2784 results about "Greenhouse" patented technology

The invention relates to the technical field of agricultural internet of things and environment intelligent control, in particular to a greenhouse environment adaptive regulation and control system based on artificial intelligence, which comprises an environment acquisition module used for acquiring multi-dimensional environment data in real time through a distributed multi-source sensor; the central controller is used for generating an optimized regulation and control strategy; the regulation and control execution module is used for driving execution equipment to carry out regulation; the central controller comprises a multi-source data fusion unit, an AI decision-making unit and a dynamic optimization engine which are respectively responsible for data filtering and fusion, generating an initial regulation and control strategy based on a space-time joint AI model, and reconstructing and optimizing the initial regulation and control strategy through a multi-target optimization algorithm. According to the method, the response real-time performance is improved through multi-source sensing and data fusion, predictive regulation and control and multi-parameter cooperation are achieved through the AI model, balance of energy consumption, growth and carbon emission is achieved in combination with multi-target optimization, and long-term self-adaption and strategy iteration of the system are supported.

The invention provides a greenhouse environment adaptive regulation and control method and system, and relates to the technical field of environment control, and the method comprises the steps: collecting multi-dimensional environment parameters in a greenhouse; based on the environmental parameters, a preset crop growth period database and weather prediction data, taking minimization of a preset cost function as a target, and adopting a model prediction control algorithm to generate an equipment linkage instruction set in a future preset time period, the preset cost function fusing environmental regulation and control deviation and operation cost; issuing the equipment linkage instruction set to each execution equipment, and executing linkage regulation and control; wherein when the equipment linkage instruction set is generated, a conflict resolution mechanism based on a dynamic priority is adopted to determine an execution sequence of a plurality of equipment instructions; the adaptive regulation and control method integrating multi-source data verification, multi-scale prediction, dynamic priority conflict resolution and multi-target cost optimization improves the accuracy, economy and crop suitability of greenhouse environment regulation and control.

The invention relates to the technical field of agricultural intelligent equipment, and discloses a multi-mode sensing fusion control system for an aerogel greenhouse heat preservation quilt. An environment multi-mode monitoring module of the system collects internal temperature and humidity distribution, illumination intensity gradient, soil moisture content profile and external meteorological change data of a greenhouse in real time to generate an environment comprehensive monitoring data set; based on the data set, the thermal insulation mode decision module analyzes the difference between the thermal energy loss rate and the thermal insulation demand of different areas in combination with the physical characteristic parameters and historical regulation and control records of the aerogel thermal insulation quilt, matches a dynamic thermal insulation strategy and generates a multi-stage partition regulation and control instruction; the cooperative parameter optimization module calculates a combination scheme of the unfolding angle, the coverage area and the thickness of the heat preservation quilt, and adjusts a partition cooperative control parameter set; the multi-mode execution controller generates a heat preservation quilt dynamic regulation and control map; the anomaly fusion diagnosis module monitors anomalies and generates a set of regulation error correction factors. The system realizes fine, dynamic and intelligent regulation and control of greenhouse heat preservation.

The invention belongs to the technical field of intelligent monitoring and control, and relates to an intelligent monitoring and control system for an agricultural greenhouse. According to the method, multi-environment factor data in a greenhouse is collected, regulation and control actions are encoded into vectors and sampled to construct a sample set, after regulation and control are executed, an environment fluctuation convergence state is detected to construct an action-environment mapping relation, and then candidate actions are generated based on an optimal interval of a crop stage; and selecting an optimal regulation and control combination through a prediction model and real-time state scoring. The system solves the problems of dependence on manpower, slow response, low precision, difficulty in multi-device cooperation, difficulty in dynamic adaptation to crop growth stage changes, low stability and energy efficiency and the like in traditional greenhouse environment regulation and control, and can realize rapid and accurate regulation of multiple factors such as temperature, light, water, gas and the like; and the environmental control automation level, the crop growth quality and the resource utilization efficiency are remarkably improved.

The invention relates to the technical field of pepper breeding regulation and control, and discloses a pepper breeding isolation shed control system based on the Internet of Things. The system comprises an environment data acquisition module, a user behavior analysis module, an isolation shed partition module, a dynamic baseline generation module, an abnormal interaction detection module, a root node decision module and a recommendation chain construction module. The environment data acquisition module acquires temperature, humidity, illumination intensity and carbon dioxide concentration data in the greenhouse in real time; the user behavior analysis module analyzes a user historical regulation and control instruction; the isolation shed partition module divides regulation and control sub-regions according to environmental differences; a dynamic baseline generation module generates a dynamic environment parameter baseline for each sub-region; the abnormal interaction detection module identifies abnormal environment fluctuation and instructions; a root node decision module screens a root node area with most significant fluctuation; the recommendation chain construction module constructs a multi-level regulation and control recommendation chain. According to the system, the accuracy and intelligence of environment regulation and control of the isolation shed are improved, and the pepper breeding requirement is met.

The invention provides an intelligent agricultural system and an agricultural greenhouse humidity intelligent control method. The method comprises the following steps: firstly, acquiring a humidity sampling data sequence in a specified monitoring time window before a current regulation and control period in an agricultural greenhouse, and calculating a humidity change trend index; comparing the humidity change trend index with a dynamic trend threshold value, distributing a weight for each humidity sampling point, and performing weighted fusion based on the weights to obtain a weighted fusion result; and then generating a humidity regulation and control reference value of the current regulation and control period based on the weighted fusion result, the current real-time humidity and the external environment parameters. And finally, the deviation between the target humidity of the crop growth model and the current real-time humidity and the humidity regulation and control reference value are input into a controller to generate a dynamic regulation and control instruction, and operation parameters of a humidity execution mechanism are regulated according to the dynamic regulation and control instruction. According to the scheme, the robustness of accurate regulation and control of the humidity of the agricultural greenhouse can be improved.

The technical scheme of the invention discloses a photovoltaic power generation and greenhouse agriculture integrated system. According to the photovoltaic agricultural integrated technology disclosed by the invention, photovoltaic power generation and greenhouse planting are innovatively combined, and a solar panel with adjustable light transmittance of 30%-90% is mounted at the top of an agricultural facility, so that sunlight is utilized for power generation, and illumination required by crop growth is reserved. The system disclosed by the invention is provided with an intelligent sensor and an AI algorithm, environment data is monitored in real time, the angle, light supplementing intensity, temperature and humidity of the photovoltaic panel are dynamically adjusted, and different crop requirements (such as low illumination of leaf vegetables and medium and strong illumination of fruits and vegetables) are accurately matched. Residual electricity is stored or connected to a grid, photovoltaic waste heat is recycled in winter for heat preservation, sun shading and cooling are conducted in summer, and efficient circulation of energy is achieved.

The invention provides a greenhouse water, fertilizer and pesticide optimization control system and method based on environment-crop-input multi-element coupling, and the method comprises the steps: deploying a sensor network, collecting greenhouse data, processing the greenhouse data, generating a space-time continuous data set, and carrying out the optimization control of the greenhouse water, fertilizer and pesticide on the basis of the generated space-time continuous data set. And quantifying a dynamic causal chain of the environment, the crops and the input, generating a decision rule base, fusing the decision rule base with a real-time simulation result of the three-dimensional digital twinborn model of the root zone, and generating a water, pesticide and fertilizer regulation and control instruction. Space-time continuous acquisition and fusion processing of greenhouse environment-crop-input product data are realized through a multi-modal sensor network, a decision rule base containing a dynamic causal chain is constructed, real-time simulation of a root zone three-dimensional digital twinborn model and Bayesian causal reasoning are combined, an accurate water, fertilizer and pesticide regulation and control instruction is generated, and the accuracy of the water, fertilizer and pesticide regulation and control is improved. The nonlinear causal relationship among multiple elements is effectively quantified, and the decision scientificity is improved.

The utility model discloses a greenhouse fruit and vegetable planting and cultivating frame which comprises cultivating frame bodies which are symmetrically arranged, placing plates capable of circularly ascending and descending along the cultivating frame bodies and cultivating pieces movably arranged on the placing plates. A circulating mechanism used for driving the placement plates to circularly lift along the cultivation rack bodies so as to balance illumination and temperature of the seedlings is arranged on each cultivation rack body, and each circulating mechanism comprises a circulating lifting part for lifting the placement plates and an anti-deviation part used for preventing the placement plates from deviating during circulation. When the device is used, the problem that in the prior art, when seedlings are located at relatively fixed positions, the temperature and illumination conditions borne by the seedlings are fixed accordingly, and consequently the growth states of the seedlings at different positions are remarkably different is solved.

The invention provides a crop growth prediction method and system based on multi-modal data, and relates to the technical field of agricultural information, and the method comprises the steps: 1, collecting real-time environment data and crop physiological data of a heterogeneous multi-source sensor in a greenhouse environment, and constructing a multi-modal original data set; 2, performing preprocessing and space-time alignment on the multi-modal original data set to form a multi-modal data fusion matrix with a unified timestamp; step 3, based on the multi-modal data fusion matrix, selecting a reference data feature set, generating an environmental evolution mode and a physiological evolution mode, and calculating a correlation degree between the two modes to determine a feature response dimension; and respectively selecting dynamic monitoring feature sets inside and outside the feature response dimension, constructing a feature evolution trajectory according to a time sequence evolution relationship, and generating a dynamic compensation coefficient. By integrating environment and crop physiology multi-source data, the association rule of crop growth and environment is predicted, and the agricultural production efficiency is improved.

The invention discloses a greenhouse intelligent management system and management method based on crop growth vigor, and belongs to the technical field of intelligent agriculture. The greenhouse intelligent management system comprises a crop growth vigor and environment monitoring system, a control system and matched operation facilities, the crop growth vigor and environment monitoring system can monitor environment information and crop growth vigor conditions of a greenhouse in real time, and meanwhile, the monitoring information is sent to the control system through a communication module; based on a crop growth identification algorithm fusing RGB and hyperspectral images and a corresponding greenhouse intelligent management method, the control system reasonably regulates and controls application parameters of matched operation facilities and dynamically regulates internal environment elements of the greenhouse, so that rapid growth of greenhouse crops is promoted in an intensive mode, and the greenhouse quality is improved. Meanwhile, the labor intensity and the cost are effectively reduced.

The invention relates to the field of intelligent control, and provides a multi-environment data collaborative intelligent regulation and control method and device in a greenhouse. The method comprises the following steps: acquiring predicted values of various environmental data at a future moment according to various environmental data influencing crop growth in a greenhouse at the current moment and meteorological data in a period of time in the future; and generating a first target control strategy of each controllable device in the greenhouse at the current moment according to the optimal values and the predicted values of the various environmental data required by the growth of the crops in the greenhouse at the current moment, and controlling each controllable device according to the first target control strategy. According to the application, the current situation that each factor in current greenhouse control operates independently and is lack of collaboration can be changed, collaborative, accurate and intelligent regulation and control of various environmental data (such as light, temperature, water, gas, fertilizer and the like) in the greenhouse can be realized, an unprecedented stable and balanced growth environment is provided for crops, and the operation energy consumption is remarkably reduced.

The embodiment of the invention discloses a reservoir carbon sink accounting method based on multi-source remote sensing data, a related device and a medium. The method comprises the following steps: acquiring multi-source remote sensing data of a target reservoir in a target time period; according to the multi-source remote sensing data, performing inversion to obtain a water surface area dynamic diagram, a chlorophyll a concentration space-time distribution diagram, a CDOM space-time distribution diagram and a water surface temperature space-time distribution diagram of the target reservoir in the target time period; the four diagrams are input into a preset carbon sequestration model for carbon sequestration processing, a unit area greenhouse gas flux value space-time distribution diagram corresponding to the target reservoir is obtained, and each pixel in the unit area greenhouse gas flux value space-time distribution diagram carries a corresponding unit area greenhouse gas flux value; and performing space-time integral processing on the space-time distribution diagram of the greenhouse gas flux value per unit area and the dynamic diagram of the water surface area to obtain a target total carbon sink amount of the target reservoir in the target time period. By implementing the method provided by the embodiment of the invention, the accuracy of reservoir carbon sink accounting can be improved.

The invention relates to the technical field of intelligent power grids, in particular to an optical storage agricultural micro-grid operation optimization method and system, and the method comprises the steps: constructing an optical storage agricultural greenhouse micro-grid, and constructing a robust optimization scheduling model under the photovoltaic output uncertainty to carry out robust optimization scheduling; based on the scheduling scheme of robust optimization scheduling, constructing a micro-grid load model for the light storage agricultural greenhouse; on the basis of the load demand in the micro-grid load model, a cost-benefit optimization model under two operation modes of autonomous investment construction and energy performance contract is constructed, and the cost-benefit is calculated on the basis of the cost-benefit optimization model under the actual operation mode, so that the energy storage capacity configuration and the operation strategy are subjected to collaborative optimization. And constructing an energy storage system capacity configuration and operation optimization model, and finally determining an energy storage configuration scheme and a multi-time scale scheduling strategy. According to the invention, the problems of photovoltaic output uncertainty, single investment operation mode and insufficient economical efficiency optimization of the existing system are effectively solved.

The invention discloses a greenhouse gas concentration time sequence prediction method based on a sudden change perception attention mechanism. The method comprises the steps of data preprocessing, sudden change intensity sequence construction with boundary processing, time sequence feature coding, sudden change perception attention weight calculation, context vector generation and concentration prediction, model training and optimization and model prediction. The method aims to solve the problem that a standard deep learning model is slow in sensing and lagged in prediction for a sudden change event in a concentration sequence, and finally realizes high-precision prediction for future concentration change, especially a sudden concentration peak value by endowing the model with the capability of actively identifying and reinforcing the learning of a historical sudden change mode. The urgent demand for early warning of abnormal emission in practical application is met. The method is particularly suitable for processing foundation observation data with small resolution and even higher resolution, has the core value of improving the prediction capability of concentration dramatic change driven by sudden emission events, and can be widely applied to key scenes such as accurate carbon emission monitoring, environmental pollution early warning and climate model simulation.

The invention discloses a greenhouse environment dynamic optimization regulation and control system and method based on Internet of Things and big data, and belongs to the technical field of agricultural Internet of Things. The system comprises an Internet of Things sensing layer, a data transmission layer, a big data platform layer, a control execution layer and a user interaction layer. The sensing layer collects environment, crop and equipment data and uploads the environment, crop and equipment data to the big data platform through the transmission layer, the platform generates dynamic regulation and control parameters through a crop model library and an intelligent decision engine (fusing LSTM prediction and particle swarm optimization), the control execution layer drives equipment to execute, and the model is iteratively optimized through feedback data. According to the invention, greenhouse environment multi-factor cooperative regulation and control and crop whole growth cycle dynamic adaptation are realized, the problems of low precision, high energy consumption and poor adaptability of traditional regulation and control are solved, the crop yield and quality can be obviously improved, the energy consumption is reduced, and the method is suitable for various facility agricultural greenhouses.

The invention discloses a light-temperature coupling-oriented eucommia ulmoides seedling environment precise regulation and control method and system, and belongs to the technical field of agricultural facility seedling culture, and the method comprises the following steps: obtaining real-time environment parameters in a greenhouse, and generating an environment data set; establishing a light-temperature coupling knowledge base based on the matching relationship between the illumination intensity and the temperature in different growth stages; according to the real-time illumination intensity value in the environment data set and the light-temperature coupling knowledge base, calculating a currently required compensation temperature value, and generating a temperature regulation and control instruction in combination with an actual temperature value in the environment data set; generating an illumination compensation strategy according to the temperature regulation and control instruction and a seedling growth stage identifier in the environment data set; and executing the temperature regulation and control instruction and the illumination compensation strategy, and outputting a linkage control signal to preset environment regulation and control equipment. According to the method, a light temperature compensation algorithm adaptive to the growth stage and a humidity feedback correction mechanism are adopted, dynamic cooperative regulation and control of the greenhouse environment can be achieved, and the seedling growing quality and the resource utilization efficiency are remarkably improved.

The invention relates to the technical field of environment monitoring and atmospheric component source analysis, in particular to a method and system for quantitatively evaluating greenhouse gas source contribution. The method comprises the following steps: extracting background concentration and calculating abnormal quantity through robust local weighted regression based on station hourly greenhouse gas concentration data; combining the abnormal quantity with meteorological data, calculating a comprehensive event score, performing event layering, and identifying a strong event window; a backward trajectory is calculated for the strong event hours, and normalized spatial weight distribution is generated; according to the distribution, the observation abnormal quantity is segmented into a local contribution part and an external contribution part according to a mass conservation principle; and the source industry influencing the observation concentration can be judged according to the diagnosis ratio of the characteristic species in the sampling data. According to the method, the quantitative and auditing separation of a concentration increment source can be realized under the condition of not depending on a complex model and strong prior, and the precision, the robustness, the interpretability and the cross-site comparability of a traceability result are effectively improved.

The invention discloses a loop heat pipe and water heat storage coupling system and a working method, and belongs to the technical field of heat storage. The system has the modes of light gathering of the light-following trough heat collector, heat conduction of the loop heat pipe, heat storage of the water storage tank and warm water irrigation by using the stored heat water. When sunlight is sufficient in the daytime, the system converts solar energy into sensible heat of water in the heat storage water tank and stores the sensible heat, and the sensible heat is insulated by soil to slow down heat loss; when plants need to be irrigated, warm water in the irrigation water tank is used for irrigation; at night or in the absence of illumination, warm water in the heat storage water tank exchanges heat with air in the greenhouse, so that a lasting and stable heat supply source is provided for the greenhouse. Solar energy collected by the heat collecting groove is efficiently transmitted into the water tank through the loop heat pipe driven by temperature difference to be converted into sensible heat of water to be stored, the sensible heat is used for increasing the greenhouse temperature and irrigating plants when heat is needed at night, and the purposes of efficiently storing heat in the daytime and continuously supplying heat at night are integrally achieved.

A predictive fuzzy PID control method for an intelligent greenhouse includes: acquiring greenhouse real-time data, where the real-time data includes temperature data and humidity data; obtaining fuzzy quantity, control quantity and error data based on the real-time data, where the error data includes an error value and an error change rate; obtaining a target value based on the error data and a predictive functional control; controlling the target value and the error data for fuzzy inference, determining and adjusting parameters, and generating a control signal based on the parameters; and controlling greenhouse equipment adjustment based on the control signal to enable an output value of the equipment to approach the target value, and repeating the above steps to achieve continuous greenhouse environment control. This method achieves more accurate and efficient control of environmental parameters such as temperature, humidity, and illumination, and provides an optimal growing environment for plants.

The invention is applicable to the technical field of citrus seedling culture, and provides citrus planting culture equipment which comprises a bottom plate and an open frame, the open frame is located over the bottom plate, the distance between the open frame and the bottom plate is adjustable, a heat preservation bag is fixedly mounted between the bottom plate and the open frame, and the interior of the heat preservation bag is used for citrus seedling culture; a plurality of first assembly seats are fixedly mounted on the periphery of the bottom plate, a plurality of lug blocks are fixedly mounted on the periphery of the open frame, the lug blocks correspond to the first assembly seats in position, polished rod shafts are rotatably mounted on the first assembly seats, and lifting screw rods are mounted on the lug blocks in a threaded mode. According to the cultivation equipment for citrus planting, the heat preservation bag can be stretched into a closed greenhouse or folded to be stored along with lifting, heat preservation and moisture preservation of large-batch citrus cutting seedlings are guaranteed through cooperation with the sealing plate, one-time overall opening and closing can be achieved in the inspection stage, the labor intensity of manual bag opening is remarkably reduced, and the management efficiency is improved.

The invention relates to the technical field of agricultural facility control, and discloses a greenhouse environment regulation and control method and system for plant cultivation. The method comprises the following steps: initializing an environmental parameter benchmark based on a plant growth model, wherein the plant growth model is predefined according to a plant type and a growth cycle; collecting multi-source environment sensor data in the greenhouse in real time; performing time sequence smoothing processing on the multi-source environment sensor data to obtain a smooth environment data sequence; calculating a deviation degree between the smooth environment data sequence and an environment parameter reference, wherein the deviation degree is a weighted sum of parameter deviations; identifying a potential environmental anomaly when the degree of deviation exceeds an adaptive threshold; analyzing the duration and the change trend of the potential environmental anomaly, and confirming a real environmental anomaly event; the occurrence frequency and the influence range of the real environment abnormal event are counted, and an environment risk index is calculated; and dynamically adjusting the environment monitoring frequency and the control strategy according to the environment risk index.

The intelligent water-saving and energy-saving vegetable greenhouse comprises a controller, a greenhouse roof and an initial rainwater discarding device, a plurality of rainwater guiding grooves are formed in the greenhouse roof, the initial rainwater discarding device comprises a rainwater container and a U-shaped pipe, a filtering cavity is formed in the top of the rainwater container and communicates with the rainwater container through a water collecting opening, one side of the filtering cavity communicates with the rainwater guiding grooves, and the other side of the filtering cavity communicates with the U-shaped pipe; one end of the U-shaped pipe is communicated with the bottom of the rainwater container, the other end of the U-shaped pipe is a drainage end, the water flow of the drainage end of the U-shaped pipe is smaller than that of the water collecting opening, the water collecting opening is connected with a floating ball through a rope, and the radial size of the floating ball is larger than the radial size of the water collecting opening; a drip irrigation pipeline is arranged in the greenhouse and communicated with the water storage tank through a water pump, and the controller is connected with the water pump. According to the greenhouse, the rainwater collection efficiency and cleanliness can be improved, the integrated design of rainwater collection, automatic irrigation and temperature and humidity adjustment is achieved, and the integration, the environmental protection property and the resource utilization efficiency of the greenhouse are improved.

The invention relates to the technical field of energy storage cabinets. The invention discloses a temperature control system of an energy storage cabinet, which comprises a controller and an energy storage cabinet body, a temperature and humidity sensor and a battery state detection module are arranged in the cabinet, and the temperature and humidity sensor and the battery state detection module are electrically connected with the controller to detect and transmit data in real time. The cooling module comprises a multi-stage heat dissipation fan and a heat dissipation channel, the heating module is composed of a heating wire and a heat conduction plate, and the ventilation module comprises a ventilation shutter and an exhaust pipeline which are both controlled by the controller. The communication module transmits data to a remote terminal, and the alarm module gives an audible and visual alarm when the temperature is abnormal. According to the invention, the hierarchical regulation and control logic of the greenhouse humidity control system and the self-adaptive control idea of the air conditioner are fused, and accurate temperature control and energy saving are realized through the dynamic adjustment of the temperature threshold through the state of the battery, the hierarchical heat dissipation of the multi-stage fan, the cooperative control of temperature and humidity and the intelligent switching of the ventilation module. Meanwhile, the system has the functions of remote monitoring, abnormity alarm and data optimization, improves the safety and operation and maintenance efficiency of the energy storage system, and prolongs the service life of equipment.

The invention provides a cold region greenhouse multispectral visual positioning picking method and system. The method comprises the following steps: synchronously acquiring an RGB image, an NIR image, greenhouse environment temperature and humidity data and depth information of a target; based on the RGB image, the NIR image and the environment data, image correction is carried out through an image correction network, and a sharpened RGB image is output; inputting the corrected RGB image, NIR image and depth image into a maturity identification model for processing, and outputting the maturity grade and three-dimensional space coordinates of the fruit; based on the three-dimensional coordinates and maturity information of the fruits and a pre-constructed environment three-dimensional point cloud map, a collision-free optimal picking path is planned for a mechanical arm; the mechanical arm is controlled to execute the picking action, the success rate of the picking result and the recognition confidence coefficient serve as feedback signals, and online fine adjustment is conducted on parameters of the image correction network and the maturity recognition model; the problems of low picking efficiency, low precision and high damage rate of the greenhouse in the cold region are solved.

The invention discloses a multi-mode sensing environment control system and a disaster early warning method based on a greenhouse, and the system comprises a multi-mode sensing module which is used for collecting various environment parameters in the greenhouse, the multi-mode sensing module at least comprises a temperature sensor, a humidity sensor, an illumination sensor, a gas concentration sensor, a soil humidity sensor and image acquisition equipment; the method comprises the following steps: S1, acquiring environmental parameter data and crop growth images in a greenhouse in real time by a multi-modal sensing module; according to the invention, a multi-mode sensing technology is used, environment and crop information is comprehensively collected, and the evaluation accuracy is improved; a plurality of intelligent algorithm models are adopted for data processing, so that a basis is provided for environment control and disaster early warning; multiple modes are provided for disaster early warning, and timely information transmission is ensured; crop growth abnormity is monitored in real time through image analysis; the disaster early warning threshold value can be dynamically adjusted, and the early warning accuracy and timeliness are improved.

The invention discloses an Internet of Things big data agricultural informatization monitoring and management system and an implementation method thereof, through monitoring, analyzing and researching environmental factors such as illumination, soil humidity and temperature in related environments of crops, and in cooperation with related peripheral electronic elements, an implementation system with operability is formed; therefore, the plant growth environment is regulated and controlled, and finally the purpose of regulating and controlling the plant growth environment is achieved. Meanwhile, people can regulate and control the temperature, the illumination intensity and the soil humidity in the plant growth environment in real time through the network. The monitoring system comprises greenhouse field data acquisition, an environment sensor can acquire parameters such as temperature, humidity, illumination intensity, soil moisture and the like inside and outside the greenhouse in real time, the parameters are presented to a management user in a visual mode, and various sound-light alarm information is provided according to requirements of planted crops. The real-time monitoring effect is achieved in the field of agricultural ecological environment monitoring, real-time monitoring of the agricultural ecological environment is achieved, and high efficiency and high quality of the farm production process are guaranteed.

The application discloses a greenhouse environment control method based on adaptive control vector parameterization, comprising the following steps: constructing a nonlinear optimal control problem of a type of greenhouse environment dynamic constraint optimization based on constraint conditions in a greenhouse environment, and converting the problem into a nonlinear programming NLP problem of a type of limited dimension parameters; based on a control vector parameterization method, solving the NLP problem by fusing engineering constraint rules and a penalty function method aiming at different constraint conditions on fixed time grid nodes of a "coarse grid"; adaptively subdividing the fixed time nodes of the "coarse grid", and constructing new "refined" time grid nodes; based on the new "refined" time grid nodes, re-solving the NLP problem until a termination condition is met to stop the solving, so that the best solution result of the NLP problem is obtained. The application solves the problem of low calculation efficiency in the solving process of the control vector parameterization method for the dynamic optimal control problem in the prior art.

The invention provides a multi-dimensional greenhouse monitoring system and method based on the Internet of Things. The method comprises the following steps: determining a multi-dimensional state vector of a greenhouse environment through multi-source sensing data in the greenhouse environment; extracting disturbance response characteristics of the greenhouse environment variables to crop growth from the multi-dimensional state vector, and generating coupling sensitivity of greenhouse environment variable regulation and control based on the disturbance response characteristics; determining a multivariable regulation deviation value of the greenhouse in the current crop growth stage according to the multi-dimensional state vector of the current greenhouse, and further generating a target regulation coefficient of each environment variable in the greenhouse according to the regulation deviation value; determining a regulation and control coupling relationship among different environment variables in the greenhouse according to all the target regulation and control coefficients; and determining a multivariable combined control instruction of greenhouse environment regulation and control by regulating and controlling the coupling relationship and the coupling sensitivity, and further performing multivariable stable control on the environment variables in the greenhouse. By adopting the scheme, multi-variable collaborative optimization control can be realized in a complex and changeable greenhouse environment, so that the regulation and control deviation is reduced.

A horticulture crop growing system comprising a growing tray preparation station wherein a plurality of growing trays are pre-treated, filled with a growing bed and planted, a growing tray displacing system for transferring planted growing trays from the growing tray preparation station to a horticulture crop growing facility, a closed structure with a controlled environment treating system and one or more tray inlet / outlet ports, and one or more horticulture crop growing modules. The horticulture crop growing module comprise a plurality of parallelly disposed single-rowed growing towers, each comprising an array of vertically supported growing trays, a growing tray manipulation system for receiving planted growing trays via the tray inlet port and loading them to allocated growing towers, and for displacing the growing trays between the growing towers and through treating stations within the horticulture crop growing module, and for displacing the growing trays to the growing tray outlet port.