610 results about "Chlorophyllin" patented technology

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 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.

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 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 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 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 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 relates to the technical field of water environment remote sensing monitoring, solves the technical problem of systematic overestimation or underestimation under the condition of algae bloom outbreak or strong stratification due to the fact that a water body is regarded as an optical uniform monolayer parameter in a traditional method, and particularly relates to a stratified light field correction chlorophyll-a remote sensing inversion method and system for an eutrophic lake. Performing vertical type identification and three-layer layering by using multispectral / hyperspectral remote sensing reflectivity, a synchronous chlorophyll-a vertical profile and a diffusion attenuation coefficient, calculating light field weight and light path weighted concentration of each layer, constructing a layered light field correction coefficient, performing layered light field correction on the remote sensing reflectivity, and establishing an empirical chlorophyll-a inversion relationship; and generating a chlorophyll-a spatial distribution map and an algae bloom risk map. According to the method, the layered light field correction coefficient with clear physical significance is constructed to correct the water surface remote sensing reflectivity, so that the inversion precision and robustness under strong layering and complex optical conditions are improved.

The invention belongs to the technical field of remote sensing image data processing, and relates to a multi-feature fusion coastal zone culture pond extraction method, which comprises the following steps: obtaining spectral features and polarization features based on an obtained Sentinel-1 image and an obtained Sentinel-2 image; calculating and evaluating an NDWI time sequence based on the NDWI to generate a time sequence synthesis NDWI image; a water body main body is obtained through the hierarchical feature fusion decision tree; obtaining morphological characteristics based on the water body object; obtaining the chlorophyll a concentration and the dynamic characteristic factor of the chlorophyll a concentration based on the Sentinel-2 image; and extracting a culture pond through a random forest classifier, and generating a culture pond spatial distribution diagram. According to the method, the spectral features, the polarization features, the morphological features, the chlorophyll a concentration and the chlorophyll a concentration dynamic feature factors are fused, and the decision tree and the random forest classifier are fused through the hierarchical features, so that the problems of low accuracy and poor stability of existing culture pond extraction are solved.

The invention belongs to the technical field of water ecology risk early warning, and provides an algal bloom risk remote sensing intelligent identification method and system, and the method comprises the steps: obtaining remote sensing image data, meteorological data and water quality data of a target region, and carrying out the preprocessing; performing frequency domain feature extraction to generate a three-dimensional frequency domain feature vector; fusing the global feature representation obtained by modeling and the generated three-dimensional frequency domain feature vector by using a multi-task deep learning network to obtain a feature map; obtaining an algae bloom binary segmentation probability graph and a continuous value distribution graph of chlorophyll a concentration based on the characteristic graph; generating an image semantic embedding vector by utilizing a semantic embedding head, and performing semantic alignment on the image semantic embedding vector by adopting a pre-trained knowledge graph to generate a research and judgment information text; and performing cross validation on the study and judgment information text, the algae bloom binary segmentation probability graph and the continuous value distribution graph of the chlorophyll a concentration to obtain a final algae bloom risk judgment result. According to the invention, the algal bloom risk remote sensing intelligent identification is realized.

The invention relates to the field of gene engineering and molecular breeding, and discloses application of a ZmTCP5 gene in regulation and control of heat resistance of corn. According to the invention, three types of knockout mutants of the gene are obtained by using a CRISPR / Cas9 technology, and three overexpression materials are created. At normal temperature (25-28 DEG C), the material has no obvious phenotypic difference with a wild type; however, under high temperature stress (45 DEG C), the ZmTCP5 knockout mutant has enhanced heat resistance, reduced reactive oxygen species (ROS) accumulation, and significantly increased ROS scavenging enzyme activity, chlorophyll content and photochemical efficiency; on the contrary, the ZmTCP5 overexpression strain is more sensitive to heat stress, which indicates that the ZmTCP5 negatively regulates the heat resistance of the corn. The research provides a theoretical basis for analyzing a heat-resistant molecular mechanism of the corn, and also provides an important gene resource for creating a new germplasm of the heat-resistant corn.

The invention belongs to the technical field of satellite remote sensing application, and discloses an active and passive satellite remote sensing fused ocean three-dimensional chlorophyll field reconstruction method and system. According to the method, satellite-borne laser radar data is subjected to correction, signal processing and biological optical model inversion, and a chlorophyll a concentration vertical section along an orbit is obtained; forming a training set by the passive satellite observation optical variable and the marine environment variable of which the profile is matched with the space-time, so as to train a long short-term memory (LSTM) neural network model; and utilizing the trained model to reconstruct a three-dimensional chlorophyll a concentration field of a target area according to passive observation and environment variables of the target area. By fusing the advantages of a satellite-borne laser radar ICESat-2 satellite and a passive optical remote sensing satellite, three-dimensional chlorophyll a concentration field detection based on active and passive fusion remote sensing is developed, the structure and function of a marine ecosystem can be deeply known, and three-dimensional dynamic observation of the ocean is realized.

The invention relates to a novel camellia saponin derivative feed additive and a preparation method thereof, and belongs to the technical field of feed additives. The modified chitosan is used in the flocculation and impurity removal process for preparing the camellia oleifera saponin derivative, and the modified chitosan reduces the loss of effective components caused by flocculation and impurity removal while ensuring the purity of the camellia oleifera saponin derivative; soybean lecithin and gelatin are used as capsule walls, the camellia saponin derivative and pyropheophorbide-a are wrapped, pyropheophorbide-a can generate singlet oxygen under excitation of visible light, phospholipid bonds are damaged, the camellia saponin derivative is released, and therefore the slow release effect is achieved; the novel camellia saponin derivative feed additive prepared by the invention has good antibacterial property and timeliness.

The invention relates to the technical field of prefabricated dishes, and discloses a color-protecting and vitamin-retaining method for green peppers in green pepper and pig tripe prefabricated dishes, which comprises the following steps: selecting fresh green peppers, sorting, cleaning, and soaking with a citric acid-calcium chloride mixed solution to obtain pretreated green peppers; performing vacuum pre-cooling on the pre-treated green peppers to obtain pre-cooled green peppers; selecting fresh pork tripe, cleaning, and marinating with spices to obtain marinated pork tripe; mixing the precooled green peppers with the marinated pork tripe, then packaging the mixture by adopting vacuum packaging to obtain a vacuum-packaged green pepper and pork tripe prefabricated dish, and finally performing low-temperature sterilization to obtain the green pepper and pork tripe prefabricated dish. The green peppers are soaked in the citric acid-calcium chloride mixed solution, and low-temperature vacuum pre-cooling is performed, so that degradation of chlorophyll is reduced, the color of the green peppers is protected, and vitamins are reserved at the same time.

The invention belongs to the technical field of biochemical pharmacy, and particularly relates to a bezoar intermediate and a preparation method of bezoar. The preparation method comprises the following steps: mixing preparation raw materials and biochemical reaction liquid to obtain a mixed raw material solution; the preparation raw materials comprise bilirubin, organic active raw materials and vitamins, the biochemical reaction liquid is sterile purified water, and the bilirubin comprises bilirubin or biliverdin; and reacting the mixed raw material solution to obtain the bezoar intermediate. The content of combined bilirubin in the bezoar intermediate prepared by the preparation method provided by the invention is 75%-85%, the content of combined biliverdin is 73%-83%, and the yield is 83%-87%. According to the method, the poisoning and destroying effects of mycotoxin on the content, the yield, the biological activity and the oxidation resistance of the bezoar intermediate are effectively eliminated. The content, the yield, the oxidation resistance and the biological activity of the bezoar intermediate are greatly improved, the performance is stable, and the quality of bezoar is ensured.

The invention provides an application of potato laccase StLAC19 in a shade avoidance reaction. The overexpression of the StLAC19 gene can significantly enhance the shading tolerance of potato plants, and effectively improve the lignin content, the osmotic regulation substance content, the chlorophyll content, the stalk strength, the CAT and POD enzyme activity and the PRO and MDA content; on the contrary, interference on expression of the StLAC19 gene results in a contrary result. The result shows that the StLAC19 gene positively regulates and controls the shading reaction of potato plants. The screening of varieties with high expression of the StLAC19 gene is helpful for obtaining materials with stronger shade-avoiding tolerance, so that the gene can be used as a target gene for breeding shade-avoiding tolerance type potato varieties.

The invention discloses a rice chlorophyll content prediction method, device, medium and equipment, and relates to the technical field of crop growth, and the method comprises the following steps: obtaining canopy spectral data and physicochemical parameters of rice in a to-be-detected area; dividing the rice canopy into three concentric circle areas based on the leaf inclination angles of different leaves of the rice, and obtaining the layered rice canopy; constructing a hierarchical radiation transfer model HRTM for simulating a rice hierarchical spectrum, and inputting the physical and chemical parameters into the hierarchical radiation transfer model HRTM to generate hierarchical spectrum data; constructing a lookup table LUT based on the physicochemical parameters and the layered spectral data, taking the lookup table LUT as an analog data set, improving the analog data set by adopting a quartile method, and determining analog spectral data; based on the simulated spectrum data, constructing an inversion model for acquiring the chlorophyll content of the rice layers layer by layer; and inputting hyperspectral data to be measured into the inversion model, and determining a corresponding chlorophyll content prediction result.

The invention discloses a time sequence reconstruction and machine learning coupled chlorophyll monitoring data breakpoint restoration method, and belongs to the technical field of water quality monitoring. The invention discloses a chlorophyll monitoring data breakpoint restoration method based on coupling of time sequence reconstruction and machine learning, and the method comprises the following steps: S1, collecting water quality monitoring data, and cleaning the monitoring data to obtain preprocessed data; s2, performing time sequence reconstruction on the preprocessed data to obtain a weekly average 1 data set; s3, respectively constructing a radial basis function neural network model and a back propagation neural network model by taking the chlorophyll concentration as a response variable and the conventional water quality parameter as a predictive variable; s4, performing performance evaluation on each model by taking a root mean square error, an average absolute percentage error, goodness of fit and relative error distribution statistics as evaluation indexes, and screening out an optimal model; and S5, applying the conventional water quality parameters in the breakpoint interval of the chlorophyll monitoring data in the water body to the optimal model, and outputting the restored chlorophyll concentration value to complete the dynamic restoration of the breakpoint.

The method comprises the following steps: S1, collecting unmanned aerial vehicle multispectral image data of a tea garden, and synchronously collecting measured values of chlorophyll and nitrogen content of tea leaves in a corresponding region; s2, preprocessing the multispectral image data, and constructing a training set and a test set; s3, an LSSCM-MobileTeNet detection model is constructed, the model takes MobileNetV3 as a reference framework, a multi-Stage Stem structure (Multi-Stage Stem) is introduced to carry out hierarchical feature extraction, and an original bottleneck layer is replaced with a lightweight spatial spectrum convolution module (LSSCM-Block); and S4, training the LSSCM-MobileTeNet detection model by using the constructed data set, processing the multispectral image of the area to be detected by using the trained model, and outputting predicted values of the chlorophyll content and the nitrogen content. The method has the advantages that RMSE and MAE are remarkably reduced, R2 is improved, and higher prediction precision is achieved; according to the method, the chlorophyll nitrogen content of the tea leaves can be rapidly detected on a large scale; the multi-stage Stem structure enhances the ability of the model to extract the hierarchical features of the multispectral image.

The invention discloses a polypeptide fertilizer beneficial to cold resistance of crops and a preparation method thereof, and belongs to the technical field of agricultural fertilizers, the fertilizer is prepared by taking livestock and poultry blood (preferably pig blood) as a raw material, preparing high iron protein peptide through a compound protease hydrolysis process, and matching with a trace element compound, humic acid and a carrier. The preparation process comprises the steps of enzymolysis preparation of the ferritin peptide, fertilizer mixing, granulation, drying and the like. The polypeptide fertilizer disclosed by the invention takes the high-iron protein peptide as a core, the contained small-molecular amino acid polypeptide can enhance the antioxidant enzyme activity of crops at low temperature and reduce malonaldehyde accumulation, the porphyrin iron can promote chlorophyll synthesis and energy metabolism, and the high-iron protein peptide and the porphyrin iron cooperate to remarkably improve the cold resistance of the crops. Meanwhile, by cooperating with trace elements and humic acid, the cold resistance is enhanced, nutrient absorption of crops is promoted, the yield and quality are improved, multiple effects of resisting cold, improving quality, increasing yield and the like are achieved, the raw materials are wide in source, the cost is low, the preparation process is simple, and large-scale production and application are facilitated.

The invention relates to the technical field of agricultural drought monitoring and precise irrigation, in particular to a crop water stress diagnosis method and system based on sunlight-induced chlorophyll fluorescence, crop canopy oxygen absorption band spectral data is collected through a hyperspectral sensor, and a sunlight-induced chlorophyll fluorescence intensity SIF value is solved by adopting a fluorescence extraction algorithm. The method comprises the following steps: taking an SIF value of a crop canopy without water stress as a reference canopy SIF value, determining a difference between the SIF value of a to-be-detected crop canopy and the reference canopy SIF value, then generating a relative SIF water deficit index SIFWDI by combining a preset formula, establishing a mapping relation between the SIFWDI and a soil water deficit index by utilizing a regression analysis model, and outputting a diagnosis report. Variable irrigation decision making and early warning are triggered according to the stress level, and model parameter self-adaptive optimization is achieved through a feedback mechanism. The system realizes lossless, real-time and high-precision water stress monitoring and irrigation management.

The embodiment of the invention discloses a time-space prediction method and device for sun-induced chlorophyll fluorescence. The method comprises the following steps: acquiring a time-space data set; based on the spatio-temporal data set, taking a county administrative unit as a reference unit to perform area aggregation, dividing a large-scale area into a plurality of homogenized sub-areas, and selecting a representative spatial point from each sub-area; extracting the SIF of the corresponding representative spatial point and variable feature information of the key covariable, and constructing a four-dimensional space-time cube based on the variable feature information; sTL decomposition is carried out on the four-dimensional space-time cube to obtain a trend term, a seasonal term and a residual term, and flattening processing is carried out on the trend term, the seasonal term and the residual term to obtain a time sequence feature vector; the time sequence feature vector is input into a trained LSTM-Transform model for prediction, and a trend-season point value prediction result is obtained; and jointly interpolating the point value prediction result and a preset covariable to a preset target prediction area by using an ANUSPLIN thin plate smooth spline interpolation method, and generating an SIF large-scale space-time prediction map.

The invention discloses a preparation method of a fructus cannabis protein peptide product with effects of resisting fatigue and improving exercise ability, which comprises the following steps: performing preliminary crushing, ultralow-temperature freezing micro-crushing and low-temperature continuous phase-change extraction degreasing on fructus cannabis, performing step-by-step hydrolysis by using polysaccharase and 2.4 L bacterial protease to extract fructus cannabis protein, performing low-temperature salting-out, and performing freeze-drying to obtain the fructus cannabis protein peptide product with the effects of resisting fatigue and improving exercise ability. The fructus cannabis protein peptide powder is prepared through low-temperature ultrasonic dissolution promotion, high-pressure micro-jet homogenization, compound enzyme C hydrolysis, 1% pepsase, high-temperature enzyme deactivation, chlorophyll removal through counter-current extraction, hydrogen peroxide decoloration, ultralow-temperature instantaneous concentration, low-temperature vacuum belt type drying and low-temperature ultrahigh-pressure sterilization. The fructus cannabis protein peptide prepared by the invention has the following remarkable effects: 1, the exercise ability is remarkably improved, the accumulation of lactic acid and urea nitrogen is reduced, the exhaustion time is prolonged, and the physical and mental fatigue is relieved; 2, the content of hepatic glycogen and muscle glycogen is obviously increased, the energy reserve of the body is increased, the explosive power and the reactivity are improved, and the athletic performance is improved; and 3, the antioxidant level of the body is improved, and fatigue and immune recovery after exercise are promoted.

The invention belongs to the technical field of microorganisms, and discloses chlorella sorokiniana M6, application of the chlorella sorokiniana M6 and a method for synchronously synthesizing xanthophyll and protein. The preservation number of the Chlorella sorokiniana M6 provided by the invention is GDMCC NO.66205, the Chlorella sorokiniana M6 has an excellent capability of co-producing lutein and protein, and is almost free of chlorophyll synthesis, so that the Chlorella sorokiniana M6 has very important economic value and practical significance for realizing commercial application of microalgae protein and lutein production.

The invention provides a spiropolyspora pink and selenium compound nutrient solution and application thereof in biological prevention and control and growth promotion of tomatoes, the prevention and control effects of different treatment combinations on tomato gray mold are studied, disease indexes and prevention and control effects of different treatment combinations are compared, activity changes of stress-resistant related enzymes SOD, POD and MDA are compared, and the disease resistance of the tomato gray mold is improved. A photosynthetic related index is chlorophyll content. The incidence rate of the spiropolyspora roseum and the selenium-enriched nutrient solution in the treatment group is lowest, the prevention and treatment effect is optimal, and the chlorophyll content is highest, so that the compounding agent can obviously improve the photosynthesis of the plants. Different compounding agents have an induction effect on the disease resistance of tomatoes. The selenium content of the compounded tomatoes is obviously higher than that of a control group and a single selenium-rich nutrient solution, the ratio of the optimal selenium-rich combination to the selenium-rich nutrient solution is 2: 3, the selenium content of tomato fruits can reach 52.67 g / kg, and it is indicated that the selenium absorption of the tomatoes can be promoted through the spiropolyspora pink.

The invention provides a peptide-enriched organic fertilizer as well as a preparation method and application thereof, and belongs to the technical field of organic fertilizer preparation. The preparation method of the peptide-enriched organic fertilizer provided by the invention comprises the following steps: mixing corn soaking water with aspergillus oryzae seed koji, and fermenting for the first time to obtain the peptide-enriched organic fertilizer; the aspergillus oryzae mold starter is obtained by inoculating aspergillus oryzae YY-21 into a culture medium and fermenting 2. According to the preparation method provided by the invention, the peptide-enriched organic fertilizer with high amino acid nitrogen content, high small peptide content, high soluble protein content and high thallus biomass can be prepared, and when the peptide-enriched organic fertilizer is used for planting lettuce, the plant height, the fresh weight of a single plant, the root length, the leaf number and the leaf area of the lettuce can be remarkably increased; the chlorophyll content, the vitamin C content and the total sugar content in the lettuce can be remarkably improved, and the nitrate content of lettuce leaves can be reduced.