18 results about "Environmental variation" patented technology

The invention discloses a green plant flowering phase monitoring method, device and system based on deep learning, and the method comprises the steps: achieving the cross-modal dynamic enhancement of visible light and near-infrared images through a learnable convolution kernel, and generating a vegetation active mask to precisely position a vegetation region; a channel attention mechanism is combined with mask weighted fusion spectrum and texture features, and non-vegetation noise interference is inhibited; the method comprises the following steps: dynamically associating climate parameters and image features through cross-modal cross attention, and capturing non-linear influence of environmental change on the flowering phase; finally, by means of a cascade residual classifier and a one-way time sequence gating network, multi-scale features and time sequence rules are fused, and high-precision and high-robustness green plant flowering phase monitoring in a complex environment is achieved.

The invention discloses a feed additive ratio control method and system based on environmental perception. The method comprises the steps that breeding environment, feed characteristics, equipment operation and animal physiological parameters are collected, and a high-quality parameter set is generated after preprocessing; calculating an environment stress index based on a multi-factor data fusion algorithm and analyzing a change trend; combining historical and current data, using time sequence prediction and a machine learning algorithm to predict additive requirements and generating an optimal proportioning strategy; a control instruction is generated according to the strategy, and the additive conveying and spraying process is regulated and controlled; and a matching strategy and control parameters are corrected in real time through closed-loop feedback, so that accurate regulation and control are realized. The device can dynamically adapt to environmental changes, reduce animal stress, and improve mixing uniformity and nutrition retention effect.

The invention discloses a crop adaptability evaluation model based on a genotype-environment interaction effect and application of the crop adaptability evaluation model, and relates to a biological information and artificial intelligence cross technology. Carrying out data preprocessing, environment mean value calculation and centralization, constructing a linear regression model, obtaining parameters representing adaptability, carrying out model verification and output, and carrying out adaptability grading; the average performance of the material is measured through the intercept, the slope reflects the response degree of the material to the environmental change, and the two jointly determine the environmental adaptability characteristics of the material. The invention provides a robust method for processing noise and missing values in a multi-environment test, establishes a unified adaptability quantitative standard to classify the adaptability of different materials, is beneficial to improving the directionality and accuracy of crop breeding, and accelerates the breeding process of high-yield and stable-yield corn varieties.

The invention discloses a forest tree genotype-environment interaction modeling method based on multi-modal deep learning, and relates to the technical field of forest tree breeding, the method comprises the following specific steps: multi-modal data acquisition: adopting a high-throughput phenotype platform, a whole genome sequencing technology and soil nutrient detection equipment to acquire multi-modal data; sNP locus genotype data, soil key physicochemical index environmental data and growth-related morphology and biomass parameter phenotype data of forest trees are collected respectively; forest genotype, environment and phenotype multi-modal data are collected through the system, the genotype-environment interaction algorithm and the phenotype prediction model are constructed after preprocessing and fusion, the model can accurately predict forest phenotypes, the breeding screening period is remarkably shortened, the breeding selection precision and efficiency are greatly improved, and the method is suitable for large-scale popularization and application. The method effectively solves the problem that a traditional breeding mode is short in time and efficiency, enables breeding work to respond to market demands and environmental changes more quickly and accurately, and provides powerful support for sustainable development of the forestry industry.

The invention provides an intelligent premixing control method and device based on machine learning, equipment and a medium, and relates to the technical field of machine learning, and the method comprises the steps: constructing a machine learning model obtained through training based on historical environment control data, plant growth data and environment disturbance response data, and embedding the machine learning model into a control system, intelligent preliminary adjustment and closed-loop feedback control of the environment of the premixing box are realized; after the environmental parameters are stable, stable gas is conveyed to the plant box body through the air duct, and dynamic compensation is conducted on environmental changes in the conveying path based on the model; a sensor is deployed in the plant box body to monitor disturbance data and input the disturbance data to the disturbance adaptation sub-module, the disturbance trend is predicted, and feed-forward compensation data is output; when the environment in the plant box body is abnormal, the system automatically exhausts air and supplements stable gas, meanwhile, the control strategy is dynamically adjusted, and a whole-process self-adaptive regulation and control mechanism is constructed. According to the invention, self-adaptive pre-regulation and control of the breeding environment can be realized.

The invention provides an intelligent regulation and control system and method for a breeding environment of epimedium seedlings, and the method comprises the steps: obtaining the growth environment parameters of a seedling breeding environment, obtaining a plurality of groups of growth environment parameters, and obtaining an environment effective data set through the determined parameter fluctuation abnormal indexes of all groups of growth environment parameters; determining an environment change trend index of each group of environment effective parameters in the environment effective data set according to the reference dynamic regulation and control threshold, and obtaining an environment comprehensive regulation and control urgency degree according to the environment change trend indexes of all groups of environment effective parameters; determining a self-adaptive regulation and control trigger threshold value through the environment comprehensive regulation and control urgency degree; determining an environment stability index of the environment effective data set according to the determined environment reference value of the environment effective data set; the seedling breeding environment is intelligently regulated and controlled according to the self-adaptive regulation and control trigger threshold value and the environment stability index, an environment regulation and control execution scheme of the seedling breeding environment is obtained, and the seedling breeding environment intelligent regulation and control efficiency is effectively improved when the seedling breeding environment is intelligently regulated and controlled.

The present invention discloses a method for monitoring the changes in the ecological environment of urban plains based on the investigation of small mammals. First, after determining the pellet collection points of long-eared owls, the proportion of forest, shrub, grassland, and cultivated land coverage areas, biomass, and vascular plant species richness within the predation range area are obtained; then, based on the pellets of long-eared owls, the parameters for measuring the biodiversity index are calculated: the species richness of prey animals, the appearance frequency of prey animals in the pellets, the predation intensity of prey animals, and the biomass proportion of prey animals; finally, based on the above indicators, the comprehensive index of the ecological environment quality in the urban plain area is calculated to determine the changes in the ecological environment of the urban plain area. The use of this method solves the problem of the inability to long-term observe the biodiversity of small mammals, and provides technical support for long-term observing small mammals with good indicator functions to judge the changes in the comprehensive quality of the urban ecological environment.

The application discloses a green plant flowering period monitoring method, device and system based on deep learning, cross-modal dynamic enhancement of visible light and near-infrared images is realized through a learnable convolution kernel, a vegetation activity mask is generated to accurately locate a vegetation area, channel attention mechanism is combined with the mask to weight and fuse spectral and texture features, and non-vegetation noise interference is inhibited, cross-modal cross-attention is used to dynamically associate climate parameters and image features, nonlinear influence of environmental changes on the flowering period is captured, finally, a cascaded residual classifier and a one-way time sequence gating network are used to fuse multi-scale features and time sequence rules, and high-precision and strong-robust green plant flowering period monitoring in a complex environment is realized.

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 invention relates to the technical field of ecological restoration, in particular to a photovoltaic plant vegetation restoration intelligent management system, which comprises a data acquisition module, an environment sensing module, a vegetation growth monitoring module, a restoration evaluation module, an intelligent optimization control module and a decision support module, and is characterized in that multi-source data is acquired in real time through a sensor network deployed in a photovoltaic plant; big data analysis and a machine learning technology are combined, the vegetation restoration effect is accurately evaluated, a management strategy in the restoration process is optimized, measures such as irrigation, fertilization and weeding of a photovoltaic plant area are automatically adjusted so as to improve the vegetation restoration efficiency and effect, and by dynamically adjusting a restoration scheme, optimizing resource allocation and maintaining ecological balance, the vegetation restoration efficiency and effect are improved. According to the method, environmental changes can be effectively coped with, refined and sustainable vegetation restoration management is achieved, the ecological restoration efficiency of the photovoltaic plant area can be improved, technical support can be provided for other similar ecological restoration projects, and the method has wide application prospects.

The application discloses a kind of automatic cultivation equipment management method and system of clam, by obtaining historical clam cultivation environmental change data and clam behavior data, using association rule mining method to identify the influence characteristics of environment on clam behavior.Based on influence characteristics, assess the growth suitability of clam under different environmental parameters.At the same time, through camera equipment monitoring target clam cultivation equipment of clam behavior, capture its behavior state.Combined with influence characteristics and clam behavior state, assess the growth suitability of clam under current environment, generate growth suitability assessment data.Finally, according to the evaluation data, adjust the operating parameters of clam automatic cultivation equipment, realize the intelligent management of clam cultivation environment.This method optimizes the efficiency of clam cultivation by making full use of environmental data and behavior monitoring, improves the growth suitability, and provides an innovative management means for clam cultivation industry.

The invention relates to the technical field of agricultural production management, in particular to a crop growth environment and insect pest induction relation analysis method and system.The method comprises the following steps that air temperature, air humidity, rainfall, illumination, soil water content and insect pest situation records are collected, time is collected in a unified mode, and environment and insect pest situation observation information is aligned; analyzing plant height and leaf and accumulated temperature change, dividing growth stages, aligning environmental change fragments before and after the stages, classifying environmental change and pest occurrence sequences in the growth stages, and forming an environment-induced pest trigger chain record set. According to the method, growth stage sections are divided according to the crop growth state change process, growth stage switching and pest situation change are closely aligned in the time sequence, pest occurrence is analyzed in the definite growth stage and environment change background, and environment condition change is no longer presented in an isolated interval; a clear and coherent analysis basis is provided for analysis of the crop growth environment and insect pest induction relation in agricultural production management.

The invention provides a garden plant irrigation method and system, and the method comprises the steps: extracting the sensitivity characteristics of a garden plant in each sub-region to environment changes at different growth stages through historical growth data and microenvironment data, and determining the growth response coefficients of the garden plant in each sub-region at different growth stages based on each sensitivity characteristic; determining the humidity-growth dynamic adaptation degree of each sub-region according to the correlation characteristic between the soil humidity and the plant growth index in each piece of monitoring data, and calculating the irrigation demand difference value of the garden plants in different sub-regions in the current microenvironment based on the humidity-growth dynamic adaptation degree of each sub-region; according to each irrigation demand difference value and each growth response coefficient, determining partition irrigation regulation and control quantity of different sub-regions in the garden region; and performing self-adaptive irrigation regulation and control on each subarea according to the irrigation regulation and control quantity of each subarea. By adopting the scheme, the zoned and differentiated irrigation regulation and control of the garden plants in different growth stages can be realized based on the growth suitability of the garden plants.

The present invention discloses a method and system for environmental monitoring and phenological correlation processing based on GEE. By obtaining environmental factors of the area to be studied, using Landsat 8 remote sensing data on the GEE platform for environmental monitoring according to the environmental factors to obtain spatio-temporal maps of the environmental factors, determining the temporal soil-adjusted vegetation phenology index from the maximum vegetation index and the minimum vegetation index, taking the temporal soil-adjusted vegetation phenology index as the phenological measurement value for phenological correlation analysis, analyzing the phenological correlation between the phenological measurement value and the index value corresponding to the surface environmental variables based on the support vector regression algorithm, using remote sensing data in GEE to monitor environmental changes and correlate them with phenological data, it is possible to identify the peaks and valleys of each parameter in the environmental factors, revealing a significant correlation between TSPI and environmental factors, improving the efficiency of diverse analysis and processing of environmental monitoring and phenological correlation, and enhancing the accuracy of phenological correlation assessment.

The invention provides a river ecological evaluation method and device based on screened periphytic diatom multi-parameter indexes, and relates to the technical field of ecological evaluation. The method comprises the following steps: acquiring periphytic diatom data and response parameters of each diatom to environmental change, wherein the response parameters comprise species parameters and functional parameters; then different river periphytic diatom multi-parameter indexes are constructed based on the response parameters; then, based on the constructed river periphytic diatom multi-parameter index, an MMI score is calculated; and evaluating the ecological condition of the river according to the MMI score. The method further comprises the step of evaluating the advantages and disadvantages of different MMI indexes from the five aspects of accuracy, deviation degree and the like so as to select the most suitable MMI index. According to the method, the environmental change response sensitivity can be improved through the functional characters, meanwhile, multi-dimensional evaluation is achieved in combination with the species parameters and the functional parameters, natural variable interference is reduced, and the scientificity and accuracy of river ecological evaluation are remarkably improved.

The present application relates to the technical field of agricultural production management, in particular to a crop growth environment and pest-induced relationship analysis method and system, comprising the following steps: collecting air temperature, air humidity, precipitation, light, soil moisture and pest records and collecting time uniformly, aligning environment and pest observation information, analyzing plant height, leaf and accumulated temperature changes to divide growth stages, aligning environment change segments before and after the stages, classifying growth stage environment change and pest occurrence sequence, and forming an environment-induced pest trigger chain record set. In the present application, the growth stage section is divided by the crop growth state change process, the growth stage switching and pest change are closely aligned in time sequence, the occurrence of pests is placed in a clear growth stage and environment change background for analysis, and the environment condition change is no longer presented in an isolated interval, thereby providing a clear and coherent analysis basis for the analysis of the relationship between crop growth environment and pest-induced relationship in agricultural production management.

The invention relates to a typical grassland miscanthus sinensis population dynamic regulation and control method based on precipitation, belongs to the technical field of livestock breeding, and aims to solve the problem that the trampling intensity is not matched with the growth requirement of miscanthus sinensis due to the fact that traditional grazing management cannot respond to the dynamic change of the environment in time and a quantitative regulation and control basis is lacked. According to the technical scheme, the method is characterized in that environmental index data such as soil volume moisture content, precipitation and effective accumulated temperature in a target grassland area are obtained in the grazing season and matched with a pre-established grazing decision query table to obtain an optimal trampling intensity interval, and grazing is executed with the interval median as the initial trampling intensity; and repeating the step of acquiring and matching the environmental indexes according to a preset period in the grazing season, and calibrating and updating the trampling intensity. The method can accurately adapt to environmental changes, dynamically regulate and control the miscanthus longicoides population in the typical grassland, maintain stable growth of the miscanthus longicoides population and balance of a grassland ecosystem, and is suitable for a grazing management scene of the typical grassland.

The present invention is applicable to the field of virtual reality, and provides a method and system for gesture capture based on virtual reality. The system includes: an environmental state and gesture capture module, a gesture state evaluation and analysis module, a virtual reality connection module, and an optimal state analysis module. This solution deeply analyzes the captured gesture data to quickly identify whether there are abnormal gestures in plants, which helps to take timely measures. When an abnormal gesture is recognized, multiple growth environment control groups can be established, and the growth conditions of plants in different environments can be simulated through virtual reality technology to help researchers or agricultural workers understand the impact of environmental changes on plant growth. For plants with normal gestures, the system can also generate predictions of future growth gesture changes, providing a scientific basis for the long-term management of plants.