955 results about "Chlorophyllin" 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.

A ginkgo canopy chlorophyll content estimation method based on an airborne hyperspectral image comprises the following steps: firstly, acquiring a ginkgo leaf canopy hyperspectral image and a chlorophyll content data set; and establishing a rapid prediction model for predicting the average content of chlorophyll in the canopy by using the hyperspectral image of the ginkgo leaf canopy, and predicting the corresponding chlorophyll content by using the model and taking the hyperspectral image of the ginkgo leaf canopy as input. The model establishment process comprises the following steps of: firstly, preprocessing an image by using multiple scatter correction (MSC); secondly, carrying out characteristic wave band selection on the preprocessed spectral data by adopting a competitive self-adaptive reweighted sampling algorithm CARS; and finally, substituting the screened characteristic wave band spectral data and the actually measured chlorophyll value into an XGBoost-RF machine learning model, and establishing a hyperspectral chlorophyll content estimation model. The method provided by the invention is a nondestructive rapid detection technology, and can monitor the plant health condition in real time.

The invention discloses a method and a system for adjusting a tea leaf fixation process and a medium, and aims to realize cooperative regulation and control of enzyme activity and aroma substances in a fixation process. The method comprises the following steps: collecting multi-modal sensing information in a fixation process, and carrying out feature extraction on the multi-modal sensing information to obtain multi-dimensional features; dynamically constructing an LSTM prediction model based on the multi-dimensional features, and outputting an enzyme activity prediction value sequence; the enzyme activity predicted value sequence is input into a fuzzy PID controller for reinforcement learning optimization, a temperature-rotating speed adjusting instruction is generated, and adjusting parameters of the controller are dynamically updated through a reward function of the chlorophyll retention rate and the energy consumption ratio; according to the temperature-rotating speed adjusting instruction, adjusting the hot air temperature of each temperature area of the enzyme deactivation chamber, the rotating speed of the enzyme deactivation cylinder and the axial segmentation wind speed; and collecting fixation end point judgment information, and terminating fixation when a preset condition is met. According to the invention, the problems of unstable quality and overhigh energy consumption caused by parameter solidification in the traditional enzyme deactivation process are solved, and dynamic optimization and accurate control of the enzyme deactivation process are realized.

The invention belongs to the technical field of gene engineering, and particularly relates to application of a ZmDIR11 gene in regulating and controlling salt stress resistance of corn. After salt stress treatment, it is found that the ZmDIR11 gene mutant corn plant growth vigor and material accumulation are significantly lower than those of a wild type B73 material, an oxidative metabolism regulation path, proline accumulation, chlorophyll synthesis, lignan accumulation and a Na < + > / K < + > transport pathway are inhibited, and related metabolic indexes are significantly lower than those of the wild type B73 material. It is shown that the ZmDIR11 gene positively regulates and controls the salt-resistant metabolic pathway of the corn in the corn, and the salt tolerance of the corn is reduced after the ZmDIR11 gene is mutated. The research provides a new gene resource for corn salt-tolerant breeding by utilizing gene engineering.

The invention discloses a sea surface chlorophyll concentration prediction method and system based on remote sensing and deep learning, and relates to the technical field of marine ecological environment, and the method comprises the steps: inputting the marine chlorophyll a data of a target region into a trained marine chlorophyll concentration prediction model, and obtaining the marine chlorophyll concentration information of the target region; wherein the prediction model is an improved marine chlorophyll a concentration prediction model based on a convolutional long short-term memory network; the periodic feature extraction module is used for extracting historical synchronous related periodic features based on historical synchronous chlorophyll concentration data, and the noise removal and fusion module is used for eliminating noise output by the recent spatial-temporal feature extraction module and the periodic feature extraction module and fusing output results to obtain a marine chlorophyll a concentration data prediction result. According to the invention, the sea surface chlorophyll concentration can be predicted more accurately.

The invention belongs to the technical field of chlorophyll content inversion, and discloses a canopy chlorophyll content inversion method based on feature extraction and a machine learning model, and the method comprises the following steps: employing a mode of combining an unmanned plane carrying a multispectral camera and ground actual measurement; collecting and processing the chlorophyll content data of the potato canopies, and planting a quilt index; carrying out feature selection by using a Pearson correlation analysis and competitive self-adaptive reweighted sampling algorithm; constructing a chlorophyll content inversion model based on machine learning; and carrying out chlorophyll content inversion model optimization based on a grey wolf optimization algorithm and a sparrow optimization algorithm, and carrying out precision evaluation. According to the canopy chlorophyll content inversion method based on feature extraction and the machine learning model, comparative study is carried out on the single growth period and the whole growth period of potatoes, the optimal inversion model suitable for the single growth period and the whole growth period is determined, and the accuracy and applicability of the study are guaranteed.

The invention discloses a chlorophyll calculation method based on a double-branch multi-scale fusion network model, which belongs to the technical field of chlorophyll calculation, is used for calculating a chlorophyll a concentration value, and comprises the following steps: taking the chlorophyll a concentration value of a measurement site as a label of a water body characteristic sequence data set, dividing the data set into a training set and a verification set, inputting the training set into the double-branch multi-scale fusion network model to obtain a trained double-branch multi-scale fusion network model; inputting the verification set into the double-branch multi-scale fusion network model to obtain a verified double-branch multi-scale fusion network model; and taking the neighborhood spectral feature tensor and the neighborhood inherent optical feature tensor as the input of the verified double-branch multi-scale fusion network model to obtain the chlorophyll a concentration value of the pixel at the corresponding position. According to the method, the calculation result of the chlorophyll a with relatively high precision is obtained through neural network processing, and the complex relationship among the concentration change of the chlorophyll a in the water body, the spectrum and other optical parameters can be captured.

The invention discloses a water source chlorophyll a concentration prediction model design method based on machine learning. The method comprises the following steps: acquiring chlorophyll a concentration data in a to-be-predicted region for a continuous period of time; carrying out data preprocessing on the chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing a concentration prediction model, carrying out data preprocessing on chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing different concentration prediction models, and inputting the processed chlorophyll a concentration data and physicochemical parameters into the prediction models to obtain a chlorophyll a concentration data prediction result; and comparing prediction results of different prediction models, and determining the prediction model. According to the prediction model design method, the WT-GRU model is adopted to preprocess the data through wavelet transform, the wavelet transform effectively extracts key time scale characteristics through signal decomposition, and the accuracy of chlorophyll a concentration prediction is remarkably improved.

A photon counting laser radar-based open water chlorophyll a concentration profile detection method is used for accurately acquiring profile concentration distribution of chlorophyll a in a water body. According to the method, ICESat-2 photon counting laser radar data and MODIS satellite remote sensing images are combined, a photon propagation process is modeled by using a Monte Carlo simulation technology, energy attenuation and scattering characteristics of photons in a water body are calculated, an extinction coefficient and a backscattering coefficient are further derived, and chlorophyll a concentration distribution is inversed according to the parameters. High-precision water chlorophyll a concentration profile data can be provided by accurately simulating the propagation process of photons in a water body and considering the optical characteristics and environmental factors of the water body. MODIS remote sensing data is used for assistance, the accuracy and reliability of an inversion result are further improved, and accurate chlorophyll concentration detection can be achieved in different open water body areas. The method is suitable for monitoring the chlorophyll concentration of water bodies such as oceans, lakes and rivers, and provides a scientific basis for water body ecological environment evaluation.

The invention discloses a regional GPP assimilation inversion system fusing foundation-multi-source satellite chlorophyll fluorescence, and belongs to the technical field of terrestrial ecosystem carbon cycle monitoring. The signal unmixing and simulation generation module is used for unmixing the total fluorescence of the mixed pixels into pure chlorophyll fluorescence (SIF) and driving an ecological model to generate SIF and GPP simulation values based on environmental stress data; the state component decoupling module is used for decoupling the total GPP analog value into a light response fast component, a light response slow component and a light response memory component; the state evolution prediction module is used for predicting dynamic evolution of the slow component and the memory component; and the self-adaptive assimilation inversion module is used for performing inversion on the region GPP by taking the slow component and the memory component as state variables, taking the SIF observation value as observation data and combining a prediction result of the state evolution prediction module. According to the method, systematic deviation caused by model simplification or data singleness in a traditional method is overcome.

The invention discloses a corn yield prediction method and system based on chlorophyll fluorescence and deep learning, and the method comprises the steps: obtaining the yield prediction related data of a target region for many years in the past, and carrying out the preprocessing of the yield prediction related data; screening yield prediction related data of the hot and dry years according to a preset threshold value, and constructing a training data set under a stress condition based on the yield prediction related data of the hot and dry years; constructing a deep neural network model, and training, verifying and testing the deep neural network model by adopting the training data set to obtain a corn yield prediction model based on DNN; and inputting to-be-predicted data into the trained corn yield prediction model, outputting a corn yield prediction result, and generating a spatial distribution diagram. According to the method, the spatial distribution goodness of fit between a prediction result and official statistical data is remarkably superior to that of a traditional method, and a quantifiable and low-cost solution is provided for precise agricultural decision making under the extreme climate condition.

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

The invention relates to the technical field of environmental monitoring, in particular to a forest vegetation coverage growth monitoring system and method based on big data, and the method comprises the steps: extracting vegetation patch chlorophyll dispersion, texture direction and near-infrared uniformity to generate a microstructure feature set, comparing homogeneity intensity, a reference value and an adjacent condition marker to generate a connected state identifier, and carrying out the analysis of the connected state identifier; and according to the soil moisture capacity and the illumination radiation quantity, abnormal items of chlorophyll reflectivity deviation degree and near-infrared band concentration degree are removed. According to the method, by analyzing the chlorophyll ratio of the sub-pixels and the neighborhood texture, the near-infrared uniformity is quantified, and the heterogeneity precision is improved. And the multi-dimensional homogeneity strength is established by standardized parameters. And establishing a communication criterion by fusing edges, terrains and homogeneity. And performing dynamic screen spectrum abnormity early warning. And integrating the water retention, illumination and correction data of the soil to establish a dynamic model, and improving the prediction temporal-spatial resolution.

The invention discloses a vegetable disease incubation period detection method and system based on bimodal time sequence collaborative fusion, and the method comprises the steps: obtaining a leaf image through building an acquisition environment, constructing a training sample data set, and constructing a downy mildew incubation period spectral feature adaptive enhancement (AW-FPF) module; the method comprises the following steps: decomposing a hyperspectral signal into low-frequency and high-frequency components through spectrum time sequence adaptive wavelet decoupling, obtaining an enhanced feature tensor through spectrum multi-scale pathological feature frequency-time dual-path aggregation fusion frequency domain and time domain paths, and obtaining a first feature sequence through weighted screening by using a multi-head attention mechanism; a downy mildew incubation period prediction (HyChl-TFNet) model containing a hyperspectral branch, a chlorophyll fluorescence parameter branch, a feature fusion branch and a classifier is constructed, bimodal features are processed and fused to output a day number prediction result, accurate recognition of the downy mildew incubation period is achieved, the detection precision can be controlled to the day number level, and the detection accuracy is improved. And an accurate time basis is provided for early prevention and control of diseases.

The invention belongs to the field of crop science and agricultural molecular biology, and particularly relates to application of a BAK gene to enhancement of flood tolerance of rape, and the BAK gene is a BnaA04.BAK7 gene; the login number of the BnaA04. BAK7 gene in a gene pool is BnaA04g015000WE, and the login number of the BnaA04. BAK7 gene in the gene pool is By destroying the expression of the BAK gene, it is found that after the expression of the BAK gene is destroyed, the flooding tolerance of the brassica napus under flooding stress is weakened, and the invention discloses for the first time that hypocotyl growth of the brassica napus under flooding stress is slow and chlorophyll content is reduced due to function deletion of the BAK gene. New gene resources and theoretical guidance are provided for research and application of the flood tolerance of the rape.

The invention provides application of molybdenum disulfide nanoparticles in preparation of a tomato leaf fertilizer, and particularly relates to the technical field of tomato fertilizers. The molybdenum disulfide nano-particles act on tomato plants in a foliage spraying manner, so that the growth of the tomato plants is promoted and / or the fruit quality is improved. The molybdenum disulfide nanoparticles are used as the foliar fertilizer to significantly increase the plant height and stem diameter of tomatoes, increase the chlorophyll content and trace element content of plants, increase the vitamin C content, soluble sugar content and soluble solid content of tomato fruits, and significantly increase the accumulation of Mo and S of plants through the sulfur-molybdenum synergistic effect, so that the yield of the tomatoes is increased. A scientific basis is provided for reasonable application of the nano molybdenum fertilizer, and a way is opened up for high-yield and high-quality cultivation of tomatoes.

The invention belongs to the technical field of remote sensing information processing, and particularly relates to a water chlorophyll a concentration inversion-oriented remote sensing time-space-spectrum fusion method, which realizes improvement of spatial resolution of a chlorophyll a concentration inversion sensitive wave band by fusing complementary information of different sensors on time-space-spectrum resolution. The method comprises the following steps of: establishing a water body chlorophyll a concentration inversion-oriented MSI and OLCI space-time spectrum fusion deep learning network, embedding a time sequence dynamic adjustment module, and establishing a water body chlorophyll a concentration inversion-oriented MSI and OLCI space-time spectrum fusion deep learning network for water body chlorophyll a concentration inversion. According to the method, the network can intelligently combine time and space features to generate a more accurate prediction image, an Adaboost machine learning joint inversion model is constructed based on time-space-spectrum fusion data and corresponding limited ground station data, and high-precision remote sensing inversion of the concentration of chlorophyll a is realized.

The invention discloses a chlorella pyrenoidosa white mutant strain FZU65-W1 and application of the chlorella pyrenoidosa white mutant The classification name of the chlorella pyrenoidosa white mutant strain FZU65-W1 is chlorella pyrenoidosa, the chlorella pyrenoidosa white mutant strain is preserved in the China General Microbiological Culture Collection Center on September 11, 2024, the preservation number is CGMCC N0.46151, and the preservation address is No.3, Yard 1, Beichen West Road, Chaoyang District, Beijing. Compared with an original strain FZU65, the chlorella pyrenoidosa white mutant strain FZU65-W1 has the advantages that the growth speed of algae cells under the heterotrophic condition is higher, and meanwhile, the cells are white due to the fact that chlorophyll and carotenoid are not synthesized in the cells. The chlorella pyrenoidosa white mutant strain FZU65-W1 can be used for producing high-quality protein through heterotrophic fermentation, so that not only can a later-stage protein extraction and preparation process be simplified, but also the sensory acceptance of algae powder can be improved, and a high-quality germplasm resource is provided for industrial production and application of microalgae protein.

The invention discloses a potato cold resistance identification method and system, and relates to the technical field of cold resistance identification. The method comprises the following steps: cultivating potato tissue culture seedlings in a standardized manner, and randomly dividing the seedlings into a low-temperature stress group and a normal-temperature control group; performing standardized low-temperature treatment including slow descent and constant-temperature stages on the stress group; the method comprises the following steps: acquiring multi-source data such as chlorophyll fluorescence parameters, canopy thermal imaging, digital image phenotypes and biochemical indexes, and forming a unified data set after derivative index calculation and standardized fusion; inputting the fusion data into an improved neural network model, training the model through a particle swarm optimization algorithm, and outputting a quantitative potato cold resistance index; automatically evaluating the cold resistance grade according to the potato cold resistance index and generating an identification report; standardization of the identification process, fusion analysis of multi-dimensional indexes and intelligent result analysis are achieved, and the accuracy, efficiency and practicability of potato cold resistance identification are remarkably improved.

The invention provides a water chlorophyll a concentration inversion method and system based on multi-modal data and a lightweight model, and relates to the technical field of water environment remote sensing evaluation. The method comprises the following steps: firstly, acquiring a Gaofeng No.5 satellite remote sensing image, a sentinel No.3 satellite image and ground actual measurement data, and completing image preprocessing and water body pixel extraction; constructing a hyperspectral index and an aquatic vegetation index, and fusing the hyperspectral index and the aquatic vegetation index with the water body temperature, the pH environmental factors and the spectral reflectivity to form a multi-dimensional feature sample set; a core feature subset is obtained through random forest and XGBoost coupling feature selection, and a lightweight student model is trained based on knowledge distillation; and constructing a to-be-predicted feature sample for the to-be-predicted time phase image and the environment factor, inputting the to-be-predicted feature sample into the lightweight student model to obtain a chlorophyll a concentration predicted value, and generating a spatial distribution map and a quality control map layer. According to the invention, high-precision, low-redundancy and efficient deployment chlorophyll a concentration inversion is realized.

The invention relates to the technical field of agricultural quantitative remote sensing and intelligent agriculture, and discloses a multi-source remote sensing data radiation correction and chlorophyll content cross-platform inversion method, which comprises the following steps: synchronously obtaining ground actual measurement, unmanned aerial vehicle and satellite remote sensing data, calculating vegetation indexes, and screening sensitive characteristic variables by using Pearson correlation analysis; establishing a radiation correction model by adopting a ratio averaging method, performing radiation normalization processing on satellite data by taking unmanned aerial vehicle data as a reference, and eliminating sensor response differences; an SPAD inversion model is constructed and optimized based on Z-score standardization, the SPAD inversion model is migrated and applied to corrected satellite data, a regional scale chlorophyll distribution diagram is generated through pixel-by-pixel calculation, and then a differentiated farmland management strategy is formulated. According to the method, the difficulty of multi-source data collaboration is solved, the regional scale crop chlorophyll monitoring precision is improved, and scientific decision support is provided for large-area crop fertilization.

The invention relates to the technical field of saline-alkali soil improvement and microorganisms, in particular to bacillus pumilus S3-9 and application thereof. The bacillus pumilus S3-9 is preserved in the China General Microbiological Culture Collection Center on December 19, 2024, and the preservation number of the bacillus pumilus S3-9 is CGMCC (China General Microbiological Culture Collection Center) No. 33120. The bacillus pumilus S3-9 provided by the invention has the effects of reducing salinity in saline-alkali soil, increasing nutrients of the saline-alkali soil and improving the diversity and stability of rhizosphere microbial communities of the saline-alkali soil, and can be applied to improving soil fertility, promoting nutrient circulation, improving soil quality, relieving soil salinization and the like. The strain can also be used for increasing the yield of sweet sorghum in saline-alkali land, has a remarkable growth promoting effect on plant height, overground part fresh weight and underground part fresh weight, can effectively improve the K < + > / Na < + > ratio, chlorophyll content, superoxide dismutase activity and catalase activity, and is beneficial to enhancing the adaptive capacity of plants to salt stress.

The invention provides a water chlorophyll a concentration inversion method based on a hyperspectral remote sensing image, which relates to the technical field of remote sensing inversion and comprises the steps of image preprocessing, water pixel extraction, actually measured sample space registration, spectral feature construction, model score optimization, concentration prediction and the like. According to the method, an optimal scheme is screened in a feature-model combination through cross validation and a unified scoring function, the optimal scheme is applied to whole image calculation, and a chlorophyll a concentration spatial distribution map and a matched quality control map layer are output. The method has the characteristics of high precision, self-adaption and high engineering practicability, and is suitable for a water quality remote sensing inversion scene driven by multi-source hyperspectral data.

The invention belongs to the technical field of dressing preparation, and particularly relates to a preparation method of a photocuring methacrylated gelatin hydrogel dressing, which comprises the following steps: step 1, dissolving methacrylated gelatin in a phosphate buffer solution to prepare a methacrylated gelatin solution with the mass concentration of 5-15%; 2, adding a natural photoinitiator into the solution obtained in the step 1, and uniformly stirring and mixing; 3, adding the traditional Chinese medicine active ingredient powder, and stirring until the powder is uniformly dispersed to obtain a mixed solution; and 4, pouring the mixed solution into a mold, and curing and molding under the irradiation of visible light. Chlorophyll or riboflavin is used as a natural photoinitiator and is directly dispersed in a methacrylated gelatin solution in a physical blending mode, a curing light source enables chlorophyll to completely avoid a chemical initiator, meanwhile, the process is simplified into physical stirring to replace nanoparticle synthesis and modification, visible light reduces the risk of tissue burn, the production cost is reduced, and the method is suitable for industrial production. The method is suitable for primary medicine.

The invention belongs to the technical field of agricultural remote sensing, and discloses a method for evaluating cold resistance of pennisetum alopecuroides based on unmanned aerial vehicle multispectral images, which comprises the following steps: acquiring agronomic characters related to cold resistance, such as chlorophyll content, overwintering rate and the like of the pennisetum alopecuroides before and after low-temperature stress through manual measurement, and acquiring the multispectral images by using an unmanned aerial vehicle; the image is preprocessed, and various vegetation indexes such as DVI, NDVI and RVI are extracted; on the basis of agronomic character data, a comprehensive cold resistance index CRI is constructed by adopting principal component analysis and a membership function method; using the vegetation index as an independent variable and CRI as a dependent variable, and using a machine learning algorithm to construct a prediction model; and finally, realizing rapid and nondestructive evaluation on the cold resistance of a large-range pennisetum alopecuroides group by utilizing the model, and outputting a five-level cold resistance grading result according to a CRI value. According to the method, the problems of low efficiency and high difficulty of traditional manual evaluation are solved, high-throughput and precise cold resistance identification is realized, and reliable technical support is provided for cold-resistant breeding and cultivation management of pennisetum alopecuroides.

The invention provides a cotton verticillium wilt resistance detection method based on an unmanned aerial vehicle chlorophyll detection model, relates to the technical field of data detection, and solves the technical problem of low cotton verticillium wilt resistance detection efficiency. The method comprises the following steps: acquiring RGB data and multispectral data of a specified field area of cotton through image acquisition equipment arranged on an unmanned aerial vehicle; based on the RGB digital orthoimage, the multispectral digital orthoimage and chlorophyll content data measured on site by different varieties of cotton, utilizing a machine learning algorithm to construct chlorophyll detection models of chlorophyll of different varieties of cotton in different growth periods by adjusting initial model parameters; based on the optimal chlorophyll detection model and the verticillium wilt disease index data, K-means clustering analysis and GIS space analysis are carried out, chlorophyll change rule data and verticillium wilt resistance levels of different varieties of cotton are obtained, and the verticillium wilt resistance levels of different varieties of cotton serve as cotton verticillium wilt resistance detection results.

The invention discloses a virulence factor gene relative abundance prediction model based on water quality physicochemical indexes, and relates to the technical field of ecological environment protection, and the virulence factor gene relative abundance prediction model comprises the following steps: taking a plurality of river water samples, obtaining virulence factor gene relative abundance of the river water samples, and obtaining water quality physicochemical index data of the river water samples; temperature, redox potential, ammonia nitrogen, nitrate nitrogen, nitrite nitrogen, total nitrogen, total dissolved solids, pH, conductivity, chlorophyll-a, chemical oxygen demand, dissolved oxygen, total organic carbon, total phosphorus, organic nitrogen, phosphate, carbon-nitrogen ratio and organic nitrogen / total nitrogen are taken as explanatory variables, and relative abundance of virulence factor genes is taken as a target variable. And establishing a regression model by adopting a GBM algorithm in an h2o packet in R software, and predicting the relative abundance of the virulence factor gene of the river water sample to be detected by utilizing the prediction model. The research provides a brand new method for predicting the relative abundance of the virulence factor gene and analyzing the environmental response of the virulence factor gene, and the method is suitable for identification and interpretation of a high-dimensional nonlinear ecosystem with complex interaction.

The invention provides a method for cooperatively regulating and controlling chlorophyll-a concentration variability through vortex-upwelling, and relates to the technical field of ocean remote sensing and environmental monitoring. A multi-source satellite and reanalysis data set is obtained, and a stationary time sequence is constructed; identifying a dominant variation period in chlorophyll-a concentration, sea surface temperature and sea surface height anomalies, and extracting a monthly abnormal value sequence; performing complex empirical orthogonal function analysis on the sea surface height anomaly, extracting a dominant spatial variation mode of the sea surface height anomaly, and identifying a vortex distribution position and form; classifying and screening target vortexes according to vortex types and seasons, carrying out normalization processing on the chlorophyll-a concentration, aligning to vortex centers, and analyzing a spatial response relationship between the vortexes and the chlorophyll-a concentration; and drawing a density time-depth profile map and a CHL-SLA composite map by combining surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, and analyzing an influence mechanism of vortex-upwelling on chlorophyll-a horizontal distribution.

The invention provides a seawater chlorophyll a concentration three-dimensional remote sensing detection method, medium, equipment and product, and relates to the technical field of ocean remote sensing. The method comprises the following steps: acquiring satellite remote sensing data and photon counting laser radar data of a to-be-detected area; obtaining a seawater chlorophyll a concentration profile based on the diffusion attenuation coefficient; the method comprises the following steps: constructing an improved VGGNet model which comprises a multi-branch VGGNet, replacing standard convolution in the VGGNet with depth separable convolution, and introducing an SE attention mechanism into a backbone network; the time and position variables, the satellite remote sensing data, the photon density, the backscattering coefficient, the diffusion attenuation coefficient and the water depth serve as input variables of the model, the seawater chlorophyll a concentration profile serves as a target variable, and the model is trained; and inputting new satellite remote sensing data into the trained model to generate three-dimensional distribution prediction of the seawater chlorophyll a concentration. The method can better extract the features in the data, improves the prediction precision of the model, and is suitable for a complex water body environment.

The invention discloses a potato seedling grading environment parameter dynamic optimization regulation and control method, and relates to the technical field of agricultural informationization. Based on an Internet of Things sensor deployed in a target area, stem diameter, chlorophyll content and plant height data of potato seedlings are collected, the seedlings are divided into weak seedlings, middle seedlings and strong seedlings by adopting K-means clustering, and the weak seedlings, the middle seedlings and the strong seedlings are classified into a weak seedling classification model, a middle seedling classification model and a strong seedling classification model; setting the duration of the initial light period; and carrying out gradient photoperiod test on each type of potato seedlings by taking the stem elongation rate and the chlorophyll content as constraint conditions. Through grading photoperiod modeling and ant colony algorithm dynamic optimization, the limitation of traditional fixed photoperiod regulation and control is broken through, the initial photoperiod range is set based on the seedling physiological difference, and the photoperiod-growth rate response curved surface model is constructed in combination with the stem elongation rate and chlorophyll content constraint conditions. The influence of different photoperiod combinations on seedling growth is quantified, accurate matching of photoperiods and seedling requirements is ensured, excessive growth or premature senility is avoided, and the growth rhythm stability and the resource utilization efficiency are improved.