112 results about "Cyanobacterial bloom" patented technology

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.

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

The invention 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.

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.

The embodiment of the invention is suitable for the field of environment monitoring of computer vision, and provides a cyanobacterial bloom monitoring method based on multi-modal data fusion and deep learning, in the monitoring method, after obtained initial data is preprocessed, variation point detection is introduced to reduce human factor interference, reduce the calculation amount and enhance the model adaptability; according to the method, a convolutional neural network (CNN) is utilized to extract multi-layer features, the features are input into a long short-term memory (LSTM) network for time sequence modeling, the model performance is improved, on the basis, the features of the CNN and the LSTM are fused, and the model accuracy is further improved; finally, variation point detection is introduced again, rich feature information and multi-mode complementarity are utilized, image key information is enhanced, and detection precision and model robustness are improved. The monitoring method provided by the embodiment of the invention breaks through the limitation of spatial-temporal characteristic splitting of a traditional method, significantly improves the prediction continuity and accuracy, and provides high-reliability support for early warning of cyanobacterial bloom.

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.

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.

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.

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.

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.

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.

The invention provides a fixed cyanobacterial bloom collector, and relates to the technical field of cyanobacterial bloom collection, the fixed cyanobacterial bloom collector comprises a collector shell, the collector shell is provided with a cyanobacterial inlet, a steel wire mesh is installed in the collector shell, an inclined screen type filter is arranged above the steel wire mesh, and the inclined screen type filter is fixedly connected with the collector shell; a collecting assembly is installed on the outer side of the collector shell, a drainage groove is formed in the bottom of the collector shell, a water inlet is formed in the drainage groove, a submersible water impeller is installed on the inner side of the drainage groove, a first drainage pipe is installed on the collector shell and communicated with an inner cavity of the drainage groove, and an air flotation assembly is installed between the drainage groove and the steel wire mesh. The cyanobacterial bloom collecting device is simple in overall structure and convenient to use, efficient and automatic cyanobacterial bloom collecting is achieved through the matched design of the inclined screen type filter, the air floating assembly, the floating hose and the collecting assembly, and the common problems of low efficiency and manual intervention in manual or semi-automatic equipment are solved.

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.

The invention relates to the technical field of cyanobacterial bloom control in water, and particularly discloses a compound enzyme-bacterium synergistic preparation for cyanobacterial bloom control, a preparation method and a use method. The preparation comprises the following components in percentage by mass: compound enzymes (10-20% of cellulase, 8-12% of beta-1, 3-glucanase and 3-8% of lipase), functional bacteria (10-20% of bacillus subtilis, 8-15% of trichoderma harzianum and 5-10% of bacillus amyloliquefaciens), an enzyme stabilizer (3-8% of trehalose), an adsorption carrier (15-25% of Fe < 3 + > modified charcoal and 8-15% of diatomite) and auxiliary components. The preparation method comprises the steps of carrier modification, solid fermentation and microcapsule embedding. During use, 3-20kg / 1000m < 3 > of water is added for the first time, 2-10kg / 1000m < 3 > of water is added every 7-14 days in the maintenance stage, and synchronous aeration is performed. Through the synergistic effect of beta-1, 3-glucanase and trichoderma harzianum, the algicidal rate is 92%, and the spore germination inhibition rate is 82%; the Fe < 3 + > modified charcoal improves the stability of the enzyme in an alkaline environment (80% of activity is retained when the pH value is 9); the method is suitable for eutrophic water with chlorophyll a greater than or equal to 30 [mu] g / L, and has no chemical residue risk.

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.

The invention relates to the technical field of microbial engineering, and particularly discloses a compound microbial agent for treating cyanobacterial bloom, bacillus velezensis provided by the invention destroys algae cells and inhibits the activity of related biological enzymes by secreting heterocyclic algal killing active substances, and simultaneously causes the leakage of cellular contents (such as algal toxins, polysaccharides and lipids); an extracellular enzyme system (such as alkaline phosphatase and beta-glucosidase) of the sphingomonas decomposes free algal toxins into low-toxicity linear peptide fragments and hydrolyzes beta-1, 4-glucosidic bonds in EPS at the same time, so that algae-derived organic fragments are converted into small-molecular soluble substances, and a substrate is provided for follow-up metabolism of rhodococcus; lipase and esterase secreted by rhodococcus can hydrolyze triglyceride and waxy components, fatty acid is converted into energy and a biosurfactant through a beta-oxidation pathway, and hydrophilicity of frond fragments is enhanced to promote overall degradation efficiency.

The cyanobacterial bloom salvaging device comprises a rubber boat, the top of the rubber boat is fixedly connected with a sewage collecting pool, the rear side of the sewage collecting pool is fixedly connected with a lifting mechanism, the top of the front side of the lifting mechanism is fixedly connected with an air flotation machine, and the air flotation machine is fixedly connected with a water outlet of the air flotation machine. A slag scraping plate is fixedly connected to the top of the air flotation machine, a shovel plate is fixedly connected to the front side of the top of the sewage collection tank, a traction rope roller is arranged in the middle of the top of the rear side of the sewage collection tank, and a positioning mechanism is fixedly connected to the middle of the rear side of the sewage collection tank; by arranging the rubber boat, the sewage collection tank, the air flotation machine, the slag scraping plate, the shovel plate, the traction rope roller and the positioning mechanism, efficient salvage and collection of cyanobacterial blooms are achieved; an operator only needs to float the whole device on the surface of a water pool through a rubber boat, and cyanobacterial blooms are conveyed into a sewage collection pool through a shovel plate and separated through an air flotation machine.

The invention belongs to the technical field of biology, and particularly relates to a method for increasing the fucoxanthine accumulation amount by using microcystis subjected to depolymerization treatment to feed palmarthria formosana. According to the method, microcystis subjected to depolymerization treatment are added in the culture process of the palmitothecium for heterotrophic culture, meanwhile, continuous inflation is conducted, the microcystis subjected to depolymerization treatment are swallowed by the palmitothecium, and the higher fucoxanthine accumulation amount in the palmitothecium is obtained by means of the culture mode. The experiment proves that after the microcystis is subjected to pretreatment and depolymerization, the removal efficiency of the microcystis is higher by the brown algae, the limitation of treatment of harmful cyanobacteria and ingestion of protozoa is made up, and meanwhile, an integrated solution is provided for biological treatment of cyanobacterial bloom and resource utilization of the microalgae; meanwhile, a new way is provided for obtaining fucoxanthine with higher accumulation amount.

The present invention discloses a strain of Saccharothrix violaceirubra NBAL-S001 and its application. The Saccharothrix NBAL-S001 of the present invention is isolated from soil, and its fermentation liquid can inhibit the photosynthesis of harmful algae blooms - Microcystis aeruginosa, and has a very high algae-dissolving effect, with a 5-day algae-dissolving rate of up to 97%. The Saccharothrix NBAL-S001 of the present invention is a biological control strain with potential development and application value in preventing and controlling cyanobacterial blooms.

The invention provides application of a macleaya cordata extract as a plant source algaecide in cyanobacterial bloom treatment, and relates to the technical field of plant source algaecide product development and environmental protection. The total alkali content of the macleaya cordata extract is 50-80%, and the main effective components of the macleaya cordata extract comprise 40-60% of sanguinarine and 10-20% of chelerythrine. The blue-green algae comprise microcystis aeruginosa, anabaena, nematonema, synechocystis, oscillatoria and nostoc. As a plant source in China, the macleaya cordata has great development and utilization values, has the advantages of wide antibacterial spectrum, no drug resistance, no residue, good safety, wide distribution and the like, and has great development prospects.

This application relates to the field of aquatic environmental pollution monitoring technology, specifically disclosing a method for extracting the area of ​​cyanobacterial blooms in lakes. The method utilizes remote sensing data to calculate the FAI index and abundance value of the monitoring area. Based on slope and statistical analysis, the FAI threshold is determined, yielding the area and spatial distribution of cyanobacterial blooms in the study area based on FAI. The F(S)-L method is used to determine the threshold for extracting cyanobacterial blooms based on abundance, obtaining the area and spatial distribution of cyanobacterial blooms in the study area based on mixed pixel decomposition. A comparative analysis of the results of cyanobacterial bloom extraction using mixed pixel decomposition and FAI shows that the overlapping areas extracted by the two methods represent the true cyanobacterial bloom regions, thus obtaining the area and spatial distribution of cyanobacterial blooms in the study area. This method solves the problem of difficulty in determining the abundance threshold, thereby improving the accuracy of cyanobacterial bloom extraction, and also has the advantages of being rapid, objective, and easy to promote.

The invention discloses intelligent blue-green algae outbreak emergency treatment equipment and method, and belongs to the technical field of water treatment equipment.The intelligent blue-green algae outbreak emergency treatment equipment comprises a water pumping mechanism installed on a buoyancy base, and further comprises a conveying and filtering mechanism, a collecting mechanism, a drainage mechanism, a chemical adding mechanism and a transmission mechanism. According to the intelligent blue-green algae outbreak emergency treatment equipment, the single motor drives the transverse rod to rotate, then the spiral blades are synchronously driven to convey blue-green algae, the transmission mechanism drives the extrusion assembly and the chemical adding mechanism to operate, multi-link cooperation of blue-green algae conveying, dehydration and chemical adding can be achieved, a traditional framework of respective driving of multiple motors is replaced, and the practicability is high. And the equipment complexity is reduced, the energy consumption is reduced, the fault rate of a motor is also reduced, and meanwhile, all links are synchronously linked, so that the process blockage caused by lagging of a single link can be avoided, and the overall processing efficiency is improved.

The invention discloses a compound microbial agent and application thereof in prevention and control of cyanobacterial bloom, two algicidal bacteria are obtained through screening, the preservation number is CCTCC NO: M 2024698, and the preservation number is CCTCC NO: M 2024699, and the compound microbial agent is proved to have obvious inhibition effects on a plurality of cyanobacteria bacteria such as microcystis aeruginosa, lacustrine triphora, anabaena and cylindrocystis, and can be used for preventing and controlling cyanobacteria bloom. And the composite bacterial agent combined by the two algicidal bacteria has a remarkable algicidal effect on algal blooms in natural water areas, can be applied to the field of biological prevention and control of freshwater cyanobacterial blooms, and provides an ideal choice for treatment of cyanobacterial blooms in water bodies.

The present application relates to a kind of preparation method of floating catalyst carbonized corncob load flower-shaped copper sulfide, through simple chemical precipitation method, in situ synthesis copper sulfide flower-shaped microspheres, and using the adhesion ability and chelating metal ion ability of polydopamine, copper sulfide is deposited on carbonized corncob, and constructs the photocatalytic efficiency high, reusable floating catalyst carbonized corncob load flower-shaped copper sulfide.The present application selects carbonized corncob as floating carrier, can achieve the effect of resource recycling, waste utilization, practices the concept of environment-friendly, sustainable development.And the synthesis method is simple and mild, solve the technical difficulty of traditional hydrothermal method high temperature and high pressure, low cost, high efficiency and economy.The prepared floating catalyst carbonized corncob load flower-shaped copper sulfide can efficiently remove Microcystis aeruginosa under visible light (λ≥420 nm) irradiation for 180 min, and has good stability, and has good application prospect in the treatment of cyanobacterial bloom.

The invention discloses an algicidal bacterium TL3 with efficient algicidal activity and application thereof, the algicidal bacterium TL3 is named as Enterobacter cloacae TL3 and is preserved in Guangdong Microbial Culture Collection Center located at No.100, Xianlie Middle Road, Guangzhou City, Guangdong Province, the preservation number is GDMCC No: 65777, and the preservation date is January 13, 2025; the invention also provides an algicidal bacterial agent containing the algicidal bacteria TL3 for degrading or inhibiting water-blooming cyanobacteria. The algicidal bacterium TL3 is obtained by screening and separating in the algicidal period by taking the algicidal habitat of the Lake Taihu as a strain resource library of the efficient algicidal bacterium, has an efficient inhibition effect on microcystis aeruginosa, has a certain algicidal effect on chrysosporium oospermum, cuscuta larakii and anabaena pseudosciaena, is used for various types of cyanobacterial bloom water bodies, and has a good algicidal effect on the water bodies. The application prospect is wide.

The present application relates to a kind of blue-green algae bloom ground multispectral remote sensing monitoring system and method, to solve the technical problems such as low resolution and weather influence of satellite remote sensing to inland lake blue-green algae bloom monitoring frequency low, this system Ground multispectral remote sensing observation subsystem includes electric control box, ground support, setting on two-dimensional turntable of ground support and blue-green algae bloom multispectral camera fixedly set on two-dimensional turntable;Blue-green algae bloom multispectral camera contains at least 4 spectral bands;Ground multispectral remote sensing service subsystem includes control acquisition module and data processing analysis module.The method includes calibration to blue-green algae bloom multispectral camera, start ground multispectral remote sensing monitoring system;Record the multispectral image of multiple angles of entire lake area;Multispectral image data analysis extraction;With the change trend of satellite remote sensing data comparison;Repeat the operation of recording multispectral image, according to the result of comparative analysis gives blue-green algae bloom warning.

The invention relates to the technical field of environment monitoring, in particular to a Taihu lake cyanobacterial bloom extraction method based on sentinel No.2 images. The method comprises the following steps: S1, obtaining and preprocessing remote sensing data: correcting by using a Sen2Cor plug-in to obtain L2A-level data, and re-sampling waveband data to 10m through SNAP software; s2, water area contour extraction: according to the multispectral remote sensing data in the step S1, respectively calculating NDWI of each group of data, and according to a threshold value, completely excluding a land part to obtain a Taihu Lake water area range mask to be applied to subsequent cutting; and S3, contour extraction of cyanobacterial bloom: calculating the NDVI of each group of data, wherein the extraction threshold value of the NDVI is-0.0693. The Taihu Lake and cyanobacterial bloom images obtained according to the method have good consistency with field sampling data, and can be subsequently used for visual evaluation of the Taihu Lake cyanobacterial bloom.

The application discloses a material for treating water bloom by a photocatalytic method, a preparation method and application of the material, and the preparation method comprises the following steps: adding bismuth nitrate, iron nitrate and polydiallyldimethylammonium chloride into ethylene glycol, heating and reacting, and the reaction condition is that the ethylene glycol is refluxed and heated at 150-200 DEG C for 2-6 hours; after cooling, centrifugation and washing are carried out, the obtained precipitate is dried, and the obtained material is Fe-doped BiOCl, namely Fe-BiOCl; wherein the adding ratio of the bismuth nitrate, the iron nitrate, the polydiallyldimethylammonium chloride and the ethylene glycol is 60-100 mg: 30-50 mg: 3-6 mL: 60-100 mL. The application provides a preparation method of the functional material Fe-BiOCl for treating water bloom, and the functional material prepared by the method can effectively inhibit the formation of algae under the photocatalytic action, and is especially suitable for treating blue-green algae bloom.

The present application relates to blue-green algae bloom control technical field, disclose a kind of sodium percarbonate combined with EM bacteria reinforced severe blue-green algae bloom control method, the present application uses sodium percarbonate combined with EM bacteria to control severe blue-green algae bloom, on the basis of adding sodium percarbonate 5mg / L further add EM bacteria 0.075mg / L, the algal cell density of experimental group is always lower than the control group of simply adding sodium percarbonate, and after adding sodium percarbonate 6h immediately further add EM bacteria, and the reducing effect is most remarkable, it shows that EM bacteria has the effect of reinforcing the effect of sodium percarbonate to control algae, and it is best to add EM bacteria after adding sodium percarbonate 6h, in the experimental group of adding EM bacteria 6h, algal cell density removal rate is 94.19% when experimental duration=48h, and the algal cell density removal rate of control group of only adding sodium percarbonate is only 69.77%.

The invention relates to a cyanobacterial bloom extraction method based on spectrum and phenological characteristics, and relates to the field of ecology, and the method comprises the steps: selecting a vegetation index PCI which is excellent in cyanobacterial bloom recognition, building a localized phycocyanobilin inversion model, and carrying out the homogeneity test of pixels around a sampling point; by calculating a PCI gradient image time sequence, cyanobacterial bloom and background water boundaries are identified, and a threshold value for distinguishing cyanobacteria and non-cyanobacteria pixels is determined by using a maximum gradient average value; constructing a cyanobacterial bloom recognition model based on the LSTM neural network; calculating cyanobacterial bloom outbreak intensity and coverage area, identifying a hot spot area and a cold spot area, and depicting distribution characteristics of cyanobacterial bloom in space; a PCI time sequence data set is synthesized, the evolution trend of a cyanobacterial bloom key phenological index time sequence is described, the innovation of the method lies in that spectrum and phenological characteristics are fused, cyanobacterial bloom spatial and temporal distribution is accurately analyzed, and guarantee is provided for water environment treatment and drinking water safety.