180 results about "Environmental factor" patented technology

The invention relates to the cross technical field of artificial intelligence and environment monitoring, and discloses an algal community structure change prediction algorithm and system based on multi-source data fusion, and the algorithm comprises the steps: obtaining water quality, weather and plankton multi-source time sequence data; performing time alignment and missing value interpolation; eliminating and screening key environment factors through recursive features; performing dynamic weighted fusion on the multi-modal features by using a space-time attention mechanism; inputting a three-layer stacked LSTM network to output future algae dominant species abundance prediction; and model parameters are corrected on line based on measured data. The system comprises a multi-source data acquisition module, a preprocessing module, a key factor extraction module, a space-time attention fusion module, a dynamic prediction module and an adaptive correction module. According to the method, the prediction accuracy and stability are remarkably improved, and algal bloom early warning and ecological regulation are effectively supported.

The invention provides a seaweed farm ecosystem prediction method, system, device and medium, and belongs to the technical field of seaweed farms, and the method specifically comprises the steps: collecting basic data of a seaweed farm ecosystem, and forming an ecological data set; preprocessing the ecological data set; determining a seaweed field prediction index, using a DMD mode to decompose and extract a time-delay main mode, then using convergence cross mapping to quantify causal intensity, constructing an environmental factor and biological index space-time diagram, and learning a causal edge weight through a graph attention network; predicting index change in combination with the incidence relation, and performing hybrid prediction by adopting a pulse disturbance-recovery coupling model and digital twinborn reinforcement learning; and visually outputting the prediction result. According to the method, a seaweed field ecological system monitoring and predicting system of data, information, knowledge and decision is constructed, the ecological system state characterization capability and risk early warning perspectiveness are improved, and scientific and efficient technical support is provided for marine ecological protection and management.

The invention belongs to the technical field of environment regulation and control, and discloses a livestock breeding environment dynamic prediction and regulation and control method based on machine learning. Comprising the following six key steps: firstly, preprocessing multi-source data, and constructing a standardized feature data set; secondly, performing time sequence feature extraction and environment-biological response correlation analysis to obtain a multi-factor interaction network; then, constructing a dynamic coupling model of the environmental parameters and the production efficiency; a multi-target reinforcement learning regulation and control model is constructed based on the animal husbandry production efficiency prediction space; predictive environmental parameter regulation and risk assessment are carried out in combination with real-time monitoring data; and finally, intelligent environment intervention is executed, and a regulation and control strategy is iteratively optimized through production index feedback. According to the method, the breeding environment is converted from passive response to active prediction, complex correlation understanding between environmental factors and biological indexes is established, and multi-target balance optimization of production efficiency, energy consumption and animal welfare is achieved.

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

The invention discloses a soil fertilizer nutrition customization regulation and control method and system. The system comprises a data acquisition module, an environment compensation module, a soil nutrient dynamic prediction module, a target demand management module, a multi-target optimization module, a safety evaluation module, a monitoring self-adaptive learning module and the like, soil nutrition and safety indexes and external environment factors are acquired, data are corrected by using a compensation function, nutrient changes are predicted, a target range is set according to different growth stages of crops, and a monitoring self-adaptive learning module is established. A multi-objective optimization model is established based on organic fertilizer contribution and missing nutrients, an optimal formula is generated by comprehensively considering crop requirements, cost and environmental risks, and the model is dynamically updated through continuous monitoring and feedback closed loop after fertilization. According to the method, the fertilizer utilization rate and the fertilization precision can be remarkably improved, the soil acidification and heavy metal risks are reduced, and the balance of yield, quality and ecological safety is achieved.

The invention discloses a multi-factor coupling ganoderma lucidum environment digital twinborn regulation and control method oriented to high-yield requirements, particularly relates to the technical field of intelligent cultivation of edible mushrooms, and is used for solving the problems of equipment action oscillation and recessive physiological stress caused by physiological response hysteresis of ganoderma lucidum in an existing environment regulation and control system. Carbon dioxide concentration and illumination intensity predicted values are generated in real time by constructing ganoderma lucidum growth digital twins, and an oxidative stress mark is activated by combining hypha respiratory entropy matching degree analysis and an active oxygen accumulation curve; when the matching degree exceeds a threshold value and oxidative stress exists, a metabolic overload signal is generated, and whether the carbon dioxide predicted value is in a physiological hysteresis interval or not is judged; delay compensation and metabolic overload suppression coefficient adjustment are performed on the carbon dioxide predicted value in the delay interval; and finally, inputting the compensated carbon dioxide value and the illumination prediction value into an environment factor coordination rule base, generating a ventilation opening degree and light supplementing power instruction to drive execution equipment, and ensuring the morphological development stability of the sporocarp and the synthesis efficiency of the active component.

The invention discloses a corn genetic performance prediction method fusing multiple environmental factors. The corn genetic performance prediction method comprises the following steps: acquiring a parent genotype data matrix, a hybrid phenotype data vector and a multi-environmental time sequence climate data tensor; a climate hysteresis effect matrix is constructed, and hysteresis influence degrees of different climate factors on corn growth and development are quantified; constructing an environment time-space relation matrix and an enhanced genome relation matrix considering non-equal contributions of sections; constructing a linear hybrid prediction model fusing the space-time environment information; estimating a variance component through a constraint maximum likelihood method, and solving model parameters based on a Henderson hybrid model equation; and predicting the phenotypic character of the to-be-predicted corn hybrid in the target environment by using the solving parameters. According to the method, the genotype data and the multi-environment time sequence climate data are fully utilized, the genotype effect, the environment space-time effect and the interaction effect of the genotype effect and the environment space-time effect are effectively integrated, and the accuracy of corn hybrid phenotype prediction is remarkably improved.

The invention discloses a method for predicting abundance of plankton by using marine ecological observation data, which belongs to the technical field of marine organism prediction, and comprises the following steps: collecting multi-source marine environment data to obtain a data set; processing short-term correlation characteristics among variables from the data set to obtain an environmental factor interaction vector; processing the time dynamic change of the environmental factor interaction vector to obtain a plankton abundance change trend; enhancing the association strength of the environmental factor interaction vector and the plankton abundance change trend to obtain a weighted dynamic feature set; extracting subsets having influence on plankton abundance prediction from the weighted dynamic feature set to obtain a prediction feature group; and constructing a regression model according to the prediction feature group, and processing the newly obtained multi-source marine environment data by adopting the regression model to obtain a plankton abundance prediction value. The method for predicting the abundance of the plankton by using the marine ecological observation data solves the problems that the current prediction result is lagged or not accurate enough, and the timeliness and reliability of a decision are influenced.

The invention provides a hydrogen-rich water concentration dynamic adjustment method, and relates to the field of concentration adaptive adjustment, and the method comprises the following steps: S1, data acquisition; step S2, data preprocessing; s3, key influence factors are extracted; s4, acquiring the initial concentration of the hydrogen-rich water; and S5, obtaining the optimized concentration of the hydrogen-rich water: obtaining a dynamic concentration adjustment amount, an extreme environment adjustment amount and an implicit stress adjustment amount through reinforcement learning dynamic optimization, extreme environment adaptation and implicit stress early warning, and obtaining the final concentration of the hydrogen-rich water. According to the method, self-adaptive adjustment of the concentration of the hydrogen-rich water can be performed according to the growth state of the plant and environmental factors, the accuracy of the concentration of the applied hydrogen-rich water is ensured, and the problem that normal growth of the plant is affected due to too high or too low concentration of the hydrogen-rich water is avoided.

The invention relates to the technical field of seawall ecological transformation, and discloses a slope type seawall inner slope ecological transformation method which comprises the following steps: selecting a seawall section suitable for ecological transformation according to three first-level indexes, namely a seawall shoreline foundation, hydrology and water quality conditions and surrounding environment conditions; constructing a pool in the selected seawall section according to the terrain; constructing a pool base; and constructing a plant community in the water pool according to the life type, the phenological period and the salt tolerance of the plant. According to the method, a proper transformation section can be selected in combination with various environmental factors of a transformation area, then a corresponding water pool is constructed in combination with the landform, then a proper ecological material is selected to construct a substrate, and different plant communities are constructed in the water pool according to the characteristics of different plants. A natural-imitated seawall ecological space is built, the flood prevention and tide blocking functions of the seawall are guaranteed, the hygrophytes, aquatic plant communities and planktons can be utilized, an inhabiting and foraging environment is built for aquatic animals and hydrophilic birds, and the ecological service function of the seawall is improved.

The invention discloses a cryptosporidium infection early warning method based on pasture environment and oocyst detection rate analysis, which comprises the following steps: collecting pasture multi-medium environment samples and corresponding parameters, and acquiring oocyst concentration by adopting a real-time quantitative PCR (Polymerase Chain Reaction) and a molecular diagnosis model; analyzing association between the oocysts and environmental factors through an oocyst propagation and environmental dynamics model; a key environment variable weight is determined by using MaxEnt and GloWPa-Crypto algorithms, and an oocyst diffusion path is simulated; and fusing multi-dimensional data to divide risk levels, and outputting the infection risk value and duration of each region through iterative operation. According to the method, through multi-medium sampling, multi-model fusion and multi-data deep fusion, the defects of single data, inaccurate propagation process description and insufficient data fusion in the prior art are overcome, the actual infection risks of different areas of the pasture can be accurately reflected, and a precise basis is provided for prevention and control.

The invention relates to the technical field of species ecological niche modeling, and provides a coastal wetland waterfowl ecological corridor construction method and system, and the method comprises the steps: collecting target waterfowl species distribution point data, and obtaining a waterfowl species distribution data set; performing habitat suitability modeling on environmental factor data and the waterfowl species distribution data set through a species distribution model to obtain a coastal wetland ecological source land; habitat suitability prediction modeling is carried out on the environment variables through a random forest algorithm, resistance value assignment is carried out on suitability indexes, and a coastal wetland comprehensive resistance surface is obtained; and carrying out multi-scene corridor path identification on the coastal wetland comprehensive resistance surface and the coastal wetland ecological source land to obtain the coastal wetland waterfowl ecological corridor. According to the method, the functionality of the ecological corridor under different ecological backgrounds can be comprehensively reflected, so that the ecological suitability of the constructed corridor is improved.

The invention relates to the technical field of environment monitoring and intelligent sensors, and particularly discloses a soil pollutant detection and analysis system based on an intelligent sensor. The system comprises a sensor array module, a data preprocessing and fusion module, an ecological toxicity dynamic evaluation module and a decision output module. According to the invention, the biological response sensing unit capable of directly reflecting the ecological effect is integrated, and the ecological toxicity dynamic evaluation model fusing the concentration, the biological response and the environmental factors is innovatively constructed, so that direct and dynamic quantification from the pollutant content to the ecological risk is realized. According to the method, the problem that an existing assessment method is insufficient in biological correlation is solved, the risk assessment result is closer to the real ecological environment effect, and a reliable basis is provided for precise treatment.

The invention relates to the technical field of pest control, in particular to a method for evaluating habitat suitability and diffusion trend of codling moths and application. The method comprises the following steps: S1, collecting occurrence distribution data of codling moths; s2, acquiring a plurality of pieces of bioclimate variable data, human activity influence indexes and apple yield data of codling moth occurrence distribution, constructing a multi-dimensional environmental factor data set according to the plurality of pieces of bioclimate variable data, the human activity influence indexes and the apple yield data, and performing cross validation on the data set; and S3, based on a Biomod2 set model, establishing a plurality of single ecological models according to the occurrence distribution data and the multi-dimensional environmental factor data set, selecting high-accuracy models according to TSS and AUC values of the single models, integrating the models to generate a set model, and obtaining a habitat suitability result of codling moths and a contribution rate of main environmental factors. Scientific reference is provided for early warning, monitoring and prevention of codling moths.

The invention belongs to the technical field of precise environment control for pig breeding, and particularly relates to a method and system for pig breeding environment-behavior correlation modeling and cooperative regulation and control. The method comprises the following steps: S1, modeling an association mechanism of a multi-element environment factor and a pig abnormal behavior: fully mining the association between the multi-element environment factor and the pig abnormal behavior in an existing knowledge base by introducing a pre-trained large language model LLM, and modeling; s2, construction of an environment-behavior collaborative decision-making system guided by the large language model: constructing a multi-source real-time data acquisition system, synchronously acquiring and fusing environment and pig behavior data, and constructing the environment-behavior collaborative decision-making system based on the fine-tuned large language model for realizing multi-factor collaborative attribution and intelligent regulation and control. According to the method, the pre-trained large language model and the multi-source perception data are fused, and the method has the advantages that the processing efficiency can be guaranteed, and meanwhile accurate modeling and analysis of the complex environment-behavior relation can be achieved.

The invention relates to the technical field of jerusalem artichoke breeding and planting, and discloses a jerusalem artichoke variety breeding and planting method and system. The method comprises the following steps: collecting phenotype data of jerusalem artichoke plants at different growth stages Constructing a multi-dimensional growth model containing an incidence relation between environmental factors and phenotypic data, wherein the environmental factors are obtained by monitoring a planting area sensor array in real time; screening candidate jerusalem artichoke strains in the target environment parameter interval based on the phenotype-environment response curve; hybridizing and matching the candidate strains to obtain filial generation seeds, and sowing the filial generation seeds in a controllable environment test field; extracting gene expression profile data of offspring seeds, and establishing a genotype-phenotype incidence matrix in combination with the response curve; transplanting the dominant group to a field, and collecting growth dynamic data to update the multi-dimensional growth model; an environment control strategy is adjusted according to the updated model, and dominant groups are directionally cultivated; tubers are harvested and subjected to quality detection, and the result is fed back to the incidence matrix to optimize the next breeding process.

The invention relates to the technical field of landform prediction, in particular to a tidal flat evolution prediction method and system based on a landform model. Comprising the following steps: acquiring landform data, environmental parameter data and biological membrane characteristic data of a target tidal flat area; the landform data comprises water depth distribution and sediment porosity; the environmental parameter data comprises tidal flow velocity, fluid shear force, illumination intensity and water body temperature; the biological membrane characteristic data comprises biological membrane thickness obtained by in-situ sampling; constructing a dynamic viscosity coefficient model of the biological membrane based on the thickness of the biological membrane and the temperature of the water body; the dynamic viscosity coefficient model represents a nonlinear enhancement effect of the biological membrane on the viscosity of the sediment; establishing a biofilm growth rate feedback model based on the water depth distribution and the illumination intensity; the feedback model quantifies the coupling relationship between the biofilm growth of the sedimentation area and the microtopographic factor; according to the method, the problem of evolution prediction misalignment under the dynamic interaction of the environmental factors can be solved.

The invention relates to a probiotic product stability prediction model and a construction method and application thereof. The invention provides a new method capable of predicting the stability of the probiotic product earlier, faster and more accurately, 14 potential influence factors such as strain physiological characteristics, preparation processes and environmental factors are incorporated into a consideration system, and a machine learning algorithm is utilized to capture a complex nonlinear relationship between the potential influence factors and the stability, so that the stability of the probiotic product can be predicted more early, faster and more accurately. The accuracy and the reliability of the prediction model are greatly improved, the prediction timeliness is high, the universality and the guidance are high, and digital management can be realized.

The present application relates to the technical field of geomorphology prediction, in particular to a tidal flat evolution prediction method and system based on a geomorphology model. The method comprises the following steps: obtaining geomorphology data, environmental parameter data and biological membrane characteristic data of a target tidal flat area; the geomorphology data comprises water depth distribution and sediment porosity; the environmental parameter data comprises tidal current velocity, fluid shear force, light intensity and water temperature; the biological membrane characteristic data comprises biological membrane thickness obtained by in-situ sampling; a biological membrane dynamic viscosity coefficient model is constructed based on the biological membrane thickness and the water temperature; the dynamic viscosity coefficient model represents a nonlinear enhancement effect of the biological membrane on sediment viscosity; a biological membrane growth rate feedback model is established based on the water depth distribution and the light intensity; the feedback model quantifies the coupling relationship between biological membrane growth in a deposition area and microtopographic factors; the present application can solve the problem of inaccurate evolution prediction under dynamic interaction of environmental factors.

This invention discloses a method and system for determining the propagation conditions of wood-eating insect populations based on data modeling. Specifically, it relates to the field of computer data modeling and simulation optimization technology. The method addresses the problem that existing data modeling methods for establishing propagation conditions of wood-eating insect populations suffer from drift in model parameter mapping relationships and poor reproducibility of output propagation conditions in subsequent batches due to neglecting latent variables such as cross-interface microbial communities. The method establishes an initial mapping model by acquiring environmental parameters and population response data from multiple historical batches. For the data in the initial stage of the current batch, a residual sequence is calculated, and a non-random structure is determined based on the monotonic change trend of the residual fluctuation amplitude with the substrate fermentation process. When a non-random structure exists, the partial dependence relationship between the residual sequence and each environmental factor is extracted along the drift trajectory of the fermentation process as a characterization of the latent variable effect. After concentration screening, the initial mapping model is corrected, and the corrected mapping model is then used to simulate and optimize the output propagation conditions adapted to the latent variable environment.

The invention relates to the technical field of intelligent medicine, and discloses a microorganism and environmental factor association modeling method and system.The method comprises the steps that according to distribution data of features in a standardized data set, information entropy values of the features are determined; screening out features of which the feature weights are higher than a preset threshold in the feature entropy set to obtain a key feature set; taking the key feature set as a node, taking the interaction relationship as an edge, and taking the association strength as an edge weight to obtain an association model of the microorganisms and the environmental factors; performing performance evaluation on the association model by using the independent verification data set to obtain a model verification report of the association model; inputting the standardized data set of the target microorganisms and the target environmental factors, and obtaining an association network diagram of the target microorganisms and the target environmental factors, wherein the model verification report is a passed association model; according to the method, the accuracy of correlation modeling of microorganisms and environmental factors can be improved.

The invention discloses a fish egg hatching rate prediction optimization method based on deep learning, and belongs to the technical field of deep learning. Hatching environment parameters such as water temperature, dissolved oxygen and illumination intensity and roe physiological state parameters are collected, and a unified feature identifier is established; by introducing environment disturbance parameters such as temperature fluctuation, dissolved oxygen change and pollutant concentration and combining with a hatching rate index for correction, a dynamic response diagram under a real working condition is generated, and the robustness of a prediction model in a complex environment is improved; by constructing a multi-modal prediction strategy and a priority weighted multi-factor collaborative prediction mechanism, efficient collaboration and conflict arbitration among environmental factors are realized; and a real-time evaluation signal is generated in combination with sensor feedback, and the deep learning network is continuously updated, so that online optimization and adaptive adjustment of the prediction model are realized. The method can effectively improve the prediction precision of the roe hatching rate, improves the stability, energy efficiency and success rate of the hatching process, and has good agricultural applicability and expansibility.

The present application relates to the technical field of ecological environment, in particular to a water ecological system evaluation system and method based on a water habitat coupling mechanism model. The technical problem is to integrate key ecological mechanisms while maintaining high computational efficiency. The system includes: an input layer that receives measured data related to the water environment of the target water habitat; a configuration layer that selects several indicators from the geographic indicators, hydrological indicators, hydrodynamic indicators and meteorological indicators input by the input layer, and configures multiple different environmental factors; key ecological processes are integrated through selective mechanism coupling, and interspecific competition factors are configured for algae and biological interaction factors are configured for fish; the simulation layer uses a differential evolution algorithm to automatically calibrate and optimize the parameters configured by the configuration layer; obtain the spatial and temporal changes of aquatic plants, algae and fish; the output layer outputs the spatial distribution results of the three species of aquatic plants, algae and fish, and provides suggestions for scenario analysis results and environmental economic balance decision-making.

The invention relates to the technical field of ecological environment monitoring, and discloses a quantitative identification method for the influence of hydropower development impounding on a terrestrial ecosystem, and the method comprises the following steps: S1, laying a plurality of sampling points along the boundary of a reservoir towards the land direction; s2, collecting an ecological response variable and an environmental factor variable of each sampling point and a horizontal distance from the sampling point to a reservoir boundary as water storage influence variables, and constructing the collected data into a data matrix; s3, taking the data matrix as a response variable; s4, introducing an environment factor variable as a control variable based on a sorting analysis result; and S5, performing variance decomposition analysis based on a partial sorting analysis result, and calculating an independent explanation variance and a total explanation variance of the water storage influence variable. The land space gradient sampling points are arranged based on the water storage boundary, the spatial propagation characteristics of the water storage influence on the land are collected in a covering mode, and the effects of enhancing the representativeness of the sampling points and ensuring the capture integrity of the ecological gradient are achieved.

The invention discloses a method and a system for evaluating a hysteretic effect of an influence factor in a species growth-suitable area, and the method comprises the steps: obtaining the distribution data of a to-be-researched species, and simulating the habitat condition of the to-be-researched species according to the distribution data, thereby obtaining a habitat distribution diagram; acquiring environmental factor data influencing the growth of the species to be studied, projecting environmental factors into the habitat distribution map, and processing to obtain environmental factor data of the suitable growth area; calculating the explanatory force of the environment factor data of the growth-suitable area to the spatial differentiation of the growth-suitable area of the species to be researched, and screening out key factors according to the explanatory force; and performing causal relationship analysis on each key factor by adopting a dynamic causal inference method GCCM to obtain a causal relationship and strength, and evaluating the hysteresis effect of the key factors according to the causal relationship and strength. According to the method, the problem that the time delay effect of an ecological system on environment change is difficult to depict in the prior art can be solved, and the influence of global climate change and human activities on biocenosis can be evaluated more accurately.

The application provides a soil environmental pollutant risk prediction method and system, which comprises the following steps: determining toxic substances having an influence on plants and animals of a species in a soil environment, and establishing a species sensitivity curve; obtaining environmental factors affecting the cumulative exposure of the toxic substances in an industrial park, and determining main environmental factors affecting the cumulative exposure of the toxic substances based on the environmental factors; based on the main environmental factors, a time series evolution prediction model is established by combining a time series and a multivariate autoregressive model; based on the species sensitivity curve and the cumulative exposure of the toxic substances in the future soil environment predicted by the time series evolution prediction model, a risk prediction model is established by combining a joint probability density model, the risk prediction model is used at least for predicting the probability of the soil environment of the industrial park being polluted by the toxic substances in the future; and the probability prediction value of the soil environment being polluted by the toxic substances in the future is predicted based on the prediction model.

The invention discloses a marine organism diversity knowledge graph construction method. The method specifically comprises the steps that multi-source marine biological diversity generation records and biological classification order element data are cleaned, and terrestrial biological records and terrestrial biological order elements which are possibly mixed in the multi-source marine biological diversity generation records and the biological classification order element data are removed; performing entity extraction and relationship extraction on the cleaned terrestrial biological record to obtain a finally constructed knowledge graph; the quality of key entities stored in the atlas is controlled mainly through multiple methods, and error attributes are corrected or abandoned; other auxiliary data sources are introduced, and information such as depth and environmental factors is complemented for sampling site entities in the atlas. The invention provides a set of full-process method for integrating multi-source heterogeneous marine biological diversity data and related environmental factor data through a knowledge graph, and semantic segmentation and rapid fusion of marine biological data can be realized; meanwhile, by means of the flexible structural characteristics of the knowledge graph, life science related data such as genes and proteins can be easily expanded on the basis of the graph, and the expansibility is extremely high.

The invention relates to the technical field of lobster breeding, and discloses a method for increasing the survival rate of lobster larvae in the floating period. The method comprises the following steps: dividing a larva density monitoring area according to physical structure parameters of a culture pond, and continuously collecting initial water environment parameters to obtain water environment time sequence data; on the basis of the time sequence data, key water quality fluctuation nodes are marked by adopting a dynamic time axis generation technology, and a water quality change map with time marks is obtained; performing multi-dimensional environmental factor mapping processing by combining the spatial distribution information of the larva density monitoring area and the water quality change map to generate a larva living environment pressure matrix; introducing an adaptive learning mechanism, carrying out pattern recognition processing on a dissolved oxygen fluctuation section and an ammonia nitrogen accumulation point in the pressure matrix, and establishing an environmental stress response rule base; and dynamically analyzing the operation instruction of the breeding equipment by using the rule base, and generating a feeding path optimization strategy and an oxygenation intensity adjustment strategy.

The utility model discloses a robinia pseudoacacia volumetric water content and environmental factor acquisition device. The device comprises an adjustable mounting rod; the environment factor acquisition part is arranged on the adjustable mounting rod and is used for acquiring environment factor data in real time; the plurality of robinia pseudoacacia data acquisition parts are used for respectively acquiring robinia pseudoacacia trunk data of the plurality of robinia pseudoacacia; and the electric appliance mechanism is arranged on the adjustable mounting rod, is electrically connected with the environment factor acquisition part and the robinia pseudoacacia data acquisition part, and is used for controlling the environment factor acquisition part and the robinia pseudoacacia data acquisition part to work and sending the acquired environment factor data and robinia pseudoacacia trunk data to an external monitoring terminal for processing and analysis. According to the utility model, the environmental factor data is acquired in real time through the environmental factor acquisition part, and the robinia pseudoacacia trunk data is acquired in real time through the robinia pseudoacacia data acquisition part and is sent to the external monitoring terminal for processing and analysis, so that the relationship between the volume water content of the robinia pseudoacacia and the environmental factors can be obtained; and real-time monitoring and continuous recording can be realized while the efficiency is high.

The application discloses a fruit tree load intelligent decision-making method based on pollen tube behavior and carbon accumulation balance, and belongs to the field of fruit tree growth management. The method collects physiological data such as fruit tree pollen tube growth and leaf photosynthesis in real time through multiple sensors, combines environmental factors and carbon balance parameters, constructs a multi-source data fusion model, and realizes fine perception and dynamic optimization and regulation and control of fruit load under different growth states of fruit trees. The system introduces an abnormal data detection and self-adaptive evolution mechanism to improve the adaptability and robustness of the model to complex field environments and various varieties. The application can effectively improve the yield and quality of fruit trees, optimize the utilization efficiency of carbon resources, and promote the intelligent and fine management of orchards.