477 results about "Water requirement" patented technology

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a meteorological data fused water-saving irrigation control method, device and equipment and a medium. According to the method, a historical meteorological data set is constructed through multi-source meteorological data fusion, a dynamic water demand table is generated in combination with a crop water demand characteristic database, and a crop water demand model is established based on soil moisture content data. A grid irrigation unit division and growth period coupling soil moisture content response matrix construction technology is adopted, a cooperative constraint is established between a water demand threshold value and a water saving benefit through a multi-objective optimization learning algorithm, and a personalized irrigation scheme is generated. A soil moisture content dynamic evaluation matrix containing a dynamic time warping operator is designed, and dynamic matching of the soil layered soil moisture content and a standard template is achieved. The contradiction between meteorological response lag and low water resource utilization rate in traditional irrigation is effectively solved, accurate irrigation decision is realized through multi-dimensional data fusion and an intelligent optimization algorithm, and the water-saving benefit and the agricultural water resource utilization efficiency are improved.

The invention relates to the technical field of intelligent water conservancy, and discloses a cross-regional water transfer project intelligent scheduling method and system, and the method comprises the steps: building a digital twin system based on a geographic information system, hydrological monitoring data and a spatial topological structure, and integrating a meteorological evolution prediction model, a basin hydrological response model and a water demand prediction model; predicting a water demand and an adjustable water amount by using a space-time convolutional neural network and a gating circulation unit; establishing a multi-objective optimization model taking water supply benefit, ecological influence and energy consumption cost as optimization objectives; an optimal water transfer scheme is generated through a Markov decision process and multi-agent cooperation; and carrying out robustness evaluation on the scheme and generating an emergency scheduling plan. According to the method, the scheduling efficiency and adaptability of the water transfer project are remarkably improved, and efficient configuration of water resources and quick response under extreme situations are achieved.

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 the field of power system supply and demand interaction, and discloses an intelligent greening irrigation system and a water-saving control method, the system comprises a multi-source data acquisition module, a space-time tensor modeling module, a dynamic decision generation module, an edge calculation acceleration module, a distributed execution control module and a drip irrigation module; the method comprises the following steps: collecting multispectral, meteorological and soil moisture data of a plant canopy in real time to generate an original data set; a fusion data set is obtained through space-time alignment and normalization preprocessing; the water demand is predicted based on an LSTM-Transform model, and an irrigation safety interval is screened; the irrigation uniformity and water pressure distribution are simulated and evaluated through multi-physics field coupling; a double-delay DDPG algorithm is used for optimizing valve opening parameters; the model and the execution error are corrected through Bayesian modeling, and an NDVI feedback optimization strategy is combined. According to the invention, a double-delay depth deterministic strategy gradient algorithm is introduced for dynamic optimization of an irrigation strategy, so that the capability of quickly adapting to environmental changes is achieved.

The invention relates to the field of intelligent agriculture, and discloses an intelligent drip irrigation control method, system and device and a storage medium, and the method comprises the following steps: data collection: obtaining soil humidity data, environmental meteorological data and crop growth state data of a target area through a plurality of sensors, and transmitting the data to a data processing module; the system comprises a data acquisition module used for acquiring soil humidity data, environmental meteorological data and crop growth state data and transmitting the acquired data to a data processing and storage module; the device comprises a machine case shell, and an intelligent control chip, a wireless communication module, an electromagnetic valve and a water flow meter are installed in the machine case shell. By integrating a sensor network, intelligent analysis and a real-time feedback mechanism, the water demand of crops is accurately calculated, irrigation is automatically adjusted, and water resource utilization is optimized; a personalized irrigation plan is made by combining a deep neural network and time sequence analysis, and the water requirements of crops in different growth stages are ensured.

The invention relates to the technical field of automatic control, and further relates to a water and fertilizer integrated multi-objective optimization intelligent regulation and control system based on artificial intelligence driving, and the system comprises a water and fertilizer load analysis unit and a water and fertilizer combined regulation and control criterion generation unit. The water and fertilizer load analysis unit is used for acquiring meteorological data from a meteorological monitoring system; simultaneously receiving data from the soil sensor and the leaf surface sensor, acquiring growth state data of the crops, and evaluating water demand and fertilizer demand of the crops in unit area at each time; and the water and fertilizer combined regulation and control criterion generation unit is used for dynamically calculating the water application amount and the fertilization amount of the crops in unit area at each time according to the soil state and the root system absorption capacity of the crops by using an AI dynamic resource optimization engine. According to the invention, the utilization efficiency of moisture and nutrients is effectively improved, the waste of resources is reduced, and the intelligence, adaptability and robustness of regulation and control are enhanced.

An intelligent fertilization and water supply method and system. The method comprises the following steps: step a, comprehensive soil monitoring, involving: comprehensively monitoring various soil indexes by means of a plurality of sensors, so as to obtain real-time data of the soil indexes; b, consideration of crop-specific requirements, involving: for the varieties and growth stages of different crops, thoroughly studying the unique moisture and nutrient requirements of the crops, so as to ensure the rationality and accuracy of a fertilization amount; c, comprehensive consideration of environmental factors, involving: collecting data of surrounding environmental factors; d, utilization of an intelligent irrigation mode, involving: by using a cyclic irrigation mode, an intermittent irrigation mode, etc., in combination with the water requirement pattern of each crop and a change in soil moisture, realizing efficient water utilization; e, implementation of a dynamic fertilization plan, involving: on the basis of a change in a crop growth condition and in soil fertility, dynamically adjusting fertilization time, frequency and dosage, so as to ensure that the nutrient supply matches the requirements of each crop; and f, implementation of a precise fertilization technique, involving: precisely delivering fertilizer to an absorption part of each crop, so as to improve the efficiency of nutrient utilization.

The invention relates to the technical field of intelligent gardens and precise irrigation, and particularly discloses an intelligent irrigation monitoring method and system. According to the method, a multi-source sensor array is deployed, Kalman filtering is adopted to fuse environmental data, and a three-dimensional state vector input reinforcement learning model is constructed to generate an irrigation decision; predicting a vegetation water demand by combining a gradient lifting decision tree, forming a graded irrigation strategy and converting the graded irrigation strategy into a water pump control instruction; soil humidity feedback data are collected in real time, decision model parameters and filtering rules are dynamically adjusted, and closed-loop optimization is achieved. Through multi-source data fusion and a self-adaptive decision-making mechanism, the irrigation precision and the water resource utilization rate are remarkably improved, meanwhile, the response capacity of the system to the vegetation growth dynamic state and the environment change is enhanced, and the beneficial effects of optimization process closed loop, decision dynamic adaptation and controllable resource consumption are achieved.

The invention relates to the technical field of agricultural irrigation, and particularly discloses a farmland irrigation control method and system, and the method comprises the steps: constructing a multi-sensor fusion monitoring network, and collecting soil, weather and plant physiological data in real time; transmitting data to an edge computing node through LoRa wireless communication; utilizing a lightweight neural network model deployed by edge computing nodes to calculate and predict crop water demand and accurate irrigation volume in combination with evapotranspiration; the irrigation strategy is executed through the intelligent irrigation execution system; and remote monitoring and abnormity alarm are realized by means of a remote monitoring and management platform based on WebGIS. Precise and intelligent management of farmland irrigation is realized, the utilization rate of water resources and fertilizers can be effectively improved, irrigation abnormity can be timely handled, the labor management cost is reduced, and the growth requirements of crops are guaranteed.

The invention discloses an agricultural irrigation control method and system, and relates to the technical field of irrigation control, and the method comprises the following steps: constructing a crop water demand model based on the fusion of crop physiological sensing data and a data driving model, and outputting a target irrigation flow; calculating a control error between the target irrigation flow and the actually acquired flow, and generating a control signal through a model prediction control strategy; inputting a static mapping model fitted by a Sigmoid function, and superposing the output of a dynamic correction model driven by state sensing data to generate a final driving signal; feedback data is collected, a driving signal and the feedback data are subjected to closed-loop comparison, and irrigation control strategy parameters are adaptively optimized through a reinforcement learning controller; according to the method, the crop physiological sensing data and the data driving model are fused, the space and time sequence feature fused water demand model is dynamically constructed, and the problems that agricultural irrigation cannot be accurately controlled and the irrigation strategy is rigid are solved in combination with a static and dynamic composite control mapping mechanism.

The invention discloses 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 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 discloses an intelligent agricultural planting management method and system, and belongs to the technical field of intelligent agriculture, and the method comprises the steps of data layer construction, water and fertilizer prediction layer construction, planting optimization layer construction and agricultural planting management. The space-time adaptive water and fertilizer prediction layer construction method combining the crop physiological load and the crop growth stage is adopted, the water demand and nitrogen, phosphorus and potassium nutrient demands of crops in the future stage are accurately predicted, the crop growth state and environment changes are dynamically responded, the water and fertilizer demand difference in different growth stages is considered, and the water and fertilizer prediction efficiency is improved. The accuracy and applicability of prediction are improved through time-space fusion, and accurate fertilization can be realized; a planting optimization layer construction method combining digital twinborn and multi-objective optimization is adopted, a water and fertilizer management strategy is subjected to refined and dynamic optimization under the condition of considering a crop growth stage, a soil nutrient condition and a future weather condition, and an optimal management strategy capable of improving the yield, saving water and fertilizer resources and reducing the nitrogen leaching risk is generated.

The invention relates to a multi-source data driven farmland irrigation decision-making method and device, equipment and a medium. The method comprises the following steps: acquiring multi-source heterogeneous data; carrying out fusion processing on the multi-source heterogeneous data, and constructing a unified farmland information database; performing spatial analysis on the data in the farmland information database; based on the analysis result, a preliminary irrigation prescription scheme is generated in combination with soil variation information and a crop water demand rule; on the basis of the preliminary irrigation prescription scheme, dividing the farmland into different irrigation areas, and calculating to obtain differential irrigation parameters of each irrigation area; integrating the differentiated irrigation parameters to obtain a differentiated irrigation strategy corresponding to each irrigation area; and dynamically optimizing a farmland irrigation decision based on each irrigation strategy. By adopting the method, multi-source data such as soil humidity, meteorological conditions and crop growth states can be effectively integrated, and a decision-making method capable of analyzing and generating an optimal irrigation scheme in real time is constructed.

The invention provides an irrigation district water resource scheduling optimization method based on cloud computing, and the method comprises the following steps: collecting irrigation district multi-source data in real time through a space-air-ground integrated sensing network, and uploading the data to a cloud platform after preprocessing; the method comprises the following steps: constructing digital twin bodies of an irrigation area in a cloud platform, dividing the irrigation area into a plurality of sub-areas according to water consumption attributes, and integrating hydraulics and hydrological agriculture models; real-time data and future weather information are fused, and a water demand prediction value is generated through a water demand prediction model by taking the sub-region as a unit; a scheduling scheme is generated by adopting a multi-objective optimization algorithm based on the predicted value, and the minimum economic benefit, water resource utilization rate and energy consumption are taken as optimization objectives; and performing simulation evaluation on the scheme by using the digital twinborn body, outputting a performance index aided decision, and finally determining an optimal scheduling scheme. According to the method, accurate prediction, dynamic optimization and intelligent scheduling of the water resources in the irrigation area are realized, and the utilization efficiency of the water resources and the irrigation management level are improved.

The invention relates to the technical field of fertilization and irrigation regulation and control, and discloses a fertilization and irrigation two-way regulation and control system under water demand and fertilizer demand coupling modeling, and the system constructs the fertilization and irrigation two-way regulation and control system based on the water demand and fertilizer demand coupling modeling. The multi-source data acquisition module is used for acquiring soil, crop growth period and meteorological data through a layered soil sensor, crop image acquisition equipment and a meteorological acquisition point; the water and fertilizer scheme generation module calculates irrigation amount, fertilization type and dosage based on data, and adjusts intervals in combination with temperature; the operation execution module controls the operation of the irrigation and fertilization equipment and records the state; and the data correction module compares the sensor with the sample data to generate a correction scheme. The system realizes accurate regulation and control through a closed loop of data acquisition, scheme generation, execution and correction, simultaneously covers equipment state monitoring and early warning, and ensures efficient fertilization and irrigation to adapt to crop requirements.

The invention provides an irrigation district reservoir and canal system water resource joint optimization scheduling method and device based on partition decoupling, and the method comprises the steps: obtaining the topological information of a target irrigation district reservoir and canal system, carrying out the irrigation division, and determining the water supply and demand of a future time period; based on the topological information, aggregating and generalizing each reservoir and channel in the irrigation area reservoir and channel system into a plurality of aggregated reservoirs and aggregated channel systems according to a coupling relationship, and dividing the target irrigation area reservoir and channel system into a plurality of subareas; channel water distribution flow feasible regions of different types of aggregation channel systems are deduced step by step from bottom to top from the last-stage channel; a double-layer water resource joint optimization scheduling model is established, the upper layer takes the maximum sum of the ratio of the water supply amount to the water demand amount of each subarea as the optimization target, and the lower layer takes the shortest water delivery time and the minimum water delivery loss of each subarea as the optimization target; the model is solved, the optimal water supply amount of each partition and the flow of the reservoir and the channel of each partition are obtained, and a scheduling optimization scheme is obtained. The method can maximize the water-saving benefit of the irrigated area.

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 provides an agricultural irrigation control method, system and device driven by an AI large model, and a medium, and belongs to the technical field of agricultural irrigation control, and the method comprises the steps: building an irrigation knowledge base and an irrigation decision set for a target crop based on the AI large model; constructing a target crop parameter set and a water demand prediction model, and training the water demand prediction model by using the target crop parameter set in combination with the irrigation knowledge base; target crop parameters collected in real time are input into the trained water demand prediction model, and target crop water demand prediction is carried out; and screening out an irrigation strategy from the irrigation knowledge base and the irrigation decision set according to the predicted value of the water demand of the target crop, and controlling the irrigation equipment based on the irrigation strategy. Through the combination of the AI large model and the deep learning model, the water demand of the crops is accurately predicted, the irrigation strategy is generated, the irrigation decision accuracy is improved, the waste of water resources is reduced, and the irrigation efficiency is improved.

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 ecological orchard management. According to the orchard management data platform and the control method thereof, the method comprises the steps that environmental data are collected through a soil sensor, a meteorological station and an unmanned aerial vehicle, space-time alignment and standardization processing are carried out on the environmental data, and a structured data set is generated; based on the structured data set, a farming control instruction set is generated through water demand prediction, pest and disease risk assessment and economical efficiency optimization; according to the farming control instruction set, the irrigation equipment, the fertilization equipment and the unmanned aerial vehicle are driven to execute tasks through priority arbitration and dynamic threshold adjustment; execution results of irrigation equipment, fertilization equipment and an unmanned aerial vehicle are collected, orchard output data are combined, and model parameters generated by dynamic decision instructions are optimized, so that sudden meteorological changes and production demand fluctuations are responded in real time, resource waste is reduced, the system adapts to a multi-equipment collaborative scene, and instruction conflicts are avoided.

The invention provides an irrigation district water diversion quantity multistage distribution method under total quantity index constraint, comprising the following steps: dividing an irrigation district into a plurality of calculation units based on an administrative division boundary and a canal system network; respectively calculating the initial water demand of each calculation unit under the living water demand, the industrial water demand, the agricultural water demand and the ecological water demand; based on the initial water demand and according to multiple water resource rigid constraint indexes, performing at least one round of checking and pressure reduction on the initial water demand of each calculation unit to obtain an initial distribution water amount of each calculation unit; and carrying out secondary distribution on surplus water generated after checking and pressure reduction, and compensating the calculation units of which the water demand is not met to obtain the final distribution water of each calculation unit. According to the method, fair, reasonable and efficient distribution of water resources in a complex system with cross coupling of administrative division and canal system networks is realized, and the problems of conflict between a scheme and actual management requirements, water consumption contradiction and the like caused by a traditional single-dimension distribution method are avoided.

The invention belongs to the technical field of irrigation management and control, and provides a multi-source sensing data-driven irrigation area water demand prediction and accurate water distribution method. The method comprises the steps of obtaining multi-source data of a target irrigation area when a preset irrigation condition is met; performing quantitative evaluation on the first soil water content data and the second soil water content data based on the meteorological data to determine respective confidence degrees, and giving dynamic weights based on the confidence degrees, the temporal and spatial variation characteristics and the actual condition of the irrigation area; performing fusion processing on the first soil water content data and the second soil water content data based on the dynamic weight to obtain third soil water content data; on the basis of the third soil water content data and the crop growth stage data of the target irrigation area, the water demand of the stage is predicted; and generating a control instruction of the intelligent valve of the water delivery canal system based on the water demand. The accuracy of water demand prediction can be remarkably improved, the problems of water resource waste and insufficient supply are reduced, and efficient utilization and scientific management of water resources in an irrigation area are achieved.

The invention relates to the technical field of intelligent agriculture, in particular to an intelligent water-saving irrigation system for intelligent agriculture, which comprises a multi-source heterogeneous sensing module for collecting farmland soil data, crop physiological data, meteorological environment data and irrigation equipment operation state data; the AI dynamic decision-making module constructs a TFT time sequence prediction model, calculates the real-time water demand and the ecological adaptation coefficient of crops by combining a crop physiological coupling algorithm, and dynamically generates a personalized irrigation scheme; the self-adaptive regulation and control module is used for implementing a personalized irrigation scheme and carrying out water, fertilizer and pesticide integrated precise putting and multi-water-source dynamic scheduling through a magnetic control driving mode and a flow closed-loop control algorithm; the irrigation effect feedback module verifies the execution effect of the personalized irrigation scheme in real time, and dynamically executes a feedback optimization action in combination with a preset deviation threshold value; and the block chain data security module performs encrypted storage and access traceability on the irrigation whole process data. Therefore, the problems of insufficient prediction accuracy, insufficient water, fertilizer and pesticide feeding precision and the like in the prior art are solved.

The invention discloses an irrigation index prediction method based on meteorological data and field moisture capacity, and relates to the technical field of intelligent agriculture, and the method comprises the steps: collecting real-time meteorological data, real-time soil volumetric water content data, soil matrix potential data and future precipitation forecast data, and carrying out quality control and time alignment processing to obtain a multi-source data set; the method comprises the following steps: calculating evapotranspiration of a reference crop according to real-time meteorological data, obtaining actual water demand of the crop in combination with a crop coefficient, calculating a moisture stress coefficient according to real-time soil volumetric water content data and a field moisture capacity static parameter value, and obtaining a basic irrigation demand index in combination with future precipitation forecast data. According to the method, the evapotranspiration amount is separated by adopting the Shuttleworth-Wallace model, the irrigation index integrating the crop evapotranspiration amount, the water deficit amount and the effective precipitation amount is generated on the basis of multi-period rolling water balance calculation and risk decision rules, and online calibration of soil parameters and optimization of irrigation decision are achieved.

The present disclosure relates to an intelligent water precise irrigation control system and method for cultivation of fruit vegetables in solar greenhouses. The system includes: an information acquisition module for acquiring information by means of sensors, the acquired information including at least one of soil information, ground environment information, irrigation device use process information, and crop information; a water demand prediction module for inputting the acquired information into a deep neural network to predict water demand of fruit vegetables; and an irrigation control module for forming a control instruction according to the predicted water demand, so as to irrigate periodically and quantitatively. In this way, irrigation time and irrigation amount can be decided automatically according to growth conditions and environmental parameters in the whole process. Intelligent irrigation can be achieved with high working efficiency and accuracy, and the water-saving, quality-improved and efficiency-improved production of fruit vegetables can be achieved.

The invention belongs to the technical field of cascade pump station scheduling optimization, and particularly discloses a cascade pump station scheduling optimization method and terminal equipment based on data driving, and the method comprises the steps: presetting the target water demand of a last-stage pump station at the # imgabs0 # moment and the target water level of a water outlet pool of each pump station; according to the target water demand and the target water level, determining a neighborhood output tracking error of a water outlet pool of each pump station; determining an estimated value of a time-varying parameter between an input error of a water inlet pool and an output error of a water outlet pool of each pump station; and according to the estimated value of the time-varying parameter, the neighborhood output tracking error and the water level of the water inlet pool of each pump station at the # imgabs 1 # moment, determining the water level of the water inlet pool of each pump station at the # imgabs 2 # moment. According to the method, the local control strategy is dynamically adjusted according to the time-varying parameters, self-adaptive control over the single pump station can be achieved, and the single pump station can achieve expected control along with the target water demand of the last-stage pump station. According to the method, the consistency control of the cascade pump stations can be realized on the basis of not depending on a model, the scheduling optimization complexity is reduced, and the robustness is high.

This disclosure relates generally to system and method for estimating crop water requirement using multi-sensor data fusion. Increasing global population is imparting pressure on both agriculture for food demand and limited freshwater resources for consumption. Estimating crop water requirement reduces water demand for crop production. The method divides soil and crop into multiple vertical and horizontal profiles to estimate water balance thereby reducing the errors in estimation of crop water requirement. Additionally, the method has capability to interlink the multiple data sets such as satellite based earth observations, weather observations from IoT sensors, Weather forecasts from global circulation models, and crop knowledge base for crop water requirement estimation. The method is based on spatio-temporal modeling for multi-layer crop and soil water balance and helps to generate the additional insights on crop water requirement like moisture at different levels in soil profile, crop canopy growth at different locations.

The invention discloses an intelligent irrigation system and method for improving the utilization rate of cotton planting water resources, and the system comprises a data collection module which is used for obtaining the multi-source data of a cotton planting region in a preset time period, and carrying out the preprocessing of the multi-source data, so as to obtain a data set containing the three dimensions of soil, crops, and meteorology; the water demand prediction module is used for predicting the data set obtained by the data acquisition module by using a trained CNN-TCN-Attention model and generating a cotton field soil water demand; the irrigation module is used for formulating an intelligent irrigation strategy in combination with a cotton growth stage based on the data set obtained by the data acquisition module and the cotton field soil water demand predicted by the water demand prediction module, and irrigating a cotton planting area according to the intelligent irrigation strategy; the visual platform is used for monitoring the irrigation process in real time; efficient water saving and precise irrigation are achieved, and the utilization rate of cotton planting water resources is increased.

The invention provides a water resource optimization scheduling method and system, computer equipment and a storage medium, and belongs to the field of water resource optimization scheduling, and the method comprises the steps: obtaining the local water supply amount, the department water demand amount and the water demand interval of each water receiving area; under the common constraint of the three parameters, the calculated economic output corresponding to the water resource input, the ecological service value of the water area, the water satisfaction quantitative result and the carbon emission in the water transfer process are used as optimization indexes, and the monthly scale is used as a time unit; according to an optimization result, a monthly water resource scheduling scheme is generated through iteration in sequence, and an annual water resource scheduling scheme is formed through integration; and determining a scheduling scheme with the optimal comprehensive benefit from the annual water resource scheduling schemes as a final water resource scheduling scheme. According to the method, the ecological benefit and social benefit of water transfer in the water transfer system can be coordinated, the ecological service value of a water area is remarkably improved and the carbon emission is reduced while the water demand of a water receiving area is ensured, and the comprehensive optimization of water resource scheduling is realized.