118 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 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.

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 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 XGBoost chlorophyll a concentration aerial remote sensing inversion method based on characteristic wave band selection, and relates to the technical field of environmental monitoring. By constructing an integrated input characteristic vector, an optimal characteristic wave band most relevant to the chlorophyll a concentration, an enhanced chlorophyll a index and specially designed correction characteristics are fused; and more targeted information is provided for the model. Moreover, correction features obtained through calculation of spectral difference values of adjacent shadow regions and non-shadow regions are introduced, and training data containing samples of the two regions are utilized to train an XGBoost model, so that the XGBoost model can autonomously learn and quantify a composite interference effect brought by illumination and suspended matter concentration change. Therefore, according to the technology, inversion noise caused by complex environmental factors can be effectively inhibited, high-precision and full-coverage inversion of the chlorophyll a concentration under different illumination and turbidity conditions is realized, and the robustness and the practical application value of the method are remarkably improved.

The invention belongs to the technical field of water environment monitoring and early warning, and discloses a lake and reservoir chlorophyll a concentration prediction method based on an SO-KNN model. According to the invention, multi-time scale meteorological cumulative effect features are introduced to enrich information representation, and an SO-KNN intelligent prediction model is constructed. According to the method, under the conditions of data scarcity and non-equilibrium, the chlorophyll a concentration, especially the high-precision and strong-generalization prediction capability of the water bloom risk critical point, is remarkably improved. The model is simple in structure and efficient in calculation, the common defects of overfitting, insufficient generalization ability and the like of a complex machine learning model in the scene are effectively overcome, and a reliable and practical innovative technical solution is provided for early water bloom warning of northern reservoirs and water areas with similar data conditions.

The invention discloses an intelligent water bloom prediction and early warning system and method based on an AI large model. The system is mainly composed of a data acquisition module, a data preprocessing module, an AI large model module, an early warning module and a visualization module. The data acquisition module is responsible for widely collecting multi-source data of a target water area. A water quality sensor, an underwater robot and other equipment go deep into a water body to accurately collect physical and chemical parameters of the water body, a meteorological station is utilized to obtain comprehensive meteorological data in real time, hydrological data is obtained from a water conservancy department database or on-site hydrological monitoring equipment, and satellite remote sensing data is obtained by means of a satellite receiving station or a third-party remote sensing data platform. Macroscopic information such as water color, chlorophyll a concentration distribution and water area change is covered. The invention belongs to the field of water environment monitoring and early warning, and particularly relates to an intelligent water bloom predicting and early warning system and method based on an AI large model.

The present application relates to the technical field of water environment treatment and ecological restoration, and discloses a water transparency improving device and method based on an intelligent linkage dosing system, the water transparency improving device based on the intelligent linkage dosing system comprising a water transparency sensor, a suspended substance concentration sensor, a chlorophyll a sensor, a data acquisition and discrimination module, a medicament dosing control module, a medicament storage and dosing module and a medicament spraying module; the water transparency sensor, the suspended substance concentration sensor and the chlorophyll a sensor are arranged in a target water body; the data acquisition and discrimination module is connected with the water transparency sensor, the suspended substance concentration sensor and the chlorophyll a sensor respectively; the medicament dosing control module is connected with the data acquisition and discrimination module and the medicament storage and dosing module respectively; and the medicament storage and dosing module is connected with the medicament spraying module. The present application realizes intelligent and automatic improvement of water transparency.

The present application belongs to the technical field of water ecological risk early warning, and provides an algal bloom risk remote sensing intelligent identification method and system, remote sensing image data, meteorological data and water quality data of a target area are acquired and preprocessed; frequency domain feature extraction is performed to generate a three-dimensional frequency domain feature vector; a multi-task deep learning network is used to fuse the global feature representation obtained by modeling and the generated three-dimensional frequency domain feature vector to obtain a feature map; based on the feature map, an algal bloom binary segmentation probability map and a continuous value distribution map of chlorophyll a concentration are obtained; a semantic embedding head is used to generate an image semantic embedding vector, a pre-trained knowledge graph is used to perform semantic alignment on the image semantic embedding vector to generate research and judgment information text; the research and judgment information text, the algal bloom binary segmentation probability map and the continuous value distribution map of chlorophyll a concentration are cross-validated to obtain a final algal bloom risk judgment result. The present application realizes algal bloom risk remote sensing intelligent identification.

The invention provides a method for distinguishing chlorophyll a concentration of an upwelling region influenced by different types of cyclones, and relates to the technical field of ocean remote sensing and environment monitoring. Preliminarily judging the influence of vortex on chlorophyll a concentration distribution; dividing the research area into a near-shore area and a far-shore area according to the water depth, respectively drawing vortex center-chlorophyll a concentration fusion maps for the two areas, and comparing the influence of cyclone and anti-cyclone of the two areas on chlorophyll a concentration distribution; dividing the vortex into a near-shore vortex and a far-shore vortex according to the ratio of the distance from the vortex center to the coastline to the vortex radius, respectively drawing vortex center-chlorophyll a concentration fusion graphs, and comparing the influence of cyclone and anti-cyclone on the chlorophyll a concentration distribution; and matching the temperature and salt data of the Argo buoy with the vortex position, analyzing the correlation between the potential density abnormity and the chlorophyll a concentration, and distinguishing the influence mechanism of vertical nutrition input and horizontal advection on the chlorophyll a concentration.

This invention presents a method for retrieving chlorophyll a concentration in surface water based on satellite remote sensing. The method acquires remote sensing images from a satellite that match the acquisition time of the water quality data. Based on the spectral information extracted from these images, a new grayscale image is generated. A grayscale co-occurrence matrix (HCEM) is calculated from this new image. The HCEM is then used to mine the texture features contained in the remote sensing images, and each texture feature is quantified. This results in a significantly improved model performance compared to models trained solely using spectral information. Furthermore, with the same amount of raw data, this invention achieves higher accuracy, which is beneficial for the precise measurement of surface water eutrophication.

The application provides a backwater area water bloom prediction method and device, and relates to the technical field of environmental science. The method comprises the following steps: acquiring geographical hydrological data and environmental monitoring data of a target hydrological environment, and constructing a backwater area three-dimensional hydrodynamic water temperature model according to the geographical hydrological data and the environmental monitoring data; constructing a backwater area three-dimensional eutrophication model based on water temperature, water depth, horizontal direction flow velocity and wind speed obtained from the backwater area three-dimensional hydrodynamic water temperature model; constructing a BP neural network model based on the backwater area three-dimensional eutrophication model and training the BP neural network model; and obtaining a water bloom risk prediction result of the backwater area of the target hydrological environment by using the neural network model. The application provides data support for chlorophyll a calculation of the backwater area eutrophication model by constructing the backwater area three-dimensional hydrodynamic water temperature model, without the need to add a large number of high-frequency detection equipment, thereby greatly reducing the model operation time and improving the model prediction efficiency.

The invention belongs to the technical field of water quality detection, and discloses a chlorophyll a concentration inversion model construction method and system based on regional adaptation. A quality control method; a Chl-a estimation model is established; and an error analysis method. According to the method, two satellite systems of Sentine-3 OLCI and Sentine-2 MSI of the European Spatial Administration are adopted to carry out modeling and verification of Chl-a remote sensing estimation on two lakes, namely the nebula lake and the Fuxian lake which are located in the same region and have great difference in nutrition level, and research of business remote sensing inversion.

The invention relates to the technical field of water environment monitoring, and discloses a lake water quality multi-parameter deep learning inversion framework based on hyperspectral data, and the framework comprises the following steps: S1, data preparation and preprocessing: obtaining the hyperspectral data of lake water quality and corresponding water quality in-situ data, and preprocessing the hyperspectral data and the water quality in-situ data; s2, constructing a feature extraction and parameter inversion model which sequentially comprises a one-dimensional convolutional neural network module, a bidirectional long-short-term memory network module and a three-dimensional attention module, and inputting the preprocessed hyperspectral data into the model. According to the method, loss distribution can be dynamically optimized according to inversion requirements of different water quality parameters, the accuracy and generalization ability of simultaneous inversion of multiple parameters such as chlorophyll a, total suspended solids and transparency are remarkably improved, dependence of a traditional model on specific water body types is broken through, and the method can adapt to lake water bodies with different hydrological and optical characteristics.

The invention relates to the field of water purification, and discloses a targeted multi-dimensional water quality purification device and an intelligent operation method thereof.The targeted multi-dimensional water quality purification device comprises a water purification unit, an ecological floating island main body and aeration equipment, an aquatic plant planting area is arranged at the top of the ecological floating island main body, an artificial filler biological membrane is hung at the bottom, and a multi-cavity microbial agent storage tank is arranged in the ecological floating island main body for targeted addition of microbial agents according to water quality data; the energy supply unit is composed of a solar photovoltaic panel at the top of the floating island and a storage battery; the propelling unit controls the floating island to move; the monitoring unit collects pollutant concentration, dissolved oxygen DO and chlorophyll a data in real time through a bottom water quality sensor group and wirelessly transmits the data to the cloud platform; the cloud platform calculates a pollution index based on the pollutant concentration, and remotely regulates and controls the operation of the aeration equipment and the microbial inoculum adding and propelling unit in combination with analysis results of DO and chlorophyll a; according to the invention, dynamic identification and precise treatment of the polluted area are realized, and the water quality purification efficiency is improved.

The invention provides a water chlorophyll a prediction model generation method and device based on a space-time transmission mechanism and an agent model and electronic equipment, and relates to the field of water component analysis and prediction.The method comprises the steps that the optimal time lag transmitted from the upstream to the downstream and the upstream optimal time lag chlorophyll a concentration are determined; a flow gating function is constructed based on a set flow threshold value, and flow gating characteristics are generated in combination with the upstream optimal time-delay chlorophyll a concentration; determining a time-space coupling feature set based on the optimal time lag and upstream optimal time lag chlorophyll a concentration and flow gating features; calculating the importance degree of each feature in the full feature set based on a time sequence prediction agent model, recursively eliminating the feature with the lowest importance degree until the number of the features in the full feature set is reduced to a preset value, and obtaining an optimal feature subset; and training based on the optimal feature subset to obtain a water chlorophyll a prediction model. According to the method, the prediction precision is improved, and meanwhile, technical support is provided for eutrophication early warning.

The chlorophyll a prediction method based on spatial heterogeneity perception graph timing confrontation network relates to the technical field of chlorophyll a prediction, and is used for solving the problems that the subjectivity is strong in response to spatial heterogeneity by artificial partition, the partition boundary is not fine enough, and the statistical characteristic difference in the region is large, etc.The reconstructed daily scale chlorophyll a concentration remote sensing data and numerical simulation sea surface temperature data SST are taken as inputs, through spatial heterogeneity partition based on the time evolution behavior of chlorophyll a, graph convolution network GCN spatial coding, time convolution network TCN time coding and regional discriminator constraint, short-term prediction of the spatial distribution of chlorophyll a concentration in the future several days is realized, so that the representation ability of the model to the inhomogeneous change process of chlorophyll a in the complex offshore sea area is improved.

The application discloses a kind of multicomponent plankton algae concentration measurement method based on fluorescence spectrum stratification partitioning.The application belongs to the field of marine aquatic ecological environment monitoring technology.The algorithm will unknown to be analyzed mixed three-dimensional fluorescence spectrum according to the following stratification partitioning analysis idea to be analyzed.First layer: full spectrum is involved in analysis, and the purpose is to analyze cyanophyta concentration and cryptophyta concentration, and obtain the difference spectrum in full spectrum region.Second layer: partitioning analysis, take the region containing effective spectral feature to participate in analysis, and the purpose is to analyze chlorophyll a concentration of green algae, diatom, dinoflagellate and yellow algae.The algorithm is significantly superior to common analysis algorithm in reducing misidentification, especially in processing such as diatom, dinoflagellate and yellow algae with high similarity living body fluorescence spectrum.

The present invention relates to a method for inverting chlorophyll a concentration in an estuary based on multi-model fusion, and relates to the technical field of water quality monitoring. ESTARFM is used to fuse MODIS remote sensing images and Landsat 8 remote sensing images to obtain high-temporal and spatial resolution remote sensing images, thereby solving the problem that existing remote sensing images cannot simultaneously achieve high-temporal and spatial resolution, resulting in poor accuracy in inverting chlorophyll a concentration in the estuary. The reflectivity of the band combination screened out from the high-temporal and spatial resolution remote sensing images is used as input, and the corresponding monitored chlorophyll a concentration is used as output to construct a random forest model and a GBR model, respectively. A TPE search weight method is used to establish a fusion model of the random forest model and the GBR model, thereby solving the problem that a single machine learning model in the existing technology cannot adapt to the complex environmental conditions of the estuary, resulting in low accuracy in inverting chlorophyll a concentration in the estuary.

The invention discloses a water body classification and chlorophyll a concentration segmented inversion method based on a dynamic threshold value, which comprises the following steps of: constructing a water body nutrition state classification system by using a ratio index and an improved floating algae index (AFAI) according to the difference of remote sensing reflectivity of spectra of different types of water bodies in different wavebands, and constructing a regression model based on measured data, and the original static classification threshold is dynamic, so that self-adaptive adjustment of water body classification is realized. And finally, respectively constructing Chla inversion algorithms on the oligotrophic water body and the moderate algal bloom water body. Compared with a traditional fixed threshold value classification method, the dynamic threshold value can automatically adjust the classification boundary according to the environmental change, and the stability of the model under the seasonal change is enhanced. Meanwhile, compared with a single inversion algorithm, the segmented inversion method can significantly improve the estimation accuracy of Chla, and effectively improves the adaptability of an inversion model to water bodies in different nutritional states.

The application discloses a five-dimensional data fusion method for identifying apple anthracnose leaf blight based on a patrol robot, relates to the technical field of anthracnose leaf blight identification, and solves the problems of limited sensing dimension, great light interference and inaccurate spatial positioning in the prior art. The method comprises the following steps: S1, acquiring the curvature feature of the surface of an apple leaf, collecting an RGB-D image of the leaf, constructing a three-dimensional topological structure of the leaf, and positioning the spatial distribution of a disease spot; S2, acquiring the spectral reflection information of the apple leaf, quantifying the biochemical components of chlorophyll a and carotenoids, and constructing a vegetation index feature set sensitive to diseases; S3, collecting the temperature information of the apple tree crown layer, constructing a transpiration anomaly detection model, and realizing multi-modal fusion of thermal infrared and point cloud data; and S4, integrating the spatial distribution, spectral reflection and thermal radiation information, constructing a five-dimensional phenotype feature matrix model, and comprehensively analyzing the apple phenotype features. The application can significantly improve the recognition accuracy under the conditions of leaf shielding, uneven light, leaf posture change and environmental interference.

This invention relates to the field of optical monitoring technology for water environment, and discloses a method for analyzing and characterizing eutrophication components of water bodies based on hyperspectral feature inversion. The method includes: retrieving the intrinsic absorption spectrum sequence of pure water as a physical constraint benchmark; calculating the ratio of the hyperspectral reflectance to be measured to the benchmark to generate a modulation vector; determining the fractional-order differential sequence related to the sampling wavelength; extracting trough features through morphological baseline correction; completing the fractional-order differential transformation by combining the order sequence; extracting the characteristic trough depth and skewness parameters; and outputting the concentrations of chlorophyll a and colored soluble organic matter using an unmixing model. This invention utilizes the benchmark to drive the dynamic evolution of the differential operator, achieving topological separation of the scattering background and trace component absorption characteristics in different bands, suppressing local waveform distortion caused by background scattering heterogeneity, and improving the accuracy of component inversion under complex matrices.

A kind of offshore chlorophyll a spatiotemporal prediction method based on local background collaborative modeling, it relates to deep learning technical field, including: S1, based on multi-source business ocean product obtains multi-source heterogeneous data after pre-processing, obtains the historical offshore environmental spatiotemporal data sequence of offshore research area, then it is divided into training set, verification set and test set according to time sequence;S2, the training set corresponding offshore environmental spatiotemporal data sequence is input into offshore chlorophyll a spatiotemporal prediction model and is carried out distributed parallel training and parameter optimization, obtains the offshore chlorophyll a spatiotemporal prediction model after training;S3, the latest input offshore environmental spatiotemporal data sequence is carried out short-term prediction by offshore chlorophyll a spatiotemporal prediction model after training and obtains chlorophyll a prediction result;The present application solves the problem that the background constraint of existing model is insufficient, and the problem that the details are described fuzzy, improves the prediction accuracy and stability, realizes the high-resolution Chl-a short-term prediction of complex nearshore sea area.