153 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 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 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 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 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.

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 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 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.

The present application provides a method and device for inverting the flow field of ocean near-surface submesoscale processes. The method provided by the present application includes: screening representative sea areas of ocean near-surface submesoscale; obtaining structural image information of phytoplankton fronts and filaments of ocean submesoscale processes; obtaining displacement velocity field information at the same position; matching the structural image information and the displacement velocity field information to construct a first training sample set; correcting the first training sample set based on a second data set and a third data set to obtain a second training sample set; training an initial model using the second training sample set to obtain a flow field inversion model; obtaining multiple submesoscale sea surface water color images of a sea area to be detected; and processing the submesoscale sea surface water color images using the flow field inversion model to obtain a displacement velocity field. The method and device for inverting the flow field of ocean near-surface submesoscale processes provided by the present application are used to solve the problem of low inversion accuracy of the current ocean flow field inversion algorithm for submesoscale processes.

This invention discloses a data-driven method for monitoring marine phytoplankton abundance using underwater acoustic networks. The method includes: constructing and training a multi-level spatiotemporal feature ensemble model based on historical marine phytoplankton abundance and corresponding marine environmental data to determine the optimal spatial location and monitoring time window of the underwater acoustic network nodes; constructing a teacher model for channel prediction, including a multi-layer cascaded structure and residual modules, and compressing the teacher model into a student model using spatiotemporal knowledge distillation; training the student model to achieve real-time channel state prediction at the underwater acoustic network nodes; matching suitable underwater acoustic communication devices and seasonal optimal routing strategies to each underwater acoustic network node based on the optimal spatial location and real-time predicted channel state, and performing adaptive modulation and coding at the link level, while simultaneously monitoring phytoplankton abundance using the underwater acoustic network. This invention can improve data throughput and prediction accuracy under strict energy consumption constraints.

The application discloses a kind of phytoplankton photosynthetic activity turbidity correction device and method, belong to phytoplankton monitoring technical field, the device includes excitation emission optical structure, light source driving module, signal detection module and main control module;The application constructs water turbidity scattering and initial fluorescence relative increment relationship by measuring 750nm water turbidity scattering and combined light-induced fluorescence kinetics curve, by calculating the correlation coefficient of both, the influence of water turbidity scattering on phytoplankton photosynthetic activity detection is corrected into photosynthetic activity correction model.The device structure is simple, and the measurement period is short, and it is non-invasive, and the underwater in-situ detection of phytoplankton photosynthetic activity of turbid water can be carried out, which provides a new way for rapid and accurate measurement of phytoplankton photosynthetic activity.

The application relates to the technical field of water ecological environment monitoring, and discloses an automatic monitoring device for planktonic algae in water and a monitoring method for planktonic algae in water. The automatic monitoring device for planktonic algae in water comprises a water sample preparation module, a water sample slice preparation module and a water sample detection module. The water sample preparation module is used for collecting a water sample and obtaining an algae concentrate of the water sample. The water sample slice preparation module is used for preparing a slice from the algae concentrate and transferring the slice to the water sample detection module. The water sample detection module is used for taking a picture of the planktonic algae in the slice and identifying and counting the algae in the picture by using a preset image visual algorithm. The device realizes automatic sampling, automatic precipitation collection, automatic slice preparation, automatic picture taking, automatic identification and counting of the planktonic algae, does not require manual attendance, saves labor cost, effectively avoids differences in identification results caused by differences in identification abilities of different laboratories and detection personnel, improves the standardization degree of algae detection, and facilitates popularization and application.

This invention provides a geochemical method and system for identifying phytoplankton community succession, relating to the fields of aquatic environmental geochemistry and ecological environment monitoring. The method involves simultaneous sampling of a target water body to determine water chemical parameters, dissolved inorganic carbon, particulate organic carbon, and stable carbon isotopes, while simultaneously acquiring data on the species composition and relative abundance of phytoplankton. Based on the water chemical parameters, the forms and concentrations of dissolved inorganic carbon in the water body are determined to ascertain the carbon source utilization pattern. The stable carbon isotope shift in the water body is determined based on the dissolved inorganic carbon, particulate organic carbon, and stable carbon isotopes. Furthermore, the stage of phytoplankton community succession in the target water body is identified by comparing the carbon source utilization pattern and the stable carbon isotope shift with the indicators. This invention enables early identification of the community succession process based on changes in carbon source utilization patterns, even before changes in phytoplankton community morphology become apparent.

The invention relates to the technical field of aquaculture cleaning equipment, in particular to an aquaculture pond phytoplankton cleaning device. Comprising a floating plate and a collecting structure arranged at one end of the floating plate, a guiding device is arranged at one end of the floating plate, a propeller is arranged on the downward working face of the floating plate and is far away from the guiding device, the collecting structure comprises a conveying groove, and a hinge piece is arranged at the position, close to the center, of the downward working face of the conveying groove; one end of the hinge piece is fixedly connected with the upward working face of the end, away from the guide device, of the floating plate, a hydraulic device is arranged on the downward working face of the conveying groove, and the end, away from the conveying groove, of the hydraulic device is fixedly connected with the upward face of the floating plate. The device has the advantages that the floating plate can move in the culture pond, phytoplankton in the culture pond is continuously collected and conveyed into the spiral conveying device through the collecting structure arranged on the floating plate, then the phytoplankton is conveyed into the collecting box through the spiral conveying device, and centralized treatment is facilitated.

The invention discloses a buoy system for ocean engineering ecological environment monitoring, and belongs to the technical field of ocean ecological environment monitoring, the buoy system comprises a buoy seat, and the buoy seat is provided with a surface sampling mechanism for sampling phytoplankton and a stratified sampling mechanism for sampling water bodies with different water depths. According to the invention, through the inclined surface layer mounting rack and the staggered surface layer interception grids, the vertical projection of each layer of sample is not overlapped in the airing process, the interference on the lower layer of sample caused by the infiltration of the upper layer of seawater is effectively avoided, and the surface layer interception grids are driven by the ratchet ring to realize a step-by-step climbing mechanism; through the meshing design of the annular sampling box and the one-way ratchet wheel, it is guaranteed that only samples at different depths at the sampling position are allowed to enter the fixed-point power column during rotation each time, in the seawater flowing process, seawater is extracted through the annular sampling box and the negative-pressure one-way sampling valve, and aliasing sampling on the vertical face is reduced.

The present application relates to a kind of marine Lidar signal noise removal method, device, equipment and medium, method includes: the information sampling of the sea area to be measured generates airborne laser radar signal, through pre-processing eliminates background noise and corrects sea surface fluctuation error, based on fast fourier transform extraction underwater depth profile frequency domain feature, to the amplitude replacement and spectrum reconstruction of electric noise concentrated frequency band, generate noise reduction radar signal, the method provided in the present application overcomes the vertical resolution loss caused by traditional vertical sliding average algorithm and weak signal error filtering problem, by frequency domain filtering replacement technology retains the original data of non-noise frequency band, while removing electric signal clutter, maintain original vertical resolution, effectively capture the microscale marine dynamic information such as thermocline, suspended solids concentration mutation interface and thin layer phytoplankton aggregation, improve marine environment monitoring precision.

The application discloses a kind of multicomponent plankton algae concentration measurement method based on fluorescence spectrum stratification partitioning.The application belongs to the field of marine aquatic ecological environment monitoring technology.The algorithm will unknown to be analyzed mixed three-dimensional fluorescence spectrum according to the following stratification partitioning analysis idea to be analyzed.First layer: full spectrum is involved in analysis, and the purpose is to analyze cyanophyta concentration and cryptophyta concentration, and obtain the difference spectrum in full spectrum region.Second layer: partitioning analysis, take the region containing effective spectral feature to participate in analysis, and the purpose is to analyze chlorophyll a concentration of green algae, diatom, dinoflagellate and yellow algae.The algorithm is significantly superior to common analysis algorithm in reducing misidentification, especially in processing such as diatom, dinoflagellate and yellow algae with high similarity living body fluorescence spectrum.

The utility model discloses a sampling device for phytoplankton detection. The sampling device comprises a fixed frame, a containing frame arranged on the fixed frame and a fixed square frame fixedly mounted on the inner wall of the containing frame. According to the sampling device for phytoplankton detection, the jacking assembly capable of jacking the bearing frame and the translation assembly for driving the jacking assembly to move left and right are arranged on the containing frame, and when the sampling device is used, the translation assembly can be started to drive the jacking assembly to move to the position below the bearing frame to be jacked up and a plankton sample storage bottle; according to the plankton sampling device, the bearing frame can be jacked up by the jacking assembly, so that the plankton sample storage bottle jacked up on the inner side of the bearing frame is lifted, different planktons can be conveniently and quickly selected for sampling, and meanwhile, the phenomenon that the sample storage bottle possibly inclines in the falling process and cannot accurately fall into the limiting sleeve can be prevented; the influence on subsequent detection or sampling operation is avoided.

The utility model relates to a water conservancy comprehensive information monitoring device for planktonic animals and plants, which belongs to the field of sampling devices and comprises a solar panel. A plurality of buoys are arranged on the solar panel; the floating cylinder comprises an upper cylinder, a lower cylinder and an upper cylinder, wherein a first cavity is formed in the upper cylinder; a top hole is formed in the upper surface of the upper cylinder, and a cover plate is arranged at the top hole; a second cavity is formed in the lower cylinder; water permeable holes; various types of sensors are respectively mounted on the upper surfaces of the second cavity and the upper cylinder; wherein collection assemblies are arranged in the two floating barrels, each collection assembly comprises a water drawing pipe, a water drawing pump, a water outlet pipe and a sampling barrel, and a connector A and a connector B are arranged on the side wall of each sampling barrel; and one end of the liquid outlet pipe is connected and communicated with the liquid outlet end of the stationary liquid pump, and the other end of the liquid outlet pipe is detachably connected with the connector B. The detection device can automatically collect zooplankton water samples and phytoplankton water samples, and can automatically add stationary liquid.