modeling of space heterogeneity perception and equation physical knowledge embedding is achieved, and the accuracy and reliability of chlorophyll a concentration prediction are improved.

The invention discloses a water area chlorophyll a concentration remote sensing inversion method and system, a medium and equipment, relates to the technical field of environmental monitoring, and provides an improved genetic algorithm for feature selection based on Sentinel-2 remote sensing satellite images and actually measured Chl-a concentration data of a Wuliang vegetarian sea by taking the Wuliang vegetarian sea as a research area, and an XGBoost model is optimized in combination with a dung beetle optimization algorithm DBO. And inversion is carried out on the Chl-a concentration of the ouyew vegetarian sea. A remote sensing characteristic wave band of Chl-a concentration is screened through an improved genetic algorithm, the remote sensing characteristic wave band is input into a DBO-XGBoost model, and a DBO-XGBoost Wuji prime sea Chl-a concentration inversion model is established. The result shows that under the same inversion model, redundant information between wave bands is effectively reduced, the complexity of the model is reduced, and the prediction performance of the model is improved at the same time. The decision coefficient R2 of the-DBO-XGBoost model is 0.9379, and the decision coefficient R2 of the- The root-mean-square error (RMSE) is 2.2566 [mu] g / L, the mean absolute error (MAE) is 1.9019 [mu] g / L, and powerful support is provided for monitoring of the concentration of the Wuliang vegetarian sea Chl-a.

The invention relates to the technical field of ocean observation, and provides a multi-element sea surface chlorophyll a concentration prediction method based on feature processing. The method comprises the following steps: acquiring historical sea surface chlorophyll a concentration data, historical sea surface temperature data, historical sea surface salinity data and historical ocean current velocity data in a to-be-predicted region for a continuous period of time, inputting the data into different channels of a convolutional network, extracting five channel feature maps, and performing down-sampling processing; inputting the four channel feature maps subjected to down-sampling processing into a first SELayer layer, calculating the weight of each channel feature map to obtain four weighted channel feature maps, obtaining four spatial feature maps by adopting an attention mixing module, obtaining four weighted spatial feature maps by adopting a second SELayer layer, and fusing the feature maps by adopting a convolutional network and up-sampling processing to obtain a new channel feature map; and outputting the sea surface chlorophyll a concentration data in the to-be-predicted region in a future period of time through the full connection layer.

The invention relates to the technical field of lake and reservoir water bloom prediction, in particular to an intelligent lake and reservoir water bloom prediction system and method based on a DTW algorithm. Comprising a data acquisition module, a matrix construction module, a weight distribution module and a display alarm module. Concentration values of chlorophyll a at different positions of a reservoir are collected through a data collection module, the concentration values are arranged according to a time sequence and divided into two parts, a matrix building module is used for calculating the similarity between a comparison sequence Xc and a training data set Xt, a prediction matrix is built with a time step length n as a row vector and a sub-sequence with the similarity m as a column vector, and a prediction result is obtained. According to the model, only chlorophyll a is adopted as monitoring data, the requirement for original data is greatly reduced, the calculation amount of the model is reduced through the DTW algorithm, and the accuracy and robustness of a prediction result are guaranteed by dynamically adjusting the weight between different similarities.

The invention relates to the technical field of marine ecological environment regulation and control, in particular to an offshore wind power driven ecological floating island blue carbon capture and regulation self-adaption method, which comprises the following steps: S1, acquiring the flow velocity gradient, chlorophyll a concentration and dissolved inorganic carbon content of a target sea area, and acquiring torque fluctuation data of a main shaft of a wind power device; s2, calculating a unit area carbon flux threshold value of the floating island; s3, generating a floating island matrix permeability adjusting instruction according to the carbon flux threshold value; s4, triggering a salinity gradient power generation module to generate compensation electric energy; s5, activating a micro-fluidic chip by using the compensation electric energy, and controlling the opening and closing frequency of pores of the mangrove forest; and S6, dynamically correcting the distribution of balancing weights of the floating island, so that the deposition rate of blue carbon is stabilized in a target interval. According to the method, wind power energy prediction, carbon flux calculation and floating island self-adaptive structure regulation and control are integrated in real time, efficient and stable operation of the blue carbon capture process of the floating island in the complex marine environment is achieved, and therefore the carbon fixing efficiency is remarkably improved.

The invention relates to an estuary chlorophyll a concentration inversion method based on multi-model fusion, and relates to the technical field of water quality monitoring. The ESTARFM is adopted to fuse the MODIS remote sensing image and the Landsat8 remote sensing image to obtain the high temporal-spatial resolution remote sensing image, and the problem that the high temporal-spatial resolution of the existing remote sensing image cannot be realized at the same time, so that the inversion precision of the estuary chlorophyll a concentration is poor is solved. The method comprises the following steps: respectively constructing a random forest model and a GBR (Guarantee Bit Rate) model by taking the reflectivity of a wave band combination screened from a high-temporal-spatial-resolution remote sensing image as input and the correspondingly monitored chlorophyll a concentration as output; a fusion model of a random forest model and a GBR model is established by adopting a TPE search weight method, and the problem of low accuracy of estuary chlorophyll a concentration inversion caused by the fact that a single machine learning model cannot adapt to complex estuary environment conditions in the prior art is solved.

The invention provides a method for culturing beneficial algae by resource utilization of cyanobacterial blooms, which comprises the following steps: 1, obtaining cyanobacterial (microcystis) blooms, and concentrating the cyanobacterial (microcystis) blooms until the concentration of chlorophyll a is 80-150mg / L; 2, carrying out continuous hydrodynamic disturbance on the high-concentration cyanobacterial bloom, wherein the disturbance intensity is suitable for preventing the cyanobacterial bloom from blackening and stinking; 3, continuously disturbing for 20-60 days to obtain cyanobacterial bloom which turns light yellow green or white; 4, tap water is used for diluting cyanobacterial blooms with changing colors, the concentration during dilution is 0.5-2.3 mL / cm < 2 >, and beneficial algae such as diatom and green algae are inoculated for culture under the conditions of normal temperature and weak light; and 5, when the culture is performed for 12-30 days, the growth density of the beneficial algae reaches the highest peak, and the cell density of the beneficial algae can reach up to 2.5 * 10 < 6 >-5.6 * 10 < 7 > cells / cm < 2 >. According to the method, algal toxin can be removed, beneficial algae such as diatom and green algae can be cultured through resource utilization of the cyanobacterial bloom, and a new way is provided for control and resource utilization of the cyanobacterial bloom.

The invention discloses a global chlorophyll a remote sensing inversion optimization method and device covering a high latitude area, and the method comprises the steps: carrying out the space-time matching of sea surface temperature based on an actual measurement data set of the actual measurement remote sensing reflectivity and the actual measurement chlorophyll a concentration, and carrying out the integrated construction of a training data set, the measured remote sensing reflectivity (412 nm, 443 nm, 490 nm, 555 nm and 670 nm), the sea surface temperature, the longitude, the latitude and the measured chlorophyll a concentration of five wave bands are included; a BP neural network is applied, a global chlorophyll a remote sensing inversion model covering a high latitude area is constructed, the training data set is adopted for training, an initial weight and a threshold value of the BP neural network are obtained through iterative solution optimization of a genetic algorithm, and a trained global chlorophyll a remote sensing inversion model is obtained; and inputting the satellite remote sensing reflectivity to be inverted and the matched sea surface temperature, longitude and latitude into the trained global chlorophyll a remote sensing inversion model, and performing inversion to obtain the chlorophyll a concentration. The method can effectively improve the global remote sensing inversion precision of chlorophyll a.

The invention relates to the technical field of data processing, and provides a marine chlorophyll a spatial-temporal variation and trend prediction method and system, and the method comprises the following steps: obtaining wind speed and sea surface temperature data for a to-be-predicted region; for the wind speed data, combining the strength of Ekman transportation and the angle of a coastline to quantify coastwise upwelling, and obtaining an upwelling index calculated based on the wind speed; using a DINEOF method to reconstruct missing data in the sea surface temperature data, and performing interpolation processing on the sea surface temperature data; and predicting the upwelling index and the sea surface temperature data subjected to interpolation processing by adopting a Chl-a long-term trend prediction model which is obtained based on correlation analysis and is related to the sea surface temperature and the wind speed, so as to obtain the concentration and the change trend of the sea chlorophyll a. According to the method, the precision of wind speed data quantification processing is improved, and meanwhile, missing data are processed, so that the accuracy of the concentration and the change trend of marine chlorophyll a can be improved.