46 results about "Irrigation scheduling" patented technology

The invention discloses an agricultural and pastoral area water network regulation and storage project scale determination method and system. The method comprises the following steps: collecting historical data of an agricultural and pastoral area, dividing the agricultural and pastoral area into a plurality of secondary sub-areas, respectively calculating the water demand and available water consumption of each secondary sub-area, and identifying a water shortage time period and a water shortage area; constructing a regulation and storage project layout scheme set, and calculating the regulation and storage capacity and project parameters of each regulation and storage project; constructing a single-target optimization model, simulating a regulation and storage result of each regulation and storage project layout scheme, evaluating each regulation and storage project layout scheme, and screening out an optimal regulation and storage project layout scheme; and constructing an irrigation scheduling optimization model based on the optimal regulation and storage project layout scheme, solving the model to dynamically optimize and adjust the water storage time, the water discharge time and the water discharge amount of the regulation and storage project, and generating an agricultural and pastoral area water network regulation and storage project layout scheme. The water resource utilization efficiency of agricultural and pastoral areas is improved, the water resource supply and demand contradiction is relieved, and sustainable development of agriculture and animal husbandry and ecological environment improvement are promoted.

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 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 relates to the technical field of agricultural irrigation, in particular to an intelligent irrigation dynamic decision-making method and equipment, and constructs an intelligent irrigation fuzzy reasoning and dynamic decision-making method by fusing multi-source remote sensing data and rainfall data and combining water demand characteristics of crops in different growth stages. It can be understood that the method is based on regularly updated remote sensing data, the moisture state of crops is dynamically monitored, and plot-level irrigation decision suggestions are generated. A manager can scientifically formulate an irrigation strategy according to suggestions in combination with the local actual water resource condition and the irrigation system, precise irrigation regulation and control are achieved, and the irrigation efficiency and decision scientificity are improved.

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.

The invention, which relates to the technical field of crop evapotranspiration simulation, discloses a paddy rice evapotranspiration estimation method considering the depth of a waterflooding layer, comprising the following steps: acquiring meteorological data in a research area, waterflooding layer depth data of a paddy field, paddy rice growth data and actually measured evapotranspiration data; considering an influence mechanism of the depth of the flooded layer on evapotranspiration, and adding the influence of the depth of the flooded layer on the canopy resistance to a canopy resistance model (such as a Jarvis canopy resistance model); considering the influence of the depth of the waterflooding layer on water layer heat storage, and constructing a water temperature-surface heat flux model; the canopy resistance model and the earth surface heat flux model are coupled into a Shuttleworth-Wallace model to improve the evapotranspiration model; based on an improved Shuttleworth-Wallace model, whole-growth-period evapotranspiration prediction under irrigation of different flooding depths is realized, and the method is used for evaluating the water-saving effect of each irrigation mode. By improving the Shuttleworth-Wallace model, the influence mechanism of the depth of the flooded layer on evapotranspiration is brought into rice evapotranspiration simulation, the rice evapotranspiration simulation precision can be improved, and a basis is provided for formulating a reasonable irrigation system.

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 present invention relates to the field of agricultural irrigation technology, specifically to a method and device for intelligent irrigation dynamic decision-making. By integrating multi-source remote sensing data with precipitation data and combining it with the water requirements of crops at different growth stages, a method for intelligent irrigation fuzzy reasoning and dynamic decision-making is constructed. This method, based on regularly updated remote sensing data, dynamically monitors crop moisture status and generates plot-level irrigation decision recommendations. Based on these recommendations, managers can combine local water resource conditions and irrigation schedules to scientifically formulate irrigation strategies, achieve precise irrigation control, and improve irrigation efficiency and scientific decision-making.

The present invention discloses a method for joint allocation of multiple water sources in oasis agricultural irrigation areas with complex canal systems. The method first simulates the typical annual Longkou water inflow process of abundant, normal, dry and extremely dry years by using a long series of Longkou water intake data based on the P-III frequency analysis method. Then, based on the system coupling principle, the irrigation system engineering distribution and the hydraulic coefficient calculation process are coupled, and the net water demand and gross water demand of each crop in the metering unit are calculated according to the planting plan and the irrigation system. Finally, based on the self-iterative optimization principle, water resources are allocated for each metering unit according to the water balance of the whole process of water demand, the groundwater quality and the water distribution principle, with the goal of minimizing the degree of water demand damage in the whole process. The method has the advantages of strong robustness, easy global convergence and fast calculation speed, and has good fitting effect with the actual water distribution process. The calculation results can be used to guide the scientific water distribution of irrigation area agriculture.

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.

This application discloses a method and system for intelligent scheduling of irrigation pipe networks based on a hydraulic model. By constructing a hydraulic irrigation simulation model, it is possible to simulate the hydraulic behavior of the pipe network under different operating scenarios, thereby achieving accurate simulation and prediction of the hydraulic behavior within the irrigation pipe network. On this basis, based on the type of crops in the target area and their water requirements during their growth cycle, and with the goal of optimizing water supply quality, an optimization objective function for intelligent scheduling of the irrigation pipe network is established. Not only is the minimum absolute error between the actual irrigation volume and the theoretical water demand considered, but key conditions such as water supply constraints, head valve working pressure constraints, and pipeline pressure constraints are also incorporated to ensure the scientific nature and rationality of irrigation scheduling. Therefore, it can significantly improve irrigation efficiency, save water resources, promote the healthy growth of crops, and provide effective technical support for solving problems such as water resource shortages and poor irrigation management in modern agriculture.

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 present invention discloses a precise water-saving irrigation method for leafy vegetables and root crops, which relates to the technical field of precise crop irrigation. An environmental anomaly degree is generated from the obtained monitoring data. If it exceeds the environmental anomaly threshold, predictions are made on the planting conditions and crop water requirement rules data. Irrigation characteristics are extracted from the obtained prediction data, and the irrigation scheme library matches a corresponding water-saving irrigation system for the crop planting area according to the irrigation characteristics. A crop water-saving irrigation digital twin model is used to test the water-saving irrigation scheme. A feasibility is constructed from the obtained test data, and the feasibility of the irrigation scheme is verified based on the feasibility. Corresponding response measures are taken according to the verification results. A drainage anomaly degree is constructed from the obtained drainage state data. If the drainage anomaly degree exceeds the expectation, drainage emergency treatment is carried out in the crop planting area. Due to the differences in water requirement characteristics and growth periods of different crops, a water-saving and high-yield irrigation system is provided for the refined management of various crops.

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 present invention provides a method for predicting crop irrigation water requirement based on the AquaCrop model and SVR, comprising the following steps: S1, obtaining meteorological station data of the planting area, soil data of the planting area, crop parameter data and field management data; S2, performing local debugging of the AquaCrop model by the trial-and-error method; S3, using the AquaCrop model to select appropriate irrigation strategies and simulate different irrigation regimes; S4, based on the simulation results of the AquaCrop model, combining with SVR to predict the daily crop irrigation water requirement in the next growth period. The present invention utilizes the existing meteorological station data and crop planting management data, combines the crop model with the machine learning method, can select appropriate irrigation strategies according to the water resource supply and demand conditions and crop growth water requirement characteristics in different regions, and predicts the daily crop irrigation water requirement in the next year through the support vector machine combined with the particle swarm optimization algorithm, improves the prediction accuracy, and can provide an effective reference basis for local agricultural planning water use.

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 present invention provides a method, device, and electronic device for automatically correcting the agricultural irrigation rate of an irrigation project. The method first obtains time-series data on the net irrigation quota for various representative crops in the irrigation project. Then, based on the obtained time-series data and a preset design guarantee rate, a design irrigation schedule for each representative crop is determined. The initial irrigation rate for the irrigation project is then determined based on the design irrigation schedules for each representative crop. This initial irrigation rate is then iteratively corrected based on preset correction requirements, ultimately yielding the design irrigation rate for the irrigation project. The present invention improves the efficiency of the agricultural irrigation rate correction process in irrigation projects, thereby improving the calculation efficiency of the design flow rate for irrigation water distribution projects, and facilitating efficient analysis and comprehensive demonstration of the economic rationality of irrigation water distribution project construction plans and scales.