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283 results about "Algal bloom" patented technology
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An algal bloom or algae bloom is a rapid increase or accumulation in the population of algae in freshwater or marine water systems, and is often recognized by the discoloration in the water from their pigments. The term algae encompasses many types of aquatic photosynthetic organisms, both macroscopic, multicellular organisms like seaweed and microscopic, unicellular organisms like cyanobacteria. Algal bloom commonly refers to rapid growth of microscopic, unicellular algae, not macroscopic algae. An example of a macroscopic algal bloom is a kelp forest. Algal blooms are the result of a nutrient, like nitrogen or phosphorus from fertilizer runoff, entering the aquatic system and causing excessive growth of algae. An algal bloom affects the whole ecosystem; it can have benign results like simply feeding higher tropic levels to more harmful effects like blocking the sunlight from reaching other organisms, causing a depletion of oxygen levels in the water, and, depending on the organism, secreting toxins into the water. The process of the oversupply of nutrients leading to algae growth and oxygen depletion is called eutrophication. Blooms that can injure animals or the ecology are called "harmful algal blooms" (HAB), and can lead to fish die-offs, cities cutting off water to residents, or states having to close fisheries.
Multi-temporal-spatial-scale cyanobacterial bloom early warning method for middle and large lake and reservoir water areas
PendingCN120725433AData processing applicationsIndication of weather conditions using multiple variablesSensing dataEarly warning system
The invention discloses a multi-temporal-spatial-scale cyanobacterial bloom early warning method for middle and large lake and reservoir water areas, and belongs to the field of cyanobacterial bloom prediction and risk monitoring. The method comprises the following steps: generating a pixel-level FAI index based on target water area remote sensing data, resampling meteorological data into a pixel level, then constructing a spatial-temporal distribution data set, training an Autoformer-ST-GNN time sequence model to realize FAI index prediction, dividing cyanobacterial bloom levels according to the FAI index prediction, and obtaining a global change trend; water quality monitoring points are arranged in key areas to collect data, historical water quality and meteorological data are utilized to train a DMC-PatchTST model fused with a blue-green algae migration period, and multi-time-scale prediction of the density of blue-green algae at the monitoring points is achieved; and finally, combining the global trend with a monitoring point prediction result to construct a space-time multi-scale cyanobacterial bloom comprehensive early warning system. According to the method, multi-source data and multiple models are fused, so that cyanobacterial bloom time-space multi-scale comprehensive early warning is realized, and the method is accurate and comprehensive.
Owner:ZHEJIANG UNIV
Algae community structure change prediction algorithm and system based on multi-source data fusion
The invention relates to the cross technical field of artificial intelligence and environment monitoring, and discloses an algal community structure change prediction algorithm and system based on multi-source data fusion, and the algorithm comprises the steps: obtaining water quality, weather and plankton multi-source time sequence data; performing time alignment and missing value interpolation; eliminating and screening key environment factors through recursive features; performing dynamic weighted fusion on the multi-modal features by using a space-time attention mechanism; inputting a three-layer stacked LSTM network to output future algae dominant species abundance prediction; and model parameters are corrected on line based on measured data. The system comprises a multi-source data acquisition module, a preprocessing module, a key factor extraction module, a space-time attention fusion module, a dynamic prediction module and an adaptive correction module. According to the method, the prediction accuracy and stability are remarkably improved, and algal bloom early warning and ecological regulation are effectively supported.
Owner:FUJIAN AGRI & FORESTRY UNIV +1
Intelligent monitoring method and system for cyanobacterial bloom outbreak
The invention relates to the technical field of data processing, and discloses an intelligent monitoring method and system for cyanobacterial bloom outbreak. The method comprises the following steps: collecting a water surface spectrum and underwater particle size data, carrying out atmospheric correction, calculating a normalized algae index and a blue-green wave band ratio, inputting the normalized algae index and the blue-green wave band ratio into a U-Net network to obtain a water bloom coverage area, carrying out integral interpolation on the particle size data to obtain a vertical section distribution curve, and calculating a surface layer enrichment degree and a floating trend index, and establishing a water surface-underwater association relationship through random forest regression training, and inputting the multi-dimensional features into a CNN-LSTM model to predict a water bloom outbreak probability and determine an early warning level. According to the method, the problem that the cyanobacterial bloom three-dimensional structure cannot be comprehensively described due to the lack of effective fusion of the water surface spectral data and the underwater vertical section data is solved, the problem that the early warning timeliness of cyanobacterial bloom outbreak is insufficient due to the lack of a multi-source data time sequence analysis model is solved, and the spatial integrity and early warning advance of cyanobacterial bloom monitoring are improved.
Owner:GUANGDONG HONGYU ECOLOGICAL ENVIRONMENT TECH CO LTD
Intra-day high-frequency automatic monitoring method and system for cyanobacterial bloom
InactiveCN120847356AMolecular entity identificationTesting waterEnvironmental resource managementWater quality
The invention provides an intraday high-frequency automatic monitoring method and system for lake cyanobacterial bloom based on a GOCI-II satellite, and aims to solve the problems that an existing method is susceptible to interference of thin cloud, low in recognition precision, high in false positive rate, low in efficiency and the like, and the processing flow depends on manpower. The system is based on an improved AFAI index, fine cloud detection, cloud expansion processing and classification correction strategies are combined, misrecognition caused by thin clouds and shadows is effectively restrained, and the extraction accuracy and robustness are improved. The system has the whole-process unattended processing capacity from GOCI-II data automatic downloading, preprocessing, algal bloom recognition, thematic map making to report output, can generate a monitoring report within one hour after satellite imaging, and supports intra-day multi-temporal cyanobacterial bloom dynamic monitoring. The method has been successfully applied to lakes such as Taihu Lake, lakes, Chaohu Lake and Hongze Lake, is particularly suitable for monitoring and early warning cyanobacterial bloom in large and medium lakes, and has wide application prospects in the fields of water environment supervision, water quality risk control, ecological assessment and the like.
Owner:SUZHOU CHENYANG HENGRUI INFORMATION TECH CO LTD +1
Lake cyanobacterial bloom detection method and system fused with remote sensing image
ActiveCN121091282ARadio wave reradiation/reflectionSynthetic aperture radarArbitrary Fluorescence Unit
The invention relates to the technical field of remote sensing monitoring, and discloses a lake cyanobacterial bloom detection method and system fused with a remote sensing image. The method comprises the following steps: acquiring a multispectral remote sensing image and a synthetic aperture radar image of a target lake; calculating a phycocyanobilin characteristic index and a chlorophyll fluorescence peak index according to the characteristic wave band reflectivity, and screening pixels meeting discrimination conditions to obtain an optical water bloom distribution mask; calculating a radar inversion phycocyanobilin index through a nonlinear regression model according to the dual-polarization backscattering coefficient to obtain a radar water bloom distribution mask; and fusing the double masks to obtain a cyanobacterial bloom monitoring result. The method solves the problems that an existing cyanobacterial bloom detection method cannot realize all-weather monitoring, lacks cyanobacterial specific recognition capability and is insufficient in reliability of a single data source, and improves the timeliness, accuracy and reliability of cyanobacterial bloom detection.
Owner:SOUTH CHINA INST OF ENVIRONMENTAL SCI MEP
Marine algal bloom disaster early warning system based on big data
The invention discloses a marine algal bloom disaster early warning system based on big data, particularly relates to the technical field of marine algal bloom disaster early warning, and is used for solving the problems that an existing algal bloom disaster monitoring means is lagged, early warning response is not timely and multi-source environmental data is difficult to fuse. The method comprises the steps of obtaining multi-source marine environment data including algae bloom position data and ocean current motion data, performing time-space synchronous calibration and standardization processing on the data, establishing a risk early warning area identification model, and calculating a dynamic correction coefficient in combination with an environment modulation factor. Key early warning features such as propagation trend intensity, influence range and duration are extracted to construct a multi-dimensional early warning signal vector, the multi-dimensional early warning signal vector is input into a pre-trained early warning level prediction model to generate a prediction early warning level, and a dynamic early warning trigger condition is set based on economic value and ecological sensitivity; and finally generating a grading early warning signal and pushing the grading early warning signal to a disaster early warning terminal in real time, thereby realizing high-precision, grading and dynamic early warning of the algae bloom disaster.
Owner:PUTIAN UNIV
Lake and reservoir algal bloom prediction and early warning system and method based on PCA-RBF neural network and time-space fusion
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.
Owner:JIAXING UNIV
Lake cyanobacterial bloom pixel level prediction method based on multi-source data fusion
A lake cyanobacterial bloom pixel level prediction method based on multi-source data fusion belongs to the technical field of algae prediction, and comprises the following steps: collecting lake pixel level multi-source basic data in a satellite image and carrying out preprocessing, calculating an algae index to generate a binary distribution product, carrying out space-time matching according to a zoning factor suitability parameter table, and carrying out prediction according to the zoning factor suitability parameter table. Inverting a blue-green algae proliferation rate and adjusting a factor weight; calculating a pixel comprehensive suitability degree; identifying a hysteresis effect factor through correlation analysis and causal test; screening a high impact factor through feature sorting, constructing a diffusion rule, extracting an initial water bloom pixel and determining a diffusion starting point; and constructing a neighborhood iterative diffusion model by using the space-time dynamic pixel-level suitability matrix, and iteratively simulating and outputting a pixel-level water bloom prediction map. According to the method, through multi-source pixel-level data standardization processing and partition threshold modeling, the coupling diffusion model is optimized in combination with the multi-source data, accurate water bloom prediction is achieved, and the space-time precision and practicability of pixel-level prediction are improved.
Owner:JIANGSU CLIMATE CENT
Layered light field correction chlorophyll-a remote sensing inversion method and system for eutrophic lake
The invention relates to the technical field of water environment remote sensing monitoring, solves the technical problem of systematic overestimation or underestimation under the condition of algae bloom outbreak or strong stratification due to the fact that a water body is regarded as an optical uniform monolayer parameter in a traditional method, and particularly relates to a stratified light field correction chlorophyll-a remote sensing inversion method and system for an eutrophic lake. Performing vertical type identification and three-layer layering by using multispectral / hyperspectral remote sensing reflectivity, a synchronous chlorophyll-a vertical profile and a diffusion attenuation coefficient, calculating light field weight and light path weighted concentration of each layer, constructing a layered light field correction coefficient, performing layered light field correction on the remote sensing reflectivity, and establishing an empirical chlorophyll-a inversion relationship; and generating a chlorophyll-a spatial distribution map and an algae bloom risk map. According to the method, the layered light field correction coefficient with clear physical significance is constructed to correct the water surface remote sensing reflectivity, so that the inversion precision and robustness under strong layering and complex optical conditions are improved.
Owner:ANQING NORMAL UNIV
Lake cyanobacterial bloom intelligent early warning method based on unmanned aerial vehicle remote sensing and image recognition
The invention discloses a lake cyanobacterial bloom intelligent early warning method based on unmanned aerial vehicle remote sensing and image recognition, and the method comprises the steps: fusing an unmanned aerial vehicle multispectral image and a convolutional neural network, extracting the spectral features of cyanobacteria, and calculating the concentration distribution; image registration and a dynamic model are combined to track water bloom boundary change and drift trajectory, satellite remote sensing is used to verify precision and invert biomass density, early warning levels are divided according to the precision and the biomass density, decision information is generated, and intelligent monitoring and accurate early warning of cyanobacterial bloom are realized. The comprehensive technical effects of water bloom dynamic monitoring, accurate early warning and efficient management are achieved, and a scientific basis is provided for water environment treatment.
Owner:YUNNAN ACAD OF ENVIRONMENTAL SCI
River and lake water bloom prediction method and system based on integrated diffusion learning model
PendingCN121303474ABiological neural network modelsForecastingHydrometryEnvironmental resource management
The invention discloses a river and lake water bloom prediction method and system based on an integrated diffusion learning model, and the method comprises the steps: carrying out the preprocessing of an original water quality time sequence according to the water quality monitoring data of a target river and lake region, and constructing a standardized water quality time sequence input data set; disturbing the data set based on a conditional diffusion generation model, and constructing a plurality of initial condition diversified input disturbance sets; a plurality of input disturbances of the disturbance set are sent into a deep neural network prediction model for parallel prediction, so that a plurality of algal bloom prediction orbits are formed to jointly form an integrated prediction result under disturbance driving; and statistical analysis and fusion processing are carried out to form prediction result distribution with uncertainty quantification capability, and visual display is carried out. According to the method, the uncertainty of the prediction result can be quantitatively described while the prediction precision is kept, and a more reliable decision basis can still be provided for water environment scheduling, ecological early warning and emergency response especially under extreme hydrological conditions such as flood and drought.
Owner:HOHAI UNIV +1
Lake and reservoir water source algal bloom risk early warning system based on 16S / 18SrRNA gene expression quantity threshold
PendingCN120683234AMicrobiological testing/measurementMicroorganism based processesCyanobacteriaGene expression level
The invention belongs to the technical field of lake and reservoir water source risk prediction, and provides a lake and reservoir water source algae bloom risk early warning system based on a 16S / 18SrRNA gene expression quantity threshold, and the method comprises the following steps: S1, collecting a sample from a lake and reservoir water source surface layer water body; s2, using 7-gate water bloom algae specific primers for blue-green algae, green algae, diatom, euglena, dinoflagellate, chrysophyta and cryptoalga; s3, based on the qPCR standard curve, calculating the copy number of the 16SrRNA / 18SrRNA gene of the seven water bloom algae; s4, when the gene expression quantity of a certain algal bloom algae continuously reaches 106-7 copy number / mL for 7-12 days, determining that the algal bloom algae is in a window phase; according to the threshold value of the gene expression quantity of the water bloom algae 16SrRNA / 18SrRNA, the window period of water bloom algae cells can be accurately recognized, so that early warning of the algae bloom risk in the lake and reservoir water source is achieved, and early warning of algae bloom outbreak in the lake and reservoir water source is achieved one week or above in advance.
Owner:INST OF URBAN ENVIRONMENT CHINESE ACAD OF SCI +2
Method for Early Warning of Algal Bloom Levels Based on Ordinal Forests Model
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.
Owner:XIAMEN UNIV
Algal bloom risk remote sensing intelligent identification method and system
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.
Owner:SHANDONG UNIV
Method for automatically monitoring dynamic change of algal bloom based on remote sensing image
A method for automatically monitoring dynamic changes of water blooms based on remote sensing images comprises the following steps: step 1, acquiring remote sensing reflectivity data of a multi-source remote sensing satellite in a screened time sequence range, calculating a spectral index, automatically determining an extraction threshold through pixel gradient statistics and an Otsu algorithm, and accurately identifying a water bloom area; and 2, calculating the water bloom area based on the multi-temporal data, researching the seasonal change, the long-term trend and the spatial distribution rule of the water bloom area in combination with a time sequence analysis method, and evaluating the accuracy of water bloom inversion by comparing the actually measured chlorophyll-a concentration with a water bloom inversion result. The application can monitor the change of the algal bloom area in real time.
Owner:CHINA YANGTZE POWER
Urban shallow lake ecological protection and restoration method and system integrated with intelligent management and control
PendingCN122010283AData processing applicationsWater aerationEnvironmental resource managementRestoration method
The invention discloses an urban shallow lake ecological protection and restoration method and system integrated with intelligent management and control, and relates to the technical field of water environment ecological management and intelligent water affairs, and the method comprises the steps: constructing a three-dimensional monitoring network to synchronously collect water quality, image and meteorological data, and carrying out the fusion to generate a multi-modal data set; a deep learning model is utilized to realize pollution source tracing, water quality prediction and algae bloom early warning in parallel; based on the prediction result, outputting a multi-facility cooperative regulation strategy through a reinforcement learning agent; and converting the strategy into a hierarchical instruction to drive an execution unit, and feeding back the treated environment state to the model and the intelligent agent to form closed-loop optimization. According to the invention, whole-course intelligent management and control from monitoring to execution are realized, systematicness, accuracy and perspectiveness of lake treatment are effectively improved, and water environment risk response capability and ecological restoration effect are significantly enhanced.
Owner:GUANGDONG RES INST OF WATER RESOURCES & HYDROPOWER
Algae bloom dynamic grading response method and device based on AI prediction
The invention relates to the technical field of algal bloom prevention and control, and discloses an algal bloom dynamic grading response method and device based on AI prediction, and the method comprises the following steps: dividing a target lake into a plurality of monitoring regions with preset areas; collecting water quality data and meteorological data in each monitoring area; generating an environment feature vector based on the water quality data and the meteorological data; through a preset first prediction model and a preset second prediction model, according to the environment feature vector, determining a water bloom outbreak probability of each monitoring area; performing weighted fusion on each water bloom outbreak probability to obtain a prediction probability, and determining a risk level of each monitoring area in the target lake; determining a corresponding disposal scheme based on the risk level; and a pre-deployed edge calculation controller is used to control the target processing device to carry out processing operation. According to the method, the accuracy and response speed of algal bloom outbreak prediction can be remarkably improved, and accurate and efficient automatic prevention and control are realized.
Owner:SOUTH CHINA INST OF ENVIRONMENTAL SCI MEP
Method for removing algae and purifying water, related synergistic composition, application and preparation method
PendingCN120622639AWater contaminantsWater/sewage treatment by neutralisationEmergency treatmentEnvironmental engineering
The invention belongs to the technical field of algae removal and water purification, and particularly relates to an algae removal and water purification method, a related synergistic composition, application and a preparation method. An organosilicon quaternary ammonium salt modified clay SiQAS-MC material is used as a matrix, one or more of high-valence metal ions or high-charge-density polymers of the high-valence metal ions are introduced, and after a composite material is formed, the composite material is put into a water body for use; or when the SiQAS-MC is used, one or more of high-valence metal ions or polymers with high charge density are added, and the SiQAS-MC is synchronously added into the water body for use. In conclusion, the synergistic composition can significantly improve the removal efficiency of SiQAS-MC on various algae, has triple water purification effects of removing phosphorus, descending turbidity and adjusting pH, and is suitable for emergency treatment of harmful algal blooms in water and remediation of polluted water.
Owner:INST OF OCEANOLOGY - CHINESE ACAD OF SCI
Method and system for multi-source algae image target detection
The present disclosure relates to a method and system for multi-source algae image target detection, and relates to the field of monitoring of algal bloom events in fresh water. The method includes first crawling images of algae of a selected species by using a built automated algae crawling tool, where the images include all formats; classifying and labeling algae in the algae images, and forming a source domain dataset by using all the classified and labeled algae images; performing transfer learning by using a faster recurrent revolutional neural network (Faster RCNN) with reference to a target domain dataset, to obtain a multi-source algae image target detection model; and finally performing identification and classification by using the multi-source algae image target detection model.
Owner:MACAU UNIV OF SCI & TECH
Water bloom prediction method and system based on ecological niche fitness
The invention provides a water bloom prediction method and system based on ecological niche fitness, and belongs to the technical field of water bloom prediction. In the training stage, a water sample collection method based on high time resolution is adopted, the fluctuation condition of the density of different types of algae along with time can be effectively reflected, the growth trend of the different types of algae can be more clearly reflected, and the prediction precision of the model is improved; in the prediction stage, the ecological niche fitness of competitive growth of different algae is quantitatively analyzed through a multi-fractal detrending coupling fluctuation analysis method, and the competitive growth relation of different types of algae under natural conditions is reflected; water quality data monitored by an automatic monitoring station in real time and ecological niche fitness of competitive growth of different algae are used as input, the concentration of key dominant algae can be predicted in real time, the spatial-temporal heterogeneity and the rapid dynamic change process of the algae are reflected, and an effective theoretical support is provided for early warning of algal bloom outbreak.
Owner:四川省生态环境监测总站
Precise water pollutant identification system based on multispectral image fusion
ActiveCN121236607AGeneral water supply conservationScene recognitionCloud processingData acquisition
The invention relates to the technical field of water body pollution monitoring, in particular to a multispectral image fused water body pollutant accurate recognition system, which comprises a data acquisition module, a cloud processing module, a boundary processing module, a pollution recognition module, a diffusion prediction module and a visualization module, the system constructs sub-pixel representation of a water pollutant boundary by using a differential geometry manifold theory, and realizes high-precision pollutant boundary description through a multi-scale analysis and curvature flow optimization technology; enhancing pollutant characteristic expression by adopting multispectral image fusion and an optimal wave band selection technology; using a support vector machine model to accurately identify various pollutant types such as oil films, oil spots, algae blooms and the like; based on a boundary fine description result and a pollutant type identification result, the diffusion trend of pollutants is accurately predicted in combination with historical flow, wind direction and wind speed data, and the system improves the water pollutant boundary identification precision and enhances the identification capability of complex boundary forms and low-contrast regions.
Owner:JIANGXI NORMAL UNIV
Lake and reservoir dominant algae community structure prediction method and system based on machine learning and storage medium
The invention discloses a lake and reservoir dominant algae community structure prediction method and system based on machine learning and a storage medium, and relates to the technical field of water environment monitoring and ecological prediction. Comprising the following steps that multi-source heterogeneous spatio-temporal data are obtained and fused, and the multi-source heterogeneous spatio-temporal data are historical time sequence data of a target water area; generating a feature vector from the fused multi-source heterogeneous spatio-temporal data; constructing a multi-output regression machine learning model, and inputting the feature vector into the multi-output regression machine learning model for training to obtain a multi-output prediction model; and inputting real-time monitoring data into the multi-output prediction model to generate a prediction result of a future dominant algae community structure. According to the method, whether cyanobacterial bloom or diatom bloom exists in the future can be clearly early warned, so that a manager can take targeted measures, for example, a specific algicide or ultrasonic equipment is used for the cyanobacteria, blind pesticide application is avoided, and the cost and the secondary pollution risk are reduced.
Owner:ZHEJIANG UNIV OF WATER RESOURCES & ELECTRIC POWER
Water quality detection method based on remote sensing technology
PendingCN120744371APolarisation-affecting propertiesColor/spectral properties measurementsWater qualityEngineering
The invention discloses a water quality detection method based on a remote sensing technology, and belongs to the field of water quality detection, and the detection method comprises the following specific steps: I, obtaining various remote sensing data in real time through various platforms and devices, and dynamically distributing the weight of each remote sensing data for data integration; iI, preprocessing the integrated remote sensing data, and predicting and complementing the remote sensing data in a missing time period in real time according to a historical water quality parameter change trend; iII, identifying the remote sensing data subjected to classification preprocessing and complementation, performing water quality parameter inversion, and identifying characteristic spectrums and polarization responses of different types of pollutants according to an inversion result; iV, tracing a pollution source according to a pollutant identification result, evaluating a physiological state and a potential outbreak risk of algae in the water body, and carrying out algae bloom early warning; according to the method, the minute-level water quality change is predicted, the problems that traditional monitoring points are sparse and the timeliness is low are solved, the water quality parameter recognition capability is remarkably enhanced, and the pollution recognition accuracy is improved.
Owner:JIANGSU BORN ENVIRONMENTAL PROTECTION TECH CO LTD
Algicidal foam compositions and methods of making and using thereof
Owner:UNIV OF MARYLAND
Blue-green algae bloom remote sensing recognition system based on multi-source satellite and intelligent threshold correction
The invention relates to a cyanobacterial bloom remote sensing recognition system based on a multi-source satellite and intelligent threshold correction, and the system comprises an intelligent image retrieval and screening module which is used for calling a multi-source image according to a daily frequency / weekly frequency period and generating a candidate set; the water body mask and preprocessing module is used for cloud removal, cutting, water body extraction and non-target plaque removal; the water bloom index and threshold value module is used for automatically calculating an ABDI or FAI index according to an image source and carrying out peak height proportion threshold value segmentation; the pattern spot extraction and clustering module is used for extracting algal bloom patches, merging pattern spots and removing small patches; and the standardized product generating and pushing module is used for generating and pushing grids, vectors, daily frequency brief reports and weekly frequency thematic reports. According to the invention, daily frequency / weekly frequency automatic and intelligent identification and achievement business output of cyanobacterial blooms can be realized, and the precision and continuity of remote sensing monitoring are significantly improved.
Owner:北京首创大气环境科技股份有限公司 +1
Method for promoting synthesis of microcystis astaxanthin by adjusting concentration of stress substance
The invention provides a method for promoting synthesis of microcystis astaxanthin by adjusting the concentration of a stress substance, which comprises the following steps of: firstly, culturing microcystis algal bloom or microcystis aeruginosa to a logarithmic phase by adopting a BG11 culture medium under the conditions of specific temperature, illumination and shaking table rotating speed, and adjusting the cell density by centrifuging and resuspending; then, NaCl, KCl, beta-violet ketone, longifolene and H2O2 with different concentrations are added into the algae liquid respectively, control is set, through multi-dimensional index measurement, the algae cell density, the ROS level, the photosynthetic pigment content, the photosynthetic performance and the level of astaxanthin and a precursor thereof are monitored when treatment is carried out for 0 day, 2 days, 4 days and 6 days, the expression quantity of astaxanthin biosynthesis related genes is further analyzed, and the astaxanthin biosynthesis related genes are obtained. Therefore, the optimal concentration of NaCl, KCl, beta-violet ketone, longifolene and H2O2 is determined, the effect of promoting astaxanthin synthesis is compared, and a theoretical basis and a precise regulation and control method are provided for industrial production of astaxanthin by using microcystis.
Owner:ZHEJIANG FORESTRY UNIVERSITY
Cyanobacterial bloom analysis method based on 720-degree panoramic photograph
PendingCN120976305AImage analysisCharacter and pattern recognitionImaging processingComputer graphics (images)
The invention relates to a cyanobacterial bloom analysis method based on a 720-degree panoramic photo, and belongs to the technical field of image processing. According to the method, shooting is carried out right above a cyanobacterial bloom distribution center point to obtain a 720-degree panoramic photo, the 720-degree panoramic photo is unfolded in a plane coordinate mode to carry out direction calibration, then a coordinate orientation grid is established, cyanobacterial blooms are identified, position statistics is carried out, and finally the cyanobacterial bloom area is calculated. The 720-degree panoramic picture is adopted for analysis, and the height angle is the lowest, so that the area calculation error is small, and the precision is high. And the efficiency of single investigation and analysis can be greatly improved.
Owner:KUNMING DIANCHI PLATEAU LAKE RES INST +1
Blue-green algae identification and quantification method and device based on unmanned aerial vehicle remote sensing image and deep learning, and medium
PendingCN121788931AImprove detection accuracyImprove adaptabilityImage analysisGeometric image transformationUncrewed vehicleEngineering
The invention discloses a blue-green algae identification and quantification method and device based on unmanned aerial vehicle remote sensing images and deep learning and a medium, and relates to the technical field of information data processing. Combining meteorological data and water quality monitoring data to construct an adaptive dynamic environment algorithm to extract EXIF metadata including camera parameters and attitude angle information, and establishing a geometric projection model to perform coarse orthographic correction on an original aerial image; the method comprises the following steps: extracting a multi-scale cyanobacterial bloom image feature map by stages based on a ResNet architecture and in combination with a feature pyramid network FPN fused with an attention mechanism, and obtaining an instance mask of cyanobacterial bloom through an anchor-free region proposal network, ROI Align and a head network; based on an improved GIS space projection and deep learning algorithm, carrying out high-precision area measurement and calculation on a binary mask image converted from the instance mask; according to the method, the influence of irrelevant interference on blue-green algae identification can be reduced, and the accuracy and comparability of blue-green algae identification and area calculation are improved.
Owner:TAIHU BASIN HYDROLOGY & WATER RESOURCES MONITORING CENT (TAIHU BASIN WATER ENVIRONMENT MONITORING CENT)
Self-adaptive sky-ground-water integrated water bloom monitoring method and platform
ActiveCN121409943AScattering properties measurementsColor/spectral properties measurementsOptical propertySky
The invention provides a self-adaptive sky-ground-water integrated algal bloom monitoring method and platform, which are used on the basis of establishing an intelligent algal bloom identification facility for dense time sequence sky-air-ground collaborative observation by using three-dimensional monitoring means such as an unmanned aerial vehicle, satellite remote sensing and near-ground hyperspectrum. According to the method, a dynamic calibration mechanism for capturing a dynamic mapping relation from inherent optical attributes to apparent optical attributes is combined to realize self-adaptive calibration of spectral feature positions of algae, so that real-time, efficient and accurate intelligent algal bloom recognition processing is promoted, an algal bloom change trend is presented in a spatial-temporal distribution mode in a multi-dimensional, multi-view and multi-scale manner, and the algal bloom recognition accuracy is improved. And effective data support is provided for algal bloom early warning of a major water source.
Owner:YANGTZE BASIN ECOLOGY & ENVIRONMENT MONITORING & SCIENTIFIC RESEARCH CENTER YANGTZE BASIN ECOLOGY & ENVIRONMENT ADMINISTRATION MINISTRY OF ECOLOGY & ENVIRONMENT OF THE PEOPLES REPUBLIC OF CHINA +1
Lake and reservoir chlorophyll concentration prediction method based on SO-KNN model
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.
Owner:DALIAN UNIV OF TECH