199 results about "Phytoplankton" patented technology

The invention provides an ocean near-surface sub-mesoscale process flow field inversion method and device. The method provided by the invention comprises the following steps: screening an ocean near-surface sub-mesoscale representative sea area; obtaining the structure image information of the frontal surface and the filament of the phytoplankton in the marine sub-mesoscale process; acquiring displacement velocity field information at the same position; the structure image information and the displacement velocity field information are matched, and a first training sample set is constructed; correcting the first training sample set based on the second data set and the third data set to obtain a second training sample set; training the initial model by using the second training sample set to obtain a flow field inversion model; obtaining a plurality of sub-mesoscale sea surface water color images of the sea area to be detected; and processing the sub-mesoscale sea surface water color image by using the flow field inversion model to obtain a displacement velocity field. The invention provides an ocean near-surface sub-mesoscale process flow field inversion method and device. The method and device are used for solving the problem that a current ocean flow field inversion algorithm is not high in sub-mesoscale process inversion precision.

The invention provides a fine-grained phytoplankton microscopic image enhancement classification method and a model building method thereof, and belongs to the technical field of underwater image enhancement classification, and the method comprises the following steps: firstly, collecting phytoplankton samples from a plurality of sea areas, and obtaining image data through a microscopic imaging technology; screening, preprocessing and data enhancement are carried out on the images, and a high-quality training set and a high-quality test set are constructed; according to the method, a double-branch deep learning model HySwinFormer is developed, the double-branch deep learning model HySwinFormer comprises a multi-scale context capture branch based on hierarchical vision TransFormer and a local detail enhancement branch designed by adopting a parallel convolution kernel, and joint expression of features is realized through a designed weighted fusion strategy; and training and optimizing the model to obtain a final model. Experimental results show that the classification performance of the HySwinFormer model on multiple data sets is superior to that of an existing mainstream deep learning model, and the classification effect is remarkably improved.

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 relates to the field of phytoplankton image edge detection, in particular to a marine phytoplankton image edge detection method based on a non-local mean denoising mode. The method comprises the following steps: putting a marine phytoplankton image file needing to be detected into a folder of a program item, automatically reading a code into an image in the folder by a program, and loading a to-be-processed image; edge details related to the marine phytoplankton image are reserved based on a non-local mean denoising algorithm; aiming at the image data after the edge details are reserved; carrying out edge extraction on the image based on a Canny edge detection algorithm; and outputting a marine phytoplankton image with edge details reserved. Through combination of non-local mean denoising and Canny edge detection, noise can be more effectively removed, image details and structure information can be kept, and the influence of noise on edge detection is reduced, so that clearer and more accurate edges are extracted; and in an image with relatively large noise, the edge extraction precision and the image quality can be remarkably improved.

The invention discloses a phytoplankton microscopic image classification model construction method, and belongs to the field of intelligent detection. For the problems of unstable microscopic image quality and small difference between phytoplankton classes, a double-branch ResNeXtViT model is provided: a ResNeXt branch enhances the texture feature extraction capability through a wavelet convolution attention module, and improves the adaptability to irregular shapes in combination with an adaptive convolution module; a Transform branch is used for capturing global context information; the feature fusion module integrates local details and a global structure. A combined data enhancement strategy including noise addition and color migration is adopted, and a joint loss function is designed to optimize intra-class compactness and local discrimination capability. Experiments show that the accuracy of the method on a Chaohu lake phytoplankton data set reaches 94.11%, which is superior to that of ResNet, ViT and other models, and the method can be deployed to an intelligent analyzer to realize rapid water quality monitoring.

The invention relates to the technical field of marine ecological environment monitoring and data analysis, and particularly discloses a sea area phytoplankton biodiversity index prediction method and system based on multi-model integration and feature engineering. After preprocessing, constructing four groups of nonlinear interaction characteristics of a temperature-salt relationship, oxygen-salt balance, chlorophyll chemical oxygen demand coupling and a nitrogen-phosphorus ratio based on environmental factors, combining station characteristics with basic environment and interaction characteristics, carrying out variance threshold screening, inputting a characteristic set into a multi-model integration framework containing models such as linear regression and gradient lifting, and carrying out multi-model integration; training and tuning according to a time sequence segmentation strategy, selecting model output according to a decision coefficient, using a result if the decision coefficient of the support vector regression model is within a preset range, and otherwise, taking a gradient lifting and extreme gradient lifting tree model to predict a mean value. And the prediction accuracy and the model generalization, stability and reliability are improved.

The invention relates to the technical field of ecological health assessment, and discloses a lake and reservoir water ecological health assessment technical method, which comprises the following steps: S1, acquiring survey monitoring data of a lake and a reservoir, including biological data and water quality data, and constructing a lake and reservoir water ecological index system; s2, determining a score level according to a total standard score value of each hierarchical level index score of the lake and reservoir water ecological index system; and S3, determining the water ecological health level of the lake and the reservoir based on the score value corresponding to each score level. According to the invention, a comprehensive lake and reservoir water ecological index system is constructed based on various biological data and water quality data. The index system comprises multiple levels such as phytoplankton, zooplankton and organic matter, the ecological health state of the lake or reservoir can be comprehensively evaluated from multiple dimensions, and the limitation of single index evaluation is avoided.

The invention discloses a method for inverting marine primary productivity based on airborne dual-wavelength laser radar remote sensing data, and the method comprises the steps: building a dual-wavelength high-precision water optical characteristic inversion model based on the airborne dual-wavelength laser radar remote sensing data, and obtaining a high-precision marine phytoplankton absorption coefficient vertical section; and inverting the marine primary productivity vertical section distribution coupled with the high-precision phytoplankton absorption coefficient. According to the invention, one laser wavelength is suitable for detecting an offshore sea area, the other laser wavelength is suitable for detecting an open sea area, and the dual-wavelength ocean laser radar improves the maximum detection depth of near and open seas and the inversion precision of the primary productivity vertical section distribution of a typical sea area. And the ocean carbon cycle space-time evolution precision and the observation capability in the aspect of ocean upper-layer energy conversion are further improved.

The invention discloses a phytoplankton primary productivity in-situ fluorescence detection device and method, and belongs to the technical field of aquatic ecology monitoring. The device adopts a modular integrated design and comprises a multi-band LED excitation light source, a laser induction module, a photomultiplier receiving system and an anti-vibration circuit structure, and synchronous detection of chlorophyll concentration and photosynthetic activity is realized through a short-optical-path optical design. The method is based on spectrum inversion and fluorescence kinetic analysis, a standard spectrum library is constructed, chlorophyll concentration distribution and photosynthetic parameters are calculated through least square fitting, and primary productivity data are output in combination with a photoresponse model. The device has the diameter of 67 mm, supports deployment of Argo and other mobile platforms, solves the problems that a traditional method is poor in timeliness and equipment is discrete, and has the advantages of being high in detection efficiency, high in anti-interference performance and wide in applicable water depth range.

The present invention provides a device for quantitatively collecting surface phytoplankton samples, comprising a sampling tube, wherein a bottom opening is provided at the bottom of the sampling tube, an annular baffle is provided on the inner side of the lower part of the sampling tube, an inlet valve is hinged on the baffle, a baffle is provided above the baffle, the inlet valve is connected to a float through a pull rope, the pull rope is passed through a hanging ring, the float is located above the baffle, a water outlet is provided on the lower side wall of the sampling tube, the water outlet is located above the baffle, a scale is provided on the side wall of the sampling tube, the starting end of the scale is flush with the center line of the water outlet, a side-permeable top cover is provided on the top of the sampling tube, a handle is provided above the side-permeable top cover, a float ring is detachably provided on the upper part of the outer wall of the sampling tube, and a counterweight is detachably provided on the lower part of the outer wall of the sampling tube; the sampling tube can be conveniently used for daily manual sample collection, and can also be applied to a buoy monitoring system, so that surface phytoplankton samples around the buoy body can be collected through auxiliary facilities, thereby improving the accuracy of the buoy monitoring system.

The invention discloses a marine phytoplankton abundance monitoring method based on data driving and an underwater acoustic network, and the method comprises the steps: building and training a multi-level spatial-temporal feature integration model based on the historical marine phytoplankton abundance and the corresponding marine environment data, and solving the optimal spatial position and monitoring time window of an underwater acoustic network node; constructing a teacher model which comprises a multi-layer cascade structure and a residual module and is used for channel prediction, and compressing the teacher model into a student model by adopting spatio-temporal knowledge distillation; the student model is trained, and channel state real-time prediction is realized at the underwater acoustic network node; on the basis of the optimal spatial position of an underwater acoustic network node and a real-time predicted channel state, adaptive underwater acoustic communication models and seasonal optimal routing strategies are matched, adaptive modulation and coding are executed at a link level, and monitoring of abundance of phytoplankton is completed based on an underwater acoustic network. According to the invention, the data throughput and prediction precision can be improved under strict energy consumption constraints.

The invention relates to the technical field of marine environment remote sensing monitoring, and discloses a marine ecological environment monitoring system based on satellite remote sensing. And the receiving unit synchronously acquires vertical sections of four paths of returned signals of co-polarization elastic scattering, cross-polarization elastic scattering, laser-induced fluorescence and water body Raman scattering. A data processing unit establishes a collaborative inversion framework integrated with multiple physical constraints, and the core of the collaborative inversion framework is to construct a cost function comprising an observation constraint term and a Raman optical closed collaborative constraint term. The function is solved through an iterative optimization algorithm, and vertical sections of ecological parameters such as phytoplankton and suspended solids are inverted. According to the method, the Raman signal is taken as a physical reference, the forced inversion result is self-consistent in optics, the ill-conditioned problem in traditional inversion is effectively solved, and the accuracy and reliability of three-dimensional profile monitoring of the marine ecological environment are remarkably improved.

The invention discloses a method for determining a chlorophyll concentration value of phytoplankton based on ocean heat waves. The method comprises the following steps: acquiring ocean green ecological remote sensing image data and ocean heat wave data of a target sea area; and inputting the ocean heat wave data and the ocean green ecological remote sensing image data of the target sea area into a chlorophyll concentration determination model, so that the chlorophyll concentration determination model determines the ocean heat wave grade of the target sea area according to the ocean heat wave data, inputting the ocean green ecological remote sensing image data into a chlorophyll concentration determination sub-model according to the ocean heat wave grade, enabling the chlorophyll concentration determination sub-model to extract corresponding multispectral remote sensing image features and ocean microwave remote sensing image features, and outputting a phytoplankton chlorophyll concentration value of the target sea area; and transmitting the phytoplankton chlorophyll concentration value of the target sea area to a marine ecological monitoring system to assist the marine ecological monitoring system in sea area monitoring. The method can improve the accuracy of determining the chlorophyll concentration value of the phytoplankton and the application reliability.

The invention provides a phytoplankton chromatography sequence identification method and a phytoplankton chromatography sequence model building method, and belongs to the technical field of image enhancement identification. The method comprises the following steps: firstly, acquiring microscopic chromatography sequence data of phytoplankton, performing view field extraction and serialization recombination, and constructing a three-dimensional data set; then, constructing a three-dimensional recognition model containing physical perception and a sequence aggregation mechanism, extracting single-frame semantic features by the model by adopting a parameter-shared twin network, and introducing a physical definition prior module to calculate a space-frequency domain quality score of a slice; secondly, designing a deep perception sequence aggregation module, and adaptively aggregating key features of a high signal-to-noise ratio by taking definition scores as gating signals and combining spatial context information between slices; and finally, training and optimizing the model based on the image-level weak supervision label to obtain an optimal model. According to the method, the problems of information truncation and out-of-focus noise interference caused by extremely shallow depth of field of high-power microscopic imaging are solved, and full-depth-of-field stereoscopic perception can be realized under the condition that frame-by-frame fine labeling is not needed.

The invention belongs to the technical field of resources and environment, and discloses an ecological niche theory and machine learning integrated ecological intelligent zoning method, which comprises the steps of environmental factor and phytoplankton community data measurement, sampling point ecological niche width and overlap calculation, key environmental factor identification and ecological environment zoning. According to the method, data such as hydrochemical element phytoplankton communities, ecological niche overlapping and width can be effectively integrated, and a machine learning algorithm is applied to perform analysis, so that factors which have remarkable influence on an ecological system are identified, and scientific and reasonable ecological zoning is performed on a research area according to the key influence factors. The method not only can accurately reflect regional ecological characteristics, but also can provide important data support for ecological protection, species distribution prediction and water resource management. Meanwhile, the method can provide an effective basis for ecological environment monitoring, ecological restoration and regional environment planning in practical application, and has high application value and wide popularization prospect.

The invention discloses a method for determining a toughness and catastrophe threshold value of a functional group of a water ecosystem, and relates to the technical field of water environment ecological engineering.The method comprises the steps that the state of a target water ecosystem is determined according to the total nitrogen content, the total phosphorus content and the chlorophyll content of a water body in the target water ecosystem, the state of the target water ecosystem is eutrophication or non-eutrophication; determining the biomass of phytoplankton based on the target water ecosystem state; utilizing an Ecopath model to construct a static food network relation model of the target water ecosystem; based on the target water ecosystem state, the biomass of the phytoplankton and the static food network relation model, determining a toughness threshold value and a catastrophe threshold value of the biomass of each functional group; each functional group is a plurality of groups for dividing species in the target water ecosystem. According to the method, the water ecosystem functional group toughness threshold value and the catastrophe threshold value which are simple to operate and high in accuracy can be provided.

The invention provides a phytoplankton density identification method and system based on ocean water temperature. The method comprises the following steps: acquiring a water temperature data set of a preset three-dimensional space of each grid region in a target ocean region within a preset time range; performing feature extraction on each water temperature data set to obtain a time sequence dynamic feature, a vertical layering feature and a space correlation feature of the ocean water temperature of each grid region; inputting the time sequence dynamic features, the vertical layering features and the spatial correlation features into a preset recognition model, so that the recognition model performs feature fusion on the time sequence dynamic features, the vertical layering features and the spatial correlation features based on an attention mechanism and a full-connection network to obtain fusion feature vectors, and generating the phytoplankton density of each grid region according to each fusion feature vector, thereby improving the accuracy and efficiency of phytoplankton density identification.

The application discloses a kind of plankton detection method, system and electronic equipment, it is related to computer vision technical field.The present application can be based on the image data of different regions, different river reaches of the river to be detected by using river plankton detection model, accurately, quickly get the plankton detection result of each pixel.And, multiple domain self-adaptive feature extraction modules are arranged in the river plankton detection model of the application, the distinguishing ability of target and background difference in image data can be improved, multiple boundary alignment modules can enhance the target feature extraction capability, the accuracy of plankton detection can be significantly improved, and the economic and ecological cost of plankton management can be effectively reduced.

The invention belongs to the technical field of aquaculture, and particularly relates to a breeding system for combined breeding of zooplankton and phytoplankton and a method for combined breeding of zooplankton and phytoplankton. By means of the breeding system, the phytoplankton releases oxygen and generates organic matter through photosynthesis, and abundant bait is provided for the zooplankton; the zooplankton is fed by the phytoplankton, nutritive salt in the generated waste is dissolved in water and becomes a nutrition source of the phytoplankton, high-density growth of the zooplankton and the phytoplankton is achieved in a limited breeding space, and land resources are effectively saved; and in combination with an intelligent monitoring system, water body environment parameters can be monitored and adjusted in real time, plankton is ensured to grow under the most suitable environment condition, the feed conversion efficiency is improved, and water body pollution is effectively reduced. The primer aquatic bacillus can increase dissolved oxygen in a water body, promote decomposition of organic matters in the water body and release of inorganic nutritive salt, and further support growth of phytoplankton.

The invention discloses a phytoplankton space distribution prediction method based on environment DNA and machine learning, and belongs to the technical field of ecological environment evaluation and treatment. Comprising the following steps: determining a sampling site, and collecting a phytoplankton eDNA sample; carrying out DNA extraction and treatment on the collected eDNA sample, and calculating a species abundance matrix; screening a training data set for training a prediction model according to the sampling time and the sampling site of the eDNA sample; inputting the training data set as an independent variable of a training set, inputting the species abundance matrix as a dependent variable of the training set, and training a prediction model; and inputting remote sensing image data into the prediction model to obtain a species spatial distribution diagram. Compared with the prior art, the method has the advantages that efficient and high-species-resolution spatial distribution prediction of algae species such as blue-green algae can be achieved, the phytoplankton prediction model is trained through the machine learning model, automatic operation of spatial prediction of various phytoplanktons is achieved, the arrangement density of sampling points is reduced, and the monitoring efficiency is improved.

The invention relates to the field of ecological environment monitoring production, in particular to an efficient separation type ecological planktonic animal and plant collecting net which comprises a frame, the middle of the frame is provided with a first-layer net used for intercepting large sundries, and the top of the first-layer net is provided with a lifting hook. The bottom second-layer net is used for intercepting phytoplankton, the third-layer net located on the same axis with the inner side of the second-layer net is used for intercepting zooplankton, rotating force is generated through the propellers under the action of water flow, and the auger push plate is made to be of a conical structure and rotate on the inner side face of the third-layer net. One end of the corrugated plate on the outer side inclines along the inner side face of the third layer of net, plankton can be pushed to move in a dispersed mode to avoid local accumulation and blockage during rotation, the plankton can be accurately intercepted by means of component force, and the wave trough positions of the three corrugated plates are not located on the same horizontal plane to generate a staggered stirring effect, so that filtering is more comprehensive and uniform.

A deep water drawing step of drawing deep water that exists in a deep region of the sea to a surface region of the sea with an upwelling pipe (1); and a phytoplankton culturing step of culturing the phytoplankton in the upwelling pipe (1) are included to produce, as a basic producer of a food chain, a living marine resource, such as fishes and shellfishes, with phytoplankton produced in the phytoplankton culturing step.

The invention relates to the technical field of ecology, in particular to an ecological evaluation method based on community specific species, which comprises the following steps: S1, evaluating phytoplankton ecological indexes by selecting an evaluation criterion, and standardizing the phytoplankton ecological indexes; s2, on the basis of the ecological index SEI of the phytoplankton, determining the change amplitude grade of the ecological condition of the phytoplankton; and S3, establishing a standard for grading the ecological indexes of the phytoplankton on the basis of verification and analysis of historical monitoring data. The phytoplankton has high sensitivity to environmental change, the change of the community structure and number of the phytoplankton can quickly reflect the change of the water body environment, the water body pollution problem can be found in time by regularly monitoring the change condition of the phytoplankton, early warning can be given out, and the ecological evaluation method based on the phytoplankton is provided for related departments. The health condition of the water ecosystem can be comprehensively and systematically evaluated, and a scientific basis is provided for protection and management of water resources.

The invention relates to the technical field of regional marine environment evaluation, in particular to a method for predicting the seawater quality pollution degree based on sensitive phytoplankton. Comprising the following steps: collecting a water body sample of surface seawater in an offshore area; dNA in the water body sample is filtered and extracted, and high-throughput sequencing analysis is carried out after PCR amplification; performing species classification annotation on each sequence, and constructing a data matrix according to obtained phytoplankton OUT data; calculating a Spearman correlation coefficient; constructing an inter-species correlation matrix, and selecting proper species to construct a co-occurrence network; exporting edge data and point data, and screening nodes with the maximum betweenness centrality or the highest tight centrality as key species; calculating an organic pollution index (OPI) and carrying out Spearman correlation analysis to determine OPI sensitive phytoplankton; and establishing a prediction model for prediction to obtain a predicted environmental pollution index. The method has the advantages that model complexity and noise influence are reduced; the prediction accuracy reaches 100% when the water quality is seriously polluted.

The invention discloses a spectral imaging-based phytoplankton volume detection method, which comprises the following steps of: selecting a plurality of spherical phytoplankton, and acquiring transmissivity spectral images and spectral image pseudo-color images of the spherical phytoplankton through a microscopic hyperspectral imager; the target range is marked in a perfect circle mode, the thickness of each pixel point in the target range is calculated according to the radius of the target range, and the thickness of each pixel point is the length of a chord which passes through the point and is perpendicular to the connecting line of the point and the circle center of the target range; constructing a training data set and training a prediction model constructed based on a random forest algorithm; acquiring a transmissivity spectral image of the phytoplankton with the volume to be measured, and inputting the transmissivity spectral image into the trained prediction model to obtain a thickness prediction result of each pixel point; and determining the actual area of a single pixel point according to the image resolution and the actual size of the image, and obtaining the volume of the phytoplankton with the volume to be measured in combination with a thickness prediction result. The device can be applied to phytoplankton in different shapes, and the application range is wider.

The invention discloses an ecological restoration device for promoting assimilation and purification of plankton by direct-drive rotary oxygenation in the field of ecological restoration, which comprises an aeration platform, a purification unit, a floating bed for planting aquatic plants and a carrier filler for microbial adsorption, an energy storage unit, a water supply unit, a water supply unit and a water supply unit, comprising a vertical wind driven generator, a solar power generation panel and an energy storage battery, and the control unit comprises a controller and a dissolved oxygen monitoring probe used for monitoring dissolved oxygen in a water body; the energy storage battery supplies energy to the control unit and the air blower. The invention provides an ecological restoration device for promoting assimilation and purification of plankton through direct-drive rotary oxygenation, the device can be directly driven to rotate by using reaction torque of aeration, the device not only can reduce loss of energy conversion, but also can recover energy of aeration airflow, and a controller intelligently controls oxygenation and aeration and adjusts the growth microenvironment of aquatic organisms, so that the ecological restoration device is suitable for ecological restoration of aquatic organisms. The assimilation purification effect is achieved.

The invention provides a novel mudflat efficient photovoltaic breeding system. The novel mudflat efficient photovoltaic breeding system comprises an arc-shaped ditch bottom, a side ditch, a pool wall, floating objects, a duckweed growth layer, a photovoltaic panel frame, a photovoltaic panel, a pipeline, a control box, a photovoltaic panel support, a first-layer annular pillar lamp, a second-layer annular pillar lamp, a third-layer annular pillar lamp, a fourth-layer annular pillar lamp, a bottom table top and a sponge sleeve. The floating objects comprise phytoplankton and zooplankton, a storage battery, a control panel and a photoelectric detection device are arranged in the control box, the arc-shaped ditch bottom, the side ditch, the pond bottom, the pond wall and the bottom table top form a pond body mechanism facilitating fish gathering and fish lifting, and the photovoltaic panel support, the photovoltaic panel frame, the photovoltaic panel, the control box, the storage battery in the box and the control panel form a photovoltaic mechanism of the fishpond. The first-layer annular pillar lamp, the second-layer annular pillar lamp, the third-layer annular pillar lamp and the fourth-layer annular pillar lamp form a light supplementing mechanism beneficial to photosynthesis, namely growth of phytoplankton, growth of the phytoplankton is beneficial to growth of zooplankton, and growth of fishes is beneficial to growth of the zooplankton.

The present invention provides a method for regulating multiple water sources in a lake based on a hydrodynamic-water quality-ecological model, which relates to the field of water source regulation technology, including steps of obtaining data, clarifying the occurrence mechanism of spatiotemporal heterogeneity of lake water environment, establishing a model, determining the water quality control threshold for maintaining the health of lake water ecology, and proposing the best multi-water source regulation scheme. The present invention, by improving the BLOOM module, incorporates the phytoplankton characterization into the simulation of the competition mechanism of lake phytoplankton, creates a hydrodynamic-water quality-ecological comprehensive model suitable for lake water environment simulation, improves the accuracy of lake water environment simulation, and thus can better accurately determine the water quality control target of lake water environment health; secondly, by using a variety of water resources for comprehensive regulation of multiple water sources, the water quality of the lake is improved while also improving the comprehensive utilization of regional water resources. The results of the present invention provide scientific and technological support for the water quality management of Chagan Lake, and are also the requirements for implementing the "one lake, one policy" precise governance.

The invention relates to the technical field of classification cultivation of oysters, in particular to a classification cultivation device for crassostrea hongkongensis, which comprises a classification mechanism, a cultivation mechanism, a material taking mechanism, a material supply mechanism and an oxygen supply mechanism. A stirring shaft is filled with phytoplankton particles, then a second oxygen pipe and the stirring shaft are communicated, an oxygen supply machine and an electric heater are turned on, then after seawater in the culture box reaches a proper temperature, male oysters and female oysters in the culture box are mutually fused and bred, then a PLC is externally connected, a first electromagnetic valve and a second electromagnetic valve are turned on, and the stirring shaft is opened. Then sea water in the water tank rushes seedlings in the water tank into the nutrition tanks one by one, then an electric push rod and a toothed plate are started to enable a stirring shaft with a toothed ring to circularly rotate clockwise and anticlockwise, then phytoplankton particles and oxygen in the stirring shaft flow into the nutrition tanks from through holes, and then the seedlings are stirred by the stirring shaft; each position in the nutrition box is filled with phytoplankton particles, feeding of oysters at each position is facilitated, systematic professional cultivation is achieved, and the yield and quality of the oysters are improved.

The present invention provides a method and system for quantitatively tracing the chemical oxygen demand (COD) of lake water bodies and internal and external pollution, relating to the technical field of quantitative pollution tracing. The method comprises: performing three-dimensional fluorescence spectral analysis on water samples at different points in the lake to obtain fluorescence excitation / emission matrix spectrum (EEMS) data; performing parallel factor analysis on the EEMS data to identify and classify the fluorescent components of dissolved organic matter (DOM) and determine the type characteristics of each component; using ridge regression analysis to establish a standardized multiple linear regression equation based on the maximum fluorescence intensity of the fluorescent components and the corresponding COD values, and calculating the percentage contribution of each fluorescent component to the COD of the lake water body; analyzing the impact path of potential variables on the migration and transformation of DOM, and quantifying the direct and indirect contributions of different pollution sources to each fluorescent component through path coefficients; and calculating the total contribution of rivers entering the lake, sediment release, and phytoplankton to the COD of the lake water body.