6391results about "Watering devices" patented technology

The invention relates to the technical field of intelligent agriculture and ecological internet of things, in particular to an ecological irrigation decision dynamic optimization method and related equipment. Comprising the following steps: acquiring multi-source environment data, acquiring a soil profile humidity gradient in real time through a soil humidity sensor array, acquiring future rainfall probability distribution, a temperature change rate and a wind speed predicted value in combination with a weather forecast interface, and synchronously accessing a geographic information system to acquire terrain elevation and crop distribution data. The three technical bottlenecks of model dimension collapse, parameter estimation instability and optimization response lag in a traditional irrigation decision-making system are systematically solved by constructing a space-time coupling analysis framework and a closed-loop optimization mechanism of multi-source heterogeneous data.

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a high-standard farmland intelligent irrigation system based on Internet of Things and data analysis, and the system comprises a soil moisture content sensing module which collects data through a multi-source sensor to construct a three-dimensional soil moisture content distribution model, and generates a soil moisture content characteristic spectrum; the soil moisture content prediction module generates water demand prediction data based on the soil moisture content characteristic spectrum, the meteorological data and the crop growth stage; the irrigation strategy module fuses terrain elevation and pipe network pressure parameters to generate an irrigation control map; the equipment state monitoring module collects water pump current waveform and other data to generate equipment health degree parameters; the pipe network optimization module optimizes pipe network topology and generates an adjusting instruction; the multi-source data fusion module generates fusion evaluation indexes by using an evidence theory, an entropy weight method and the like; and the intelligent execution module generates an execution control instruction accordingly. All the modules cooperate to achieve precise irrigation, and the utilization efficiency of water resources and the intelligent level of farmland management are improved.

The invention relates to a supply and demand multi-source data fusion farmland water-saving irrigation water system design and dynamic water distribution optimization method, which is characterized by comprising the following steps: S1, crop-soil-weather multi-source data coupling modeling, S2, water-saving water system design optimization, S3, dynamic water-saving water distribution strategy, and S4, intelligent terminal control system control. According to the scheme, accurate configuration of water resources is achieved through the artificial intelligence technology, the irrigation water consumption is reduced to the maximum extent, and meanwhile the crop growth requirement is met.

The invention relates to the field of power system supply and demand interaction, and discloses an intelligent greening irrigation system and a water-saving control method, the system comprises a multi-source data acquisition module, a space-time tensor modeling module, a dynamic decision generation module, an edge calculation acceleration module, a distributed execution control module and a drip irrigation module; the method comprises the following steps: collecting multispectral, meteorological and soil moisture data of a plant canopy in real time to generate an original data set; a fusion data set is obtained through space-time alignment and normalization preprocessing; the water demand is predicted based on an LSTM-Transform model, and an irrigation safety interval is screened; the irrigation uniformity and water pressure distribution are simulated and evaluated through multi-physics field coupling; a double-delay DDPG algorithm is used for optimizing valve opening parameters; the model and the execution error are corrected through Bayesian modeling, and an NDVI feedback optimization strategy is combined. According to the invention, a double-delay depth deterministic strategy gradient algorithm is introduced for dynamic optimization of an irrigation strategy, so that the capability of quickly adapting to environmental changes is achieved.

The invention discloses an artificial intelligence rice water and fertilizer real-time monitoring method and system, and relates to the technical field of agricultural intelligent decision making, and the method comprises the steps: inputting a farmland feature data set into a soil thermodynamic diagram generation model, carrying out the high-resolution reconstruction of the farmland feature data set through a GAN adversarial network, and generating a whole-field high-precision soil thermodynamic diagram; based on the whole-field high-precision soil thermodynamic diagram, a collision relation between the fertilization amount and historical farming data is detected according to an FCL causal algorithm, a preliminary causal diagram is generated, and a causal diagram structure of the fertilization amount and the historical farming data is constructed by adopting a multiple linear regression method; based on a causal diagram structure, the multi-order causal effect of the fertilization amount, the soil parameters and the historical yield is analyzed through a dynamic allocation algorithm, and a water and fertilizer regulation and control strategy is formulated in combination with a multi-objective optimization algorithm. According to the method, the soil thermodynamic diagram generation model is constructed, so that the fuzzy problem of the edge of the field and the salinization area is solved, a high-fidelity soil space state substrate is provided for water and fertilizer regulation and control, and invalid irrigation is reduced.

The invention specifically relates to the technical field of intelligent irrigation, and discloses a meteorological prediction-based irrigation dynamic regulation and control system, which comprises a data acquisition module, a data analysis module, a strategy generation module, a strategy execution module, a feedback regulation module and a man-machine interaction module, the data acquisition module integrates a water quantity acquisition terminal, a crop state acquisition terminal and a meteorological monitoring terminal, acquires multi-source irrigation decision data of a target irrigation farmland in real time, judges a water shortage risk level of the target irrigation farmland through the data analysis module, and generates an irrigation strategy through the strategy generation module; the strategy execution module executes an irrigation strategy through an electrically-driven intelligent water-saving valve, the feedback adjustment module dynamically adjusts the irrigation strategy based on sensor feedback data, and the man-machine interaction module provides a data visual interface, dynamically optimizes the irrigation strategy in combination with real-time meteorological changes and irrigation feedback, divides water shortage risk levels, and provides a water-saving management system. The utilization efficiency of water resources is effectively improved, and the applicability and reliability of the system are improved.

An intelligent fertilization and water supply method and system. The method comprises the following steps: step a, comprehensive soil monitoring, involving: comprehensively monitoring various soil indexes by means of a plurality of sensors, so as to obtain real-time data of the soil indexes; b, consideration of crop-specific requirements, involving: for the varieties and growth stages of different crops, thoroughly studying the unique moisture and nutrient requirements of the crops, so as to ensure the rationality and accuracy of a fertilization amount; c, comprehensive consideration of environmental factors, involving: collecting data of surrounding environmental factors; d, utilization of an intelligent irrigation mode, involving: by using a cyclic irrigation mode, an intermittent irrigation mode, etc., in combination with the water requirement pattern of each crop and a change in soil moisture, realizing efficient water utilization; e, implementation of a dynamic fertilization plan, involving: on the basis of a change in a crop growth condition and in soil fertility, dynamically adjusting fertilization time, frequency and dosage, so as to ensure that the nutrient supply matches the requirements of each crop; and f, implementation of a precise fertilization technique, involving: precisely delivering fertilizer to an absorption part of each crop, so as to improve the efficiency of nutrient utilization.

The invention relates to the technical field of intelligent gardens and precise irrigation, and particularly discloses an intelligent irrigation monitoring method and system. According to the method, a multi-source sensor array is deployed, Kalman filtering is adopted to fuse environmental data, and a three-dimensional state vector input reinforcement learning model is constructed to generate an irrigation decision; predicting a vegetation water demand by combining a gradient lifting decision tree, forming a graded irrigation strategy and converting the graded irrigation strategy into a water pump control instruction; soil humidity feedback data are collected in real time, decision model parameters and filtering rules are dynamically adjusted, and closed-loop optimization is achieved. Through multi-source data fusion and a self-adaptive decision-making mechanism, the irrigation precision and the water resource utilization rate are remarkably improved, meanwhile, the response capacity of the system to the vegetation growth dynamic state and the environment change is enhanced, and the beneficial effects of optimization process closed loop, decision dynamic adaptation and controllable resource consumption are achieved.

The invention discloses a crop environment quantitative evaluation and decision making system based on growth stage self-adaption, and relates to the technical field of agricultural intelligent decision making. Acquiring an environment original data sequence of field multi-type sensors through a data acquisition module; the dynamic identification module analyzes the sequence by using a preset crop growth stage discrimination model, identifies the current growth stage and outputs a corresponding key environment parameter weight template; the feature fusion module performs weighted fusion on the original data according to the template to generate an environment feature vector with stage adaptability; the state evaluation module converts the vector into a growth state quantitative evaluation value through a growth state evaluation model; and the decision trigger judgment module calls a corresponding preset dynamic threshold interval according to the growth stage for comparison, and generates a decision trigger instruction when the growth state quantitative evaluation value deviates. According to the invention, stage self-adaptive intelligentization of crop growth monitoring and decision making is realized, and the accuracy of environment state evaluation and the timeliness of management decision making are improved.

The invention relates to the technical field of agricultural irrigation, and particularly discloses a farmland irrigation control method and system, and the method comprises the steps: constructing a multi-sensor fusion monitoring network, and collecting soil, weather and plant physiological data in real time; transmitting data to an edge computing node through LoRa wireless communication; utilizing a lightweight neural network model deployed by edge computing nodes to calculate and predict crop water demand and accurate irrigation volume in combination with evapotranspiration; the irrigation strategy is executed through the intelligent irrigation execution system; and remote monitoring and abnormity alarm are realized by means of a remote monitoring and management platform based on WebGIS. Precise and intelligent management of farmland irrigation is realized, the utilization rate of water resources and fertilizers can be effectively improved, irrigation abnormity can be timely handled, the labor management cost is reduced, and the growth requirements of crops are guaranteed.

The invention discloses an intelligent water and fertilizer integrated irrigation system based on an Internet of Things sensor and a large language model, and relates to the technical field of intelligent agriculture. The system obtains soil, weather and plant growth state data in real time through Internet of Things sensors, including a soil sensor, a weather sensor and a multispectral sensor, deployed in a farmland; the method comprises the following steps: based on a large language model architecture, integrating preset agricultural field knowledge base data, performing field adaptive fine tuning on model parameters by adopting a data migration technology, and constructing an intelligent irrigation control model with a dynamic decision-making function; the control model analyzes sensor data and crop growth requirements, generates a water and fertilizer supply strategy, calls an internet-of-things control interface of the water and fertilizer all-in-one machine, and automatically adjusts irrigation water quantity and proportional supply of nutrient elements such as nitrogen, phosphorus and potassium. According to the invention, Internet of Things perception and large model decision are fused, and different environments are adapted through fine adjustment, so that closed-loop precise control is realized, and the water and fertilizer utilization rate and the crop yield are remarkably improved. Actual measurement shows that the system can improve the water and fertilizer utilization rate by 30% or above, and the crop yield is increased by 15-20%.

The invention discloses a traditional Chinese medicinal material standard base intelligent irrigation and fertilization method based on the Internet of Things and a decision making system, and relates to the technical field of agricultural informatization. Through multi-source data acquisition and intelligent analysis, the limitation of an existing irrigation and fertilization method is solved, accurate decision making and resource optimization are realized, comprehensive data are acquired by utilizing a soil moisture content sensor, a meteorological station and plant image acquisition equipment, and by combining principal component analysis, a long-short-term memory neural network and a block chain technology, the intelligent irrigation and fertilization method is realized. According to the system, the water and fertilizer requirements are accurately predicted, an irrigation and fertilization scheme is optimized, data credibility and traceability are ensured, meanwhile, real-time control and collaborative operation of equipment are achieved through edge calculation and a wireless sensor network, the resource utilization efficiency is improved, the production cost is reduced, and scientificity and sustainability of traditional Chinese medicine planting are remarkably improved.

The invention discloses an intelligent agricultural water-saving irrigation system and a control method, and relates to the technical field of agricultural irrigation. Comprising the following steps: a land area planning module performs equal-area division on a farmland, sets a detection node array, and monitors water retention conditions of different soil depths; the moisture gradient characterization module calculates the vertical moisture gradient and the direction of the vertical moisture gradient of the evaluation area in real time through edge calculation. And the irrigation water amount correction module fuses and generates a target water-holding capacity based on the leaf stomatal conductance signal and the moisture gradient. The irrigation circulation termination control module uses pulse type infiltrating irrigation, and sets double termination conditions of the water-holding capacity change rate and the permeability. The closed-loop feedback adjustment module detects the soil water content after irrigation, characterizes the water leakage risk and feeds back and adjusts the irrigation water amount, closed-loop control is formed, and effective utilization of water resources is ensured.

This application provides a wind-resistant, stable power generation agricultural-solar hybrid system, including pillars, beams, photovoltaic supports, and photovoltaic panels erected in farmland. The photovoltaic panels are equipped with a buffer mechanism and an overload release device. The buffer mechanism effectively absorbs kinetic energy and reduces the swaying amplitude and speed of the photovoltaic panels through its elasticity, and automatically resets the photovoltaic panels after strong winds. The overload release device protects the photovoltaic panels from damage caused by strong winds. This application overcomes the technical defects of existing static wind-resistant systems, reduces system wind resistance, can withstand super typhoons, provide stable power generation and irrigation, and promote agricultural production.

The invention belongs to the technical field of agriculture, particularly relates to a cranberry water demand dynamic sensing system and a method thereof, and aims to solve the problems that an existing traditional irrigation method is often based on a fixed time table or simple soil humidity measurement, the actual water demand condition of cranberries cannot be timely and accurately reflected, and insufficient irrigation or excessive irrigation is easily caused. In order to solve the problems that in the prior art, water resources are wasted, and the growth and yield of cranberries are possibly influenced, the invention provides the following scheme: the system comprises a monitoring module, a control module and a display module, wherein the monitoring module comprises stem flow rate, leaf water potential, soil water content at different depths and positions, air temperature and humidity, illumination intensity and wind speed data; according to the cranberry water demand monitoring system, accurate monitoring, real-time evaluation and scientific prediction of the cranberry water demand condition can be achieved, then a reasonable irrigation decision and environment regulation and control are guided, and the irrigation efficiency and the water resource utilization efficiency are improved.

The invention discloses a farmland soil humidity intelligent monitoring system based on the Internet of Things, and relates to the field of resource optimization decision crossing, a soil humidity monitoring module executes a soil humidity adjustment plan, monitors farmland soil humidity time sequence data in real time, and obtains a farmland soil humidity monitoring result through time sequence analysis and K-means clustering; obtaining an association rule between the soil humidity time sequence data and the crop yield; and the humidity monitoring report generation module is used for optimizing the soil humidity adjustment plan by tracking an optimal soil humidity threshold value in real time by utilizing a dynamic programming hybrid optimization algorithm according to the optimal soil humidity range, and generating a farmland soil humidity monitoring report. Through an optimization model coupled by a crop moisture production function and a multi-objective genetic algorithm, and in combination with Monte Carlo global sensitivity analysis, an optimal soil humidity range is identified, so that accurate quantitative modeling of a humidity-yield nonlinear relationship is realized, the defect of a static soil humidity threshold is avoided, and the sensitivity of moisture stress early warning is improved.

The present application relates to a water sprinkler, including a housing, a solar module, a battery module, a water inlet / outlet pipe, and a control module, where the water inlet / outlet pipe and the control module are arranged in the housing, the solar module is arranged outside the housing and rotatably connected to the housing, the water inlet / outlet pipe is provided with at least one control valve, the control module is in communication connection with the control valve and controls the opening and closing of the control valve, and the solar module, the control module and the control valve are each electrically connected to the battery module.

The invention relates to an irrigation area water regimen monitoring method, device and system using a sensor technology, and belongs to the technical field of irrigation area water regimen adjusting systems, and the method comprises the following steps: S1, receiving model parameter update uploaded by each irrigation sub-area under a federated learning framework, and using the model parameter update to iteratively update a central side irrigation area digital twinborn model; s2, acquiring an actual measurement value of the sensor and manual intervention data from a system log; s3, calculating a global average distortion index and a manual intervention rate; the global average distortion index is used for representing a deviation degree between the digital twin model and a physical world reflected by a sensor actual measurement value, and the manual intervention rate is used for representing a decision conflict degree based on manual intervention data statistics; s4, on the basis of the preset weight, the global average distortion index and the manual intervention rate are combined, and the man-machine system trust entropy is worked out. According to the method, a benchmark which does not depend on the state of the sensor and has definite physical significance is provided for the measured value of the sensor.

The invention relates to the field of intelligent irrigation, and discloses an intelligent irrigation system and method for a crop root system, and the system comprises a root zone sensing module which is used for collecting soil environment parameters through multi-mode soil sensor arrays disposed at soil layers of different depths, generating a sensing matrix, and transmitting the sensing matrix to a root zone sensing module; the sensing matrix is used for representing water potential, pH value, nutrient concentration and temperature information in each soil layer; and the root system density analysis module is connected with the root zone sensing module, and is used for constructing a root system density weight matrix based on root system space distribution information obtained by underground computed tomography, and multiplying the sensing matrix by the root system density weight matrix element by element to generate a root zone state vector. Accurate generation of regulation and control signals is achieved, the micro-fluidic execution module can adjust the irrigation flow and the fertilization proportion in real time according to the actual requirements of the root zone, and therefore it is guaranteed that the rhizosphere environment is always close to the optimal physiological interval.

The invention discloses a garden partition intelligent water-saving irrigation regulation and control system based on the Internet of Things, and belongs to the field of irrigation regulation and control systems. According to the system, a basic water demand model containing static characteristics of plants, soil and the like is established for each subarea through a central processing controller, real-time environment data and weather forecast are fused, and future irrigation water demand and optimal time are calculated in a prospective mode. The core innovation of the method lies in that after irrigation, the system performs feedback iterative optimization on a basic water demand model by comparing the efficiency deviation of the actual change and the predicted change of the soil humidity to form a self-learning closed loop. According to the method, conversion from passive response to active prediction is achieved, the method has the advantages of being accurate, prospective and self-adaptive, the method can continuously adapt to environmental changes, and the intelligent level and the water saving effect of irrigation are remarkably improved.

The invention belongs to the technical field of garden irrigation, and particularly discloses a garden landscape intelligent irrigation management system which comprises a sensing layer, a transmission layer, a platform layer and an execution layer. The sensing layer is used for collecting garden environment and plant related data; the transmission layer is used for transmitting the data acquired by the sensing layer to the platform layer; the platform layer is used for realizing data visualization, intelligent decision generation and equipment control; the execution layer executes irrigation operation according to an instruction of the platform layer; according to the system, through accurate monitoring and intelligent control, water resource waste and irrigation cost are effectively reduced. Manual inspection and operation workloads are reduced through automatic operation, one administrator can manage thousands of mu of gardens, and the management efficiency is improved. The risk of plant diseases and insect pests can be reduced through on-demand irrigation, and the stability and attractiveness of the garden landscape effect are kept. The environment, irrigation and plant growth data accumulated by the system provides a scientific decision basis for garden maintenance, and is convenient for long-term planning and management strategy optimization.

The invention discloses a rice field irrigation control method, and belongs to the field of rice field irrigation, and the method comprises the steps: obtaining soil humidity, water level and meteorological data from a rice field through a sensor network, storing the data in a pre-established distributed database, and obtaining a multi-source data set; according to the data set in the unified format, key features of soil humidity, water level and meteorological data are extracted by adopting a principal component analysis method, and a feature vector set is obtained; if the water consumption of the predicted irrigation scheme exceeds a preset threshold value, the scheme is optimized and adjusted through a linear programming algorithm, and an optimized irrigation scheme is obtained; according to the optimized irrigation scheme, the water consumption in the irrigation process is monitored in real time through flow meter equipment, and accurate measurement data are obtained; and the accurate measurement data is distributed through a quota management module, a regional water resource allocation scheme is generated by adopting a dynamic quota algorithm, and a quota management result is obtained.

The invention discloses a gleying rice field intelligent irrigation and drainage regulation and control method based on reinforcement learning. The method comprises the steps that meteorological data, soil data and field surface water level data are collected; calculating crop state information based on the meteorological data and the soil data by using a crop growth model; inputting the meteorological data, the soil data, the field surface water level data and the crop state information into an offline training strategy model, outputting action suggestions, and performing verification and decision making according to preset regulation and control logic in combination with the meteorological data, the soil data, the field surface water level data, an external data source and agronomic rule base data; a control strategy is generated, irrigation, drainage or oxygenation is executed, and irrigation and drainage regulation and control are completed. Compared with the prior art, the method has the advantages that priority regulation and control logic for ORP / pH value real-time feedback is established, a prospective pre-drainage mechanism for rainfall forecast is fused, water layer constraint in the growth period and conditional filtering rules in the fertilization safety period are fused, and a perception-decision-execution closed-loop control system is formed.

The invention provides an intelligent decision-making method based on a multi-modal large model and related equipment, which are applied to an artificial intelligence technology, and the method comprises the steps: collecting a current field image of a to-be-decided region, and carrying out the preprocessing of the current field image, and obtaining a target field image; inputting the target field image into a pre-trained vision-text alignment model, and performing prediction by the vision-text alignment model by using the target field image to obtain a disease and pest diagnosis scheme corresponding to the to-be-decided region; dividing the to-be-decided region into a plurality of partitions, and processing current data of each mode in current multi-mode data of the partitions to obtain a space-time sequence of each mode; inputting the space-time sequence of each modal of the subarea into a pre-trained irrigation decision model, and performing prediction by the irrigation decision model according to the space-time sequence of each modal to obtain irrigation data of the subarea; the irrigation strategy of each subarea is generated according to the disease and pest diagnosis scheme and the irrigation data of each subarea. The cost can be reduced, and the generation efficiency and accuracy of the irrigation strategy can be improved.

The invention discloses a digital twin-driven water and fertilizer real-time dynamic balance transfer system, which comprises a sensing and data acquisition module for monitoring moisture, salinity, pH value, illumination and rainfall meteorological data of soil in real time through various sensors deployed in a farmland; and the digital twinborn modeling and simulation module is used for constructing a digital twinborn model of the farmland based on the real-time data collected by the perception and data acquisition module. According to the invention, by arranging the perception and data acquisition module, the digital twin modeling and simulation module, the intelligent decision-making and regulation module, the execution and feedback module, the historical data tracing and system optimization module, the expansion module, the networking module and the edge-cloud cooperative computing architecture, the farmland environment and the crop growth condition can be monitored in real time; the digital twinborn modeling and simulation module constructs a digital twinborn model of a farmland based on real-time data, simulates a soil environment and crop growth, and optimizes a water and fertilizer proportioning strategy.

The invention provides an installation position recommendation method of a courtyard intelligent sprinkler and a mower control method, and relates to the technical field of sprinklers. The method comprises the following steps: acquiring information of a vegetation coverage area, a movement limited area and a non-movement limited area based on a courtyard map, and performing quantitative judgment on geometric features of the movement limited area to realize accurate identification of a narrow traffic area; when the recommended installation position is determined, the range of the inner side of the movement limited area is excluded, and meanwhile the effective spraying area which can be covered by the installation position of the sprinkler can be maximized. According to the technical scheme, hindering of installation of the sprinkler on a mower or a user passing path can be avoided, and reasonable distribution of water resources can be guaranteed; meanwhile, in combination with the sprinkling irrigation coverage range and lawn mower path planning, cooperative operation of the courtyard intelligent sprinkling irrigation system and the lawn mower is achieved, and the overall efficiency and the intelligent degree of courtyard automation equipment in the operation process are improved.

The invention discloses a rice field irrigation and drainage decision-making method and system based on human-in-loop reinforcement learning, and the method is realized based on reinforcement learning, and the method comprises the steps: collecting real-time environment parameters of a target rice field in a current decision-making period; constructing an environment state vector based on the real-time environment parameters; constructing an irrigation drainage decision model of a continuous action space based on a soft actor commentator algorithm, wherein the model outputs an action according to the environment state vector; when it is judged that the output action deviates from a reasonable strategy or has a potential risk, a human-in-loop collaborative intervention mechanism in which a human expert participates is introduced, the output action is corrected, and a human-in-loop reward signal is generated through expert intervention; an output action is executed, environment feedback and a multi-target weighted reward signal containing a human-in-loop reward signal are collected, loop iteration is performed based on a closed-loop control system, and the irrigation and drainage decision model is updated; and obtaining irrigation and drainage decision output of the current decision period according to the updated irrigation and drainage decision model.

A comprehensive power supply method for agricultural irrigation equipment relates to the technical field of agricultural irrigation equipment power supply, and comprises the following steps: constructing a comprehensive power supply unit which comprises wind power generation equipment, photovoltaic power generation equipment and energy storage equipment; defining a water pump motor in the irrigation equipment as a critical load, and defining other equipment in the irrigation equipment as non-critical loads; adopting an ARI MA model to obtain prediction data of wind speed and illumination intensity; calculating the predicted power generation power of the wind power generation equipment based on the wind speed prediction data, and calculating the predicted power generation power of the photovoltaic power generation equipment based on the illumination intensity prediction data; constructing a comprehensive power supply strategy model; according to the comprehensive power generation power of each device in the comprehensive power supply unit and the consumption power of the irrigation device, a power supply mode with high irrigation stability is dynamically selected after analysis of the comprehensive power supply strategy model; according to the dynamically selected power supply mode, power is supplied to the irrigation equipment through a bidirectional inverter; the problem that traditional irrigation equipment is unstable in operation is solved.

The invention relates to the technical field of fertilization and irrigation regulation and control, and discloses a fertilization and irrigation two-way regulation and control system under water demand and fertilizer demand coupling modeling, and the system constructs the fertilization and irrigation two-way regulation and control system based on the water demand and fertilizer demand coupling modeling. The multi-source data acquisition module is used for acquiring soil, crop growth period and meteorological data through a layered soil sensor, crop image acquisition equipment and a meteorological acquisition point; the water and fertilizer scheme generation module calculates irrigation amount, fertilization type and dosage based on data, and adjusts intervals in combination with temperature; the operation execution module controls the operation of the irrigation and fertilization equipment and records the state; and the data correction module compares the sensor with the sample data to generate a correction scheme. The system realizes accurate regulation and control through a closed loop of data acquisition, scheme generation, execution and correction, simultaneously covers equipment state monitoring and early warning, and ensures efficient fertilization and irrigation to adapt to crop requirements.

The invention relates to the technical field of selenium-rich white tea production, and discloses an intelligent control system and method for automatic production of selenium-rich white tea. The system comprises a selenium-rich white tea growth environment regulation and control module, a tea growth stage recognition module, a selenium content self-adaptive supply module, a production equipment cooperative control module and a quality closed-loop feedback module. The growth environment regulation and control module collects tea garden multi-region environment parameters and establishes a dynamic monitoring set; the growth stage identification module divides growth stage identifiers in combination with the environment parameters, the bud and leaf morphological image sequence and the growth cycle time sequence data; the selenium content self-adaptive supply module generates a supply scheme and an equipment control instruction set according to the growth stage identifier and the soil selenium content data; the production equipment cooperative control module is linked with related equipment to execute a cooperative operation time sequence control process; and the quality closed-loop feedback module generates a correction amount according to the finished product detection data, and feeds the correction amount back to the growth environment regulation and control module for parameter iterative updating.