422 results about "Cyanobacteria" patented technology

The invention relates to the technical field of water environment risk monitoring, in particular to a method for evaluating the algal bloom risk of a water body. The method comprises the following steps: collecting historical monitoring data of a to-be-evaluated water body, wherein the historical monitoring data comprises blue-green algae abundance data, water quality data and hydrological data; analyzing the correlation between the cyanobacteria abundance or chlorophyll a concentration and the water quality and hydrological data of the to-be-evaluated water body; hydrological and water quality parameters with the highest correlation with the cyanobacteria abundance or chlorophyll a concentration are screened out; hydrology and water quality parameters of a water body to be evaluated are taken as predictive variables, and cyanobacteria abundance or chlorophyll a concentration is taken as a response variable to construct a Bayesian network model; the weight of each parameter in the Bayesian network model is calculated, and the algal bloom risk probability that the cyanobacteria abundance exceeds a specific threshold value under the given parameter condition is calculated according to the weights. According to the invention, the scene-based probability deduction of the stable period and the dynamic period is realized through the double-branch Bayesian network model, so that the accuracy and timeliness of algal bloom risk assessment are improved.

The invention discloses a blue-green algae growth prediction method and an intelligent regulation and control device, and relates to the field of blue-green algae growth prediction and regulation and control, and the method comprises the steps: carrying out the collection of blue-green algae growth related data, data preprocessing, random forest model construction, SVM model construction, and model weight fusion of a final random forest prediction model and a final SVM prediction model to obtain a fusion model. And dynamically optimizing the fusion model, finally obtaining the dynamically optimized fusion model, obtaining a real-time prediction result according to the dynamically optimized fusion model by using the blue-green algae growth related data collected by the sensor module in real time, and adjusting the blue-green algae growth culture conditions in real time. According to the method, the advantages of the random forest model and the SVM model are combined, a fusion model with dynamic optimization and real-time adjustment functions is developed, and the accuracy and adaptability of blue-green algae growth prediction are remarkably improved. And meanwhile, data acquisition, processing and prediction modules are integrated, so that the accuracy of blue-green algae growth prediction and the automation degree of the system are remarkably improved.

The remediation method for the moderately and severely cadmium-polluted rice field aims at strictly controlled cadmium-polluted rice fields, and is divided into two stages of strict control and safe utilization for remediation. In the strict management and control stage, an activating agent is applied to the soil in spring, then microalgae are inoculated, cadmium-enriched plants are planted, the plants are collected after being full of the microalgae, the plants are harvested when the plants grow to the enrichment extreme value, stubbles are reserved for growth, and the steps are repeated till winter; applying an activating agent in winter, planting green manure, and harvesting in spring in the next year; the process is cyclically carried out until the cadmium content of the soil is reduced to a safe utilization level. Entering a safe utilization stage, applying an activating agent in spring, inoculating nitrogen-fixing cyanobacteria, collecting the cyanobacteria after the cyanobacteria overgrow, and repeating the steps until the rice seedlings are transplanted; applying nitrogen-fixing blue-green algae, and planting rice; applying an activating agent after rice harvesting, planting winter green manure, and harvesting in next spring; the process is cyclically carried out until the cadmium content of the soil is reduced to a preferential protection level. The method is low in cost, high in remediation efficiency and good in effect, and a new way is provided for cadmium-polluted rice field remediation.

The invention discloses a plug flow type mechanical pressurization cyanobacteria control ship, which belongs to the field of ecological environment governance, and mainly comprises a ship body, an algae collection head, a buoyancy cabin, a water collection tank, a pressurization treatment cabin and a pressurization assembly, the lever principle and the electric push rod are used for pressurizing, so that compared with a mode of directly pressurizing a water body by using an air compressor, a water pump and the like, the treatment energy consumption is low, and the pressurizing speed is higher; through the combined design of the dustpan-shaped algae collecting head, the algae inlet position, the pressurization treatment bin layering and the like, the entering and the discharging of high-algae-content water are realized by utilizing the ship advancing power and the water impeller, the operation energy consumption is greatly reduced compared with the water pump sampling, and the device can be suitable for blue-green algae prevention and control in water areas such as lakes, reservoirs, water sources, landscape water bodies and the like.

The present invention discloses a method for extracting a stable body from cyanobacteria, comprising the following steps: S1. Cultivating cyanobacteria; S2. Pretreating cyanobacteria; S3. Ultrasonic disruption; S4. Differential centrifugation; S5. Ultrafiltration; S6. Ultracentrifugation; S7. Density gradient centrifugation; S8. Removing peripheral proteins. The extraction method provided by the present invention has high extraction efficiency, stable and controllable process, no toxic by-products, is green and environmentally friendly, and does not require high temperature and high pressure equipment and large instruments and equipment, thereby expanding application scenarios and reducing application costs. The present invention can obtain high polyP n Stable, active granular biomaterials with high polymerization degree, concentration, and purity, as well as high bioactivity, can meet the stringent requirements of applications in the food and daily chemical sectors, as well as clinical medicine and scientific research. They possess significant practical application value and commercial prospects, and will address the technical challenges of preparing stable, active granular biomaterials in China.

The invention provides a purple blue-like bacterium and an application of the purple blue-like bacterium in prevention and treatment of cotton diseases. The preservation number of the purple blue-like bacterium is CCTCC (China Center For Type Culture Collection) NO: M 20242093. On the other hand, the invention provides an application of the blue purple-producing bacterium CEF642N strain, namely the application of the blue purple-producing bacterium CEF642N strain serving as a biological control agent for cotton verticillium wilt. According to the application disclosed by the invention, the inhibition effect of the purple blue-like bacterium CEF642N extract component on verticillium dahliae is analyzed, and the purple blue-like bacterium CEF642N extract has the effect of preventing and treating the verticillium wilt and can be used for preparing a biological prevention and treatment agent, so that a new biological prevention and treatment means is provided for preventing and treating the verticillium wilt.

The invention discloses a lake cyanobacterial bloom intelligent early warning method based on unmanned aerial vehicle remote sensing and image recognition, and the method comprises the steps: fusing an unmanned aerial vehicle multispectral image and a convolutional neural network, extracting the spectral features of cyanobacteria, and calculating the concentration distribution; image registration and a dynamic model are combined to track water bloom boundary change and drift trajectory, satellite remote sensing is used to verify precision and invert biomass density, early warning levels are divided according to the precision and the biomass density, decision information is generated, and intelligent monitoring and accurate early warning of cyanobacterial bloom are realized. The comprehensive technical effects of water bloom dynamic monitoring, accurate early warning and efficient management are achieved, and a scientific basis is provided for water environment treatment.

The invention discloses a system and a method for analyzing neurotoxicity of cyanobacteria toxin to aquatic organisms, the system comprises a bottom plate, and also comprises an electric guide rail mechanism arranged on the bottom plate, and a bearing container mechanism arranged on a slide block of the electric guide rail mechanism; the first liquid feeding mechanism and the second liquid feeding mechanism are both arranged on the bottom plate; and the full-automatic fluorescent microscope is arranged on the bottom plate and located on the downstream of the second liquid feeding mechanism, an image acquisition module is arranged on an eyepiece of the full-automatic fluorescent microscope, and the image acquisition module is in communication connection with an analysis module. CYN solutions with different concentrations are injected into the placement groove through the first liquid feeding mechanism, a fixed solution is injected into the placement groove through the second liquid feeding mechanism, then the result of the influence of the CYN solutions with different concentrations on aquatic organisms is obtained through analysis of the full-automatic phosphor microscope, the image acquisition module and the analysis module, use is convenient, and the detection accuracy is high. And the labor intensity of workers is reduced.

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.

The utility model discloses an aeration cyclone offshore algae control system, which belongs to the technical field of offshore prevention and control of blue-green algae, and comprises an air compressor, a water tank, a heat exchanger, a cooling circulation device and a plurality of aeration cyclone mechanisms, each aeration cyclone mechanism comprises an aeration main pipe, a horizontal aeration branch pipe and a plurality of vertical aeration branch pipes, according to the utility model, active offshore algae control is realized in the time period of stronger illumination and higher air temperature, and the'gas stripping 'effect generated by the rising of bubbles is utilized, so that the Reynolds number is increased, the turbulence degree of a water body is increased, and the aeration effect of the water body is improved. Meanwhile, the normal migration rule of the blue-green algae can be destroyed due to the occurrence of the rotational flow, so that the blue-green algae cannot normally perform photosynthesis, and water layers at different temperatures can be sufficiently mixed due to the rotational flow, so that the water temperature of the surface water body is greatly reduced, and the growth of the blue-green algae is effectively controlled.

The invention discloses a blue-green algae type discrimination method based on a three-dimensional fluorescence spectrum. The method comprises the following steps: S0, obtaining a to-be-detected sample; the method comprises the following steps: S1, acquiring original three-dimensional fluorescence spectrum data; s2, preprocessing the original three-dimensional fluorescence spectrum data; rayleigh scattering and Raman scattering in the original three-dimensional fluorescence spectrum are removed, and smooth denoising is carried out on the original three-dimensional fluorescence spectrum; s3, spectral features are extracted based on a parallel factor-peak ratio method, and spectral feature vectors are obtained; and S4, carrying out missing value supplement and data standardization processing on the obtained spectral feature vectors, inputting the processed feature vectors into a trained LightGBM multi-classifier, outputting probabilities by a classification model, and selecting a category with the maximum probability. According to the method, the distribution mechanism of the blue-green algae fluorescent pigment is combined, and the fluorescent component characteristics and the relative content characteristics of the fluorescent components of the blue-green algae are comprehensively considered, so that the three-dimensional fluorescence spectrum information of the blue-green algae sample can be more comprehensively, completely and meticulously represented, and the variety discrimination of different blue-green algae with smaller difference is realized.

The invention relates to a preparation method of potassium-nitrogen co-doped cyanobacteria-based activated carbon, and relates to the field of biomass activated carbon. According to the application, the blue-green algae is taken as a substrate, is carbonized firstly, and then is co-doped and activated by adopting melamine and potassium carbonate, the process operation is simple and easy to control, the utilization rate is high, the cost is low, the economic benefit is high, and the method has great significance on recycling of the blue-green algae; besides, according to the technical scheme, under the condition of controlling the mass ratio of the carbon powder to the potassium carbonate and controlling the mass ratio of the carbon powder to the melamine, the quantity of micropores of the prepared activated carbon can be reduced and the quantity of mesopores can be increased by increasing the addition amount of the melamine; the pore size distribution of the prepared activated carbon is changed from micropores to mesopores, so that the purpose of adjusting the pore size is achieved, and the prepared activated carbon is larger in specific surface area and more excellent in adsorption performance.

The invention relates to the technical field of blue-green algae cleaning, and discloses a blue-green algae cleaning boat and a cleaning method.The blue-green algae cleaning boat comprises a boat body, a bearing frame, a cleaning assembly, a dumping and collecting box and a power supply driving assembly, and the boat is composed of two suspension bodies to improve buoyancy and stability. The bearing frame is located on the inner side of the ship body and provides a bearing area for installing the cleaning assembly and other related components. The cleaning assembly is located above the bearing frame and used for cleaning and conveying algae pollutants. And the dumping and collecting box is arranged at the tail end of the cleaning assembly and is used for receiving and storing the cleaned and conveyed algae pollutants. The power supply driving assembly provides assembly working power and supports work of the cleaning assembly. The small boat firstly runs to a preset position and is accurately positioned by additionally arranging a built-in navigation system. And after the blue-green algae arrives at the target area, the cleaning assembly starts to work, and the blue-green algae is removed from the water surface through a cutting and conveying mechanism and conveyed into a dumping and collecting box. The cleaned algae are directly conveyed into the collecting box through the conveying device, and the pollutant collecting process is completed.

The invention is applicable to the field of blue-green algae detection, and particularly provides a blue-green algae detection method based on multi-task deep learning and adaptive background removal, in the detection method, a MAGAN model is constructed, local features are extracted through a graph convolutional network, global context perception is enhanced by using a self-attention mechanism, and the detection accuracy is improved. And finally, the foreground and background distinguishing is optimized through a background removing module, so that the self-adaptive background removing task is realized. Then, a multi-task deep learning framework is adopted; the targets of the multiple tasks are learned in the same model at the same time, so that the model can share knowledge from the multiple tasks; the blue-green algae detection task and the background removal task share part of features and are cooperatively optimized, background removal is achieved, support is provided for blue-green algae recognition, the blue-green algae recognition provides reference information for background removal, the cooperative effect between the tasks is enhanced, the model can more accurately distinguish blue-green algae from other water substances, and false detection and missing detection are reduced.

The invention relates to a Tarromyces sp. Strain and application thereof, the Tarromyces sp. Strain is named as Tarromyces sp. Strain F19, the preservation number is GDMCC No: 65365, and the preservation date is October 28, 2024. The Tarromyces sp. Strain is named as Tarromyces sp. Strain F19, the preservation number is GDMCC No: 65365, and the preservation date is October 28, 2024. According to the invention, a new strain of cyanobacteria with phytopathogen resisting and plant growth promoting effects is separated from rhizosphere soil of corn and is preserved, and the strain has a remarkable inhibition effect on various phytopathogens such as fusarium oxysporum, phytophthora nicotianae or Kleiella solani, and can be used as a biocontrol bacterium for preventing and treating various plant diseases; in addition, the strain can also be used for plant growth promotion. The strain resources of the biocontrol bacteria are enriched, and the method can be applied to the development of biopesticides.

The invention discloses a cyanobacterial bloom early warning method and system based on cladocerans. The method provided by the invention comprises the following steps: S101, based on basic geographic information of a target water area, collecting a remote sensing image of the target water area; s102, judging the distribution range of cyanobacterial blooms in the target water area according to the remote sensing image of the target water area; s103, if the cyanobacterial bloom exists in the target water area, removing a water area corresponding to the cyanobacterial bloom in the target water area to obtain a water area to be sampled, dividing the water area to be sampled into at least one first sampling point, and collecting the cladocera density of the at least one first sampling point; and S104, performing early warning on cyanobacterial bloom according to the relationship between the cladocera density and a preset threshold value. By combining a remote sensing technology and a sampling technology, the water body can be accurately monitored, so that a basis is provided for treatment and early warning of cyanobacterial bloom.

The utility model discloses a device for automatically salvaging blue algae on the river surface, comprising a hull, a conveyor belt connected to a driving mechanism is obliquely arranged at the tail of the hull, a screen with a wave-shaped structure is fixed on the surface of the conveyor belt, a fixed groove is arranged on the hull below the head end of the conveyor belt, a plurality of collecting buckets are placed in the fixed groove, and drainage holes are arranged on the side walls of the fixed groove and the collecting bucket. The utility model improves the structure of the existing mowing boat, adds a screen with a wave-shaped structure on the surface of the conveyor belt, and is provided with a fixed groove and a collecting bucket. During the rotation of the conveyor belt, the blue algae on the water surface will adhere to the surface of the screen, be transported and finally fall into the collecting bucket for collection, and at the same time, the separation of water and blue algae is achieved through the screen, and the water in the collecting bucket is discharged through the drainage hole. The device can realize the automatic salvage of blue algae through a simple structure, and can salvage and clean up all the blue algae on the river surface in a short time.

The invention provides a microalgae fertilizer capable of improving salt and alkali resistance of cucumbers, and the microalgae fertilizer is a composite microalgae bio-fertilizer and comprises scenedesmus obliquus, nitzschia grain and extracellular polymeric substances generated in the culture process of the nitzschia grain. The components of the microalgae compound fertilizer disclosed by the invention are all freshwater algae strains and natural active microalgae cells, so that sodium ions and metal cations cannot be accumulated, the microalgae compound fertilizer does not contain blue-green algae, and cytotoxin which is harmful to the environment cannot be generated; the used algae strains are easier to breed, and the biomass accumulation speed of the algae strains is far higher than that of other algae strains, so that the prepared microalgae biological fertilizer has the advantages of saving time and cost. And the polyunsaturated fatty acid generated by the microalgae fertilizer can enhance the plant resistance through an osmotic regulation system and an antioxidant enzyme ROS system.

The invention discloses a rapid emergency intelligent treatment method and system for lake algal bloom outbreak, and belongs to the technical field of water environment governance, and the treatment method comprises the steps that a plurality of long-strip-shaped standard enclosure pieces are sequentially connected end to end to form an enclosure, and an algal bloom outbreak area is surrounded; geographic position information and water chlorophyll concentration information are obtained through a monitoring device in the enclosure and transmitted to a cloud processing platform, and the cloud processing platform plans a driving route according to the geographic position information and determines the dosage of a flocculating agent and a biological inhibitor according to the chlorophyll concentration; the unmanned ship loads a flocculating agent and a biological inhibitor in sequence according to the respective dosage, and enters the enclosure along the driving route to be thrown after each time of loading. Through combined application of the semi-open enclosure and an intelligent system, the whole treatment process is more efficient and convenient, the labor cost is reduced, and a more scientific, efficient and economical solution is provided for treatment of cyanobacterial bloom.

The invention discloses a preparation method and application of polydeoxyribonucleotide derived from prokaryotes. The preparation method comprises the following steps: 1) enriching blue-green algae biomass; 2) ultrasonically crushing; (3) carrying out RNA elution; 4) DNA and protein separation; (5) dissolving the DNA; (6) separating out DNA; 7) gradient cleaning of DNA; (8) dissolving the DNA; and (9) preparing PDRN. The preparation method of the water-blooming cyanobacteria PDRN is developed by taking the prokaryote cyanobacteria as the raw material, the preparation method is simple and easy to implement, the molecular weight of the PDRN can be efficiently controlled, the purity and yield of DNA are ensured by controlling the RNA elution step and the DNA and protein separation step, the antioxidant capacity of the DNA is verified, and the method is suitable for industrial production. And a new way is provided for resource utilization of the water-blooming cyanobacteria biomass.

The invention provides a blue-green algae inhibitor accurate spraying method and system based on an unmanned ship, and belongs to the technical field of blue-green algae treatment. The technical problem that the dosage of the blue-green algae inhibitor cannot be accurately controlled in the blue-green algae killing process is solved. According to the technical scheme, the spraying method comprises the following steps of collecting target water area environment data and blue-green algae concentration data, drawing a target water area route planning map, dividing the target water area route planning map into a plurality of sub-areas, judging target sub-areas needing to be sprayed with inhibitors, planning a spraying path of an unmanned ship, and determining a spraying scheme of each target sub-area. And setting a spraying path of the unmanned ship and a spraying scheme of each target sub-area, and accurately spraying the blue-green algae inhibitor to the target water area. The system comprises an unmanned ship, a planning and drawing module, a route planning module and a spraying control module. According to the method, the blue-green algae inhibitor is automatically and accurately sprayed to the target water area through the unmanned ship, the spraying path is planned, and automatic treatment of blue-green algae is achieved.

The present application relates to a cyanobacteria bloom treatment ship and a treatment method, the cyanobacteria bloom treatment ship comprises: a main hull and a floating body located on both sides of the main hull, a propeller is arranged under the floating body; a water quality monitoring device is arranged on the main hull, the water quality monitoring device comprises: a protective cover, a winding machine, a steel cable, a motor, and a water quality monitoring sensor, the water quality monitoring sensor is connected to the other end of the steel cable, the motor is used to drive the winding machine to wind or release the steel cable, and the steel cable can pass through the limiting hole at the bottom of the protective cover; an ultrasonic algae control device is also arranged on the main hull, the ultrasonic algae control device comprises: an electric telescopic rod and a plurality of ultrasonic transducers, one end of the electric telescopic rod is connected to the ultrasonic transducer. The scheme provided by the present application can detect the growth period of cyanobacteria in the water area, and adjust the working mode of the ultrasonic transducer in real time according to the different growth periods of cyanobacteria, which is conducive to better control of the number of cyanobacteria and improve the water quality in the water area.

The invention provides a blue-green algae image recognition method and system based on hierarchical self-adaption and domain driving, and the method comprises the steps: enhancing an image through employing an improved dark channel algorithm; constructing a blue-green algae biological attribute text database, and performing synonym replacement and sentence pattern recombination; multi-scale visual features are extracted through a hierarchical adaptive Swin Transform model, and key region characterization is enhanced in combination with dynamic spectrum attention; the text is input into a Bio-ALBERT model, and semantic embedding of field optimization is generated through term mask prediction and attribute relation pre-training; constructing a two-layer heterogeneous graph by using a graph attention interaction network GAIN, calculating a cross-modal association weight through a bidirectional graph attention mechanism, and outputting a cross-modal graph feature; multi-scale cross-modal association is modeled through a hierarchical graph attention fusion mechanism, and a comparison alignment loss optimization model is combined; and high-precision blue-green algae identification is realized. According to the method, through multi-scale perception, domain semantic adaptation and graph structure fusion, the accuracy of blue-green algae detection in a complex environment is improved.

This invention provides compositions and methods for providing high product yield of transgenes expressed in cyanobacteria and microalgae.

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 provides application of chlorogenic acid in inhibition of growth of carbapenem-resistant proteus mirabilis, and belongs to the technical field of development of antibacterial materials. It is proved that chlorogenic acid has a good in-vitro killing effect on carbapenem-resistant proteus mirabilis, growth of the carbapenem-resistant proteus mirabilis can be inhibited, the minimum inhibitory concentration is 5 mg / mL, a biological membrane of the carbapenem-resistant proteus mirabilis can be effectively destroyed, and the chlorogenic acid can be used for preparing the carbapenem-resistant proteus mirabilis. The invention provides the inhibition effect of chlorogenic acid on carbapenem-resistant proteus mirabilis, and the chlorogenic acid has wide application value in the fields of medicines and the like.

The invention provides a cyanobacterial bloom prediction method and system based on satellite remote sensing and weather forecast. The method comprises the following steps: selecting an optimal meteorological factor according to different contributions of meteorological factors accumulated in 1-7 days to cyanobacterial bloom outbreak; taking the optimal meteorological factor and the ATBI wave band as prediction model input; and constructing a prediction model based on a Bayesian theorem principle. According to the scheme provided by the invention, a Chinese large lake cyanobacterial bloom short-term prediction model based on the Bayesian theorem can be constructed by taking a cyanobacterial bloom monitoring result of a large lake and meteorological factor data as main data sources, and pixel scale prediction of cyanobacterial bloom in multiple days in the future is realized.

The invention discloses a blue-green algae identification and quantification method and device based on unmanned aerial vehicle remote sensing images and deep learning and a medium, and relates to the technical field of information data processing. Combining meteorological data and water quality monitoring data to construct an adaptive dynamic environment algorithm to extract EXIF metadata including camera parameters and attitude angle information, and establishing a geometric projection model to perform coarse orthographic correction on an original aerial image; the method comprises the following steps: extracting a multi-scale cyanobacterial bloom image feature map by stages based on a ResNet architecture and in combination with a feature pyramid network FPN fused with an attention mechanism, and obtaining an instance mask of cyanobacterial bloom through an anchor-free region proposal network, ROI Align and a head network; based on an improved GIS space projection and deep learning algorithm, carrying out high-precision area measurement and calculation on a binary mask image converted from the instance mask; according to the method, the influence of irrelevant interference on blue-green algae identification can be reduced, and the accuracy and comparability of blue-green algae identification and area calculation are improved.

The invention discloses a confocal laser microscope detection method for micro-plastic surface biofilm components and biomass, and belongs to the field of micro-plastic pollution monitoring. The method comprises the following steps: carrying out stratified sampling and polymer component identification on micro-plastic in an environmental water body; the method comprises the following steps: marking algae, blue-green algae, bacteria and extracellular polymeric substances in a biological membrane through specific fluorescent staining, carrying out high-resolution three-dimensional imaging by utilizing a confocal laser microscope, and realizing batch quantitative analysis of each component of the biological membrane by adopting ImageJ software. The technical problems that an existing detection method is insufficient in parameter dimension, evaluation result deviation is caused only based on partial component quantification, and the influence of the overall structure of the biological membrane on the micro-plastic migration behavior cannot be truly reflected are solved. According to the method, the biomass and spatial distribution of each component of the biological membrane can be comprehensively and accurately quantified, the accuracy of analyzing the vertical migration mechanism of the micro-plastic is improved, and multi-dimensional data support is provided for ecological risk assessment.

The invention discloses a desert blue-green algae large-scale culture nutrient solution and application thereof. The desert blue-green algae large-scale culture nutrient solution is prepared from urea, monopotassium phosphate, potassium chloride, sodium bicarbonate and medium trace element fertilizer. Wherein the concentrations of the urea, the monopotassium phosphate, the potassium chloride, the sodium bicarbonate and the medium trace element fertilizer are 500-550 mg / L, 20-30 mg / L, 10-15 mg / L, 10-15 mg / L and 10 mg / L respectively. The invention provides a nutrient solution special for large-scale cultivation of desert blue-green algae, and solves the technical problems of high cost, low yield and low blue-green algae purity of large-scale cultivation of desert blue-green algae in the prior art. The nutrient solution composition provided by the invention can balance each nutrient composition, so that the nutrient composition can be more suitable for the nutritional requirements of the growth of the desert blue-green algae.