430 results about "Farming environment" patented technology

The invention provides an agricultural environment monitoring method and system based on the Internet of Things. According to the method, multi-source data weights are dynamically distributed through a soil sensor array connected with Internet of Things nodes, and a global soil parameter set is generated; deploying a micro-electro-mechanical micro-fluidic chip in the coverage area to carry out soil solution selective permeation, converting the target ion concentration into an electric signal, and synchronously transmitting the electric signal and soil parameters; historical time series data are extracted, coherence is established through time correlation analysis, and undeployed area data are filled in combination with a spatial interpolation algorithm to construct a dynamic prediction model; according to the nutrient space change trend output by the model, crop growth requirements are matched to generate a fertilization amount adjustment instruction, and the fertilization amount adjustment instruction is issued to field fertilization equipment through the Internet of Things to execute dynamic regulation. Space-time precise sensing of soil nutrients and self-adaptive fertilization regulation and control are realized, and the utilization efficiency of agricultural resources and crop growth sustainability are improved.

The invention provides an intelligent agricultural environment monitoring system based on the Internet of Things, and relates to the technical field of data processing. The feature extraction and analysis module is used for performing segmented statistical analysis on the soil humidity original data according to the soil humidity original data in combination with historical soil humidity data, historical environment temperature data and preset near-period soil humidity feedback data, and automatically extracting a humidity change rate and an abnormal trend index data set under multiple time windows; the self-learning judgment module is used for dynamically adjusting the current judgment threshold value based on a self-learning algorithm, judging the soil humidity original data in combination with the humidity change rate and the abnormal trend index, and generating a judgment signal for judging whether local irrigation response is triggered or not; the response control module is used for automatically generating an irrigation equipment starting instruction; the irrigation execution module is used for executing irrigation operation according to the irrigation equipment starting instruction; according to the invention, the accuracy of agricultural environment monitoring is improved.

The invention relates to the technical field of agricultural environment intelligent regulation and control, in particular to an agricultural environment intelligent regulation and control method and system based on a cloud platform, and the method comprises the steps: collecting farmland environment parameters and crop image data through a multi-source sensor, filtering the farmland environment parameters and crop image data through an edge computing node, and transmitting the filtered farmland environment parameters and crop image data to the cloud platform; preprocessing the farmland environment parameters and the crop image data, and extracting time domain and space domain features; an agricultural environment state index prediction model is constructed based on a multi-head self-attention encoder, training of the agricultural environment state index prediction model is completed through historical data, the agricultural environment state index prediction model is deployed as a cloud service API, farmland environment parameters and crop image data input in real time are processed online, a control instruction is generated according to a prediction result, and the control instruction is sent to the cloud service API. Accurate and intelligent regulation and control of the agricultural environment are completed through Internet of Things protocol issuing equipment. Through dynamic feature modeling and multi-index collaborative optimization, the precision of agricultural environment regulation and control and the resource utilization efficiency are remarkably improved.

The invention discloses a microbiological culture scheme customization recommendation method and system based on soil data analysis, and relates to the technical field of microbiological culture plants.The method comprises the steps that soil samples at different positions and depths are collected, soil characteristic data are analyzed, and data processing is carried out; microbial community information is obtained through soil DNA extraction, PCR amplification and high-throughput sequencing, and distribution and functions of microorganisms are evaluated in combination with data preprocessing and diversity analysis; selecting proper microbial strains according to soil characteristics and microbial community analysis results; a microbial culture scheme is combined with agricultural management measures; the conditions of soil and microorganisms are monitored in real time, and a microorganism culture scheme is dynamically adjusted based on a feedback mechanism; through field trials and advanced performance evaluation indexes, the effect of the microbial culture scheme is verified for a long time. The method has high adaptability and sustainability, and customized microorganism management schemes can be provided in different agricultural environments.

The invention provides a crop planting area intelligent extraction method and system based on multispectral remote sensing, and relates to the field of remote sensing image processing, and the method comprises the steps: obtaining and correcting a multiband remote sensing image; calculating a vegetation index and constructing a crop growth characterization index to extract canopy features; obtaining a multi-period feature map, calculating a spatial distribution entropy, and establishing an evaluation function to determine a time sequence fusion weight; a spatial constraint function is established based on the spectral distance, and a segmentation criterion is constructed by combining the spatial constraint function with time sequence features for classification iteration. According to the method, the accuracy and the discrimination degree of crop planting area extraction are improved, and crop identification requirements in a complex agricultural environment are met.

The invention relates to the technical field of agricultural environment monitoring, and discloses a cultivated land quality monitoring method. The method comprises the following steps: acquiring soil multi-parameter data, and carrying out sensor network deployment and anti-interference processing; performing exception filtering, standardization and missing value interpolation on the data; cloud storage data are generated through sliding window segmentation and encryption compression; generating a monitoring report based on the multi-dimensional analysis and the dynamic chart; adopting a plurality of intelligent algorithms to identify abnormal modes and constructing an early warning strategy library; and finally, real-time monitoring and early warning grade division are realized. According to the invention, full-process automatic monitoring and intelligent early warning of the cultivated land quality are realized, and the monitoring precision and the decision-making efficiency are obviously improved.

The invention relates to the field of intelligent agriculture and intelligent construction, and discloses an agricultural monitoring system and method based on an intelligent helmet, and the system comprises a wearable terminal, an active sampling control module, an image recognition processing module, a multi-modal fusion module, a recognition reasoning module, and a result output module. The method comprises the steps of collecting a field crop image and environment data; calculating an uncertainty index of an identification result according to the current image; if the uncertainty is high, adjusting the acquisition angle of the camera and re-sampling; constructing the image and the agricultural environment data into a multi-modal graph structure and carrying out graph neural network processing; performing dynamic trend identification on a fusion result, and outputting a crop state; and outputting an identification result through a helmet display or linking an agricultural control system to execute related operations. According to the invention, the capabilities of identification display, voice communication, operation guidance and system integration in a complex environment are improved, and the problems of poor real-time performance and weak interaction of an existing scheme are solved.

The invention discloses a robot path planning method based on deep reinforcement learning, and relates to the fields of intelligent agriculture, path planning, robots and the like. The method comprises the following steps: firstly, sensing a farm environment, defining a state space and an action space of a robot, and setting a multi-modal weighted combined reward mechanism and an experience playback buffer area; a learnable weight coefficient is introduced into a Critic network loss function in a traditional DDPG algorithm, an entropy regularization item is added into a target function of an Actor network, then a differential game is selected through an adaptive attenuation # imgabs0 # greedy strategy to generate a control strategy or a DDPG algorithm to generate an action, finally the action or the control strategy is executed, network parameters and target network parameters are updated, and a target network is obtained. And dynamically updating the experience playback buffer area. Compared with other path planning methods, the method improves the adaptability of path planning to a dynamic environment, and also has good efficiency and safety in a complex agricultural environment.

The invention discloses an intelligent fertilization and irrigation regulation and control device and method based on deep learning. The method comprises the following steps: S1, constructing an agricultural environment original data set; s2, preprocessing the original data set to generate an agricultural data set with consistent time and space; s3, constructing a multi-layer neuromorphic calculation model which comprises a photosynthetic layer, a root system layer and a transpiration layer; s4, performing correlation analysis based on the photosynthesis dynamic curve, the root system water and fertilizer absorption curve and the transpiration-moisture supply curve, and constructing a photosynthesis-root system-transpiration synergistic curve; s5, on the basis of the photosynthesis-root system-transpiration synergistic curve, calculating a promoting factor of photosynthesis on root system absorption and a regulating factor of root system absorption on transpiration intensity, and constructing a self-adaptive regulation and control function; and S6, carrying out time sequence analysis on the self-adaptive regulation and control function, and formulating a final fertilization and irrigation regulation and control scheme. According to the method, a multi-layer neuromorphic calculation method is adopted, the photosynthesis-root system-transpiration cooperative relation is accurately modeled, and intelligent fertilization and irrigation regulation and control are achieved.

The invention discloses an agricultural environment monitoring method and system based on digital agriculture. The method comprises the steps that S1, original environment data signals are collected through a heterogeneous sensor network deployed in a farmland area; s2, performing space-time fusion processing on the original environment data signal to generate a standardized environment state signal; s3, inputting the standardized environment state signal into a pre-trained multi-mode diagnosis model to generate an environment-crop coupling diagnosis signal; s4, according to the environment-crop coupling diagnosis signal, an agricultural operation decision signal is generated, and the agricultural operation decision signal comprises quantitative parameters of the irrigation amount, the fertilization formula or the disaster prevention and control instruction; and S5, converting the agricultural operation decision signal into an equipment control instruction signal, and driving an execution mechanism to complete precise agricultural operation. According to the agricultural environment monitoring method and system based on digital agriculture, the problems of data splitting, extensive decision making and execution lagging in agricultural environment monitoring can be solved.

The invention discloses an agricultural environment intelligent control system based on artificial intelligence and a control method thereof, and belongs to the technical field of agricultural intelligent control and artificial intelligence application. According to the method, agricultural sensor data and image data are jointly packaged into a standardized AI input structure by adopting prompt engineering and semantic modeling means; a general large language model or an image recognition model is accessed, and comprehensive evaluation of complex crop states is realized through customizing a Prompt semantic structure; constructing a natural language analysis engine, and converting the control suggestion returned by the AI into a structured command; the Agent module is used for completing control opportunity judgment, control path planning and effect feedback adjustment, and strategic intervention on the whole agricultural control process is achieved.

The invention relates to the technical field of agricultural management, in particular to a smart farm environment regulation and control method and system based on big data analysis, and the method comprises the following steps: obtaining an instantaneous temperature value of the upper surface of a leaf and an instantaneous temperature value of the lower surface of the leaf through an infrared temperature sensor, obtaining an instantaneous value of stomatal conductance through a stomatal conductance sensor; and acquiring a water vapor pressure difference value of the air cavity through a water vapor pressure sensor, aligning a time sequence by adopting a multi-source data fusion algorithm, eliminating noise, and generating a dynamic physiological data set. According to the method, the leaf temperature difference, the air hole conductance and the air cavity water vapor pressure difference are collected, a real-time dynamic data set is constructed, a temperature difference amplification signal is extracted, transpiration enhancement triggering, wind speed increasing, water mist particle size decreasing and wind speed and particle size linkage regulation and control are analyzed in combination with the trend, a regulation and control weight is constructed by introducing multiple regression analysis, and the wind speed and the particle size are dynamically regulated according to the weight. A leaf temperature difference regression base line is guaranteed, the regulation and control accuracy and physiological adaptability are improved, and the environment self-adaptive regulation capability is enhanced.

The invention provides an agricultural automation linkage control method and system based on artificial intelligence. Belongs to the technical field of agricultural automation control. The method comprises the following steps: acquiring agricultural environment data and agricultural automation equipment data, and preprocessing the acquired agricultural environment data; performing feature extraction on the preprocessed agricultural environment data, and constructing an agricultural environment feature vector; and performing agricultural environment state analysis according to the agricultural environment feature vector to obtain an agricultural environment state evaluation result. Through preliminary control simulation, reinforcement learning algorithm optimization control strategy and linkage control simulation, and continuous iteration optimization of equipment control strategy, the control strategy better meets the actual production requirement, the accuracy and effectiveness of agricultural automation equipment control are improved, manual intervention is reduced, and the production cost is reduced.

The invention belongs to the technical field of livestock and poultry breeding environment intelligent control, and particularly relates to a meat duck breeding environment temperature regulation and control method and system integrating quantile regression prediction and reinforcement learning. The method comprises the following steps: collecting multi-dimensional data of a breeding environment and operation state information of environment regulation and control equipment, and performing feature construction to obtain a feature vector; using the multi-source data set to predict in-house temperature based on quantile regression to obtain predicted temperatures under different quantiles; modeling a breeding environment temperature regulation problem into a Markov decision process, and dynamically adjusting fan power and a wet curtain equipment state; and setting an online updating model step threshold, and performing actual deployment and application on the reinforcement learning decision model obtained by training and fusing quantile regression information. According to the method, the problems that the meat duck breeding environment temperature regulation and control technology still faces insufficient predictability, regulation and control lag, control strategy static performance, multi-target optimization deficiency and the like are solved.

The invention discloses an intelligent agricultural plant protection operation control system based on a big data Internet of Things, and the system comprises a sensing layer which is an agricultural environment parameter collection module which collects the environment parameters of a farmland in combination with a fine-grained expert splitting technology; the crop growth state monitoring module is used for monitoring the growth state of crops in real time; a network layer: an Internet of Things communication module which is responsible for data transmission between the sensing layer and the platform layer; the platform layer comprises a big data processing and analysis module which is used for cleaning, integrating, analyzing and mining data acquired by the sensing layer in combination with a hybrid expert model in a MoE architecture; the intelligent decision support module is used for constructing a dynamic decision optimization model and dynamically adjusting a decision scheme; the plant protection operation management module is used for designing a hybrid solver of a space-time heuristic rule and integer programming, generating a preliminary operation plan, and then adjusting and optimizing the preliminary operation plan in real time according to real-time data in combination with a dynamic decision optimization model and an expert model in the MoE architecture.

The invention belongs to the technical field of farm environment regulation and control, and discloses a farm environment regulation and control system and method, and the system comprises a multi-source sensing module, a data analysis module, an intelligent decision module, and a regulation and control execution module. According to the method, the dynamic environment coupling model is constructed, the environment state is comprehensively evaluated in combination with the multi-source sensing data, and the internal conflicts and cooperative relationships among the regulation and control parameters are accurately identified, so that the cooperative regulation and control instruction set considering multiple targets is formulated. Therefore, the problem of linkage environment imbalance caused by single regulation and control in traditional control is avoided, and the overall stability and comprehensive comfort of the breeding environment are remarkably improved. According to the invention, animal behavior perception is taken as a core feedback and feedforward signal, and conversion of a regulation strategy from passive response to active prediction is realized. The system can pre-judge the change of a local environment in advance according to the moving trend of animals and perform fine adjustment, so that stress events such as ammonia peak value or violent fluctuation of temperature and humidity are effectively avoided.

The invention discloses an intelligent agricultural environment monitoring method and a light supplement adjusting system, and particularly relates to the technical field of agricultural monitoring. An edge computing device is utilized to extract photosynthesis efficiency irregular features and illumination intensity change rate fluctuation features in a data processing module, and a delay degree evaluation module evaluates the delay abnormal degree between real-time environment data and a light supplement adjustment decision. The light supplementing control module is used for deeply analyzing delay data in a fixed time period and dynamically adjusting the illumination intensity and spectral distribution of a light supplementing lamp, so that the problem of insufficient response caused by high delay and single parameter analysis in a traditional method is effectively solved; the accuracy and the real-time performance of agricultural environment monitoring are remarkably improved, the crop growth condition is optimized, and therefore the agricultural production efficiency and the crop quality are improved.

The invention discloses an animal epidemic prevention processing method and system based on a block chain, and the method comprises the steps: building a farm basic database, formulating an epidemic prevention time table in combination with animal variety characteristics and geographic position climate conditions, and determining epidemic prevention time nodes of a farm; the method comprises the following steps: acquiring farm environment data, animal health data and epidemic prevention operation records in real time through Internet of Things equipment, and generating an epidemic prevention execution data stream; comparing the epidemic prevention execution data stream with a preset epidemic prevention measure execution list, and judging the implementation condition of epidemic prevention measures by setting a threshold value and a rule; according to the epidemic prevention execution data flow and the implementation condition of the epidemic prevention measures, a random forest algorithm is adopted to analyze the epidemic prevention effect, and an epidemic prevention effect evaluation report is generated; and an epidemic prevention effect evaluation report and epidemic situation early warning information are encrypted, stored and shared through a block chain, and data support is provided for epidemic prevention decision making. According to the invention, intelligence, precision and traceability of epidemic prevention management of the farm are realized.

The invention relates to the technical field of intelligent agricultural data auditing, in particular to a non-corresponding auditing system for data sharing of the Internet of Things, which comprises a data acquisition association unit, a multi-modal data fusion unit, a self-adaptive calibration unit and a calibration evaluation feedback unit. The multi-modal data fusion unit uses a convolutional neural network to extract features and obtains accurate fusion feature vectors through a multi-modal feature fusion model and multiple optimization technologies and deep fusion data, and the self-adaptive calibration unit dynamically adjusts calibration model parameters by means of a reinforcement learning algorithm and a deep Q network in combination with historical data so as to obtain a multi-modal feature fusion model. And the calibration evaluation feedback unit evaluates the calibration effect, feeds back optimization if the expectation is not reached, and provides decision support based on the calibrated data, thereby effectively solving the problem of data accuracy caused by the complex agricultural environment, and improving the reliability of data auditing.

The invention relates to the technical field of agricultural environment intelligent monitoring, and discloses a cultivated land crop adaptability evaluation and planting suggestion system, which comprises a plurality of distributed microenvironment decision-making units, each unit acquires soil humidity and illumination data in real time through an environment sensor, and slope information acquired by a tilt angle sensor is combined to determine the adaptability of cultivated land crops. The local processor calculates entropy values representing crop stress risks based on a dynamic weight adjustment algorithm, and entropy value interaction and collaborative decision-making among units are realized through a low-power-consumption communication network. Therefore, the system can autonomously identify the local hydrological stress risk, forms a cooperative response network based on the physical law between units, and achieves the precise perception and intelligent decision-making of a complex farmland environment.

An internet-of-things communications network deployed within an environment is disclosed. The system may be used to analyze emissions related data within an agricultural environment and provide a calculation regarding a monitored environment's carbon emissions. The system has a communications network allowing for the transmission and reception of information. The network may include a plurality of devices capable of obtaining information related to agricultural actions, an access point, and a remote database. The communications networks may deploy a modified Wi-Fi HaLow communications protocol. Further, a data analysis module may be implemented to analyze and report information related to the monitored environment. The data analysis module may be utilized in partitions to optimize processing of the system where some execution of the data analysis module may occur at the access point and other execution occurs at the remote database.

The invention discloses a facility vegetable tomato leaf miner pest comprehensive prevention and control system, and relates to the technical field of facility agriculture pest prevention and control, and the system comprises a space distribution obtaining module which obtains the space distribution data of a pest invasion path in a facility agriculture environment, and determines the protection level demand intensity of different regions; the protection parameter calculation module is used for calculating specification parameters and installation density of insect prevention grids in all areas according to the protection level requirement strength, and if the pest density of the peripheral area exceeds a preset threshold value, the aperture of the insect prevention grids is adjusted to the minimum specification, and a first-layer electric control isolation net protection barrier configuration scheme is obtained; the comprehensive control system for the greenhouse vegetable tomato leaf miner pests can effectively deal with the complexity and variability of pest invasion, and the pest control effect and sustainability of the greenhouse agricultural environment are remarkably improved.

The invention relates to the technical field of soil moisture content monitoring, and discloses a method and a system for accurately monitoring soil moisture content through remote sensing inversion of an unmanned aerial vehicle. Acquiring original spectral data of different soil types and environmental conditions through a spectral sensor to obtain an initial multispectral image data set; dividing soil types by using a support vector machine classification method according to pixel reflection characteristics to form a soil type grouping set; calculating a multispectral band reflectivity variance for each group, and screening candidate bands to construct a set; processing the candidate wave band set through a random forest regression model, adjusting the weight in combination with an environment variable, and performing iterative screening to form an optimized wave band combination sequence; extracting spectral features of the optimized wave band, and carrying out weighted fusion on the soil variation region to output a processed image; and calculating the soil moisture content by using a linear inversion model based on the pixel reflectivity of the processed image, and outputting a distribution diagram when the deviation reaches the standard. The method is suitable for different soil and environments, the wave band is accurately selected, the precision is improved, and high-quality data is provided for agricultural and environmental governance.

The invention discloses a ventilation method and device based on precise monitoring of the environment in a farm and a storage medium, relates to the technical field of analysis control, and constructs a three-dimensional pollution concentration field model by collecting data such as the pollution gas concentration, suspended particles, temperature and humidity, airflow velocity and the like of a manure ditch in real time and combining the spatial layout of a breeding house. On the basis of the model and ventilation constraints, a virtual ventilation strategy composed of multiple sets of air supply speeds, air directions and air opening degrees is automatically generated, simulation is conducted, disturbance scores are calculated, and the optimal strategy is selected to be applied to ventilation control. Environmental parameters are continuously monitored in the ventilation process, if it is detected that pollutants rise or temperature and humidity are abnormal, the strategy generation and screening process is automatically restarted, and dynamic closed-loop optimization adjustment of a ventilation scheme is achieved; dynamic ventilation control can be carried out according to the actual manure environment, the influence of gas backflow and pollution disturbance is reduced, and the influence on the farm environment is reduced; the influence on the growth health of livestock and poultry is reduced.

The invention relates to the technical field of agricultural environmental protection and groundwater pollution remediation, in particular to an agricultural non-point source groundwater pollution remediation technology screening system and method, and the system comprises a barrier technology screening module, a degradation technology screening module, a removal technology screening module and a cost accounting module; the method comprises the following steps: quantitatively evaluating the pollutant blocking capability of a blocking technology to farmland infiltration through a soil hydraulics equation, screening an aeration zone efficient degradation technology by utilizing a Fick law and a Monod kinetic model, and verifying the economic feasibility of an aquifer removal technology based on a shadow engineering method and a full life cycle cost model; the method comprises the following steps: determining optimal technologies of farmland infiltration, an aeration zone and an aquifer in sequence through each module, and generating a feasible scheme in combination with cost accounting. The problems that a traditional technology is poor in adaptability and high in cost in an agricultural scene are solved, and full-chain precise screening from a pollution source to a tail end and economic and feasible repairing technology combination are achieved.

The invention provides an intelligent cattle farm feeding management system, which belongs to the technical field of intelligent agriculture and animal husbandry, and comprises a data acquisition module, a data processing module, a control module, a user interface module, a communication module and a storage module. The system obtains cattle farm environment and cattle physiological behavior data in real time through a multi-source sensor and image acquisition equipment, intelligent analysis and decision generation are carried out through algorithms such as machine learning, reinforcement learning and fuzzy logic, and automatic management of functions such as feed feeding, environment regulation and control and health early warning is achieved. The system has the characteristics of comprehensive perception, intelligent decision making and accurate execution, the cattle farm feeding efficiency can be effectively improved, cattle health is guaranteed, and the operation cost is reduced.

The invention discloses an animal husbandry environment monitoring and early warning system based on a big data platform, and relates to the technical field of intelligent animal husbandry environment monitoring and early warning systems, and the system comprises an acquisition module which acquires farm environment data and animal behavior image data and uploads the data to an edge computing node, a preprocessing module, a data processing module and a data processing module, the edge computing node is used for preprocessing the farm environment data and the animal behavior image data, the fusion module is used for fusing the preprocessed farm environment data and animal behavior image data to form a multi-modal data set, and the prediction module is used for establishing a health and risk prediction model. Obtaining an animal health condition assessment score and a potential risk level score according to the multi-modal data set; according to the method, data from different sources are integrated, so that health and risk assessment is more comprehensive and accurate, the spatial relevance of the data is adjusted by using a geographic weighting function, and the representativeness of the data is further improved.

The invention relates to the field of animal husbandry industry environment regulation and control system optimization, in particular to a lightweight digital twinborn breeding environment monitoring method and system, and the method comprises the following steps: S1, carrying out the construction of a computational fluid dynamics (CFD) model for a breeding farm environment; s2, environment data are collected; s3, comparing model prediction with real-time data, and monitoring a breeding environment according to a comparison result; and S4, data presentation. According to the invention, through cooperative work of the flow field modeling unit, the environment data acquisition unit, the model prediction and data calibration unit and the visualization unit, light-weight, full-range and high-precision breeding environment monitoring and regulation are realized.

The invention provides lighting system for illuminating a target surface in an agricultural environment for a flock of birds, wherein the lighting system comprises a plurality of lighting devices spaced apart on an elongated axis, wherein each lighting device comprises: a connector for electrically connecting the lighting device to a power line; a housing comprising a light exit window; at least one solid-state light source arranged within the housing, wherein the at least one solid-state light source is configured to emit light source light; wherein each lighting device is configured to shape the light source light into a light beam, and configured to emit the light beam along a main optical axis via the light exit window onto the target surface; wherein adjacent lighting devices of the plurality of lighting devices are spaced apart with a spacing (S); and wherein the light exit windows of the adjacent lighting devices are arranged at a distance (H) from the target surface; wherein each respective light beam of the adjacent lighting devices has an angular light distribution with a Full Width Half Maximum (FWHM); whereinFWHM<F*2*tan-1(S2⁢H);wherein FWHM is said Full Width Half Maximum (FWHM); wherein S is said spacing (S); wherein H is said distance (H); and wherein F is a factor in the range between 0.1 and 1.

The invention provides a security optimization encryption method, system and device for data in a facility agriculture scene and a medium, and belongs to the technical field of agricultural informatization security, and the method comprises the steps: carrying out the dynamic division of security levels of data collected in a facility agriculture environment according to the data source device type, data content sensitivity and real-time requirements; distributing an encryption algorithm for the collected data according to a mapping relation between the security level and the encryption algorithm; secret keys are generated and managed based on an identity encryption mechanism, and then secret key fragmentation storage is carried out in combination with a trusted execution environment; when encryption is needed, whether the current trusted execution environment is safe or not is judged, and when the current trusted execution environment is safe, secret keys of the fragments are recombined, the multi-source heterogeneous data are encrypted through the distributed encryption algorithm, and encrypted data are generated; and carrying out secure transmission on an adaptive communication protocol after packaging the encrypted data. According to the method, the data security level is dynamically divided, the encryption algorithm is flexibly selected, the resource consumption is reduced, and the real-time performance and security are improved.