4841 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 discloses an automatic illumination adjusting method and system for an agricultural greenhouse, a medium and a product, and relates to the field of seed and seedling cultivation, the method comprises the following steps: obtaining image data of target seedlings in a seedling raising area, and determining seedling height data and leaf density distribution of the target seedlings according to the image data; determining initial illumination intensity and illumination distribution data of the seedling raising area according to the image data and the illumination parameters of the seedling raising area; when the initial illumination intensity is lower than a preset light intensity lower limit, determining a target adjustment angle of the light supplementing plate based on the seedling height data, the leaf density distribution and the illumination distribution data; adjusting the inclination angle of the light supplementing plate to a target adjusting angle, and obtaining the corrected illumination intensity of the seedling raising area in real time; and when the corrected illumination intensity is lower than the preset light intensity lower limit, adjusting the luminous intensity of the LED light source until the corrected illumination intensity is not lower than the preset light intensity lower limit. According to the invention, energy loss of agricultural greenhouse light supplement can be saved.

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 discloses a plateau multi-energy complementary agricultural energy management system and a plateau multi-energy complementary agricultural energy management method, and belongs to the technical field of new energy. Wind, light and terrestrial heat multi-energy complementation is cooperated with a battery energy storage system and a phase change heat storage system, and a central intelligent management system is used for carrying out priority scheduling and real-time monitoring and prediction on a load; efficient energy supply of irrigation, processing and greenhouse heating is achieved, the renewable energy utilization rate and the load satisfaction degree are remarkably improved, the operation cost and environmental pollution are reduced, safe and efficient operation of the system is ensured, and good economic and environment-friendly effects are achieved.

Modular prefabricated raised bed greenhouses having side panels, end panels, and roof panels are described. The side panels, end panels, and roof panels each define one or more open spaces covered by a transparent sheet. The side panels have a bottom frame and a top frame that join at an angle. The end panels have a bottom frame and a top frame that join at a zero angle to form a planar surface. Pairs of side panels can be adjacently coupled together to extend the length of the raised bed greenhouse. An end panel is coupled to the pair of side panels on each end of the raised bed greenhouse. A roof panel is coupled with each pair of side panels to form a roof. Each side panel has a door that transitions between an open and closed position for accessing the inside of the raised bed greenhouse.

The invention discloses an intelligent seedling greenhouse environment regulation and control system based on the Internet of Things, and belongs to the field of intelligent regulation and control of the Internet of Things, and the system comprises a heterogeneous sensor dynamic networking construction module which is used for deploying self-positioning wireless sensor nodes and obtaining a dynamic networking scheme based on the wireless sensor nodes; the environment data acquisition and preprocessing module is used for acquiring a multi-dimensional environment data set and preprocessing the multi-dimensional environment data set to obtain a complete environment data set; the growth stage self-adaptive control module is used for dynamically regulating and controlling a seedling culture strategy by adopting an environment variable coupling method based on the complete environment data set to obtain a self-adaptive regulation and control scheme; and the virtual feedback simulation module is used for performing calibration regulation and control through a virtual feedback mechanism and an actual acquisition device to obtain an optimized regulation and control scheme.

The invention relates to the technical field of greenhouse environment control, in particular to a greenhouse environment control method and system. The method comprises the following steps: obtaining data of a crop cultivation stage and data of a soil monitoring sensor to obtain soil state monitoring filling data; then, identifying the abnormal fluctuation state of the soil acidity and alkalinity, and estimating the root growth inhibition degree according to the state to obtain root growth inhibition gradient data; secondly, predicting the soil looseness requirement according to the root inhibition data, and adjusting the soil acidity and alkalinity to realize balance adaptation; and then, based on the soil state data, carrying out adaptive control training, constructing a soil environment regulation and control model, and sending the soil environment regulation and control model to a cloud platform to execute greenhouse environment control. According to the invention, the greenhouse environment control technology is optimized, so that the greenhouse environment control technology is more perfect.

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 discloses a greenhouse temperature data monitoring method and system based on data analysis. The method comprises the following steps: S1, collecting and constructing an environment data set by using a multi-point sensor array arranged in a greenhouse; s2, obtaining a preprocessed environment data set; s3, forming an initial partition structure; s4, when it is monitored that the environmental data change exceeds a threshold value, dynamically adjusting partition boundaries and the number of regions to obtain a self-adaptive partition result; s5, performing feature extraction on the environment data of each partition in the self-adaptive partition result to form a feature set for model training; s6, obtaining future temperature prediction results of different partitions in the greenhouse; and S7, generating greenhouse environment regulation and control suggestions in combination with future temperature prediction results of different partitions, and carrying out auxiliary regulation on a ventilation system, a heating system and a shading device. The problems of insufficient precision and poor adaptability in greenhouse temperature monitoring and regulation in the prior art are solved, and efficient technical support is provided for intelligent agriculture development.

The invention relates to the field of crop planting optimization decision making, in particular to a crop planting optimization decision making method based on digital twinning and reinforcement learning. According to the technical scheme, the method comprises the steps of collecting greenhouse multi-modal data including greenhouse environment data, crop data, soil data and management data; cleaning, aligning and normalizing the acquired data to form a structured multi-modal time series data set; identifying the growth cycle of the crop based on the collected data; constructing a crop-environment digital twinborn model which is mapped with the physical greenhouse environment in real time; constructing a digital twin simulation space; and reinforcement learning and strategy optimization are carried out in a digital twinborn simulation space, so that a global optimal control strategy is dynamically and adaptively generated. The method is suitable for crop planting optimization decision making.

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 discloses an agricultural machinery automatic driving operation method applied to an automatic driving harvester. The method belongs to the technical field of automatic driving of agricultural machinery, and comprises the following specific operation steps: firstly, acquiring internal environment information of a greenhouse by using a millimeter wave radar, and constructing a two-dimensional grid map by using a Cartograph algorithm; secondly, according to the drawn grid map, an improved PSA algorithm is adopted to complete global path planning of the automatic driving harvester; and finally, the automatic driving harvester carries a ZED camera to collect image information within 5-6 m around the vehicle, an xLSTM model is used to identify the collected image information, whether there is an obstacle around the vehicle is determined, and if there is an obstacle, a DCS algorithm is used to complete local path planning of the operation vehicle. According to the invention, farmland crops can be harvested more quickly and accurately, the labor cost is reduced, and the harvesting efficiency is improved.

The present application provides a method and system for managing the entry and exit of a greenhouse. The method includes: obtaining the basic information, health status, historical entry and exit pattern data, and environmental parameters of the greenhouse of the object to be entered or left in the target time period; using a pre-trained prediction model to perform multi-level processing on the historical entry and exit pattern data, predicting the entry and exit demand information of the object in the target time period based on the multi-level processing results, and determining the initial disinfection process of the object according to the entry and exit demand information; the multi-levels include the potential distribution learning level and the graph structure modeling level; dynamically adjusting the initial disinfection process according to the basic information, health status, and environmental parameters of the greenhouse of the object to obtain the target disinfection process; executing the target disinfection process to obtain the disinfection result of the object, and determining whether to allow the object to enter or leave the greenhouse according to the disinfection result of the object. The technical solution provided by the present application can improve management efficiency.

The invention discloses a coastal zone carbon sink data acquisition management system and method, and particularly relates to the technical field of carbon sink evaluation, and the method comprises the following steps: dividing carbon flux parameter partitions based on vegetation types, sediment characteristics and historical carbon storage levels, and establishing initial reference values; plant biomass, soil organic carbon content and non-carbon dioxide greenhouse gas flux are regularly collected according to fixed sampling points, and a carbon sink observation data sequence is formed; when data exception triggers an evaluation process, generating a carbon sink credibility index and an ecological interference response index; inputting the two indexes into a prediction model to output an adjustment vector, generating an adjustment strategy in combination with a weight, and dynamically updating an initial carbon flux reference value of each partition; according to the method, regionalized management is realized by constructing carbon flux parameter partitions, a carbon sink observation data sequence is established, an evaluation index is dynamically generated, intelligent updating of a carbon flux reference value is completed in combination with a prediction model, and the precision, responsiveness and adaptive adjustment capability of carbon sink monitoring are improved.

The invention discloses a device for collecting greenhouse gas released by a unit in sewage treatment, which comprises a floating module, a detection module and a connection module, and the detection module is mounted on the floating module through the connection module. The detection module comprises a gas-collecting hood, a limiting plate arranged at an inlet of the gas-collecting hood, an aeration unit gas-collecting cavity and a non-aeration unit gas-collecting cavity, the aeration unit gas-collecting cavity and the non-aeration unit gas-collecting cavity are arranged in the gas-collecting hood, a gas detection unit is arranged at the top of the gas-collecting hood, and the aeration unit gas-collecting cavity and the non-aeration unit gas-collecting cavity are both connected with the gas detection unit. The floating module comprises a floating block with a hole, the connecting module comprises a bottom box and a top box, the gas collecting hood is inserted into the top box, the bottom box is inserted into the floating block with the hole, the top box is inserted into the bottom box, and the floating block with the hole, the top box, the bottom box and the gas collecting hood are fixed into a whole through bolts. The aeration unit and the non-aeration unit can be measured by using the same device, so that the complexity and manual operation steps of using a plurality of devices are reduced. The working efficiency is greatly improved, and the field working time is shortened.

The invention discloses a plant greenhouse intelligent monitoring and environment optimization management system and method. The method comprises the steps that a detection module obtains environment data, resource data and equipment feedback data of a vegetable greenhouse in real time; the acquisition module acquires vegetable types, growth days and growth parameters; the health prediction module calculates the current growth index according to the growth parameters and predicts the index of the next growth stage; the establishing module calculates a difference value between the predicted growth index and the standard growth index through a target function; the parameter optimization module adopts an improved dung beetle optimization algorithm, adjusts an optimization process according to equipment feedback and outputs optimal cultivation parameters; the condition generation module generates a resource scarcity state according to the resource data and provides adjustment limitation; the processing module adjusts a theoretical range based on the optimal parameter and the resource constraint; and the in-greenhouse adjustment module dynamically adjusts the greenhouse environment according to the optimization result. Through integration of sensor data and an optimization algorithm, dynamic adjustment of the resource ratio in the vegetable greenhouse is realized, and the vegetable growth efficiency is improved.

The invention relates to the technical field of intelligent agriculture and Internet of Things control, and discloses an intelligent greenhouse control method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-factor environment state vector; strawberry image features are extracted based on YOLOv5; inputting a strawberry growth stage prediction model in combination with the time sequence to determine target environment parameters; constructing an environmental parameter prediction model; constructing a dual error term, and inputting the dual error term into a fuzzy controller to generate a preliminary control instruction; optimizing and outputting an optimization control instruction by the Q learning network; compared with a greenhouse control method which mostly depends on a fixed threshold value or a static rule in the prior art, the technical problem that dynamic target setting and self-adaptive adjustment of environmental parameters are difficult to realize especially under the condition that demands of strawberry crops in different growth stages change rapidly is solved. Intelligent greenhouse environment control in combination with the growth stage of strawberry crops is realized, and the adaptability of the greenhouse to crop growth conditions is improved.

The invention discloses a small-scale greenhouse gas emission prediction method based on a pre-training model, and belongs to the field of greenhouse gas emission prediction. According to the method, a greenhouse gas monitoring micro station is arranged in a park, data such as greenhouse gas concentration and meteorological factors are collected in real time, and high-precision traceability of a greenhouse gas emission source and accurate accounting of greenhouse gas emission can be achieved in combination with a high-precision meteorological numerical value mode, a backward trajectory tracking algorithm, a receptor model and a diffusion model; the greenhouse gas emission amount is predicted in real time by calling the pre-training model, and the prediction efficiency and accuracy are remarkably improved. Different from a mode that a traditional model depends on a large amount of historical data training, the pre-training model can be finely adjusted on the basis of a small amount of existing data, so that effective learning and prediction are realized. According to the method, a large amount of time and computing resources are saved, the environmental change can be quickly responded, and real-time and accurate emission prediction is provided.

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 invention relates to the technical field of intelligent heat supply, in particular to a multi-source energy fusion agricultural greenhouse intelligent heat supply and regulation and control system, which comprises a multi-source energy acquisition module for acquiring energy output parameters of a solar heat collector, a geothermal well and a biomass boiler in real time and monitoring external meteorological data of a greenhouse; the energy dynamic matching module is used for generating an energy scheduling strategy through a dynamic priority algorithm and outputting a target energy type and supplied energy; the energy storage buffer module is used for storing or releasing the supplied energy and outputting the temperature and the residual capacity of an energy storage medium; the multi-parameter coupling regulation and control module is used for generating a regulation and control instruction through an environment parameter coupling model; the actuating mechanism driving module is used for converting the regulation and control instruction into control signals of a heater, a fan and a humidifier; the self-learning optimization module is used for dynamically correcting the dynamic priority algorithm and the environment parameter coupling model; according to the invention, through multi-parameter cooperative regulation and dynamic optimization, crops are ensured to be in the optimal environmental conditions in different growth stages.

The embodiment of the invention discloses a water-gas interface greenhouse gas flux automatic monitoring system and a distribution prediction method. The system comprises a wind speed sensor and an air pressure and temperature sensor which are respectively used for collecting the wind speed of air at a monitoring point, the atmospheric pressure and the Kelvin air temperature; the infrared sensor is used for collecting the content of CO2, CH4 and N2O in the air of the monitoring point; the acidity sensor and the temperature sensor are respectively used for collecting the pH value and the centigrade water temperature of monitoring point water; the carbon dioxide sensor is used for collecting the concentration of bicarbonate in water at a monitoring point; the methane sensor and the nitrous oxide sensor are respectively used for collecting the contents of greenhouse gases CH4 and N2O dissolved in the monitoring point water; and the calculation module is used for calculating the flux of the greenhouse gas on the water-gas interface. According to the embodiment of the invention, the water-gas interface flux of various greenhouse gases under actual conditions can be accurately monitored.

A farming method may be shown and described. In an exemplary embodiment, plants may begin in a germination phase. Next, plants are brought to a nursery for a period of time before optionally being transplanted to one or more subsequent nurseries. Finally, plants are transplanted to a greenhouse where they may grow until they are ready for harvest. In an exemplary embodiment, the nursery phases may be vertical farms while the greenhouse phase may be a traditional, hydroponic, or other type of farm which may receive sunlight. AI may be implemented to optimize environmental conditions and robotics may be used to harvest the plants. Plants may be indexed to efficiently expedite plant growth and optimize the time and plant density / spacing in each phase.

The utility model discloses a large-span multi-span greenhouse which comprises a plurality of supporting units formed by fixedly connecting a plurality of combined beam frames end to end, a plurality of upper longitudinal beams and a plurality of lower longitudinal beams are arranged among the supporting units, and the upper longitudinal beams and the lower longitudinal beams are distributed in a staggered mode. Each combined beam frame comprises two stand columns, a cross beam, a suspension cable and a plurality of main ribs, every two adjacent combined beam frames share the same stand column, the cross beam is erected between the two stand columns, the upper ends and the lower ends of the main ribs are fixedly connected with the upper longitudinal beam and the lower longitudinal beam respectively, the lower longitudinal beam is fixedly arranged on the cross beam, and the suspension cable is arranged on the main ribs. The two ends of each suspension cable are fixedly arranged on the two corresponding stand columns, and greenhouse covering materials are installed on a net frame formed among the main ribs, the upper longitudinal beams and the lower longitudinal beams. The main ribs are made of rigid materials or flexible materials. The utility model not only replaces the conventional glass greenhouse and the conventional multi-span film greenhouse which respectively use a truss structure and an arch structure, but also has the advantages of large span and material conservation.

The invention provides a greenhouse intelligent temperature control automatic adjustment optimization method based on multi-parameter dynamic coordination, and relates to the technical field of agricultural facility environment control. The method comprises the following steps: acquiring temperature, air humidity, wind speed and airflow distribution environment data inside and outside a greenhouse in real time; the environment state is quantified through a dynamic evaporation efficiency coefficient DEEC and an airflow distribution uniformity index ADUI; the flow of a water pump, the rotating speed of a fan and the opening degree of a ventilation system are dynamically adjusted in combination with an improved PID control algorithm, and the weight of an integral term is dynamically adjusted along with the ADUI to enhance the response efficiency; a time sequence prediction model based on LSTM is adopted to fuse historical and real-time data, a parameter correction amount is predicted, and the adaptation capability of the system to a new environment is improved in combination with a transfer learning algorithm; system hardware comprises a low-power-consumption wireless sensor, a frequency conversion actuator and an embedded control terminal. Through combination of multi-parameter perception and cooperative control, accurate temperature and humidity control and energy efficiency optimization are realized, resource consumption is reduced, and automation and intelligence levels of environment adjustment are 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 discloses pest control equipment and a pest control method based on greenhouse vegetable planting, and belongs to the field of agricultural pest control. According to the technical scheme, the pest control equipment based on greenhouse vegetable planting comprises a monitoring and early warning module, a physical control module, a biological control module and an intelligent control module, and the monitoring and early warning module comprises a pest situation forecasting mechanism; real-time monitoring and early warning are achieved through multiple trapping modes and an AI algorithm, physical prevention and control and biological prevention and control means are combined, automatic adjustment is achieved through the intelligent control module, and therefore the problems that in the prior art, monitoring is not timely, the prevention and control means are single, and intelligent control is lacked are solved; the system has the advantages of real-time monitoring and early warning, synergistic effect of multiple prevention and control means, intelligent control and accurate prevention and control.

The invention discloses a greenhouse gas point source emission automatic identification method, system and device and a medium, and relates to the technical field of environment monitoring. The method comprises the following steps: acquiring a greenhouse gas emission list and satellite remote sensing data of a to-be-detected area; carrying out emission source preliminary screening according to the greenhouse gas emission list, and determining a preliminarily screened greenhouse gas source; inverting a greenhouse gas concentration distribution diagram by using satellite remote sensing data, and performing target detection and pixel segmentation on the greenhouse gas concentration distribution diagram by using a Mask R-CNN model to determine a greenhouse gas plume; the greenhouse gas plume comprises a specific position and a specific form; and performing quantitative analysis on the greenhouse gas plume by using an IME algorithm, and determining a final emission source in combination with the preliminarily screened greenhouse gas source. According to the invention, the recognition accuracy and real-time performance of the greenhouse gas emission source can be improved.