1987 results about "Agricultural irrigation" patented technology

The invention relates to the technical field of agricultural irrigation, in particular to an irrigation decision-making method based on an agricultural system model. The method comprises the following steps: calculating a growth environment index of a target farmland to generate a crop environment growth index; crop physiological status evaluation is carried out according to the crop environment growth index, short-term water demand prediction of the field is carried out, and field crop short-term water demand data is generated; performing irrigation scheme decision processing according to the short-term water demand data of the field crops to obtain optimal irrigation scheme data; irrigation equipment is regulated and controlled by using the optimal irrigation scheme data, and irrigation water quantity influence analysis is carried out to generate irrigation water quantity influence data; and performing adaptive irrigation control optimization according to the irrigation water quantity influence data so as to obtain intelligent irrigation control strategy data. Irrigation equipment can be regulated and controlled according to dynamic requirements of crops, intelligent and accurate irrigation decision control is achieved, excessive or insufficient irrigation is effectively avoided, and the water resource utilization rate is remarkably increased.

The invention discloses a water-saving control method for agricultural irrigation, a cloud platform and equipment, and relates to the technical field of irrigation water-saving control, and the method comprises the steps: determining the historical rainfall of the geographic position of a target farmland and the historical soil moisture content parameter of the target farmland; determining farmland water demand rule characteristics of the target farmland based on the historical rainfall and the historical soil moisture content parameters; constructing a farmland water-saving control model according to the farmland water demand rule characteristics; dividing a water demand area of the target farmland based on the crop information; based on the farmland water-saving control model and the soil moisture content parameter of each water demand area, obtaining a predicted irrigation value of each water demand area; acquiring a real-time irrigation value of each water demand area; and based on the predicted irrigation value and the real-time irrigation value, performing real-time regulation and control on a preset intelligent irrigation water valve in each water demand area. The problem that the utilization rate of water resources is low can be effectively solved.

The invention relates to the technical field of agricultural irrigation, and discloses a regional crop rolling correction intelligent irrigation decision-making method, which comprises the following steps of S1, collecting basic data and dividing an irrigation decision-making region; s2, crop growth model parameter sensitivity analysis; s3, localizing parameters of the crop growth model; s4, a regional space long-term irrigation decision scheme is formulated; s5, establishing a farmland information database based on remote sensing and Internet of Things technologies; and S6, constructing a regional irrigation system rolling correction system. The invention provides a regional crop rolling correction intelligent irrigation decision-making method. The method comprises the following steps: analyzing a mutual relation and an evolution trend of each element of an irrigation decision-making model; a regional grain and cotton crop irrigation system based on crop water shortage information perception-process recognition-efficient decision is constructed, and irrigation decisions are dynamically adjusted in real time according to future climate changes and field management changes, so that the balance between the water resource utilization efficiency and the crop yield is achieved.

The present invention relates to the technical field of agricultural irrigation. Disclosed are an intelligent irrigation strategy formulation method and system. The method comprises: determining the current irrigation strategy on the basis of a preset irrigation decision-making model and a Q-learning intelligent agent and according to the current agricultural state; on the basis of the current irrigation strategy, performing irrigation simulation on a target growing region to obtain a corresponding irrigation reward value and the next agricultural state, wherein the irrigation reward value is determined on the basis of the crop yield, the crop water demand, and the annual economic cost after irrigation, the current agricultural state, the current irrigation strategy, the irrigation reward value, and the next agricultural state form an empirical four-tuple, and a plurality of empirical four-tuples form an experience pool; and randomly sampling the empirical four-tuple from the experience pool as a training sample to train the Q-learning intelligent agent so as to obtain an optimal irrigation Q-value table which is used for determining a corresponding optimal irrigation strategy on the basis of any agricultural state of the target growing region. The present invention can efficiently obtain an accurate irrigation strategy.

The invention provides a dynamic self-optimization and adaptive learning method for a large model of a water conservancy system, and the method comprises the steps: data collection and preprocessing, model construction and initialization, real-time monitoring and model evaluation, difference analysis and adaptive adjustment strategy generation, model updating and optimization, continuous learning and knowledge accumulation, etc. According to the method, through dynamic data driving, intelligent difference analysis and self-adaptive optimization, conversion of a water conservancy system model from static passive to dynamic active is realized. Compared with the prior art, the method has accuracy, real-time performance, economical efficiency and sustainability, provides a landing technical path for intelligent upgrading of a water conservancy system, and has wide application potential in the fields of flood control and disaster reduction, agricultural irrigation, clean energy production and the like.

The invention relates to the technical field of agricultural irrigation management, in particular to a method and system for estimating the agricultural irrigation water consumption of an irrigation area. The method comprises the following steps: collecting soil, weather and crop data through a sensor and a detector, processing the collected data through an edge computing node, and then selecting an optimal transmission path to transmit the data through an Internet of Things gateway based on an https protocol; an irrigation area agricultural irrigation water consumption prediction model is constructed based on an intelligent algorithm of deep learning, the model stores, preprocesses and analyzes received data, then modeling and prediction are carried out on soil humidity, meteorological conditions and crop growth conditions, and finally an irrigation water consumption estimation result is obtained; and updating the irrigation area agricultural irrigation water consumption prediction model to ensure the accuracy and timeliness of model prediction. According to the method, through an advanced sensor technology, the Internet of Things, big data analysis and a machine learning algorithm, accurate estimation and optimal management of the agricultural irrigation water consumption of the irrigation area can be realized.

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 agricultural water-saving irrigation system and a water-saving irrigation method thereof. Relates to the technical field of agricultural irrigation. The height adjusting device comprises clamping assemblies and a plurality of lifting air cylinders, and the top of each lifting air cylinder is provided with the corresponding clamping assembly; and the two water supply pipelines are correspondingly arranged at intervals, each water supply pipeline is fixed to the lifting air cylinder through a clamping assembly, a plurality of irrigation pipelines are arranged between the two water supply pipelines at intervals, and each irrigation pipeline is provided with an irrigation spray head assembly. The agricultural water-saving irrigation method comprises the following steps: step 1, height adjustment; 2, adjusting the irrigation water flow; step 3, carrying out drip irrigation; 4, sprinkling irrigation is carried out; and 5, pressure adjustment. According to different irrigation requirements, switching between spray irrigation and drip irrigation can be achieved through the irrigation spray head assembly, so that more accurate moisture control is achieved, and the irrigation process is simple, direct and practical.

The invention discloses an agricultural irrigation area water resource intelligent management system and method, and relates to the field of intelligent management, and the method comprises the steps: building a correlation model of a crop growth stage and a basic irrigation threshold value through real-time collection of original weather station data, future 24-hour weather forecast, crop types, planting dates and other multi-dimensional data; and in combination with soil humidity sensor data and a channel pump station state, the basic irrigation starting threshold is dynamically adjusted by using an FAO formula or a deep learning algorithm to form a dynamic irrigation decision threshold. And then multi-source data is input into an irrigation decision module, an irrigation starting and stopping instruction is generated by judging the relation between the irrigation state and the soil humidity threshold value, and closed-loop management is achieved. Thus, the limitation of traditional irrigation static threshold management is broken through, and through meteorological data dynamic adaptation and multi-source data fusion decision making, on the premise of guaranteeing crop growth water demand, the water resource allocation efficiency is optimized, and the intelligent management level of an agricultural irrigation area is improved.

The invention relates to a modularized farmland water-saving irrigation device applied to the field of agricultural irrigation, through the arrangement of a self-monitoring filter unit, on one hand, the blockage condition can be judged through automatic detection of water pressure, on the other hand, after blockage, a filter disc for effective filtration can be temporarily and automatically replaced, and compared with the prior art, the modularized farmland water-saving irrigation device has the advantages that the cost is reduced; the influence on the irrigation water amount caused by the fact that the filter element is not replaced in time or is not replaced in time is effectively avoided, it is effectively guaranteed that crops can be stably irrigated, and stable growth of the crops is effectively guaranteed; meanwhile, when blockage occurs, the water pressure of one side, far away from a water source, of the multi-position filter is obviously reduced, at the moment, the sensing bin is obviously changed, and in cooperation with the action of a micro camera and an alarm, an alarm can be given after blockage occurs for multiple times and the filter disc is automatically replaced, so that a worker can conveniently position the abnormal multi-position filter, and the working efficiency is improved. A plurality of filter discs can be replaced at a time, so that the manual replacement frequency is effectively reduced, and the influence on the irrigation process is reduced.

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 provides an intelligent water-saving control method and system for an agricultural irrigation device, and the method comprises the following steps: generating a farmland state prediction index based on soil comprehensive information, weather information and crop information, and generating a farmland irrigation strategy; calculating an irrigation emergency degree based on the farmland state prediction index; if the irrigation emergency degree reaches the highest emergency degree, the filtering irrigation module is controlled to execute alternate irrigation operation; if the irrigation emergency degree does not reach the highest emergency degree, calculating to obtain the backwashing emergency degree of the filtering irrigation module; if the backwash emergency degree is greater than or equal to the irrigation emergency degree, generating a backwash strategy, and sequentially executing the backwash strategy and the farmland irrigation strategy; if the backwash emergency degree is smaller than the irrigation emergency degree, the filtering irrigation module is controlled to execute alternate irrigation operation on the target farmland. According to the invention, intelligent irrigation and backwashing control can be realized, and a better water-saving effect is achieved.

The invention relates to a multi-source remote sensing data quantitative inversion regional crop water consumption demand data fusion method. The method comprises the steps of data acquisition, data assimilation, data fusion and formation of a multi-source heterogeneous database. A distributed multi-source heterogeneous database is built by using advanced technologies such as comprehensive grid computing and cloud computing, a high-performance storage retrieval multi-source heterogeneous database and a data product oriented to a complex irrigation data system are built, agricultural information is collected in real time, a database is formed, and a database management system is established. And finally, efficient data management and sharing are realized. Related agricultural irrigation processes are systematically monitored, including monitoring of water consumption of crops, monitoring and forecasting of agricultural irrigation and the like. An agricultural production management system and an agricultural expert system are developed and are mainly used for diagnosing water shortage, fertilizer shortage and pest and disease damage of crops. The data acquisition forms are diversified, the parameter optimization quantity is large, the acquisition of large-range farmland parameters is realized, and a complete system is formed with remote sensing irrigation monitoring.

The invention relates to the technical field of soil physical property detection, in particular to a soil moisture content intelligent monitoring control method and system, and the method comprises the steps: multi-modal sensor data collection: deploying a multi-modal sensor array in a to-be-detected soil area; interference compensation and correction; self-adaptive calibration is carried out; water phase state distinguishing and soil moisture content index calculation; 4, soil moisture content evaluation: comprehensively evaluating the actual water content, the effective water content, the soil moisture content index and the water phase state information obtained in the step 4, and setting different grade threshold values to obtain a soil moisture content evaluation result; and intelligent control decision and execution: taking the soil moisture content evaluation result as input, and generating a corresponding control decision. The intelligent monitoring and controlling system for the soil moisture content can monitor the soil moisture condition in real time and can improve the precision and efficiency of agricultural irrigation through intelligent compensation, calibration and decision optimization, and therefore the purposes of water saving and efficient crop management are achieved.

The invention discloses a regional water safety-oriented water demand prediction method, electronic equipment and a storage medium, and the method comprises the steps: firstly collecting an industrial water impact factor, a living water demand prediction impact factor, and an agricultural irrigation water demand prediction impact factor; then, a prediction equation for reflecting the future evolution trend of each influence factor is established for each influence factor, then an industrial water demand prediction model, a living water demand prediction model and an agricultural irrigation water demand prediction model are established, and the water demand prediction models are subjected to validity inspection; and finally, performing water demand prediction on a to-be-predicted area by using the inspected water demand prediction model to obtain a water demand prediction result. According to the method, economic driving, social influence, climate change and other factors are fully considered, the water demand change mechanism is researched from the internal structure of the system, the problems that in existing water demand prediction, factor selection is not comprehensive, and the simulation effect is poor are solved, and the prediction effectiveness and accuracy are improved from the two aspects of the model structure and the prediction factors.

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.

The invention belongs to the field of water resources and water ecological protection, and aims to provide a water resource collaborative configuration reservoir ecological flow checking and scheduling method, so as to scientifically and reasonably check the ecological flow target, realize coordinated management of reservoir water supply, ecological protection and agricultural irrigation, and effectively improve the comprehensive utilization efficiency of the water resources. According to the technical scheme, the method comprises the steps of 1, analyzing and determining a downstream influence river reach of a reservoir project, investigating the distribution condition of ecological protection objects in the downstream influence river reach, and further analyzing ecological protection requirements; 2, calculating ecological flow targets step by step according to ecological protection requirements of various protection objects in the reservoir project influence river reach; 3, carrying out multi-functional reservoir water resource collaborative configuration, and comprehensively determining a reservoir ecological flow target; and 4, optimal scheduling of the ecological flow of the multifunctional reservoir. On the basis of the water resource collaborative configuration result of the multifunctional reservoir, the ecological flow scheduling rule of the multifunctional reservoir is perfected by formulating a coordination degree mechanism of ecological, living, industrial, agricultural and other industries.

The invention provides an intelligent agricultural irrigation optimization method and system, and relates to the technical field of agricultural management. The method comprises the following steps: acquiring first irrigation monitoring data of a plurality of sub-irrigation areas in a target planting area, calculating a temperature influence parameter and a humidity influence parameter of each sub-irrigation area, and constructing an irrigation feature vector of each sub-irrigation area; clustering the plurality of sub-irrigation areas to construct a plurality of irrigation area sets, calculating a water demand urgency factor of each irrigation area set and optimizing an initial irrigation scheme to obtain a first target irrigation scheme, and collecting second irrigation monitoring data of the plurality of sub-irrigation areas, and calculating an irrigation gain parameter of each irrigation area set, and optimizing the first target irrigation scheme to generate a second target irrigation scheme. According to the invention, accurate and efficient irrigation water distribution is realized.

The invention relates to the technical field of intelligent agriculture and precise irrigation, in particular to an intelligent control system for agricultural irrigation. Comprising a water-saving valve module, a sensing layer module, a control layer module, an execution layer module and a data and application layer module, and the water-saving valve module is used for achieving microsecond level flow regulation and control through piezoelectric ceramic driving and energy recovery and integrating a self-maintenance function. According to the invention, the LSTM network and the Duelling DQN algorithm are utilized to accurately discriminate the soil type and formulate an extreme weather coping scheme, dynamic optimization of an irrigation strategy is realized based on low-power-consumption edge calculation and an intelligent control algorithm, the adaptability and decision accuracy of the system are greatly improved, multiple advanced technologies are integrated, and the system is suitable for large-scale popularization and application. Comprising a magneto-rheological buffer, a TSN valve controller, an SMA emergency pressure release valve and a variable frequency water pump driver, pressure balance of a pipe network, high-precision synchronization of valves and safe treatment of sudden failures are ensured, and the stability and flexibility of an irrigation system are comprehensively improved.

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.

An irrigation pipe network system layout optimization method, an apparatus, a medium, and a product, relating to the technical field of smart agricultural irrigation. The method comprises: acquiring a system state of a target rotational irrigation pipe network system at the current moment (S10); inputting the system state of the target rotational irrigation pipe network system at the current moment into a trained deep Q network mode to obtain a control action at the current moment, wherein the process of determining the trained deep Q network model comprises: pre-training a deep Q network model on the basis of a virtual rotational irrigation pipe network system, and then re-training a preliminarily trained deep Q network model on the basis of a real rotational irrigation pipe network system (S20); and optimizing the target rotational irrigation pipe network system on the basis of the control action at the current moment (S30). In the method, the deep Q network model is pre-trained and re-trained by using the virtual rotational irrigation pipe network system and the real rotational irrigation pipe network system, thereby ultimately optimizing the target rotational irrigation pipe network system on the basis of the trained deep Q network model.

The invention provides an electromagnetic valve control system and method for agricultural irrigation, and relates to the technical field of electromagnetic valve control, and the method comprises the steps: determining a lag fault-tolerant point when an electromagnetic valve for controlling an irrigation nozzle generates control lag during agricultural irrigation according to the actual condition information and control state data of the electromagnetic valve; performing coordinated correction on the action time of the electromagnetic valve based on the action limit interval and the irrigation spraying amount to obtain a coordinated correction characteristic when the electromagnetic valve for controlling the irrigation nozzle generates control lag during agricultural irrigation, and determining a control fitting threshold when the electromagnetic valve performs irrigation spraying control at different action time according to the coordinated correction characteristic; and fault-tolerant control is carried out on an electromagnetic valve for controlling an irrigation nozzle during agricultural irrigation according to the action feedback deviation. The action feedback of the electromagnetic valve can be monitored in real time in the control process of the electromagnetic valve, the control lag is recognized and corrected, accurate coordination correction is carried out, the control parameters of the electromagnetic valve are dynamically adjusted, and the control stability of the irrigation system is improved.

The invention relates to the technical field of agricultural irrigation, in particular to an intelligent biological irrigation system and method based on remote control, and the system comprises a biological sensing unit, a neural computing decision engine, a driving execution unit, and a communication optimization architecture. Wherein the biological sensing unit is used for monitoring multi-modal data of plant physiological status, rhizosphere microenvironment and meteorological parameters in real time; the neural computing decision engine is used for constructing a pulse neural network, fusing a plant dimensional management system signal transduction mechanism and a multi-agent optimization algorithm, and generating a millisecond-level dynamic irrigation strategy; the driving execution unit is used for millimeter-level water quantity adjustment and pipe network self-repairing of the irrigation execution unit; the communication optimization architecture is used for seamless coverage and secure transmission of irrigation data, and meanwhile, a strategy is optimized by applying algorithms such as reinforcement learning and the like. Therefore, the problems of water resource waste, poor irrigation effect, untimely irrigation strategy adjustment and the like in the prior art are solved.

The invention relates to the technical field of agricultural irrigation, in particular to a configuration optimization method of a collaborative energy supply photovoltaic power generation water-saving irrigation system. According to water demand characteristics of local crops in the growth period, configuration is carried out by taking the minimum water demand of the crops in different growth periods as a target, and a system flow calculation model is constructed by considering change rules of photovoltaic module utilization efficiency, water pump operation efficiency and pipeline efficiency under different irradiation intensities, valve opening degrees and water lifting heights; and determining the optimal water lifting height according to the system overall efficiency and the solar energy utilization rate. The initial investment cost, the operation cost, the maintenance cost and the replacement cost of the water-saving irrigation system are used as constraints, and a system life cycle cost model is established. The optimal configuration parameters of the photovoltaic generator set, the reservoir and the storage battery are determined by adopting a crown porcupine optimization algorithm, so that the whole life cycle cost of the system is minimized, and the lowest water demand of crops is met. The system is suitable for a water-saving irrigation system composed of a photovoltaic generator set, a reservoir and a storage battery.

The invention discloses an intelligent dry-wet alternating agricultural irrigation device and an irrigation method thereof, and belongs to the technical field of irrigation. An intelligent dry-wet alternating agricultural irrigation device comprises an inserting rod and further comprises a mounting plate, the mounting plate is fixedly connected with the top of the inserting rod, and a water conveying main pipeline is rotationally connected to the mounting plate; the multiple water distribution pipes are arranged on the main water conveying pipeline at equal intervals, and each water distribution pipe communicates with the main water conveying pipeline; the multiple transverse moving mechanisms correspond to the water distribution pipes one to one, and the transverse moving mechanisms are arranged on the water conveying main pipeline; through cooperative action of the transverse moving mechanism and the displacement assembly, the water spraying parts on the water distribution pipe are controlled to conduct dry-wet alternate irrigation on the two sides of crops at different distances in sequence, two kinds of pipelines do not need to be arranged for conducting dry-wet irrigation on the two sides of the crops, the installation cost is low, the device is stored and placed when not used for irrigation, and the use is convenient. And a farmer can conveniently observe the operation growth condition.

The invention relates to a micro-spraying system irrigation uniformity control method and system, and belongs to the technical field of agricultural irrigation energy saving. Calculating the flow data of each nozzle according to the data of the flow meters of the branch pipes; calculating the pressure distribution of each node under the condition of no throttle valve by using the irrigation area pipe network hydraulic model, and finding out the part with too high or too low pressure in the pipe network; a throttle valve is installed on a main pipe of a pipe network in an irrigation area, and the opening degree of the throttle valve is adjusted and the pressure rise of a water pump is calculated by taking the most disadvantageous spray head in the pipe network as a pressure reference; the flow condition in the pipe network is monitored in real time through the flow meter, and data are uploaded; the pressure is calculated through the pipe network hydraulic model, when the water pressure in the pipe network is abnormal, the throttling valve needing to be adjusted and the opening degree are calculated, workers are informed, and early warning is conducted in time. The service life of the system is prolonged, and the irrigation efficiency and the water resource utilization rate are improved.

The invention discloses a photovoltaic agricultural irrigation system, and relates to the technical field of photovoltaic irrigation, the photovoltaic agricultural irrigation system comprises a photovoltaic panel group rotatably arranged on a photovoltaic support, an electric box, a pump body and an irrigation pipe communicated with the output end of the pump body through an irrigation main pipe, and the photovoltaic panel group comprises a first photovoltaic panel, a second photovoltaic panel, a third photovoltaic panel and a rotating frame; a rotating frame is arranged on a photovoltaic support, a plurality of photovoltaic panels are arranged on the rotating frame, the photovoltaic panels can track sunlight to conduct angle synchronous adjustment, solar energy converted by the photovoltaic panels is stored in an energy storage battery through an electric box, and therefore a pump body can be controlled to pump water from a water storage pool to conduct drip irrigation or sprinkling irrigation on farmland; meanwhile, the second photovoltaic panel and the third photovoltaic panel which can be stored can be unfolded towards the two sides of the first photovoltaic panel to adjust the light receiving area of the first photovoltaic panel, the area, impacted by wind and rain, of the first photovoltaic panel can be reduced through relative movement folding and storage, and the safety of the second photovoltaic panel and the third photovoltaic panel is effectively improved.

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 hydraulic characteristic simulation and evaluation method and system for a rotation irrigation unit pipe network of a drip irrigation system, and relates to the field of agricultural irrigation, and the method comprises the steps: obtaining the design and operation data of a tree-shaped pipe network of each irrigation cell, and the data of an irrigation emitter; based on the design and operation data, drawing a plane layout diagram of a rotation irrigation unit annular pipe network of each irrigation cell, and establishing a pipe network hydraulic model; according to the design and operation data and the emitter data, hydraulic information parameters of the pipe network hydraulic model are determined, and model simulation is carried out; performing model calibration according to a model simulation result and a field test result; and according to a simulation result of the pipe network hydraulic model after model calibration, evaluating an irrigation emitter flow simulation effect, and evaluating an annular pipe network optimization effect. The invention provides a relatively efficient drip irrigation pipe network optimization design method and evaluation method, and a basis is provided for transformation and upgrading of a large-scale drip irrigation system.

The invention relates to the technical field of GNSS-R remote sensing, in particular to a satellite-borne GNSS-R soil humidity fusion inversion method and system based on machine learning, and the method comprises the steps: obtaining multi-source data composed of satellite-borne GNSS-R internal feature value data and SMAP external auxiliary data; the method comprises the following steps: performing preprocessing based on acquired multi-source data, taking a matched data set as input to construct a machine learning inversion model, performing track point soil humidity inversion by using an optimized inversion model, performing grid point fixed weight fusion according to a track point soil humidity inversion value, calculating the weight of each GNSS system by using an improved self-adaptive CRITIC weight method, and calculating the weight of each GNSS system according to the weight of each GNSS system. And result verification and application are carried out based on the soil humidity inversion value of the grid point, weight-fixed fusion is carried out on the characteristic value of each GNSS system track point in each grid, abnormal value interference is effectively resisted, and the output continuous grid data can be directly adapted to agricultural irrigation zoning, hydrological model input, climate research and other business scenes.