512 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 discloses a drip irrigation parameter dynamic regulation and control method combined with multi-point sensor monitoring, and relates to the technical field of irrigation regulation and control, the method comprises the following steps: monitoring a multi-point sensor arranged in a target area to obtain target monitoring data; performing actual demand analysis on the target crop to obtain actual demand data; analyzing the target soil permeability of the target area to obtain a target irrigation period; and performing irrigation processing according to the actual demand data and the target irrigation period to obtain irrigation data. The technical problems that in the prior art, real-time requirements of crops cannot be accurately obtained, irrigation is difficult to implement according to soil characteristics, and the agricultural production efficiency and sustainability are limited are solved. The effects that multiple types of monitoring data are obtained by arranging the multi-point sensors in the target area, precise irrigation is achieved, the crop growth environment is optimized, good physicochemical properties of soil are maintained, and the technical effects of high efficiency and sustainability of agricultural production are improved are achieved.

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 irrigation control method and system based on meteorological information, and relates to the technical field of irrigation control, an area to be irrigated is divided into a plurality of sub-areas, and future rainfall (first rainfall) is predicted through a preset meteorological early warning model; acquiring water absorption data of each sub-region, calculating a water absorption coefficient, and determining absorbable rainfall (second rainfall) in combination with the first rainfall; and judging the water consumption of each sub-region according to the crop growth data and the historical data, and comparing the water consumption with the second rainfall. If the water consumption is greater than the second rainfall, the sub-region needs to be irrigated; according to the method, the soil water content of each sub-region can be finely judged, and whether rainfall meets the water demand or not is evaluated, so that whether irrigation is needed or not is judged, excessive irrigation and water resource waste are avoided, meanwhile, it is ensured that crops obtain enough water, water resource management is optimized, and the crop growth effect and agricultural production efficiency are improved.

The invention relates to the technical field of agricultural irrigation, in particular to a rice water-saving intelligent irrigation decision-making method and system, and the method comprises the following steps: determining the basic information of a target rice field, and obtaining a rice intelligent water-saving irrigation path by using a random landmark method and an adaptive heuristic optimization algorithm; the water demand of rice in the target rice field is obtained in real time, and then a water demand prediction model is constructed based on CEEMD and LSTM; predicting the water demand of the target rice field by using a water demand prediction model, and obtaining rice water-saving irrigation parameters of each irrigation point on the rice intelligent water-saving irrigation path in combination with rainfall information and a rice growth stage; according to the rice water-saving irrigation parameters, the irrigation system is used for completing irrigation of the target rice field. According to the method, the accuracy and the intelligent degree of rice irrigation decision making can be improved, the complexity of rice irrigation decision making and the dependence on expert experience are reduced, and more optional schemes are provided for rice irrigation control.

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 discloses a hilly slope irrigation control system based on artificial intelligence, and the system comprises a wind speed and wind direction detection module which is used for obtaining wind speed and wind direction data; the sprinkler head adjusting module is used for adjusting the angle and the spraying distance of a sprinkler head based on the wind speed and wind direction data; the soil humidity detection module is used for being connected with a soil humidity sensor and determining irrigation requirements according to data of the soil humidity sensor; the artificial intelligence control module is used for optimizing the irrigation time and the irrigation amount through an artificial intelligence algorithm; the sprinkling irrigation system control module is used for controlling the sprinkling irrigation system to irrigate according to the optimization result; the hilly slope irrigation control system based on artificial intelligence reduces the phenomenon of non-uniform growth of crops, facilitates healthy and uniform growth of the crops, and ensures that the crops can obtain a proper amount of water supply, so that normal growth and development of the crops are promoted, the yield and quality of the crops are improved, and the agricultural production benefits are further 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 discloses an intelligent precise irrigation system based on low-altitude remote sensing data driving. The intelligent precise irrigation system comprises a low-altitude unmanned aerial vehicle, a ground sensor network module, a meteorological data integration module, a space-time self-evolution feature map network module, a space-time adaptive attention network module, a context multi-arm tiger machine algorithm module and an automatic irrigation control module. The low-altitude unmanned aerial vehicle is used for acquiring remote sensing data of crops; the ground sensor network module is used for collecting real-time environment information; the meteorological data integration module is used for multi-modal data integration; the space-time self-evolution characteristic graph network module is used for generating a moisture demand distribution graph; the space-time adaptive attention network is used for optimizing an irrigation strategy; the context dobby machine algorithm module is used for dynamically selecting an optimal irrigation strategy; and the automatic irrigation control module is used for executing the optimal irrigation strategy and implementing differential intelligent irrigation management. According to the invention, precise and intelligent irrigation is realized by using the space-time self-evolution feature map network and the context multi-arm tiger machine algorithm.

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 discloses an intelligent control system and method based on agricultural big data. The method comprises the steps that soil index data, collected by a soil sensor, of multiple preset time points in a preset time period and growth state parameters, collected by a crop sensor, of crops at the multiple preset time points are obtained; performing feature extraction and feature interaction on the soil index data at the plurality of preset time points and the growth state parameters of the crops at the plurality of preset time points to obtain soil-crop growth state interaction feature vectors; generating an irrigation control instruction based on the soil-crop growth state interaction feature vector; the irrigation operation of the farmland can be automatically adjusted, and agricultural optimization and efficient production are achieved.

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 discloses an automatic irrigation control system and method for a tea garden based on photovoltaic power generation, and particularly relates to the technical field of agricultural irrigation, photovoltaic power generation capacity, tea tree growth irrigation cycle and meteorological condition information are collected in real time through multiple types of sensors, and formatting processing is carried out to ensure data availability; information lag degree analysis is carried out based on the processed data, and feedback lag early signs in photovoltaic power generation and tea garden irrigation cooperative control are identified; according to the lag signs, extracting power supply fluctuation, tea tree irrigation demand urgency and climate environment change characteristics, and generating corresponding fluctuation indexes, demand indexes and change indexes; dividing a feedback sampling period into a high-quality period or a low-quality period by using fuzzy logic, and dynamically adjusting the sampling frequency based on the current sampling frequency in the low-quality period; the feedback sampling frequency can be dynamically adjusted, and the stability and the response speed in the cooperative control process of photovoltaic power generation and tea garden automatic irrigation are effectively improved.

The invention discloses an urban landscaping intelligent irrigation control method and device, and relates to the technical field of intelligent control, and the method comprises the steps: obtaining urban landscaping distribution information, carrying out the irrigation demand analysis, and determining a plurality of subdivided irrigation regions through clustering; for each subdivided irrigation area, generating a plurality of original irrigation schemes in combination with the growth cycle of vegetation; on the basis of Internet of Things sensing modules deployed in all regions, environment state and vegetation growth state data are collected in real time, regional weather prediction information is combined, an original irrigation scheme is dynamically optimized, a personalized irrigation scheme is generated, and intelligent irrigation control is carried out accordingly. The technical problems that in the prior art, a traditional irrigation mode lacks precise irrigation control over vegetation in different areas, irrigation cost is high, and diversification requirements are difficult to effectively meet are solved, and the technical effects that the irrigation cost and water resource waste are reduced through regional precise irrigation, and the diversification requirements of vegetation in different areas are met 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.