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421 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
Method for evaluating algal bloom risk of water body
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.
Owner:GUANGDONG PROVINCIAL HYDROLOGICAL BUREAU SHAOGUAN HYDROLOGICAL BRANCH
Long and narrow river cyanobacterial bloom information identification method and system based on remote sensing data
PendingCN119295946AHigh precisionRealize regulationCharacter and pattern recognitionSensing dataCyanobacteria bloom
The invention discloses a long and narrow river cyanobacterial bloom information identification method and system based on remote sensing data, and the method comprises the following steps: selecting a remote sensing image in a research region range through a CEE cloud platform, and carrying out the preprocessing of the remote sensing image; a Sentinel-2 satellite image and a Landsat satellite image are respectively utilized to detect a cloud layer in the preprocessed remote sensing image within a cloud cover threshold value; based on a detection result, removing cloud pixels from the preprocessed remote sensing image to obtain a cloud-removed image; analyzing the cloud-removed image by using the improved normalized difference water body index, and determining the threshold change range of the water body in each image of the cloud-removed image so as to realize the recognition of the water body area; identifying cyanobacterial bloom information in the water body area by using a floating algae index method, and determining a threshold change range of cyanobacterial bloom in each image so as to realize identification of distribution characteristics of cyanobacterial bloom; according to the method, the accuracy and flexibility of narrow and long river cyanobacterial bloom information identification are remarkably improved.
Owner:HOHAI 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
Method and system for monitoring cyanobacterial bloom
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.
Owner:GUANGZHOU INST OF GEOGRAPHY GUANGDONG ACAD OF SCI +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
Multi-scale collaborative algal bloom assessment method fusing underwater video
ActiveCN119495019Aaccurate assessmentCharacter and pattern recognitionBiological modelsUnderwaterWater quality
The invention relates to a multi-scale collaborative algal bloom assessment method fused with an underwater video, and belongs to the field of image recognition. The method comprises the following steps: S1, acquiring underwater, water surface and high-altitude images; s2, collecting water quality data and meteorological data; s3, aligning the data in time and space; s4, performing binary mask segmentation on the image by using an image segmentation technology; s5, vectorizing the binary mask of the image, and performing concat splicing on the vectorized binary mask, the water quality data and the meteorological data to serve as input; s6, an improved Transform model is used for prediction; and S7, fusing the binary masks of the images by using a weighted summation method to obtain a binary mask of the algal bloom area of the monitoring area and a fused algal bloom coverage ratio. The method provided by the invention can realize calculation and prediction of the binary mask of the algal bloom area and the fusion algal bloom coverage ratio of the monitoring area at multiple moments, and is beneficial to dynamic and accurate evaluation of the algal bloom phenomenon.
Owner:CHONGQING INST OF GREEN & INTELLIGENT TECH CHINESE ACAD OF SCI
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
Ship capable of synchronously purifying water and removing algae
ActiveCN222390402USimple structurerestore transparencyWater cleaningBatteries circuit arrangementsChemical storageMicroorganism
The utility model discloses a synchronous water quality purification algae removal ship, relates to the technical field of water treatment, and aims to solve the problem that a large-area water body needs to automatically and synchronously treat algal bloom, SS (suspended solids) and nitrogen and phosphorus pollution. A ship body is fixedly provided with an algae removal reaction bin, an SS removal reaction bin and a nitrogen and phosphorus removal bin, and liquid inlet ends of the three are respectively connected with a liquid pump I, a liquid pump II and a liquid pump III for providing liquid conveying power; an algae removal agent storage bin is fixedly arranged at the upper end or the side wall of the algae removal reaction bin; an iron ion one-way pressure permeable membrane is arranged at a liquid outlet of the algae removal reaction bin and is used for intercepting iron ions in the bin; an SS removal agent storage bin is fixedly arranged at the upper end or the side wall of the SS removal reaction bin; a funnel-shaped slope is arranged at the bin bottom of the SS removal reaction bin, and a sludge outlet is formed in the middle of the slope bottom; a filler is arranged in the nitrogen and phosphorus removal bin, and nitrogen and phosphorus removal functional microorganisms are arranged in the nitrogen and phosphorus removal bin; the device can simultaneously treat water bloom, SS (suspended solids) and nitrogen and phosphorus pollution in a water body, and is suitable for automatic treatment of a large-area water area.
Owner:HUATIAN ENG & TECH CORP MCC +1
Surveying and mapping method based on remote sensing big data analysis
ActiveCN120408261AMaterial heat developmentScattering properties measurementsIndustrial waste waterWater quality
The invention discloses a surveying and mapping method based on remote sensing big data analysis, and relates to the technical field of remote sensing big data. During operation of the system, images and spectral data of a target water body area are collected through satellite remote sensing, unmanned aerial vehicle remote sensing or a ground sensor, the collected remote sensing data are preprocessed, and the target water body area is obtained through waveband combination or spectral line analysis; the method comprises the following steps: extracting characteristic parameters of a water body by utilizing spectral information in remote sensing data, carrying out multi-dimensional analysis on field data, calculating to obtain a water body turbidity coefficient Ct, an algal bloom coefficient Ca and an oil pollution coefficient Co, and analyzing the influence of potential pollution sources, agricultural runoff and industrial wastewater discharge on the water body by combining the remote sensing data and geographic information. And performing spatial-temporal change analysis on the water body pollution condition, identifying the trend of pollutant expansion or degradation, and based on the results of pollution assessment and spatial-temporal analysis, comparing the comprehensive water quality index WQI with a preset threshold to generate water quality level early warning information.
Owner:SURVEYING & MAPPING INST OF LINYI MUNICIPAL BUREAU OF LAND & RESOURCES
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
Method for predicting spatial distribution of algal blooms in hour-scale lake based on stationary satellite
The invention discloses an hour-scale lake algal bloom space distribution prediction method based on a stationary satellite, and the method comprises the steps: obtaining the hourly wind speed, wind direction, air temperature and other meteorological data and GOCI series satellite remote sensing data, and extracting the algal bloom coverage through an algal bloom recognition index; based on the priori knowledge of wind-driven algae bloom migration and accumulation, constructing a new characteristic index by using a wind direction and a remote sensing index; selecting representative algal bloom samples, extracting data such as wind speed, air temperature and remote sensing indexes of sample points in the same time period to construct a sample database, training a machine learning model and verifying the model precision; the model is applied to a typical eutrophicated lake to test the precision performance of the model, and the algal bloom distribution condition at the current moment and the environmental data at the subsequent moment are utilized to predict the algal bloom spatial distribution change of the lake hour by hour through model iteration. According to the method, prediction and early warning of the spatial position and the outbreak intensity of lake algal blooms in an hour scale can be realized, and prevention and control of eutrophic lake algal blooms are assisted.
Owner:NANJING INST OF GEOGRAPHY & LIMNOLOGY
Algicidal bacterium and application thereof
The invention discloses an algicidal bacterium and application thereof, the algicidal bacterium is named as Bacillus sp.L1.3, and is preserved in China Center for Type Culture Collection on March 31, 2025, the preservation address is Wuhan University, Wuhan, China, and the preservation number is CCTCC NO: M 2025629. The nucleotide sequence of the 16S rRNA gene of the strain is as shown in SEQ ID NO: 1. The invention also provides application of the algicidal bacteria in treatment of water bloom of freshwater cyanobacteria, and the freshwater cyanobacteria is microcystis aeruginosa. Fermentation liquor of the strain L1.3 has a strong inhibition effect on growth of microcystis aeruginosa, secretions of the strain L1.3 can destroy cell walls, cell membranes, thylakoid and other structures of algae cells, inhibit photosynthesis of algae and cause irreversible damage to the algae, and the strain L1.3 has wide-range temperature stability and can be used for preparing the microcystis aeruginosa strain L1.3. The bacterial strain is suitable for being prepared into an algicidal bacterial agent to be applied to the field of biological control of freshwater cyanobacterial bloom.
Owner:FUYANG NORMAL UNIVERSITY
Method, device and equipment for identifying algal blooms in rivers around lakes and reservoirs and storage medium
The invention relates to the technical field of remote sensing, in particular to a method, device and equipment for identifying algal blooms in rivers around lakes and reservoirs and a storage medium. The method comprises the following steps: acquiring sentinel No.2 remote sensing image data at historical moments in a preset area, and performing radiometric calibration, atmospheric correction and super-resolution reconstruction; extracting an algal bloom sample and a non-algal bloom sample; performing feature conversion on the samples to obtain a sample feature data set; performing model training by using an XGBoost method based on the sample feature data set to obtain a water bloom recognition model; and inputting a feature data set obtained by converting the reflectivity image data of the to-be-monitored area into the water bloom recognition model to obtain a water bloom recognition result. According to the method, water bloom monitoring is carried out by adopting sentinel No.2 remote sensing image data, so that the problem that a conventional water color satellite cannot be used for monitoring due to the narrow width of a river channel around a lake and a reservoir in the related technology is solved; and the method does not need to set a water bloom identification threshold value in advance and extract a water body, so that the automation degree of water bloom identification is improved.
Owner:CHINA THREE GORGES CORPORATION +1
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
Method for identifying algal blooms of Harcasia sanguinea and gonyautogea multistriata based on spectral data
PendingCN120195108AScattering properties measurementsColor/spectral properties measurementsAlgal bloomEcology
The invention relates to the technical field of marine ecological disaster monitoring, and discloses a method for identifying algal blooms of Harcasia sanguinea and Gynea multiflora based on spectral data, which comprises the following steps: collecting water spectral data of a water area where algal blooms occur, and executing interference elimination processing on the water spectral data to obtain target spectral data; based on the sensitive wave bands and the reflectivity values of the Harcasia sanguinea and the gonyerea multistriata, identifying the water body type of the water area where the algal blooms occur; constructing a spectral reflectance curve of the Harcasia sanguinea and the gonyautoa multistriata, determining an optimal distinguishing waveband combination of the Harcasia sanguinea and the gonyautoa multistriata, and separating out an algae bloom area of the Harcasia sanguinea and the gonyautoa multistriata; the method comprises the following steps: defining discriminating double indexes of the Harcasia sanguinea and the gonyautogea multistriata, and constructing an algae species differentiating system of the Harcasia sanguinea and the gonyautogea multistriata; and according to the algal bloom growth conditions, after parameter optimization processing of the algal species distinguishing system is executed, an algal bloom identification result of the algal bloom generation water area is output. The method can improve the classification precision of algae species.
Owner:JIANGSU OCEAN UNIV +2
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
Hyperspectral-based near-sensitive lake and reservoir water quality and water bloom short-term prediction and early warning method and system
ActiveCN119804362AScattering properties measurementsTransmissivity measurementsEnvironmental resource managementData set
The invention discloses a short-term prediction and early-warning method and system for water blooms of water quality of near-sensitive lakes and reservoirs based on hyperspectrum, and belongs to the field of water quality prediction and early-warning technical methods. Combining the preprocessed water body proximity hyperspectral reflectivity with the time-space attribute information to generate a water body proximity hyperspectral reflectivity-time sequence data set; constructing a preset water quality inversion model set according to a space-time matching principle in combination with synchronously measured water quality parameters; generating a multi-water quality parameter-time sequence data set by using a preset water quality inversion model set; and building a prediction and early warning model based on the multiple water quality parameter-time sequence data set, and outputting a water quality and algal bloom prediction and early warning result. Measurement data are transmitted to a cloud server in real time, hyperspectral reflectivity data are converted into water quality parameters easy to understand by a user by constructing a water quality inversion model set, and the data are integrated on a software platform based on multi-parameter water quality; and current water quality information, historical long-term change and future short-term algal bloom prediction and early warning information can be checked anytime and anywhere.
Owner:NANJING INST OF GEOGRAPHY & LIMNOLOGY
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
Device and method for in-situ determination of denitrification rate of sediment
PendingCN120334470ATesting waterGas analyser construction detailsEnvironmental resource managementSustainable management
The invention discloses a device and a method for determining the denitrification rate of sediments in situ. According to the device, through field determination, transparent design and fluidity design, the accuracy and ecological correlation of denitrification rate determination are greatly improved, and an innovative technical support is provided for research and management of nitrogen circulation of a water body. And further determining the denitrification rates of rivers, shallow lakes and intertidal zones by using a method of combining stable isotopes with an in-situ culture room. The method can help understand back mechanisms of environmental problems such as algal blooms and oxygen deficit, improve water quality, provide important data support for ecological restoration, wetland protection and agricultural management, promote health and stability of an ecological system, provide important data for studying the effect of nitrogen circulation in global climate change, and promote implementation of sustainable management practice. Compared with a traditional laboratory measurement method, the method has remarkable advantages in the aspects of simulating natural conditions, spatial heterogeneity and ecological interaction, and the denitrification mechanism can be more comprehensively understood.
Owner:SHENZHEN UNIV
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
Intelligent algae control method and device based on target identification
ActiveCN119898851AWater/sewage treatment with mechanical oscillationsBiological modelsChlorophyll aWater quality
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.
Owner:INNOVATION CENTER OF YANGTZE RIVER DELTA ZHEJIANG UNIVERSITY
Water bloom monitoring data interpolation method and device based on second-order optimization hidden feature analysis
ActiveCN119671033Aimprove accuracyData processing applicationsComplex mathematical operationsEngineeringData mining
The invention discloses a water bloom monitoring data interpolation method and device based on second-order optimization hidden feature analysis. The method specifically comprises the following steps: S1, collecting historical water bloom monitoring data from a server and storing the historical water bloom monitoring data; s2, constructing a water bloom monitoring data matrix V according to the stored historical water bloom monitoring data; and S3, constructing a target loss function according to historical water bloom monitoring data and the water bloom monitoring data matrix V, and outputting a water bloom monitoring data interpolation value with the highest precision. The method is specially used for water bloom monitoring data, and water bloom monitoring data interpolation which accords with a statistical rule and is high in accuracy can be carried out, so that interpolation of missing water bloom monitoring data is realized. The method can be widely applied to the fields of algal bloom monitoring, environment monitoring and the like.
Owner:CHONGQING UNIV +1