1270 results about "Irrigation water" patented technology

The invention discloses a flexible load multi-target collaborative scheduling system and method, and relates to the technical field of collaborative optimization of power systems. The method is used for solving the problem of lack of accurate prediction and multi-target coordination of agricultural electricity and water utilization regulation and control. Firstly, based on meteorological data, soil moisture content and crop growth characteristics, an irrigation demand prediction model is constructed, irrigation water demand is predicted, and a water pump load power baseline is generated; then, a dynamic baseline constraint condition is generated in combination with historical behavior data and water pump start-stop logic; establishing a power grid side objective function, a user side objective function and a water affair side objective function, and introducing an underground water and carbon emission punishment mechanism; thirdly, dividing the power distribution network into sub-regions, adopting an alternating direction multiplier method to solve a region regulation and control strategy in parallel, coordinating water resource distribution conflicts through virtual interactive variables, and outputting a global scheduling instruction; and finally, collecting real-time response data and correcting the prediction model on line to realize closed-loop adaptive optimization.

The invention relates to a supply and demand multi-source data fusion farmland water-saving irrigation water system design and dynamic water distribution optimization method, which is characterized by comprising the following steps: S1, crop-soil-weather multi-source data coupling modeling, S2, water-saving water system design optimization, S3, dynamic water-saving water distribution strategy, and S4, intelligent terminal control system control. According to the scheme, accurate configuration of water resources is achieved through the artificial intelligence technology, the irrigation water consumption is reduced to the maximum extent, and meanwhile the crop growth requirement is met.

The invention discloses a deep learning-based intelligent optimization method for farmland irrigation water quantity. The method comprises the following steps of S1, obtaining a preprocessed farmland multi-mode perception data set; s2, constructing and training a multi-scale gated SIREN water content continuous prediction model by taking the preprocessed farmland multi-modal perception data set as input; s3, dividing a planned irrigation period into a plurality of time slices, defining a water volume decision vector space and constructing a differential evolution multi-target water volume optimization model; s4, outputting a Pareto optimal water quantity decision vector set meeting a multi-target constraint condition, and selecting an optimal farmland irrigation water quantity from the Pareto optimal water quantity decision vector set according to a user weight or a preset rule; s5, the optimal farmland irrigation water amount is issued to an intelligent valve control system, and an electromagnetic valve is driven to execute irrigation according to time slices. According to the invention, an intelligent irrigation prediction-decision-execution-feedback full-link closed-loop mechanism taking SIREN as a core is realized, and the method has remarkable water-saving and yield-increasing benefits and engineering deployability.

The invention discloses a method and system for estimating irrigation water consumption and water utilization efficiency, and relates to the technical field of water resource management, and the method comprises the steps: collecting the plant stem flow rate, canopy temperature and soil profile water content data of a target area through a multi-source sensing device, and obtaining a gradient and permeability distribution diagram of a geographic information system; dividing the farmland of the target area into sub-areas with different irrigation priorities based on a soil conductivity mutation point detection algorithm; constructing a dynamic water demand model fused with the plant transpiration hysteresis effect, performing space-time correlation analysis on the stem flow rate data and the soil moisture content gradient, and calculating the real-time water demand intensity of each sub-region; the method comprises the following steps: accessing short-time rainfall prediction data of a meteorological radar, dynamically correcting a future irrigation decision by adopting a sliding time window mechanism, and triggering a gradient delay irrigation instruction when the predicted rainfall amount exceeds an irrigation threshold value; and calculating the actual moisture utilization efficiency based on the inversion data of the moisture content of the irrigated soil and the unmanned aerial vehicle multispectral image.

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

The invention discloses an intelligent water and fertilizer integrated irrigation system based on an Internet of Things sensor and a large language model, and relates to the technical field of intelligent agriculture. The system obtains soil, weather and plant growth state data in real time through Internet of Things sensors, including a soil sensor, a weather sensor and a multispectral sensor, deployed in a farmland; the method comprises the following steps: based on a large language model architecture, integrating preset agricultural field knowledge base data, performing field adaptive fine tuning on model parameters by adopting a data migration technology, and constructing an intelligent irrigation control model with a dynamic decision-making function; the control model analyzes sensor data and crop growth requirements, generates a water and fertilizer supply strategy, calls an internet-of-things control interface of the water and fertilizer all-in-one machine, and automatically adjusts irrigation water quantity and proportional supply of nutrient elements such as nitrogen, phosphorus and potassium. According to the invention, Internet of Things perception and large model decision are fused, and different environments are adapted through fine adjustment, so that closed-loop precise control is realized, and the water and fertilizer utilization rate and the crop yield are remarkably improved. Actual measurement shows that the system can improve the water and fertilizer utilization rate by 30% or above, and the crop yield is increased by 15-20%.

The invention discloses an irrigation area scale irrigation water consumption monitoring method based on physical constraint machine learning, and is applied to the technical field of irrigation water consumption monitoring. Comprising the following steps: constructing a space-time continuous sequence of irrigation response characteristics of an irrigation area by using a multi-source data integration and assimilation technology; the irrigation water amount balance equation and the earth surface energy conservation law are used as regularization terms of physical constraints to be embedded into the training process of the long and short term memory deep learning model, so that an irrigation water amount prediction model is established; and dynamically estimating the irrigation water consumption of the irrigation area based on the irrigation water consumption prediction model obtained by training. According to the method, the water balance equation and the energy conservation law are embedded into the machine learning model, so that the model prediction result follows the basic physical law, and water consumption estimation violating the physical common sense is avoided. The introduction of the physical constraint improves the robustness and generalization ability of the model under an unconventional climate condition or a limited training data condition, and enhances the credibility and interpretability of a result.

The invention relates to the field of environment monitoring and ecological restoration, in particular to a farmland and river ecological collaborative monitoring and regulation system and method. The system comprises a first-stage barrage, a second-stage barrage, a gabion revetment, front-end acquisition equipment, execution equipment and a server, wherein the first-stage barrage and the second-stage barrage are arranged at intervals; the execution equipment comprises irrigation equipment, a first gate, a second gate and an ecological flocculant putting device; the server comprises a data acquisition module, a data storage module, an analysis processing module and a control execution module, inputs control parameters including a first gate opening degree, a second gate opening degree, an irrigation equipment electric valve opening degree and an ecological flocculant putting amount through an LSTM model, and predicts state variables including an upstream water level, a downstream flow, an irrigation water supply amount, a total phosphorus concentration and a total nitrogen concentration; and a multi-target optimization model including agricultural water demand, ecological flow, flood control safety and water quality purification targets is solved in combination with an optimization NSGA-II algorithm, target control parameters are acquired, and a control instruction is generated to drive execution equipment.

The invention discloses a unit area irrigation water consumption estimation method and system based on multi-source data fusion, and relates to the field of irrigation water consumption estimation. Multi-source heterogeneous data such as remote sensing images, meteorological data, soil humidity and crop growth information are fused, and observation and model errors are reduced through data assimilation; estimating crop evapotranspiration under an energy balance principle; estimating daily-scale irrigation water consumption by adopting a water balance method; a crop actual irrigation area identification model based on deep learning is introduced, actual irrigation areas of different crop types are effectively distinguished, and the influence of temporal and spatial changes on irrigation efficiency is dynamically reflected. According to the method, high-precision and rapid estimation of the comprehensive unit area irrigation water consumption of a single crop and multiple crops on different regional scales can be realized, the timeliness and accuracy of irrigation water quota evaluation are remarkably improved, and the method has strong adaptability to climate change, soil difference and crop diversity and is suitable for popularization and application. Powerful technical support is provided for precise irrigation and intelligent water conservancy management.

The invention provides an irrigation water demand prediction method based on meteorological soil crop multi-source feature fusion, and relates to the field of data prediction, and the method specifically comprises the steps: firstly collecting farmland region multi-source feature data, and constructing an original data set; then, a nonlinear disturbance relation between farmland characteristics is extracted through a high-order disturbance encoder module, and a structure energy tensor is introduced to describe high-order structure relevance; thirdly, a dynamic gating characteristic generator module is introduced, a disturbance gain factor and a time coupling factor are combined to generate a gating signal, and dynamic screening of multi-source characteristics is achieved; then constructing a cross-variable interaction modeling module, and generating a feature embedding vector to model a complex coupling relationship among weather, soil and crops; and finally, outputting an irrigation water demand predicted value by adopting a multi-layer sensing network in combination with channel mapping and a cross memory mechanism, so that efficient dynamic prediction of the farmland water demand is realized, and the water resource utilization efficiency and the accurate regulation and control capability are improved.

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 discloses a system for saline-alkali land farmland soil water and salt detection and drought and flood disaster early warning. According to the method, spectral information is extracted through a remote sensing image or an unmanned aerial vehicle image, and a salinity spectral index and a moisture spectral index are constructed. Meanwhile, the water content and the salt content of the soil at different depths are collected. Key spectrum variables are screened through an RXGBoost model, a salinity-moisture coupling relationship is synchronously calculated through a single model, and an independent calibration link is reduced for a calibration process. According to the method, a comprehensive and modular technical method and a solution are applied for the first time, maximum application of limited information is realized, and the technical requirements of efficient utilization, monitoring and management of irrigation water are met; and difficult points and key points coexist. Through deep coupling of theoretical innovation and system design, the system is realized in the field of saline-alkali soil irrigation: single data monitoring is transited to a multidisciplinary collaborative ecological hydrological theoretical system, a recessive water-salt interaction process is converted into modelable and controllable dominant decision parameters, and a systematic solution is provided for efficient utilization of saline-alkali soil agricultural resources.

The invention discloses a garden partition intelligent water-saving irrigation regulation and control system based on the Internet of Things, and belongs to the field of irrigation regulation and control systems. According to the system, a basic water demand model containing static characteristics of plants, soil and the like is established for each subarea through a central processing controller, real-time environment data and weather forecast are fused, and future irrigation water demand and optimal time are calculated in a prospective mode. The core innovation of the method lies in that after irrigation, the system performs feedback iterative optimization on a basic water demand model by comparing the efficiency deviation of the actual change and the predicted change of the soil humidity to form a self-learning closed loop. According to the method, conversion from passive response to active prediction is achieved, the method has the advantages of being accurate, prospective and self-adaptive, the method can continuously adapt to environmental changes, and the intelligent level and the water saving effect of irrigation are remarkably improved.

The invention provides a fertilization control method and system, and belongs to the technical field of smart agriculture, and the method comprises the steps: determining a target element-deficient region based on a multispectral remote sensing image of a target region crop, a crop type and a crop growth stage; inputting the multispectral reflectivity data, the soil nutrient data and the crop growth stage of the target nutrient-deficient area into a preset nutrition diagnosis model to obtain a deficient nutrient element type and a deficient degree; inputting the nutrient element type, the lack degree, the soil nutrient data, the crop growth stage, the planned irrigation water amount and the fertilizer mother liquor concentration into a water and fertilizer decision model, and calculating to obtain the target fertilizer liquor concentration and the total fertilizer demand; and controlling the fertilization device to adjust the mother liquor injection proportion and the irrigation flow based on the target fertilizer solution concentration and the total fertilizer demand. The fertilization control method and control system provided by the invention realize accurate regulation and control of water and fertilizer, not only can improve the utilization rate of water and fertilizer, but also can improve the yield and quality of crops.

The invention discloses an irrigation water demand calculation method considering fine distribution of crops, electronic equipment and a storage medium, and aims to accurately calculate the irrigation water demand and spatial distribution of the crops in a target area through a remote sensing technology, meteorological data and a soil moisture balance model. The method comprises the following steps: collecting remote sensing crop classification, weather and soil parameter data; reference crop evapotranspiration is calculated at the grid scale; configuring crop parameters; calculating root zone soil moisture change based on a water balance model; and calculating the water demand of crops and the water demand of irrigation by adopting an FAO-56 crop coefficient method. Through refined crop distribution and dynamic soil moisture simulation, high-resolution irrigation water demand distribution information is provided, and a scientific basis is provided for agricultural water resource management.

The invention provides an intelligent irrigation district management method and system based on the Internet of Things, and the method comprises the steps: obtaining the real-time irrigation water amount and water pump energy consumption data of each water conveyance channel in a target irrigation district; according to the real-time irrigation water amount and the water pump energy consumption data, generating a dynamic water resource supply and demand relation graph; determining a target water delivery channel corresponding to the water resource transaction request of the water consumption terminal of each irrigation area from a target irrigation area, and if any target water delivery channel has a condition that a water pump is overloaded or a deposition diffusion coefficient value is greater than a preset diffusion coefficient threshold value, determining the water resource transaction request of the water consumption terminal of each irrigation area based on a dynamic water price adjustment rule and a dynamic water resource supply and demand relationship graph, generating a water resource transaction price of the target water conveyance channel; and according to the water resource transaction prices of all the target water conveyance channels and the node states in the dynamic water resource supply and demand relationship graph, generating target water conveyance optimization parameters of each target water conveyance channel. The water resource allocation efficiency of the irrigation area and the operation stability of the water conveying system are improved.

The invention relates to a farmland irrigation decision-making method and device based on multi-source data, equipment and a medium. The method comprises the steps of obtaining multi-source heterogeneous data of a farmland in real time, and obtaining synchronized soil data, meteorological data and crop growth data based on the multi-source heterogeneous data; the multi-source heterogeneous data comprises soil data, meteorological data and crop growth data; obtaining a multi-source fusion irrigation index based on the synchronized soil data, meteorological data and crop growth data; obtaining an irrigation strategy based on a preset multi-target optimization model according to the multi-source fusion irrigation index; and obtaining an irrigation scheme based on the irrigation strategy. By adopting the method, the water resource utilization efficiency can be improved, and the irrigation water consumption is reduced; the crop yield is improved and the production cost is reduced; the adaptability of the system to climate change is enhanced, and the robustness of the strategy is improved.

The invention relates to the technical field of drip irrigation devices, and discloses a crop cultivation water and fertilizer recycling drip irrigation device with a regulation function, the crop cultivation water and fertilizer recycling drip irrigation device comprises a mounting rack, a drip irrigation box is fixed on the top of the mounting rack, the inner wall of the drip irrigation box is fixedly communicated with a crop drip irrigation device, and a regulating valve is mounted on the circumferential surface of the crop drip irrigation device. The device comprises a mounting frame, a cultivation bin is fixed to the inner wall of the mounting frame, a recovery box is fixed to the inner wall of the mounting frame, and a purification mechanism used for purifying and recovering waste water is arranged on the inner wall of the recovery box. The problem of water and fertilizer waste in traditional drip irrigation is avoided, the utilization efficiency of water resources is improved, the irrigation water consumption is reduced, the frequency of equipment replacement and maintenance can be reduced, the operation cost is further reduced, and the overall drying efficiency is improved.

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

The invention relates to the field of agricultural irrigation, in particular to an intelligent water distribution system and method for root space-time competition of an intercropping system, and is applied to agricultural precise irrigation, efficient utilization of water resources and management of the intercropping system. According to the system, the ground penetrating radar and the multispectral laser technology are fused, and crop root system distribution, activity and nutrient data are accurately obtained. Based on transfer learning and multi-agent reinforcement learning algorithms, a root system competition model is constructed, and an optimal water distribution strategy is intelligently decided. The deformable drip irrigation execution module achieves precise irrigation, the water consumption is greatly reduced by 20%-35%, and waste is effectively avoided. Meanwhile, the system balances the growth requirements of intercropping crops, relieves root competition, improves the overall yield and stability, and the amplification reaches 15%-25%. According to the system, the utilization efficiency of water resources is remarkably improved, and powerful support is provided for sustainable development of modern agriculture.

The invention discloses an agricultural water resource optimization method based on crop water demand and multi-objective optimization, which comprises the following steps: S1, constructing a multi-objective planning model of crop yield, irrigation moisture productivity maximization and planting water cost minimization, and converting the multi-objective planning model into a deterministic single-objective model by adopting a Zimmerman method; s2, reading the monitoring data collected in the current monitoring period and the monitoring data corresponding to the non-collection time period of the current monitoring period in the previous monitoring period; the monitoring period comprises a complete planting period of corn and wheat; s3, inputting all the monitoring data into a deterministic single-target model, and solving the single-target model by adopting Lingo programming to obtain the irrigation volume of the corn and the wheat in each growth period in the current monitoring period; and S4, according to the current growth periods of the corn and the wheat, adopting the irrigation amount of the corresponding growth period obtained in the step S3 as the guidance irrigation amount of the current growth periods of the corn and the wheat.

The invention relates to the technical field of agricultural irrigation monitoring, and discloses a multi-source sensing-based irrigation water utilization coefficient intelligent monitoring method and system, and the method comprises the steps: collecting multi-source data through unmanned plane spectrum sensing, a ground Internet of Things sensor and a crop growth model, carrying out the time-space alignment, abnormal value elimination and standardization processing of the multi-source data, and carrying out the monitoring of the utilization coefficient of irrigation water. Constructing a data set; constructing a three-dimensional digital twinborn model of the irrigation area, and simulating hydrological processes under different irrigation strategies to generate a virtual irrigation water utilization coefficient reference value; performing multi-source feature fusion on the data in the data set through a CNN-LSTM-RF model to obtain target feature data; inputting the target feature data into a PPO algorithm, dynamically optimizing irrigation parameters, maximizing an actual irrigation water utilization coefficient, and outputting an optimal irrigation strategy; irrigation is conducted based on the optimal irrigation strategy, the irrigation water utilization coefficient is monitored in real time, and when the irrigation water utilization coefficient is lower than a set threshold value, early warning information is automatically sent out; according to the invention, irrigation water utilization coefficient monitoring and management are realized.

The invention discloses an irrigation water utilization coefficient evaluation method and system based on irrigation area water use whole process simulation, and relates to the technical field of agricultural water-saving irrigation. Irrigation water utilization information is obtained through multi-source data fusion; based on the irrigation water utilization information, constructing an irrigation area water utilization whole-process simulation model; the whole-process water consumption simulation model for the irrigated area comprises main canal water simulation, field canal water simulation and field crop water simulation; calculating a water delivery utilization coefficient, a water distribution utilization coefficient and a field water utilization coefficient by utilizing the irrigation area water utilization whole-process simulation model; and multiplying the obtained water delivery utilization coefficient, water distribution utilization coefficient and field water utilization coefficient to obtain an irrigation water utilization coefficient. The irrigation water utilization coefficient is accurately measured and calculated, the water utilization efficiency of crops is accurately evaluated, and fine management of water utilization in an irrigation area and water-saving benefit evaluation can be achieved.

According to the technical scheme, the water-saving irrigation device for agricultural technology popularization is characterized by comprising a water storage tank, a water supply hole is formed in one side of the water storage tank, a water supply pipe is connected to the interior of the water supply hole, a flow dividing box is connected to one side of the water supply pipe, a water pump is connected to the interior of the water storage tank, and one end of the water pump is connected with the water supply pipe; the plurality of mounting holes are formed in one side of the shunting box; the irrigation assembly is arranged on one side of the flow dividing box and used for irrigating farmland, when irrigation water is conveyed to the water seepage pipe through the water supply pipe, micropores are formed in the outer wall of a pipeline of the water seepage pipe, stable water irrigation can be achieved, and pressure control can be conducted on the conveyed irrigation water through a pressure adjusting valve; waste caused by too fast water flow or uneven irrigation caused by insufficient water flow is avoided, soil humidity is monitored in real time through a soil humidity sensor, the irrigation amount of the water seepage pipes at different positions is controlled in cooperation with a first electromagnetic valve, utilization of irrigation water is optimized, the water saving effect is improved, and waste of water resources is reduced.

The invention discloses a loop heat pipe and water heat storage coupling system and a working method, and belongs to the technical field of heat storage. The system has the modes of light gathering of the light-following trough heat collector, heat conduction of the loop heat pipe, heat storage of the water storage tank and warm water irrigation by using the stored heat water. When sunlight is sufficient in the daytime, the system converts solar energy into sensible heat of water in the heat storage water tank and stores the sensible heat, and the sensible heat is insulated by soil to slow down heat loss; when plants need to be irrigated, warm water in the irrigation water tank is used for irrigation; at night or in the absence of illumination, warm water in the heat storage water tank exchanges heat with air in the greenhouse, so that a lasting and stable heat supply source is provided for the greenhouse. Solar energy collected by the heat collecting groove is efficiently transmitted into the water tank through the loop heat pipe driven by temperature difference to be converted into sensible heat of water to be stored, the sensible heat is used for increasing the greenhouse temperature and irrigating plants when heat is needed at night, and the purposes of efficiently storing heat in the daytime and continuously supplying heat at night are integrally achieved.

A method and apparatus for site-specific application of a fluid additive, with or without irrigation water, wherein a center pivot or lateral move irrigation system is utilized for a dedicated additive spraying system. Said spraying system including an additive pumping apparatus with a pump controller in communication with an irrigation system position sensor, and an additive pump in fluid communication with a reservoir of additive and a plurality of flow control valves. Said flow control valves each in fluid communication with an additive carrying conduit, said conduit terminating with an end-portion of conduit with spray nozzle set. Each said end-portion with spray nozzle set configured to apply additive to a ground surface below said end-portion and within the boundaries of polygon shaped areas of a field based on the position of the pipe spans, said position provided to the pump controller by the irrigation system position sensor.

The invention discloses a CatBoost-based irrigation area moisture productivity prediction method, and the method employs a CatBoost machine learning model to construct an irrigation area moisture productivity prediction model, can more effectively process the complex nonlinear relation and high-dimensional data among climate, soil, underground water and management measures, remarkably improves the precision and adaptability of irrigation moisture productivity prediction, and improves the prediction precision and adaptability of the irrigation moisture productivity. And the limitation of the traditional model in the aspects of dynamic nonlinear environment and data demand is overcome. Meanwhile, the Bayesian optimization technology is innovatively combined with the CatBoost model, automatic and intelligent optimization of model hyper-parameters is achieved, blindness of manual parameter adjustment is effectively avoided, the prediction precision and generalization ability of the model are further improved, the problem that a machine learning model is prone to overfitting is solved, and the method is suitable for large-scale popularization and application. And the robustness of the model in a complex environment is ensured. According to the method, quantifiable and generalizable decision support is provided for precise water saving, salt control and crop layout optimization.

The invention discloses a crop water distribution method and device, a medium and equipment, and relates to the field of water resource management, and the method comprises the steps: constructing a multi-target simulation-optimization model; the multi-objective simulation-optimization model comprises an EPI crop growth model and an optimization model which takes maximized economic water productivity, maximized irrigation water productivity and maximized nutritive water productivity as objective functions and takes irrigation water quantity and salt accumulation in a crop growth period as constraints; inputting the acquired meteorological data, crop data, soil data, channel data and groundwater data into a simulation model of a multi-target simulation-optimization model, determining simulation results of actual crop evapotranspiration, final crop yield and straw quantity, taking the simulation results as input of an optimization model, and determining an irrigation water distribution scheme; constructing a water-salt migration model; and updating the simulation result through the water-salt migration model so as to optimize the irrigation water distribution scheme until a preset optimization target is achieved.

The invention discloses an agricultural drought monitoring method for a humid and semi-humid area. The method comprises the following steps: acquiring a day-by-day soil moisture content historical time sequence of a drought generation year of a research site; according to the day-by-day soil moisture content historical time sequence of the research site, estimating the field moisture capacity and the wilting moisture content of various soil textures, and calculating the soil effective moisture content of the various soil textures; according to the crop irrigation water amount and the actual monitoring value of the soil water content in the key growth period of the dry crops, obtaining the soil water content when the dry crops are subjected to water stress; calculating agricultural drought monitoring indexes of the dry crops in the humid and semi-humid areas according to the soil water content when the dry crops are subjected to water stress and the soil effective water content of the corresponding soil texture, and determining agricultural drought severity grade division according to the STAD value for representing the agricultural drought grade of the research area so as to realize drought monitoring. According to the method, the problem that other indexes fail in drought monitoring of a wet or semi-wet area can be solved, and the method has relatively high universality.

The invention relates to the technical field of irrigation water, and discloses an irrigation district water optimization method and system based on dynamic data management, and the method comprises the steps: collecting the basic data of an irrigation district project and a monitoring station, the data of the irrigation district monitoring station, the monitoring data of the water volume of each branch water port in an access irrigation district, and the geographic information topological data of an integrated channel system and a land parcel; constructing a digital archive library; obtaining a water demand forecast and a recommended water distribution scheme according to the data in the digital archive, and making an actual water consumption scheme by referring to the water distribution scheme; calculating a canal system utilization coefficient and an irrigation water utilization coefficient based on the canal head water diversion amount, the water consumption data of each tail end water diversion port and the actual water consumption data of the field plot, and evaluating the water utilization efficiency; and when optimization is determined, optimizing water consumption of the irrigation area according to the canal environment data, the canal system utilization coefficient and the irrigation water utilization coefficient. According to the invention, by establishing a total-factor digital mapping relation, accurate metering and dynamic optimization of irrigation water are realized, and dynamic planning of water saving of an irrigation area is realized.