189 results about "Algal bloom" patented technology

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.

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.

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 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.

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 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.

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.

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.

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 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 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.

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

The application discloses a kind of urban lake algal bloom measurement method based on algal vertical distribution structure analysis, belong to environmental science field, the application constructs with column plane concentration level, column plane algal community composition, column plane aggregation characteristics and column plane profile form as key dimension, including 14 single index algal vertical distribution structure comprehensive evaluation index system, realizes the quantitative analysis of algal vertical distribution structure.The application establishes a set of fast response and risk classification as core algal bloom measurement process, including algal in-situ stereoscopic observation, algal vertical distribution structure comprehensive evaluation, algal vertical distribution structure classification and algal bloom and risk identification etc.Step.The method breaks through the limitation of traditional method in monitoring dimension, observation data is not fully mined and algal bloom determination standard is fuzzy etc.Limit, lays a theoretical foundation for algal bloom monitoring and evaluation system research, application in urban lake can provide technical means and scientific basis for algal bloom prediction and early warning and algal bloom precision prevention and control.

The invention discloses a water algae bloom control device for multi-channel cross flow field separation. The water algae bloom control device comprises a floating body platform, and an algae slurry circulating lifting mechanism and an algae slurry collecting and treating mechanism which are arranged on the floating body platform, the algae slurry circulation lifting mechanism is composed of a crawler belt assembly and a plurality of transverse flow separators which are evenly and fixedly arranged on a crawler belt of the crawler belt assembly at intervals, the crawler belt assembly is obliquely arranged on the floating body platform through a support, and each transverse flow separator is composed of a shell cylinder and a filter cylinder which are sleeved inside and outside; a door opening is formed in the side wall of a shell barrel, located on one side of the conveying direction of the crawler belt assembly, of each transverse flow separator, and a control mechanism used for controlling the cover plate to be opened and closed is arranged on the floating body platform. The crawler circulating lifting mechanism drives the transverse flow separators to circularly rotate to automatically take water, filter and drain water and discharge algae slurry at a fixed point, water does not need to be pumped by a water pump, the algae slurry is discharged according to the self weight, and the energy consumption of the equipment can be effectively reduced.

The present application belongs to the technical field of data analysis, and particularly relates to a high-throughput biosensing system for early warning of algal blooms and pathogens, which comprises multiple sampling units, a microfluidic chip with a multi-channel structure, and a high-throughput surface-enhanced Raman detection unit with an integrated metal nanostructure surface, and obtains the original surface-enhanced Raman spectrum of the water sample through a Raman spectrum acquisition mechanism. The system uses compressed spectrum sampling and sparse reconstruction methods to reduce the data volume and maintain the integrity of the spectrum information, constructs a pathogen Raman fingerprint dictionary through a dictionary learning method, and realizes the identification of different algal metabolites, algal toxins and pathogen-related molecular components and the concentration interval judgment thereof in combination with a sparse representation classification mechanism. Based on the type and concentration interval of the pathogens, the system further generates algal bloom risk warning and pathogen risk warning, and realizes early discovery of abnormal changes in the water body. The present application has significant advantages in stability, sensitivity, throughput and real-time performance.

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

The application provides a water quality short-term prediction method based on parameter perturbation-localized ensemble data assimilation, belongs to the technical field of water quality prediction, and is characterized in that: based on the water quality state and uncertainty of a hydrodynamic-water quality mechanism model at an initial time T1, an initial state vector is constructed and converted into an initial ensemble member to input the model; sensitive water quality parameters of the model are screened, the parameters are perturbed, and the parameter perturbation set is generated together with the model driving conditions, and then the parameter perturbation set is fused with the initial ensemble member to form a perturbation set; the perturbation set is input into the model to run to the T2 time, and a water quality prediction set is obtained; the observation data and error of the water quality at the T2 time are combined, and an EnKF or other ensemble data assimilation algorithm is used to calculate and analyze the set; finally, the analysis set is used to update the initial conditions of the model at the T2 time, the above steps are repeated, and the optimal estimation at each time is continuously output, so that the water quality short-term prediction is realized. The application can improve the water quality short-term prediction precision, save the calculation cost, accurately capture the spatiotemporal heterogeneity of water bloom, and has strong interpretability and generalizability.

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 relates to a biprism spectrograph for water body detection, relates to the technical field of spectrograph design and analysis, and solves the technical problems of insufficient real-time performance, poor long-term stability, limited index coverage and insufficient anti-interference capability of a spectral analysis technology in the prior art. The biprism spectrometer comprises a light source, a slit, a spherical reflector, a biprism assembly, a turn-back mirror, an imaging mirror, a linear array detector and a data acquisition, processing and control device which are sequentially arranged in the light path direction, the light source is connected with the slit through an optical fiber. The biprism spectrometer for water body detection is suitable for scenes such as lake and reservoir algae bloom early warning, estuary and industrial / municipal discharge port supervision, disinfection tank by-product and residual chlorine fingerprint tracking, drinking water plant online protection and the like, and can be expanded to tasks such as oil stain identification, dissolved organic matter characterization and on-site rapid fingerprint comparison.

The invention relates to a preparation method of a multi-energy coupling water body shading diaphragm plate and the shading diaphragm plate, and belongs to the field of water environment treatment. The preparation method comprises the following steps: preparing outer-layer functional shell slurry consisting of PVDF (Polyvinylidene Fluoride), Fe3O4 and SiO2 aerogel; preparing middle-layer light absorption layer slurry which is composed of nano WO3, nitrogen defect g-C3N4 and Fe3O4 and is filled with nitrogen to regulate and control the density; preparing inner-layer super-hydrophilic layer slurry formed by modified TiO2 doped with Fe < + > and V2O5; and sequentially coating the three layers of slurry by adopting a blade coating method, pre-drying, and curing and forming in an alkaline solution in a mold with a pit structure. The prepared shading diaphragm plate has a three-layer structure of an outer-layer functional shell, a middle-layer regulation and control and an inner-layer functional core, can synchronously realize efficient shading and algal inhibition, microcystin degradation and magnetic control recovery, has the advantages of wide scene adaptability and long service life, and is suitable for algal bloom treatment and pollution regulation and control of static and flowing water bodies.

A method for surface water remediation and prevention and mitigation of harmful algal blooms includes the steps of receiving nutrient-rich water from a body of water; cultivating a first type of algae in the raceway algae growth pond to absorb and reduce nutrient concentrations in the nutrient-rich surface water; flowing at least a portion of the nutrient-rich surface water treated in the raceway algae growth pond to an attached algae flow way connected downstream of the raceway algae growth pond; cultivating a second type of algae in the attached algae flow way to further absorb and reduce nutrient concentrations in the nutrient-rich surface water; and returning at least a portion of the nutrient-rich surface water treated by the raceway algae growth pond and the attached algae flow way to the body of water.

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 present application belongs to the technical field of machine vision environment monitoring, and specifically discloses a method and system for automatic identification and early warning of algal blooms based on an intelligent camera, which comprises the following steps: first, collecting original video data containing algal bloom information through an intelligent camera, and extracting image frames from the video for preprocessing and algal bloom pixel labeling to obtain training samples; then, training a BP neural network model using the training samples. Subsequently, a camera is arranged in the field to collect water surface images to be classified and transmit them to the cloud, the camera is calibrated and the parameters are stored, the collected images are preprocessed and input into the trained model, algal bloom pixels are identified and the category and coordinates are output, the coordinates are finally converted into real three-dimensional coordinates to calculate the actual area, and if the actual area exceeds the early warning threshold, early warning information is generated and sent. The present application improves the identification accuracy, can identify various algal blooms, has low cost and is convenient for long-term operation, and is suitable for monitoring of rivers, lakes and nearshore areas.

The present application belongs to the technical field of algal bloom emergency prevention and control, and particularly relates to a high-altitude mountain stream type reservoir branch bay hierarchical three-dimensional algal bloom prevention and control method and device. The method comprises the following steps: S1, through upstream unpowered diversion flood routing self-control, according to the mountain stream inflow and water level, automatically switching the diversion flood routing mode, and through pulse intermittent discharge to maintain the circulation kinetic energy in the dry season; S2, through hierarchical three-dimensional reservoir intake bottom support and energy dissipation and endogenous blockage, reducing the vertical impact force of the inflow, guiding the water flow to the two banks to form a shore-hugging circulation, and blocking the release of internal source nutrients in the bottom mud through in-situ passivation; the present application realizes hierarchical three-dimensional algal bloom prevention and control of high-altitude mountain stream type reservoir branch bays, can systematically solve the problems of poor water body exchange, strong internal source release, large water level amplitude and the like of high-altitude mountain stream type reservoir bays, realizes long-term and stable prevention and control of algal bloom, has extremely low operation and maintenance cost, and is particularly suitable for remote, high-altitude and unattended environments.

The application discloses a kind of based on toxigenic and odor-producing gene toxigenic and odor-producing cyanobacteria field rapid monitoring system and method, a kind of based on toxigenic and odor-producing gene toxigenic and odor-producing cyanobacteria field rapid monitoring system, including water sample algal automatic enrichment module, algal cell rapid lysis and DNA extraction tube, isothermal / normal temperature PCR module, palm fluorescence detector, qualitative and quantitative algorithm and toxigenic and odor-producing cyanobacteria risk warning grade calculation software, mobile phone APP software, each module is sequentially linked.The application realizes the integrated detection of toxigenic and odor-producing cyanobacteria quickly, accurately and conveniently, solves the problem that existing technology cannot accurately distinguish between toxigenic and non-toxigenic cyanobacteria and is difficult to scientifically assess the health risk of water body, while overcoming the limitations of traditional monitoring technology, such as laboratory detection, large equipment, complex operation, long detection period, etc., and meeting the timeliness, convenience and ease of operation requirements of field scenes such as grassroots monitoring, outdoor inspection and emergency disposal of sudden water bloom.

The application discloses a lake and reservoir algae bloom prediction and monitoring time window scheduling method, equipment and storage medium, comprising: based on observation data, using a data assimilation algorithm to perform online assimilation update on the state and parameters of the lake and reservoir digital twin; using the assimilated digital twin to predict the algae bloom in the future period, and performing physical constraint correction and uncertainty quantification processing on the prediction result to output prediction information; generating a detection time window candidate set according to the prediction information; based on the detection time window candidate set and a preset operation constraint, optimization solving is performed to obtain a detection time window scheduling strategy, which is issued to an unmanned detection platform, and the measured data collected after the execution of the base detection task is fed back to the observation data to perform data registration and model assimilation on the digital twin. Through the construction of a closed-loop system of perception-assimilation-prediction-scheduling-feedback, the application realizes dynamic optimization configuration of monitoring resources according to the algae bloom risk, and improves the early warning timeliness and resource utilization efficiency.

The present application relates to the field of environmental management technology, and discloses a kind of emergent aquatic plant-sediment microbial fuel cell synchronous remediation water body method, comprising the following steps: S1, constructs emergent aquatic plant-SMFC coupling system, emergent aquatic plant-SMFC coupling system includes emergent aquatic plant and SMFC, SMFC includes anode electrode, modified cathode electrode and external resistance;Emergent aquatic plant is planted around anode electrode, so that anode electrode is contacted with bottom mud;S2, preparation modified cathode electrode, and the catalytic activity of cathode electrode is enhanced by electrochemical oxidation modification;S3, domestication electric active microorganism, emergent aquatic plant-SMFC coupling system is placed in room temperature domestication, and the electricity generation capacity of anode electrode is improved by introducing bottom mud rich in organic matter or exogenous inoculation microorganism;S4, start and run system, and the concentration change of chlorophyll a, ammonia nitrogen, total nitrogen and total phosphorus in water body is monitored in real time.The present application provides a kind of green, economic and efficient algal bloom water remediation technology, and is suitable for the ecological management of algal bloom water.