659 results about "Lake water" patented technology

The invention relates to the field of river and lake water quality monitoring, and discloses a river and lake remote sensing monitoring multi-source data fusion and analysis method, which comprises the following steps: firstly, collecting satellite remote sensing images, unmanned aerial vehicle aerial images and ground monitoring station data of a river and lake area, and preprocessing to form a feature data set; and then, constructing an image classification model based on a convolutional neural network (CNN), a water quality evaluation model based on a support vector machine (SVM) and a water quality prediction model based on time sequence analysis, and respectively classifying, evaluating and predicting water quality. And finally, by defining a multi-source data fusion algorithm, carrying out fusion analysis on a model output result, obtaining a comprehensive water quality evaluation result, and generating a river and lake remote sensing monitoring report. The advantages of multiple data sources are fully utilized, comprehensive, rapid and accurate monitoring of the water quality of rivers and lakes is achieved, powerful support is provided for environmental protection and water resource management, and the method has wide application prospects and important practical value.

The invention provides a lake water chemical oxygen demand internal and external source pollution quantitative tracing method and system, and relates to the technical field of pollution quantitative tracing. The method comprises the following steps: performing three-dimensional fluorescence spectrum analysis on water samples at different point positions of a lake to obtain fluorescence excitation / emission matrix spectrum EEMS data; performing parallel factor analysis on the EEMS data, identifying and classifying the fluorescent components of the dissolved organic matter DOM, and determining the type characteristics of each component; based on the maximum fluorescence intensity and the corresponding COD value of the fluorescence component, establishing a standardized multiple linear regression equation by adopting ridge regression analysis, and calculating the contribution percentage of each fluorescence component to the COD of the lake water body; analyzing influence paths of potential variables on DOM migration and transformation, and quantifying direct contributions and indirect contributions of different pollution sources to each fluorescent component through path coefficients; and calculating the total contribution of lake-entering rivers, bottom mud release and phytoplankton to the COD of the lake water body.

The invention discloses a river and lake water level and flow prediction method and system in the field of remote sensing hydrology and hydraulic engineering, and the method comprises the steps: obtaining satellite radar altimeter data, river and lake water system data, rainfall inversion data and surface temperature inversion data based on a target river, a target lake and a watershed range file; taking the cross interval as a virtual hydrometric station observation point, extracting and processing waveform data, calculating water surface elevation information through a wavelet tracking algorithm, constructing an elevation profile group to establish an initial water level time sequence, and generating a river and lake water level inversion data set through fitting and elevation reselection. Meanwhile, NDWI data are obtained from satellite radar altimeter data, water surface width data are extracted, an inversion data set is formed by combining actually-measured river channel section data and a Manning formula inversion river channel flow rate, and finally various kinds of data are input into a pre-constructed intelligent prediction model to obtain future river and lake water level and flow rate change conditions. According to the invention, the precision of river and lake water level and flow prediction in areas lacking data is effectively improved.

The invention provides a lake water quality monitoring method and system based on deep learning, and relates to the field of deep learning. The method comprises the following steps: firstly, collecting original sensor data, remote sensing images, hydrodynamic force and meteorological data, carrying out space-time alignment processing, and constructing a lake surface rasterization space-time diagram; the graph is input into a model composed of a spectrum attention encoder, a graph convolutional network and a time sequence unit, lake global water quality index concentration distribution is predicted, a dynamic threshold value is set based on historical data, the standard exceeding condition is recognized in real time, pollution early warning is triggered, and high-precision monitoring and intelligent management of lake water quality are achieved.

The invention discloses a lake water resource-water safety collaborative annual-scale robust scheduling logic generation method, system and device and a storage medium, and the system comprises a watershed hydrology-lake body hydrodynamic coupling modeling module which is used for constructing a distributed watershed hydrology model and a lake body hydrodynamic model; the comprehensive meteorological uncertainty boundary file generation module is used for obtaining a watershed meteorological boundary file and a lake body meteorological boundary file; the drainage basin hydrological simulation module is used for forming a drainage basin inflow-water taking boundary file set covering meteorological uncertainty; the lake water level control strategy generation module can obtain a plurality of water level control boundary files; a lake body hydrodynamic simulation module; and the water level control strategy determination module is used for determining a final annual scale robust water level scheduling strategy. According to the method, the spatial-temporal heterogeneity of rainfall and a multi-scene water level control strategy are comprehensively considered, the accuracy and robustness of lake water resource management are remarkably improved, and reliable operation of the system under various meteorological conditions is guaranteed.

The invention relates to a lake water quality real-time monitoring method and system based on a sensor array. The method comprises the steps that the sensor array is arranged in a target lake, and data acquisition frequency parameters are set; acquiring working parameters of the sensor array in real time, analyzing the working parameters, determining health states of the sensors, and performing adaptive adjustment on the sensors based on the health states of the sensors; collecting water quality monitoring data in real time based on the adjusted sensor array, performing preprocessing and quality control verification on the water quality monitoring data, and constructing a monitoring data set with qualified quality control; and comparing each water quality index in the monitoring data set with qualified quality control with a preset environmental standard limit value, marking a limit value exceeding event, and performing early warning, thereby completing real-time monitoring of the water quality of the target lake. According to the invention, intelligent real-time monitoring, abnormity identification, data quality control and early warning response of lake water quality are realized, and comprehensive technical support is provided for water environment management.

The invention relates to the technical field of water level and water surface relation analysis, in particular to a lake water level and water surface relation analysis method based on a DEM and a remote sensing image, which comprises the following steps of: based on DEM and remote sensing image data, analyzing a space relation between measured data and the DEM, correcting abnormal elevation and adjusting original data, screening boundary points with small elevation difference, and calculating the water level and water surface relation of a lake; and performing statistics on water surface areas under different water levels to obtain a water level area change sequence. According to the method, through cooperative adjustment and automatic fusion based on multi-source spatial data, through elevation correction, spatial anomaly screening and time sequence change tracking, the consistency of spatial data is improved, elevation anomaly information is dynamically corrected, the discrimination capability of a water body spatial distribution boundary is enhanced, and dense water surface change information extraction is realized based on spatial overlapping data; the space node and area change trend under the water level grading condition can be automatically concluded, and the dynamic response characteristics of the lake space structure can be systematically reflected.

The invention discloses a lake ecological health evaluation method based on big data, and the method comprises the steps: inputting the current lake water quality ecological data obtained in advance into a lake ecological health model obtained through training, and obtaining the health level of a lake ecological system. Training to obtain a lake ecological health model, including: obtaining lake water quality ecological data, the lake water quality ecological data including monitoring data, regional weather data and hydrological data; an anomaly detection method based on hypothesis testing and an anomaly detection method based on prediction are combined, abnormal values existing in the monitoring data are subjected to abnormal value identification and cleaning, and updated lake water quality ecological data are obtained; and inputting the updated lake water quality ecological data into the constructed lake ecological health model, updating the weight of the constructed lake ecological health model, and obtaining a trained lake ecological health model. The method provides decision support for lake ecological health protection and basin sustainable development.

The invention belongs to the technical field of river and lake water quality monitoring, and discloses a river and lake water quality intelligent traceability monitoring system and method.Dissolved organic matter fluorescent fingerprints are analyzed in real time through a three-dimensional fluorescent monitoring unit, pollution markers are detected, and parameters such as COD, ammonia nitrogen and total phosphorus are synchronously detected through a full-spectrum monitoring unit; the video monitoring unit is used for collecting section images / videos of rivers and lakes, other sensors are used for collecting real-time values of chlorophyll, pH, conductivity, turbidity and dissolved oxygen in a water body, then the data processing module is used for carrying out normalization processing on multi-source data, and the analysis and early warning module is used for analyzing water quality early warning scores, spectrum early warning scores and video early warning scores. All scores are synthesized to obtain a comprehensive early warning score, traceability is started when comprehensive early warning is larger than or equal to the grade II, pollution sources are recognized based on three-dimensional fluorescence spectrum comparison and upstream patrol, and a river and lake water body monitoring-early warning-traceability-verification closed loop is achieved.

The invention discloses a quantitative source tracing method for river and lake water pollution in combination with a knowledge graph and machine learning. The invention relates to the technical field of water quality pollution traceability, and the method comprises the steps: collecting and processing target river section data, constructing a hydrodynamic force-water quality model, and obtaining river downstream pollutant diffusion characteristics and target section concentration time sequence characteristics under different discharge situations of each discharge port based on the discharge port position of a target river section through a unit pulse response test; the method comprises the following steps: constructing a source intensity-time-concentration relation knowledge graph, randomly extracting a sample set from the graph, training the sample set by adopting a machine learning method, learning a nonlinear mapping relation between a downstream section concentration time sequence and multi-outlet source intensity, carrying out dynamic inversion of a river pollutant diffusion process, and realizing rapid positioning and contribution quantification of a pollution source. And finally, generating probability distribution of pollution source positions by adopting a Monte Carlo sampling method to assist in improving the traceability judgment priority, thereby improving the accuracy and response speed of river water quality pollution traceability.

The invention discloses a shallow lake submerged plant planting matrix and an efficient planting method. The method comprises the steps of submerged plant planting matrix preparation, seedling preparation, culture and maintenance and seed throwing. The planting substrate comprises the following components in percentage by weight: 54-74% of common soil; 12 to 33% of sediment; 11-12% of river sand; 1% of kaolin; after the raw materials are uniformly mixed, a small amount of water is added for multiple times, so that the sticky planting substrate is formed. Propagules such as submerged plant seedlings, buds and seeds are wrapped with the planting substrate. The submerged plants and the planting substrate form a stable whole body which is not easy to separate; after the plant seedlings are maintained and cultivated for 5 days, the submerged plant seedlings are thrown and planted in the lake water area. A small amount of degradable environment-friendly substrate raw materials are added, so that the strength and toughness of the planting substrate can be improved, the substrate can be kept in water until the submerged plants are stable in rooting and germination, necessary nutrient elements can be provided for growth of the submerged plants, and rapid and stable growth of the submerged plants is promoted.

The present invention discloses an intelligent mobile artificial fish nest experimental platform for fish with sticky and sinking eggs, including an artificial fish nest bed and an intelligent mobile base, wherein the artificial fish nest bed is a field-shaped structure, buoyancy cylinders are loaded on four sides, an experimental observation camera is arranged in the middle, a group of Mecanum wheels are respectively installed at four corners of the intelligent mobile base, and an omnidirectional mobile motor, a stable support motor, an intelligent control console, a magnetic plate, an underwater camera system and a telescopic hinge are loaded on the intelligent mobile base. The present invention makes up for the lack of protection measures for fish with sticky and sinking eggs at present, provides a suitable spawning place for fish with sticky and sinking eggs, weakens the influence of underwater terrain changes caused by changes in river and lake water levels and dam flood discharge on the growth and development of sticky and sinking fish eggs, optimizes the protective effect of artificial fish nests on sticky and sinking fish eggs, and provides more technical basis for the protection of spawning grounds of fish with sticky and sinking eggs.

The invention relates to the technical field of environment intelligent supervision, and discloses a visual river and lake water area health ecological supervision and management system. A data acquisition and feature extraction module of the system is responsible for acquiring multi-source ecological monitoring data of water areas of rivers and lakes, extracting spatial change features of motion trails of aquatic organisms and a time sequence of water quality parameters from the data, and identifying abnormal features according to an index variation mode corresponding to a predefined ecological health level; and generating an ecological health abnormity response label set associated with the specific geographic position. And the health state analysis module obtains key information of the marked water area section based on the label set, analyzes a change track and a deviation interval of the health state in the supervision time frame, and determines a health state lagging time period. And the evaluation delay processing module is used for extracting related time information to calculate a time difference according to the water area subareas related to the lagging time period, screening out the water area subareas which are not evaluated completely and sorting the water area subareas, and generating an adjustable configuration scheme of the monitoring time period of the water area subareas.

The invention discloses a method and system for monitoring the lake water level based on remote sensing images, and particularly relates to the technical field of remote sensing monitoring, and the method comprises the steps: constructing a complete three-dimensional terrain model of a lake through historical remote sensing images and data of underwater detection equipment, including water surface and underwater shapes; a quantitative relation model of the terrain and the water level is established by utilizing a digital twinborn technology and actually measured water level data, the model is repeatedly tested and optimized, the precision is ensured, and the model is finally applied to real-time water level monitoring, so that the water level information of a target lake is automatically acquired and updated; the problem that a terrain-water level relation model is difficult to accurately monitor lake water level changes in the prior art is solved. The system further comprises a water body boundary correction module and a boundary contour processing module, the boundary of the lake is recognized and extracted more accurately through a remote sensing image and a machine learning model, the boundary is optimized, and accuracy and completeness of water body information are guaranteed.

The invention discloses a flood lake wetland heat flux simulation method in a watershed hydrological model, and relates to the technical field of heat flux simulation, and the method comprises the following steps: dispersing a watershed space into a slope river channel and a lake wetland, simulating the water movement of a slope river channel unit, and simulating the water movement of the lake wetland; the method comprises the following steps: simulating surface energy exchange for a lake wetland unit on the basis of simulating a hydrological process, simulating land hydrology and lake inflow runoff of a sub-basin by adopting a grid-based distributed hydrological model, respectively dividing simulation grid units for a slope river channel and a lake wetland, establishing evapotranspiration, infiltration and surface runoff models, and establishing an evapotranspiration, infiltration and surface runoff model; a two-dimensional shallow water equation is used for processing slope confluence, a one-dimensional hydrodynamic model is used for calculating river channel confluence, a lake water quantity balance model is established for calculating the lake water level, the lake submerging process is simulated, and the lake wetland distributed heat flux process is simulated. And the technical blank of flooding wetland hydrothermal simulation is filled.

The invention relates to the technical field of lake chlorophyll a inversion, and provides an inland lake chlorophyll a inversion method and system based on hyperspectral data, and the content of chlorophyll a in a lake range is inverted through a multiband index model and a regression method. Firstly, hyperspectral image data of a lake is obtained and preprocessed to remove noise and improve data quality. Then, extracting a water body range by adopting an NDWI index, and extracting wave band information of different sampling points; based on optical characteristics of lake water quality, a multiband index model considering pigment absorption and remote sensing reflectivity is constructed, and an inversion model is optimized in combination with regression analysis. According to the method, the hyperspectral remote sensing image data are collected and fused only through actual measurement of a small number of points, the constructed model is applied to the lake chlorophyll a distribution characteristics on the scale of a monitoring area, accurate inversion of the concentration of the chlorophyll a in the whole area of the lake can be achieved, and the problem that the accuracy is low when remote sensing data is singly adopted for inversion is solved.

The invention belongs to the technical field of lake remote sensing monitoring, discloses a lake water level and water reserve dynamic monitoring method based on satellite-borne active and passive remote sensing information fusion, and effectively solves the problem that lake information in remote areas is difficult to efficiently and accurately obtain. Comprising the following steps: S1, preprocessing laser data; s2, preprocessing the multispectral data; s3, taking the ICESat-2 data as a core, combining with a multispectral image covering a corresponding region, and constructing a lake bottom DEM inversion data set based on neighborhood features; s4, constructing and training an elevation inversion convolutional neural network model; s5, predicting and generating a lake bottom DEM by using the trained elevation inversion convolutional neural network model; and S6, calculating the absolute water volume of the lake according to the DEM at the bottom of the lake, and carrying out lake water level-water reserve monitoring. According to the invention, long-time-sequence hydrological observation of remote region lakes can be effectively realized with low cost.

The invention belongs to the field of lake ecological desilting, and discloses a shallow lake bottom mud ecological desilting and residual water treatment combined treatment method which comprises lake bottom mud ecological desilting, solidification dehydration treatment and residual water treatment. Ecological dredging of the lake bottom mud is implemented by adopting an environment-friendly cutter suction dredger and a sealed pipeline to convey mud, and the bottom mud is conveyed to a mud discharge field to be solidified and dehydrated. A vacuum preloading technology is adopted for solidification and dehydration of the bottom mud, pore water of the bottom mud is effectively discharged by increasing the pressure difference between soil bodies of a drainage plate, and the overall solidification effect is improved. The residual water treatment comprises the treatment of desilting and solidifying residual water, the technologies of multi-stage precipitation, mechanical aeration, aquatic plant remediation and the like are adopted, and the residual water is discharged nearby after being treated to reach the standard. The combined treatment method aims at effectively reducing endogenous pollution of lakes, promoting lake water quality improvement and guaranteeing water safety of regional lakes and reservoirs, and is particularly suitable for ecological dredging engineering of shallow lakes, short construction period and fine-particle bottom mud.

The invention discloses a lake water surface area regulation and control scheme generation method and device. According to the method, lake basic data of a region to be regulated and controlled are collected, and a high-precision digital elevation model (DEM) and a lake database are constructed, so that the landform and water body attributes of a research region can be accurately reflected; an initial simulation model is constructed by combining an environmental fluid dynamic model, a water body boundary condition, a DEM model and a lake database, and the coupling influence of a hydrodynamic process and topographic features on lake water level and water surface distribution can be fully considered; after real-time monitoring data is obtained, an initial model is optimized based on parameter prior distribution and a sample sampling strategy, and the precision and the response speed of a simulation model are remarkably improved; the target simulation model is used for simulating various water level scenes to obtain response indexes under the scenes, and a regulation and control scheme is generated based on the multi-target constraint curve and the analysis strategy, so that the regulation and control accuracy of the lake water surface area is improved.

The invention discloses a lake water body extraction method and system based on deep learning, a storage medium and electronic equipment, and relates to the technical field of remote sensing image processing. The method comprises the following steps: constructing a multi-level deep learning water body extraction model by utilizing polarization characteristic differences between a lake water body and other ground features in polarization decomposition; on the basis, accurate extraction of water boundary information is further realized through a feature learning and optimization process. According to the method, a new thought is provided for obtaining long-time series lake continuous observation data, and data support is provided for further developing seasonal change analysis, rapid flood season early warning, disaster assessment and the like of a frozen circle lake water body.

The invention relates to an internet-of-things monitoring and early warning system for lake water quality. The internet-of-things monitoring and early warning system comprises a multi-mode intelligent sensor acquisition system, a self-adaptive wireless transmission system, a distributed data storage system, an intelligent data analysis system, a visual interaction system, an intelligent early warning system and a remote intelligent control and repair system. The invention also relates to a monitoring method using the lake water quality Internet of Things monitoring and early warning system. Traditional and innovative sensors are cooperated to comprehensively collect water quality and ecological data, the biosensor accurately detects specific pollutants, the hyperspectral imaging sensor monitors algae distribution and water color change, rich and high-precision data are provided for lake water quality monitoring, the monitoring comprehensiveness and precision are improved, and the water quality monitoring system is suitable for being popularized and applied. The lake ecological protection work is powerfully supported.

The invention discloses a lake water level and water surface area conversion method and device. According to the invention, parameter optimization is carried out on an initial lake hydrodynamic model through actually measured water surface data, so that the fitting precision and simulation capability of the model to a lake dynamic change process are improved; the problem of insufficient precision caused by the fact that a traditional water level area conversion method only depends on static topographic data or single water level record is solved; meanwhile, a high-resolution water level-water surface area data set is constructed through space-time alignment of day-by-day water surface area simulation data of day-by-day scale simulation and actually measured water level data, so that a partition regression model is established under different hydrological process conditions, hysteresis and nonlinear characteristics in the water level change process are fully considered, and the accuracy of the water level change process is improved. Therefore, the adaptability and generalization ability of the water level area relation model are improved, and high-precision and two-way conversion between the lake water level and the water surface area under the complex dynamic hydrological condition is achieved.

The invention discloses a multi-branch auxiliary fusion improved YOLOv10 unmanned aerial vehicle water surface object target detection method, which comprises the following steps: (1) using an unmanned aerial vehicle to shoot lake water surface floating object images at different heights in a lake water area environment, and recording the overall condition of the lake water surface; (2) selecting a proper labeling tool, and carrying out data set labeling work on the stored pictures; (3) performing data enhancement on the pictures in the data set and the corresponding annotation information, and then dividing the enhanced data set into a training set, a test set and a verification set according to a proportion; (4) a YOLOv10 model is used and improved, a target detection network based on the multi-branch auxiliary fusion improved YOLOv10 model is built, and an obtained data set is used for model training and verification; (5) evaluating an output result; according to the invention, identification of the floating objects on the water surface is realized, so that the cleaning work of the floating objects on the lake water surface is more effectively carried out.

The invention provides an urban internal lake water pollution treatment method and system. Real-time water quality data are collected from a lake; processing the collected data; using the prediction model to predict a water quality trend and identify a pollution risk; according to the analysis result of the machine learning algorithm and the water quality model, the decision support module generates a targeted treatment strategy; according to the treatment strategy provided by the decision support module, specific treatment measures are implemented; and the intelligent management and control module is used for monitoring and managing the whole treatment process in real time. All the modules are linked to construct a whole-process and intelligent treatment system, the treatment efficiency of urban internal lakes is effectively improved, and continuous improvement of water quality and long-acting restoration of an ecological system are achieved.

The present invention discloses an integrated water level prediction method including virtual sites based on graph and sequence decomposition technology, comprising: preprocessing water level monitoring site data and dividing it into a training set and a test set; constructing a graph and sequence decomposition module (GSD), and using the training set and the test set to train the graph and sequence decomposition module GSD; constructing a virtual site water level prediction module (VS-WLP), and using the training set and the test set to train the virtual site water level prediction module VS-WLP; based on the trained graph and sequence decomposition module GSD and the virtual site water level prediction module VS-WLP, water level prediction is realized; based on the trained river and lake water level prediction model, a hierarchical integration method is used to realize integrated water level prediction. The present invention effectively improves the robustness and applicability of the prediction model. The innovative water level prediction module can be used for water level estimation in areas without monitoring stations, making up for the shortcomings of traditional hydrological monitoring systems in spatial distribution.

The invention discloses a multi-mode perception unmanned operation platform for river and lake water quality improvement and an intelligent decision-making method, and the platform comprises an intelligent control center, and an autonomous navigation system, a multi-parameter water quality detection system, a dual-mode aeration system and an intelligent medicament decision-making system which are connected with the intelligent control center. Autonomous navigation operation of a complex water area is achieved through the autonomous navigation system, the multi-parameter water quality detection system provides a multi-mode sensing sensor array, comprehensive water quality parameter detection can be conducted on the water areas of rivers and lakes, and the dual-mode aeration system conducts aeration according to dissolved oxygen parameters. A working mode instruction intelligently judged by the intelligent control center is executed, aeration treatment is conducted, the intelligent medicament decision-making system dynamically calculates the medicament adding amount according to the water quality parameters, and accurate adding is achieved. According to the system, efficient integrated collaborative operation of autonomous navigation operation, full-dimensional water quality parameter real-time monitoring, dual-mode precise aeration and an on-demand dosing mechanism is realized, and the effect and efficiency of river and lake water quality improvement can be effectively improved.

The invention provides an urban lake group low-carbon regulation and control method and system. The method comprises the following steps: acquiring geographic space, terrain, weather, hydrological observation and drainage basin management data of a target lake; on the basis, lake inflow and pollutant concentration data are calculated; simulating and calculating flow field distribution and water quality index data of the target lake by combining topographic data of the target lake; constructing an optimization model aiming at minimizing gate pump energy consumption and maximizing lake water quality improvement; and solving the optimization model based on the lake inlet flow, the pollutant concentration, the flow field distribution, the water quality index, the gate pump power coefficient, the gate pump flow, the lift and the seasonal duration to obtain gate pump regulation and control parameter set data so as to realize low-carbon regulation and control of the urban lake group. According to the urban lake group low-carbon regulation and control method and system provided by the invention, seasonal change can be allocated to optimize water quantity, lake inlet flow can be adjusted, water quality can be improved, water fluidity can be enhanced, lake ecological environment restoration can be promoted, energy consumption can be reduced, and green low-carbon treatment can be realized.

The invention discloses an internal flow lake water storage optimization calculation method considering flood control and ecological influence. The method comprises the following steps: step 1, lake water system connectivity analysis; 2, analyzing the water storage capacity of the lake; step 3, constructing a distributed hydrological model; 4, constructing and calculating a hydrology-water volume scheduling integrated model; step 5, constructing a water storage optimization model; and step 6, lake water storage optimization calculation. According to the internal flowing lake water storage optimization calculation method considering flood control and ecological influence, internal flowing lake water volume optimization scheduling considering ecological and flood control safety is realized, a scientific basis is provided for protecting the ecological and flood control safety of an internal flowing river basin under the current climate change condition, and the method has very high application value.

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 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.