35 results about "Irrigation scheduling" patented technology

The invention relates to the technical field of water conservancy monitoring, and provides an intelligent water conservancy monitoring method for an irrigation area and related equipment thereof. Water body boundary extraction is carried out on remote sensing image data to obtain an initial water body boundary data set, time sequence interpolation processing is carried out on the initial water body boundary data set to obtain a water body change trend data set, and soil moisture content prediction deduction is carried out according to a satellite cloud picture and the water body change trend data set to generate a soil moisture content prediction data set. Performing water level and area fitting on the regional DEM according to the water body change trend data set to construct a water storage capacity model, and performing crop water demand calculation according to regional data and crop data to obtain a crop water demand matrix data set; and performing constrained programming according to the soil moisture content prediction data set, the crop water demand matrix data set and the water storage capacity model to obtain a multi-target irrigation scheduling decision set. According to the application, dynamic balance between irrigation requirements and water resource supply is realized through multi-level and multi-source data fusion and intelligent scheduling.

The invention discloses an agricultural intelligent irrigation scheduling method and system based on deep learning, and relates to the technical field of intelligent agriculture, and the method comprises the steps: collecting field crop data in unit time, the field crop data comprising soil parameters, meteorological parameters and crop parameters; constructing an irrigation decision model, and analyzing a field water and fertilizer loss value according to the field crop data; setting a water and fertilizer dynamic coupling mechanism for irrigation scheduling, and adopting a dynamic compensation strategy for the field water and fertilizer loss value; based on the growth trend of field crops, constructing a farmland digital twinborn model, simulating the dynamic compensation strategy and generating a feature contribution value; and visualizing the feature contribution value to generate a farmland decision reason analysis report, thereby effectively reducing the pollution risk of agricultural activities to underground water and drainage basin water bodies, realizing an accurate irrigation strategy of agricultural soil, and avoiding resource waste.

This invention discloses a method for improving the accuracy of rice irrigation forecasts in irrigation districts based on digital twins, belonging to the field of agricultural water conservancy engineering technology. Based on digital twin technology, this invention divides the irrigation district into sub-regions using GIS and constructs a digital mapping model; it establishes a dynamic adaptive calculation system with multi-model selection, combining meteorological data completeness, rice growth period, and historical errors, and calculates the appropriate model using a dynamic weighted adaptation algorithm, obtaining the final value from the input data; it introduces soil moisture and field management correction coefficients to construct a dynamic calculation model; combined with the calculation of real-time water requirements for rice, it generates a refined irrigation scheduling plan for the sub-regions and achieves coordinated irrigation; and it corrects parameters using a gradient descent algorithm to ensure forecast accuracy. This method, through its dynamic adaptive calculation system, solves the accuracy problem in scenarios with missing data and has broad application value.

The invention discloses an irrigation water demand dynamic prediction method based on a water balance equation and a crop water production function. The method comprises the following steps: step 1, multi-source data arrangement; 2, the net irrigation water amount is calculated based on a water amount balance equation; 3, determining an evapotranspiration demand based on a crop moisture production function; step 4, determining precipitation threshold values of different drought degrees; and step 5, predicting regional irrigation water demand under different drought situations. According to the method, spatial and temporal dynamic changes of regional scale farmland irrigation water are estimated, crop evapotranspiration requirements under different yield levels are determined in combination with a crop moisture production function, and then spatial and temporal changes of irrigation water requirements under different drought degrees are predicted according to a water balance principle; compared with a traditional irrigation system, the method considers the potential influence of insufficient water on the crop yield, distinguishes the space-time difference of the irrigation water demand, and provides an effective technical support for the dynamic prediction of the agricultural irrigation water and the meeting of the agricultural drought defense requirements.

This invention discloses an irrigation scheduling system for irrigation canal systems, comprising: multiple remotely controlled gates, multiple flow meters, multiple water level gauges, and a cloud-based processing and control subsystem. Each canal has a remotely controlled gate at its inlet; the flow meters are located downstream of the remotely controlled gates; a water level gauge is installed at both the head and tail of each canal; the cloud-based processing and control subsystem sends gate adjustment commands to the remotely controlled gates based on the instantaneous flow rate detected by the flow meters. This invention detects water flow in the canals using flow meters and water level gauges and transmits the data wirelessly to the cloud-based processing and control subsystem. This allows for real-time acquisition of irrigation data for the canal system and automatic control of the gate openings, improving the automation level of irrigation canal system water management. This significantly reduces the difficulty of water resource scheduling in irrigation areas, saves labor, improves the efficiency of water resource scheduling, and further enhances irrigation area management efficiency.

The invention discloses a male parent machine-transplanted seedling method for improving hybrid rice seed production yield, saving cost and improving efficiency, which comprises the following steps: establishing a coupling model of male and female parent sowing difference period and machine-transplanted seedling age based on ecological adaptability parameters of hybrid rice parents, and implementing bed soil preparation by adopting a printing sowing and seedling raising technology; a seedling raising tray with a specific length-width ratio is adopted to complete staggered-period sowing of a first-stage male parent and a second-stage male parent in different periods, an irrigation system based on a moisture tension threshold value is implemented through a seedling field environment management strategy of temperature-light-water-gas multi-factor coordinated regulation, and two-stage male parent seedlings are unitized and integrated by applying a seedling physical form adaptation technology; and a matching technical scheme for precise field moisture management based on the plant physiological water demand rule is established. The method has the core advantages that the technical bottleneck that traditional male parent mechanical transplanting is poor in adaptability is broken through, seed production combination and cultivation crop rotation are accurately matched through the parent coupling model, two-stage male parent lossless integration and single-row mechanical transplanting are achieved in combination with a standardized seedling raising system, and the transplanting quality and density uniformity are guaranteed.

The application discloses a soilless culture substrate drainage amount monitoring device, and technical scheme points are as follows: the device comprises a substrate bag, substrate exudate enters a drainage collection barrel along a reserved special flow channel, a substrate scale in real-time operation for 24 hours timely weighs the weight change, and synchronously transmits the weight change to a software data platform, so that first drainage time and total drainage amount and other data can be mastered, irrigation amount data is combined, a drainage ratio is calculated, an important index for judging whether irrigation is reasonable is obtained, substrate parameter change conditions such as temperature, humidity, pH value and conductivity of different tanks, different substrates and different climates are determined, if the above parameters change extremely and exceed a safety threshold, a software data platform sends a warning information, equipment and irrigation conditions can be checked, and the important goals of optimizing an existing irrigation system, guaranteeing a healthy physical and chemical environment of a crop rhizosphere and saving water and fertilizer are achieved.

The application provides a kind of digital garden water resource management and intelligent irrigation scheduling system, it is related to garden engineering and water resource management technical field, including, data acquisition unit, the data acquisition unit is to the environmental state of garden area, irrigation operating state, garden space structure information and vegetation configuration parameter is collected to form basic perception data.This digital garden water resource management and intelligent irrigation scheduling system, through the unified collection of garden area environmental state, irrigation operating state, garden space structure information and vegetation configuration parameter, and based on space mapping constructs garden water resource digital twin model, comprehensively analyzes the water demand degree of each area, water supply capacity and historical water use characteristics to form global water use state information, realizes the continuous, fine modeling and analysis of the overall water use state of garden, improves the accuracy and cooperativity of cross-regional water use configuration and management.

This invention discloses a method for estimating rice evapotranspiration considering flooded layer depth, belonging to the field of crop evapotranspiration simulation technology. The method includes the following steps: acquiring meteorological data, flooded layer depth data of paddy fields, rice growth data, and measured evapotranspiration data within the study area; considering the influence mechanism of flooded layer depth on evapotranspiration, adding the influence of flooded layer depth on canopy resistance to the canopy resistance model (such as the Jarvis canopy resistance model); considering the influence of flooded layer depth on water layer heat storage, constructing a water temperature-surface heat flux model; coupling the canopy resistance model and the surface heat flux model into the Shuttleworth-Wallace model to improve the evapotranspiration model; and using the improved Shuttleworth-Wallace model to predict evapotranspiration throughout the entire growth period under irrigation at different flooded depths, for evaluating the water-saving effects of various irrigation methods. This invention, by improving the Shuttleworth-Wallace model and incorporating the influence mechanism of flooded layer depth on evapotranspiration into rice evapotranspiration simulation, helps improve the accuracy of rice evapotranspiration simulation and provides a basis for formulating reasonable irrigation systems.

The invention relates to the technical field of sponge city greening and irrigation, in particular to a multi-dimensional data fusion sponge city greening and irrigation scheduling system, which comprises a data acquisition module for acquiring soil humidity, meteorological data and water source conditions, analyzing the influence of precipitation and temperature on the soil humidity and generating a soil humidity record; the water source evaluation module analyzes rainwater accumulation and underground water level fluctuation and calculates water supply capacity, the greening demand evaluation module extracts water demand indexes and generates a water demand evaluation result, and the irrigation scheduling optimization module calculates irrigation frequency and time interval, adjusts scheduling risk and generates an optimization result. The method comprises the steps of constructing a soil humidity difference chart, deducing an evaporation trend, identifying a water loss rate, measuring and calculating supply capacity and response delay, constructing a water demand model, implementing irrigation sequencing according to priority, distributing irrigation frequency and interval, avoiding cross scheduling, comparing soil humidity with a target interval after execution, adjusting the irrigation amount and time period in real time, and forming a closed-loop mechanism.

This invention discloses a method for dynamic prediction of irrigation water demand based on the water balance equation and crop water production function, comprising the following steps: Step 1, multi-source data processing; Step 2, calculation of net irrigation water volume based on the water balance equation; Step 3, determination of evapotranspiration demand based on the crop water production function; Step 4, determination of precipitation thresholds for different drought levels; Step 5, prediction of regional irrigation water demand under different drought scenarios. The method of this invention estimates the spatiotemporal dynamic changes of farmland irrigation water volume at a regional scale, combines this with the crop water production function to determine crop evapotranspiration demand at different yield levels, and then predicts the spatiotemporal changes of irrigation water demand under different drought levels based on the water balance principle. Compared with traditional irrigation systems, this method considers the potential impact of insufficient water on crop yield and distinguishes the spatiotemporal differences in irrigation water demand, providing effective technical support for dynamic prediction of agricultural irrigation water use and meeting the requirements of agricultural drought prevention.

The invention provides an intelligent garden water-saving irrigation system based on a cloud platform. Relates to the technical field of garden engineering and information, and comprises a data acquisition module, the data acquisition module obtains environment operation data of multiple garden irrigation areas through a sensor, carries out time marking on the environment operation data, forms area environment data through the time marking, and sends the area environment data to a server; according to the intelligent garden water-saving irrigation system based on the cloud platform, unified collection and analysis of environmental data of multiple garden areas are realized by establishing a cloud platform architecture, information such as soil humidity, meteorological environment and irrigation execution state is sensed in real time, and data consistency is ensured by a time synchronization mechanism; according to the method, normalization and difference calculation are carried out on a multi-region environment data set, comprehensive environment state parameters and deviation characteristics are formed, dynamic monitoring and accurate analysis of garden water resource states are achieved, and scientificity and real-time performance of irrigation scheduling are improved.

The invention discloses a wheat soil moisture prediction method based on a mixed PPLSTM model, and the method comprises the following steps: S1, obtaining soil moisture data in a wheat growth period, and carrying out the preliminary screening of data features; s2, performing data preprocessing on the acquired soil moisture data; s3, performing dimension reduction processing on the high-dimensional features by using principal component analysis (PCA); s4, optimizing hyper-parameters of the long short-term memory (LSTM) network by using particle swarm optimization (PSO); s5, inputting the optimized LSTM network into training data for training to obtain a prediction model; s6, testing the trained model by using the verification set, and outputting a prediction result; and S7, optimizing parameters of the LSTM model according to a test result, and performing final training to obtain a model with optimal prediction precision. According to the method, the PSO optimization algorithm, the PCA dimension reduction algorithm and the LSTM network are combined, so that the problems that in a traditional method, high-dimensional input features are redundant, the time dependence relation capturing capacity is insufficient, and hyper-parameter selection depends on experience are solved. Compared with a traditional LSTM model, the method has the advantages that on the basis of keeping the sequence modeling capability, the prediction precision and stability are remarkably improved, and a more reliable decision basis can be provided for intelligent irrigation scheduling, so that the water-saving irrigation practice is effectively promoted, and the sustainable development of precision agriculture is promoted.

The invention discloses a crop growth environment intelligent monitoring and irrigation control system based on big data, and relates to the technical field of intelligent irrigation, and the system comprises a cloud scheduling server which is used for issuing an irrigation scheduling instruction with an expected execution timestamp to an edge controller; the edge controller is used for entering a first control state and calculating a pressure change rate when a local clock reaches an expected execution timestamp; if the irrigation valve is opened in a second control state in a continuous preset number of sampling periods, adding a first state flag bit to the acquired sensing data and packaging the sensing data into a data packet to be reported in an isolation period from the opening of the irrigation valve; the cloud scheduling server is used for receiving a data packet, and if a first state flag bit is analyzed from the data packet, the data packet containing sensing data is intercepted and forbidden to be input into the built-in compensation algorithm module; according to the invention, the problem that the local clock of the edge controller is easily influenced by the environment to generate drifting, and pipe network pressure resonance is caused is solved.

The application discloses a kind of field scale water salt migration drive carbonate migration time relationship monitoring and control method, comprising: based on different irrigation system setting field test processing, using profile water temperature salt monitoring system arranged in each test plot automatically continuous acquisition high-frequency data;Based on different irrigation mode stage acquisition low-frequency data;Using analytical detection technology to process low-frequency data, obtain low-frequency carbonate index data;Based on low-frequency data and high-frequency data, establish deviation function and carry out regression correction to high-frequency parameter to obtain calibrated high-frequency data;Calibrated high-frequency data and low-frequency carbonate index data are processed, to obtain synchronized water salt parameter sequence and carbonate index sequence;Using time lag regression modeling method to process synchronized water salt parameter sequence and carbonate index sequence, to obtain prediction model;Based on prediction model, identify key water salt driving factor and corresponding lag time, and generate irrigation control decision.

The invention relates to the technical field of intelligent agriculture and water resource management, and provides an agricultural intelligent water consumption management and control method and device, and the method comprises the steps: obtaining multi-source sensing data which comprises ground sensing data, remote sensing image data and meteorological background data; performing fusion processing on the multi-source sensing data to generate a standardized spatio-temporal data set; inputting the standardized spatio-temporal data set into a preset crop water demand evaluation model, and outputting the current water demand grade of the crop; inputting the standardized spatio-temporal data set into a preset regional water resource configuration model, and outputting a regional available water source priority; calling a multi-water-source configuration strategy based on the current water demand level of the crops and the priority of the available water sources of the region, and generating an irrigation scheduling instruction; and issuing the irrigation scheduling instruction to a field execution terminal to execute a variable irrigation action. The problems that in the prior art, agricultural water management efficiency is low, and water resources are seriously wasted are solved, and precise irrigation and efficient utilization of the water resources are achieved.

The present application relates to the field of artificial intelligence and data processing technology, in particular to a method and system for estimating irrigation water consumption based on adaptive dynamic feature fusion, specifically as follows: real-time collection of irrigation water consumption data; preprocessing of the collected water consumption data; constructing a dynamic lag embedding matrix based on historical water consumption information, and then fusing it with periodic features to obtain historical fusion features; constructing an irrigation water consumption estimation model for agricultural irrigation in the irrigation area based on a deep LSTM model, inputting the preprocessed data and historical fusion features into the model for training until the preset condition is met and the training is stopped, obtaining the trained model; incremental training of the trained model; inputting the newly collected irrigation water consumption data and meteorological data into the incrementally trained model to estimate the irrigation water consumption for agricultural irrigation in the irrigation area. The present application can solve the problems of prediction bias and inaccuracy caused by dynamic data, and thus improve the scientificity of irrigation scheduling and the robustness of water use efficiency.

The application discloses a large-scale irrigation area irrigation strategy making method based on supply-demand optimization adaptation, belongs to the technical field of irrigation scheduling, and can solve the problem of low irrigation benefit in the prior art. The method comprises the following steps: dividing a large-scale irrigation area into multiple irrigation subareas, and dividing the whole year into multiple irrigation time periods according to an irrigation system of preset crops in the large-scale irrigation area; determining irrigation water demand of the large-scale irrigation area in each irrigation time period and available water supply of a water supply reservoir supplying water to the large-scale irrigation area in each irrigation time period; determining an excess time period when the available water supply is greater than or equal to the irrigation water demand, and determining a deficiency time period when the available water supply is less than the irrigation water demand; determining irrigation water and excess water of each irrigation subarea in each excess time period and irrigation water and deficiency water of each irrigation subarea in each deficiency time period according to the available water supply and the irrigation water demand, and further determining an irrigation strategy of the large-scale irrigation area. The application is used for making the irrigation strategy.

The invention discloses an ecological restoration management and control method and system for interplanting poplar and jerusalem artichoke in a windy and sandy area, and relates to the technical field of ecological engineering management and control, and the method comprises the steps: firstly collecting an environment parameter EP1 before planting, and calculating a wind erosion potential index W of each grid unit, so as to match differentiated planting planning parameters, and achieve the scientific layout according to local conditions; after planting, an environment parameter EP2 after planting is obtained, the daily compensation water amount ET is calculated, precise irrigation of the composite canopy is achieved, and the water resource utilization rate is increased. Meanwhile, a biological hazard index I is calculated, a patrol scheduling scheme APL is generated, and pest control is converted into active early warning from passive response. And finally, an irrigation scheduling scheme BPL is generated and sent to an automatic irrigation control terminal, a patrol scheduling scheme APL is sent to patrol personnel, key parameters are stored in a log data set HIS, a complete management and control process from data acquisition, scientific analysis to accurate execution is completed, and long-term health and stability of an ecological restoration project are ensured.

The invention discloses a landscaping maintenance monitoring and early warning method and system, and relates to the technical field of garden irrigation, the system comprises a sensing module, an execution module and a communication module, the sensing module is used for collecting image and video data, the execution module is used for executing irrigation and fertilization operation, and the communication module is used for data interaction inside and outside the early warning system. The early warning system further comprises an image recognition module and a decision intelligent agent, the sensing module comprises image acquisition equipment and video acquisition equipment which are arranged on a fixed monitoring point and a mobile platform, and the sensing module is used for acquiring visual data of a plant growth state; the system has the advantages that plant physiological status and pest and disease damage signs are automatically analyzed through the image recognition module, simulation deduction is performed by fusing multi-source environmental data in the digital twinborn body through the decision agent, and optimized decision instructions such as scientific water and fertilizer ratio, irrigation scheduling and pest and disease damage intervention strategies can be generated.

The invention discloses an irrigation district resource regulation and control method and device under water-salt constraint, and the method comprises the steps: obtaining the hydro meteorological data and soil and crop data of a target irrigation district, dividing the irrigation district into a plurality of layers in the vertical direction, building a water-salt balance model according to the number of layers, and representing the water-salt dynamic process between different soil layers. The refinement degree of the model in the aspects of simulating irrigation area groundwater supply, evapotranspiration, salt migration and the like is improved, so that the problem of simulation result deviation caused by traditional homogenization treatment is solved. Secondly, price data, water supply data and environment data are further obtained, corresponding objective functions and constraint models are constructed, and then the objective functions and constraint models act together with a water-salt balance model to form a resource optimization model, so that multi-objective cooperative regulation and control can be realized under the condition of considering multiple factors such as economic benefits, water supply safety and ecological environment protection, and the resource optimization efficiency is improved. By combining actual irrigation area execution, it can be ensured that the irrigation system is closer to the crop water requirement rule and the water-salt safety threshold value, and the water resource utilization rate is increased.

The application belongs to the technical field of irrigation scheduling management, and specifically discloses an automatic water resource optimal scheduling management method based on a demonstration area of an irrigation area, which comprises the following steps: acquiring N irrigation areas in a current concerned irrigation area, and setting up a scheduling set containing multiple water irrigation modes for each irrigation area; constructing a selection optimization algorithm for selecting water irrigation modes for all irrigation areas one by one at each time; acquiring a local state set of each irrigation area in real time, inputting all local state sets into the selection optimization algorithm to select water irrigation modes of all irrigation areas at the same time; and having the following advantages: through real-time acquisition of the local state set and application of the selection optimization algorithm, the adaptability of the irrigation system is enhanced, the water resource allocation is optimized, and the irrigation efficiency and crop growth are ensured. Through intelligent management, water resource waste is reduced, cost is reduced, and economic and environmental sustainability of agricultural production is improved.

The invention relates to the field of automatic control and agricultural irrigation, and discloses an automatic irrigation control method. The method comprises the steps that a sensor installation list is acquired, node configuration and weather interface parameters are acquired, naming standardization, unified time service binding and step size resampling processing are carried out, and an acquisition preparation package structure is generated; soil water content threshold binding, partition association and water demand estimation are executed, and an irrigation scheduling list structure is generated; performing execution channel mapping, low-energy-consumption mode configuration and safety interlocking loading to generate an execution result recording structure; and performing execution-perception alignment under a uniform step size grid, performing deviation analysis and threshold re-calibration associated with interlocking and capability events, and performing rule version synchronous updating to generate a closed-loop configuration structure. According to the invention, self-learning closed-loop control of acquisition, analysis, execution and feedback is realized, and the method has the beneficial effects of adaptive adjustment, water saving, high efficiency, stability and reliability.

The application discloses a durian garden intelligent sprinkling irrigation scheduling method based on climate time sequence analysis, relates to the technical field of intelligent sprinkling irrigation control, and comprises the following steps: collecting state sensing data of each sprinkling irrigation partition of a durian garden, performing time sequence arrangement and partition corresponding on the state sensing data, identifying climate evolution control states of each sprinkling irrigation partition in combination with partition environment conditions, forming a partition control state sequence, classifying and corresponding the partition control state sequence according to rainfall condition changes and water requirement changes, configuring a rain-remaining control window, a post-rain verification control window and a supplemental irrigation rearrangement control window for each sprinkling irrigation partition, and performing window control processing according to scheduling target execution windows corresponding to the control windows to form a window scheduling table. The rain-remaining control window, the post-rain verification control window and the supplemental irrigation rearrangement control window configured for each sprinkling irrigation partition make the sprinkling irrigation arrangement no longer stay at single-point judgment, which is helpful to reducing invalid sprinkling irrigation, repeated sprinkling irrigation and post-rain leakage irrigation.

The invention relates to the technical field of irrigation district scheduling, in particular to an irrigation district intelligent scheduling system based on crop water demand prediction, which comprises a data acquisition module and an irrigation scheduling module, the irrigation scheduling module comprises an irrigation scheme determination unit for matching a historical irrigation scheme according to the real-time water demand index of the crop; the water absorption efficiency analysis unit is used for determining whether the water absorption efficiency is qualified or not according to the dynamic change rate of the gravity water proportion; the microelement determining unit is used for determining whether the historical scheme is qualified or not according to the concentration stability index of the interactive combination of the microelements when the water absorption efficiency is not qualified; determining a water source scheduling unit of a scheduling mode according to the acute injury index; the irrigation scheme adjusting unit is used for determining the irrigation frequency according to the daily average moisture consumption rate of the root zone and correcting by using an acute damage index; and the irrigation effect monitoring unit is used for determining whether damage repair is qualified or not according to the descending rate of the gravity water proportion and adjusting a preset stability index. The water resource utilization rate is increased, and meanwhile the crop yield is guaranteed.

The application discloses a farmland water demand prediction and irrigation control method. The application belongs to the technical field of farmland intelligent irrigation and water resource allocation. The method comprises the following steps: acquiring historical daily meteorological data, current annual daily rainfall, evaporation, soil moisture content and other basic data of a target farmland (irrigation area), and presetting meteorological data and planting structure of a prediction period; determining crop water demand and drainage capacity according to basic parameters, and correcting water supply parameters; constructing a daily water distribution model and completing model parameter calibration; determining initial conditions, combining dynamic changes of crops, and performing daily prediction by using a water balance model; generating an irrigation or drainage control scheme, and outputting an irrigation schedule in the whole growth period and annual total water demand prediction results and generating the irrigation schedule. The technical scheme can realize agricultural water demand prediction and intelligent irrigation control in an irrigation area, lays a foundation for guaranteeing food safety, and provides a decision basis for regional water resource allocation.

The invention discloses an irrigation district scheduling plan generation method suitable for a demand-determining scene. The method comprises the following steps: step 1, newly adding an annual scheduling plan file; 2, determining a high-increase and low-decrease coefficient; 3, determining the total amount of water needing to be distributed by different water sources in the next year; 4, water of different water sources in the next year is distributed to all irrigation areas; 5, determining the planned distribution total water volume of the next year; 6, determining distribution of domestic, industrial, ecological and agricultural water in each irrigation region in the next year; step 7, generating a final result table of the irrigation area scheduling plan in the next year; and finally generating a structured table according to irrigation area management requirements. The method has the advantages that the annual scheduling plan can be automatically generated in the demand mode, the cost is low, the adaptability is good, and the efficiency is high. The invention further discloses a system suitable for the irrigation district scheduling plan generation method for the demand-determining scene.

The invention relates to the technical field of slope stabilization, and discloses a precise irrigation system optimization method based on high and steep slope characteristics and a dynamic threshold, in the precise irrigation system optimization method, a'slope stability-irrigation threshold 'association mechanism is established by quantifying the critical water content theta c of the position, most prone to unbalance, of a high and steep rock slope; it is ensured that the water content of the representative easy-to-unbalance position after irrigation is smaller than or equal to theta c, and the slope instability risk is controlled; in combination with the water demand difference of grass and shrub, comprehensively considering the plant water demand, the plant wilting coefficient, the plant species and proportion and the soil key parameters, and determining a proper irrigation threshold interval; dynamic adjustment of irrigation volume and period is realized by fusing short-term weather forecast, and meanwhile, a bottom treatment mechanism for abnormal weather such as sudden heavy rain, sudden strong wind and high temperature is formulated; finally, the problems that in the prior art, side slope stability and irrigation requirements are completely disjointed, mixed plants are seriously insufficient in water requirement adaptability, irrigation regulation and control are static, and scientific basis is lacked are solved.

The invention discloses a day scale optimal irrigation system determination method based on Jensen model-dynamic programming-crop growth model coupling. The method comprises the following steps: acquiring research area data; carrying out data processing to obtain day-by-day input data of the dynamic planning model, wherein the day-by-day input data comprises the daily effective rainfall, the daily planned wetting layer depth, the daily effective rainfall of the planned wetting layer, the daily moisture sensitivity index and the daily maximum evapotranspiration of crops; developing an irrigation system optimization program for daily calculation based on a Jensen model and a dynamic programming algorithm, and calculating a daily scale optimal irrigation scheme based on the Jensen model under a water limiting condition; adjusting the irrigation scheme according to the single minimum irrigation amount to obtain a better solution set; inputting the better solution set into the verified crop growth model to calculate the crop yield and the water utilization efficiency to obtain a Pareto optimal solution set; and an optimal daily scale irrigation system is selected. The invention provides a method which gives consideration to irrigation system optimization and growth condition simulation and can provide a daily-scale irrigation system for any crop.

The invention discloses an irrigation scheduling method based on remote sensing fusion and automatic linkage of an unmanned aerial vehicle, and the method comprises the steps: building a spatial incidence relation among a canal system, a measurement and control integrated gate, a farming land parcel and crop planting distribution; specific association can be realized between crop species in different farming land parcels and the control relation of the measurement and control integrated gate to the specific farming land parcels. The unmanned aerial vehicle is used for collecting low-altitude remote sensing data, the evapotranspiration inversion model and the field moisture dynamic balance model are combined, the actual crop evapotranspiration and the net irrigation water demand are calculated for the land parcel level water demand control unit, and the limitation that in the prior art, water demand prediction depends on fixed parameters, and spatial heterogeneity is difficult to reflect is overcome. The water distribution scheduling model is constructed based on the land parcel level net irrigation water demand and the management attribute, and the optimal scheme is executed through the integrated gate, so that the problems that the water distribution scheme is disjointed with actual execution and automatic linkage is lacked in the prior art are solved, and the overall efficiency and the collaboration of irrigation scheduling are improved.