434 results about "Irrigation control" patented technology

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a meteorological data fused water-saving irrigation control method, device and equipment and a medium. According to the method, a historical meteorological data set is constructed through multi-source meteorological data fusion, a dynamic water demand table is generated in combination with a crop water demand characteristic database, and a crop water demand model is established based on soil moisture content data. A grid irrigation unit division and growth period coupling soil moisture content response matrix construction technology is adopted, a cooperative constraint is established between a water demand threshold value and a water saving benefit through a multi-objective optimization learning algorithm, and a personalized irrigation scheme is generated. A soil moisture content dynamic evaluation matrix containing a dynamic time warping operator is designed, and dynamic matching of the soil layered soil moisture content and a standard template is achieved. The contradiction between meteorological response lag and low water resource utilization rate in traditional irrigation is effectively solved, accurate irrigation decision is realized through multi-dimensional data fusion and an intelligent optimization algorithm, and the water-saving benefit and the agricultural water resource utilization efficiency are improved.

Provided is an intelligent irrigation control method for rice fields based on a cloud service platform and a system thereof. Constructing a planting condition data set and a condition coefficient, monitoring the current water supply environment of crops, and constructing a water supply coefficient. If the water supply coefficient is lower than the water supply threshold, based on the current growth data of crops, a plurality of corresponding irrigation strategies are matched from the pre-constructed crop irrigation knowledge graph, and the target strategy is selected from a plurality of irrigation strategies; constructing an irrigation observation coefficient set, and constructing irrigation coefficient. If the irrigation coefficient is lower than the efficiency threshold, use the trained strategy optimization model to optimize the target strategy, and control the irrigation coefficient based on the optimized target strategy to complete the irrigation of crops in the irrigation area.

The invention discloses an intelligent water and fertilizer integrated precise irrigation control method. The method comprises the following steps: step 1, establishing a multi-modal data acquisition network; step 2, inputting the data obtained in the step 1 into a trained LSTM neural network, and predicting future crop transpiration water demand; 3, according to nutrient concentration distribution fed back by a soil conductivity sensor in real time, combining with a water and fertilizer coupling migration model, dynamically calculating a nitrogen-phosphorus-potassium ratio of each irrigation zone, realizing precise mixing of a fertilizer mother solution and irrigation water through a three-channel proportioning pump, and implementing a variable irrigation decision; 4, dynamically adjusting the pulse irrigation duration of each branch through an MPC algorithm; and step 5, recovering the residual liquid at the tail end of the pipe network to a buffer tank, detecting the EC value of the recovered liquid through an electrochemical sensor, and taking the EC value as a reference parameter automatic correction proportioning algorithm of the next irrigation period. Accurate control over the irrigation process can be achieved, so that the water and fertilizer utilization efficiency is improved, and resource waste and environmental pollution are reduced.

The invention relates to the technical field of automatic irrigation control, in particular to a multi-sensor fusion agricultural precise irrigation system which comprises a leaf area analysis module, an environment evaluation module, a task arrangement module, an execution regulation and control module and a feedback regulation module. According to the method, the crop leaf area index is introduced to be compared with the growth stage target value, the dynamic expression of the group moisture demand is realized, the environmental evapotranspiration influence factor is generated in combination with illumination and temperature disturbance parameters, the timeliness of water demand driving force identification is enhanced, and the task execution sequence is judged by adopting regional water pressure disturbance data; a multi-region irrigation resource allocation process is optimized, a water quantity and pressure difference double-factor adjustment mechanism is utilized, the rationality and response accuracy of time length allocation are improved, water supply state analysis and task target deviation comparison are performed, dynamic supplementary irrigation regulation and control are realized, the closed-loop capability and supplementary irrigation accuracy of irrigation period control are improved, and the irrigation period control efficiency is improved. And the stable allocation capability and the irrigation response efficiency of the irrigation system under the influence of multiple variables are enhanced.

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 discloses an intelligent soil moisture content monitoring and precise irrigation linkage system, and relates to the technical field of irrigation. Key parameters such as moisture content, temperature, humidity and conductivity of soil are obtained in real time through a soil moisture content monitoring module, processed by a data acquisition and transmission module and transmitted to a control center; the control center carries out deep analysis and evaluation on the soil moisture condition through the edge computing gateway and the cloud decision controller, an accurate irrigation control instruction is generated, the irrigation control execution module carries out accurate regulation and control on irrigation equipment according to the instruction, and reasonable irrigation of a farmland is achieved. The system can comprehensively master the moisture condition of farmland soil, provides data support for precise irrigation, improves the precision and efficiency of irrigation, saves water resources, is intelligent and high in automation degree, can reduce manual intervention, reduces the labor cost, and improves the stability and reliability of irrigation at the same time.

The invention provides a high-standard farmland water and fertilizer integrated intelligent regulation and control method and system, and the method comprises the steps: obtaining terrain point cloud data and crop canopy water stress data of a high-standard farmland, generating an elevation gradient distribution feature set, and generating an elevation model according to the set; analyzing the crop canopy moisture stress data, and calculating a moisture stress index; performing spatial position matching on the terrain difference parameters and the micro-terrain difference parameters to generate a terrain compensation parameter set; establishing a time sequence optimization correlation model in the 5G edge computing node, and optimizing a pulse irrigation time sequence factor and a flow distribution weight by using the time sequence optimization correlation model according to the terrain compensation parameter set and the historical irrigation record so as to generate a multi-target irrigation control instruction; performing high-standard farmland water and fertilizer integrated intelligent regulation and control according to the multi-target irrigation control instruction and the water and fertilizer migration rate parameter corresponding to the microtopography difference parameter; water and fertilizer are accurately distributed according to needs, and the irrigation uniformity, the resource utilization efficiency and the crop yield are improved.

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.

The invention provides a farmland water efficiency data mining analysis method, which comprises the following steps of: acquiring multiple types of soil moisture content information, irrigation water quantity data and crop growth index data corresponding to different depths on different point positions acquired in multiple monitoring periods in a target farmland area to form a dynamic monitoring data set; the method comprises the following steps: acquiring a dynamic monitoring data set, performing multi-dimensional correlation analysis on the dynamic monitoring data set to generate a farmland water efficiency correlation analysis result, performing strategy matching on the correlation analysis result based on a preset water efficiency optimization strategy network to generate a water efficiency optimization regulation and control scheme, and finally feeding back the water efficiency optimization regulation and control scheme to a farmland irrigation control system. Irrigation parameter adjusting operation is triggered, and optimization of farmland water efficiency and reasonable utilization of water resources are achieved.

The invention discloses an intelligent farmland irrigation decision-making method based on digital twinning, and the method comprises the following steps: S1, collecting multi-source data of a farmland environment, and generating a time sequence farmland environment data set; s2, constructing a farmland digital twinborn model, and updating node and edge attribute information in real time; s3, constructing a time sequence diagram based on the updated twinborn model, and inputting the time sequence diagram to a time sequence diagram neural network; s4, optimizing structural parameters of the neural network of the time sequence diagram by applying a red-mouth blue-magpie optimization algorithm; s5, predicting the future soil water potential and the crop water demand trend by using the optimized network; s6, generating an irrigation control instruction according to the prediction result in combination with water resource supply; and S7, executing irrigation, monitoring the humidity of the land parcel and the crop state in real time, and updating the farmland digital twinborn model. According to the invention, by fusing the digital twinning, the time sequence diagram neural network and the Red-billed-blue-magpie optimization algorithm, accurate prediction and intelligent irrigation control of the future water demand trend of the farmland are realized, and the water resource utilization efficiency and the crop yield are significantly 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 AI-based digital twinborn irrigation area automatic irrigation method and system, and relates to the technical field of artificial intelligence, and the method comprises the following steps: collecting data, and constructing a digital twinborn model; irrigation demand prediction is carried out according to the digital twinborn model; making an irrigation control decision according to the irrigation demand prediction result; performing automatic irrigation based on the irrigation control decision content; crop growth evaluation is carried out according to the irrigation execution result; performing irrigation strategy optimization according to a growth evaluation result; maintaining and updating the irrigation system according to the irrigation strategy; and performing water resource management based on the planning result. According to the method, the digital twin model can be updated in real time and adapt to environmental changes by introducing the data stream based on the Internet of Things and a reinforcement learning mechanism. The feedback mechanism based on AI and reinforcement learning enables the digital twinborn model to more accurately simulate an irrigation environment and provide an optimal irrigation strategy under different environmental conditions.

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 provides an intelligent irrigation control method and system for a high-standard farmland. The method comprises the following steps: acquiring crop canopy image data and farmland terrain elevation data of different crop planting areas in the high-standard farmland; determining a crop growth stage based on morphological characteristic data in the crop canopy image data, and constructing a soil hydrodynamic simulation model according to a root distribution depth interval and a theoretical water absorption rate coefficient corresponding to the crop growth stage; and based on the farmland terrain elevation data and the soil porosity parameter, generating a soil moisture migration parameter by using a soil hydrodynamic simulation model, generating a pipe network pressure regulation and control instruction in combination with the soil moisture migration parameter and a preset irrigation strategy, and adjusting the water outlet pressure distribution of the intelligent irrigation pipe network according to the pipe network pressure regulation and control instruction. Generating an irrigation control instruction; accurate space-time matching of irrigation of the multi-crop rotation area is achieved, the water utilization efficiency is improved, and the problem of irrigation uniformity at the terrain difference and the rotation boundary is solved.

The invention relates to the technical field of agricultural intelligent irrigation, in particular to an intelligent irrigation control system and method based on meteorological environment acquisition, and the system comprises a sensing layer, a transmission layer, a control layer, a cloud layer and an execution layer. Then meteorological environment data collected by the sensing layer is transmitted to the control layer through a wireless transmitting module and a wireless receiving module, and a controller of the control layer is integrated with an edge computing unit, a communication unit and a decision-making unit; the problems that an existing agricultural meteorological monitoring and irrigation control system is high in network transmission load coefficient, and the edge computing capability of sensor nodes cannot be effectively utilized; the problem that an irrigation control algorithm based on a static threshold mechanism or a single statistical model lacks adaptive processing of rainfall time sequence prediction and dynamic response ability of specific requirements of crop growth stages is solved.

The invention discloses a high-water-saving accurate drip irrigation optimization method based on a population algorithm. The method comprises the following steps: S1, constructing a comprehensive input data set; s2, forming a multi-dimensional collaborative vector coding set; s3, constructing a multi-objective optimization model; s4, generating an initial improved shark search algorithm population; s5, in a global exploration stage, forming an evaluation result data set; s6, in a local development stage, recording a current optimal multi-target solution; s7, decoding the multi-dimensional collaborative vector code corresponding to the current optimal multi-target solution into a drip irrigation pipe network implementation data set and an irrigation scheduling implementation data set; and S8, generating an irrigation control instruction set based on the drip irrigation pipe network implementation data set and the irrigation scheduling implementation data set, and executing partition irrigation operation. According to the method, the pipe arrangement rationality and the water resource utilization efficiency can be optimized at the same time, and higher convergence and generalization ability are shown in complex terrains and heterogeneous crop distribution environments.

The invention discloses an agricultural irrigation control method and system, and relates to the technical field of irrigation control, and the method comprises the following steps: constructing a crop water demand model based on the fusion of crop physiological sensing data and a data driving model, and outputting a target irrigation flow; calculating a control error between the target irrigation flow and the actually acquired flow, and generating a control signal through a model prediction control strategy; inputting a static mapping model fitted by a Sigmoid function, and superposing the output of a dynamic correction model driven by state sensing data to generate a final driving signal; feedback data is collected, a driving signal and the feedback data are subjected to closed-loop comparison, and irrigation control strategy parameters are adaptively optimized through a reinforcement learning controller; according to the method, the crop physiological sensing data and the data driving model are fused, the space and time sequence feature fused water demand model is dynamically constructed, and the problems that agricultural irrigation cannot be accurately controlled and the irrigation strategy is rigid are solved in combination with a static and dynamic composite control mapping mechanism.

The invention provides an intelligent self-adaptive agricultural irrigation servo ball valve control system, which belongs to the technical field of irrigation control, and comprises a sensing layer, a control layer and an execution layer, the sensing layer is connected with the control layer, the control layer is connected with the execution layer, and the sensing layer comprises a soil parameter monitoring unit, a crop physiology monitoring unit and a space environment monitoring and prediction unit. The non-linear mapping relation between the light-temperature-humidity three-dimensional parameter space and the irrigation amount is established through the control layer, the water demand quantity of crops is accurately matched, meanwhile, the control precision of + / -2.5% of the irrigation amount is achieved, water data needed by plant growth are accurately analyzed by collecting the growth state, soil data and environment data of plants, and the water quality of the crops is improved. The water demand quantity control is more accurate, meanwhile, effect evaluation is carried out after irrigation, feedback data and timely adjustment opportunities are provided for later-stage control, and plant data of corresponding types can be set for learning to form a self-adaptive function of at least three types of crop growth models.

The invention discloses a control system and a control method for water and fertilizer integrated irrigation, and relates to the technical field related to irrigation, and the system comprises a data collection module which is used for collecting soil moisture content data and crop growth stage data; the water and fertilizer demand analysis module is used for performing water and fertilizer demand analysis and outputting soil water and fertilizer demand indexes in a preset irrigation time zone; the water and fertilizer loss compensation module is used for compensating the soil water and fertilizer demand index and outputting an irrigation target and a fertilization target; the mixing completeness optimization module is used for executing analysis and optimization of water and fertilizer mixing completeness; and the irrigation control module is used for controlling the water and fertilizer integrated irrigation device to perform water and fertilizer integrated irrigation on the preset irrigation area. The technical problem that in the prior art, due to the fact that the soil moisture content, the crop growth stage and weather factors cannot be comprehensively considered, the irrigation and fertilization precision and the water and fertilizer resource utilization efficiency are insufficient is solved, and the technical effect of improving the irrigation and fertilization precision and the water and fertilizer resource utilization efficiency is achieved.

The invention provides an integrated intelligent irrigation self-adaptive control system and method, and relates to the technical field of intelligent irrigation. Comprising the steps of collecting historical irrigation data and real-time irrigation data; preprocessing the irrigation data to obtain preprocessed irrigation data; an irrigation decision model is constructed through BiLSTM-CNN-Attention, and the irrigation demand change trend in a short time in the future is predicted; according to the irrigation demand change trend in a short time in the future, the irrigation amount needed by plants is calculated, and a self-adaptive irrigation control strategy is generated; and evaluating the irrigation decision model by using the model evaluation index, and dynamically adjusting hyper-parameters of the irrigation decision model according to feedback. According to the method, the irrigation decision model is constructed, the change trend of the irrigation demand in a short time in the future is predicted, the irrigation amount required by plants is calculated, and the self-adaptive irrigation control strategy is generated, so that the overall utilization efficiency of water resources is improved, and the irrigation work is ensured to be efficiently and continuously propelled.

The invention relates to the technical field of water conservancy irrigation control, and particularly discloses an energy-saving and environment-friendly water conservancy irrigation system informatization control method which comprises the following steps: S1, acquiring key environment parameters influencing irrigation operation in real time through a wind power detector deployed near a sprinkling irrigation opening, synchronously acquiring a reference working state and a target area of the sprinkling irrigation port; s2, abstracting natural wind into a computable wind vector by establishing a spatial reference coordinate system, and constructing a water drop kinematics model based on a physical law; with the aim of achieving accurate falling of water drops to a target area, model reverse solution is carried out, and the spray irrigation opening angle and the water pressure compensation amount needed for counteracting the wind power influence are accurately calculated; and S3, according to the solved compensation amount, the initial working parameters of the sprinkling irrigation opening are adjusted in real time, so that water drop drifting and evaporation caused by wind power are effectively inhibited, the water resource utilization rate is remarkably increased, and the purposes of energy conservation and environmental protection are achieved.

The invention relates to the technical field of agricultural engineering and ecological environment protection, in particular to a saline-alkali soil desalination improvement system and method based on brackish water recycling. The system comprises a data collection module which collects soil salinity data, meteorological data and crop growth data of the saline-alkali land in real time; the data processing and analyzing module is used for receiving and preprocessing the soil salinity data, the meteorological data and the crop growth data of the saline-alkali land, constructing a soil salinity prediction model and outputting a soil salinity prediction result; the irrigation strategy decision module is used for generating an optimal irrigation strategy by adopting a reinforcement learning algorithm according to the soil salinity prediction result and the preprocessed crop growth data; and the irrigation control system is used for performing brackish water irrigation operation on the saline-alkali soil according to the optimal irrigation strategy. Precise regulation and control of saline-alkali soil salinity can be achieved, brackish water is adopted as irrigation water, water resources are saved to a certain degree, and therefore the improvement cost of saline-alkali soil is reduced.

The invention provides an agricultural irrigation control method, system and device driven by an AI large model, and a medium, and belongs to the technical field of agricultural irrigation control, and the method comprises the steps: building an irrigation knowledge base and an irrigation decision set for a target crop based on the AI large model; constructing a target crop parameter set and a water demand prediction model, and training the water demand prediction model by using the target crop parameter set in combination with the irrigation knowledge base; target crop parameters collected in real time are input into the trained water demand prediction model, and target crop water demand prediction is carried out; and screening out an irrigation strategy from the irrigation knowledge base and the irrigation decision set according to the predicted value of the water demand of the target crop, and controlling the irrigation equipment based on the irrigation strategy. Through the combination of the AI large model and the deep learning model, the water demand of the crops is accurately predicted, the irrigation strategy is generated, the irrigation decision accuracy is improved, the waste of water resources is reduced, and the irrigation efficiency is improved.

The invention relates to the technical field of agricultural irrigation, in particular to an intelligent irrigation control system and method based on an intelligent crop growth model. The method comprises the steps of collecting temperature, humidity, illumination and soil moisture data of a crop growth environment, and taking the data as input of a machine learning model; an LSTM machine learning algorithm is utilized to construct a crop growth model used for predicting the crop growth trend and calculating the irrigation water demand; in the crop growth process, crop image data are acquired in stages and compared with a standard growth image, similarity and a correction coefficient are calculated, and the predicted growth trend is corrected in real time; according to the corrected growth trend, the irrigation water demand is dynamically adjusted, and closed-loop feedback control is formed; according to the corrected irrigation water demand, the irrigation equipment is accurately controlled for irrigation, and intelligent monitoring of crop growth and efficient utilization of water resources are achieved.

The invention relates to a drip irrigation pipe network zoning irrigation control method and system based on multi-objective optimization, and belongs to the technical field of artificial intelligence. The method comprises the following steps of collecting multi-source heterogeneous data and sampling point space coordinates, after abnormal value elimination and normalization processing, splicing features and normalized coordinates to form a joint feature vector, and obtaining irrigation partitions with continuous space and similar features through K-means clustering; constructing a multi-objective optimization model taking the zoning irrigation duration and the inlet target flow as decision variables; solving by adopting a hybrid strategy multi-objective evolutionary algorithm of chaotic mapping initialization and adaptive hybrid variation to obtain a Pareto optimal solution set; and finally, combining an entropy weight method and decision maker preference, determining an optimal irrigation parameter through a superior and inferior solution distance method, and introducing a real-time soil moisture monitoring feedback correction parameter. Partitioned precise irrigation of the drip irrigation pipe network can be achieved, multiple optimization objectives are balanced, the irrigation effect and the resource utilization efficiency are improved, and system energy consumption is reduced.

The invention belongs to the technical field of agricultural irrigation, and discloses a precision agriculture water and fertilizer integrated distribution method, which comprises the following steps: S1, data acquisition; s2, data processing and analysis; s3, formulating a water and fertilizer formula; s4, water and fertilizer distribution and conveying The method has the beneficial effects that by comprehensively collecting farmland soil, weather and crop growth data, a crop growth and water and fertilizer demand model is constructed, and accurate prediction and scientific formula making of water and fertilizer demands are achieved; by utilizing a drip irrigation and intelligent irrigation control system, water and fertilizer distribution is accurately regulated and controlled, the resource utilization efficiency is remarkably improved, waste is reduced, healthy growth of crops is promoted, and the yield and quality are improved; in addition, due to precise management, use of chemical fertilizers is reduced, environmental pollution is relieved, the ecological environment is protected, the labor intensity of farmers is reduced due to the intelligent management characteristic, and the production efficiency and flexibility are improved.

The invention discloses a rice growth period distinguishing method and an intelligent irrigation control device. Belongs to the technical field of crossing of intelligent agriculture and irrigation and water conservancy engineering and particularly relates to the technical field of rice growth period judgment and intelligent irrigation. The technical problem that an accurate and effective rice growth period judgment method for guiding intelligent rice irrigation is lacked at present is solved. The method specifically comprises the following steps: calculating a rice growth process index through an improved accumulated temperature model; and constructing a multi-factor coupling growth period judgment method, and judging the current rice growth period through the rice growth process index. The system comprises a sensing unit, a judgment and decision-making unit, an electric execution unit and an input and power supply unit, the judgment and decision-making unit receives the environment data of the rice field and the water layer depth data of the rice field, judges the current growth period of the rice according to a rice growth period judgment method, calculates the current water layer deviation and generates a control instruction to the electric execution unit.

The present disclosure relates to an intelligent water precise irrigation control system and method for cultivation of fruit vegetables in solar greenhouses. The system includes: an information acquisition module for acquiring information by means of sensors, the acquired information including at least one of soil information, ground environment information, irrigation device use process information, and crop information; a water demand prediction module for inputting the acquired information into a deep neural network to predict water demand of fruit vegetables; and an irrigation control module for forming a control instruction according to the predicted water demand, so as to irrigate periodically and quantitatively. In this way, irrigation time and irrigation amount can be decided automatically according to growth conditions and environmental parameters in the whole process. Intelligent irrigation can be achieved with high working efficiency and accuracy, and the water-saving, quality-improved and efficiency-improved production of fruit vegetables can be achieved.

The utility model relates to the technical field of water conservancy irrigation, and discloses a water conservancy irrigation diversion design structure which comprises a water supply main pipe connected with a water supply facility and used for controlling irrigation according to environmental values monitored by a water conservancy irrigation system, a plurality of diversion branch pipes are arranged on the water supply main pipe, and monitoring units are arranged on the diversion branch pipes. A water flow adjusting assembly is further arranged on the flow dividing branch pipe, and the water flow adjusting assembly comprises a pressure adjusting valve arranged on the flow dividing branch pipe; soil humidity and air temperature thresholds are set through the irrigation control system, when the thresholds are reached, the control system starts the water supply facility and remotely controls the corresponding pressure regulating valves to be opened and the opening proportion, stable water pressure in the flow dividing branch pipes is guaranteed through the pressure stabilizing valves, and the water flow sensor measures the water supply amount; after the water supply threshold value or the soil humidity threshold value is reached, the corresponding pressure regulating valve is closed to stop irrigation, the irrigation diversion proportion is flexibly controlled, and the effects of adjusting the diversion proportion and saving water resources are achieved.

The invention relates to the field of landscaping management automation, and discloses an intelligent irrigation method and system for landscaping, a controller and a sprayer. Constructing a soil moisture dynamic equation and a plant growth model according to the plant information and the environment information; based on the dynamic equation and the growth model, nozzle opening time is calculated; controlling the nozzle to open and close according to the opening time; and dynamically adjusting the opening time of the nozzle based on the environment information after the nozzle is opened. According to the invention, the soil moisture dynamic equation is constructed based on the soil humidity and weather change, and the nozzle opening time is calculated based on the dynamic equation and the growth model, so that efficient and intelligent irrigation control is realized. Compared with an existing timing automatic irrigation system, water resource waste caused by fixed-period watering is effectively avoided, meanwhile, the accuracy and timeliness of water supply are ensured, the modern requirements for water saving and precise maintenance are met, and remarkable environmental and economic benefits are achieved.