2314 results about "Irrigation water" patented technology

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

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

The invention discloses an intelligent water and fertilizer integrated precise irrigation control method. The method comprises the following steps: step 1, establishing a multi-modal data acquisition network; step 2, inputting the data obtained in the step 1 into a trained LSTM neural network, and predicting future crop transpiration water demand; 3, according to nutrient concentration distribution fed back by a soil conductivity sensor in real time, combining with a water and fertilizer coupling migration model, dynamically calculating a nitrogen-phosphorus-potassium ratio of each irrigation zone, realizing precise mixing of a fertilizer mother solution and irrigation water through a three-channel proportioning pump, and implementing a variable irrigation decision; 4, dynamically adjusting the pulse irrigation duration of each branch through an MPC algorithm; and step 5, recovering the residual liquid at the tail end of the pipe network to a buffer tank, detecting the EC value of the recovered liquid through an electrochemical sensor, and taking the EC value as a reference parameter automatic correction proportioning algorithm of the next irrigation period. Accurate control over the irrigation process can be achieved, so that the water and fertilizer utilization efficiency is improved, and resource waste and environmental pollution are reduced.

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

The invention 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 discloses a strip-shaped compound planting mode water and fertilizer zoning management method and system, and relates to the technical field of modern agriculture, and the method comprises the following steps: installing a sensor network in a field, collecting data through a soil moisture sensor, an intelligent water meter and an unmanned aerial vehicle multispectral camera, threshold values are set according to the water demand and the fertilizer demand in the crop growth cycle to judge fertigation, crop demands are predicted by applying multiple linear regression and a random forest algorithm, a fertigation plan is optimized, the growth trend is analyzed, the yield potential is evaluated, the influence of the irrigation level is researched, and an irrigation strategy adjustment suggestion is provided. The system collects field data through an integrated sensor network, accurately sets an irrigation and fertilization threshold value, predicts crop demands by using an advanced algorithm, optimizes a water and fertilizer management plan, effectively analyzes a growth trend and evaluates yield potential, provides a scientific basis for irrigation strategy adjustment, and significantly improves modern agricultural production efficiency and resource utilization efficiency.

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

The invention relates to the technical field of irrigation, and discloses an intelligent energy-saving irrigation management system and method based on environmental monitoring, and the method comprises the steps: obtaining the daily water demand of crops in a farmland through a crop growth monitoring module, setting the water flow of irrigation according to the daily water demand, then dividing the farmland into a plurality of regions, then environment parameters of soil humidity, temperature and illumination intensity of each area are monitored in real time through an environment monitoring module, and then the environment parameters are processed and analyzed through a data processing module to obtain current irrigation indexes of each area; whether the set irrigation water flow meets the irrigation requirements of all the areas or not is judged based on the current irrigation indexes of all the areas, if the areas do not meet the irrigation requirements, water supplementing is conducted on the areas in a sprinkling irrigation mode, then the water supplementing water flow is calculated based on the environment parameters, and it is guaranteed that the water content of all the areas of the farmland is consistent; therefore, the growth vigor of crops in each area is consistent.

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

The invention discloses an intelligent precise farmland irrigation direction control system based on the Internet of Things, and relates to the technical field related to intelligent irrigation. Comprising a data acquisition module, a data processing and analyzing module, a data labeling and classifying unit, a three-dimensional coordinate model building module, a nozzle angle adjusting device, a nozzle water quantity adjusting unit and a control instruction execution unit. According to the invention, the distance between the nearest nozzle and the water-requiring position is calculated, so that irrigation water can reach the water-requiring area more efficiently, the distance is accurately calculated to ensure that water can be quickly and accurately conveyed to the required area, water loss and energy waste caused by long-distance water conveying are avoided, the water spraying direction is adjusted in combination with the wind direction and the wind speed, and the water spraying efficiency is improved. The device can effectively cope with the influence of natural wind on irrigation, and by monitoring wind direction and wind speed information in real time and combining the wind direction and wind speed information with water spraying parameters of the spray head, the system accurately calculates horizontal and vertical angle adjustment values of the spray head, so that sprayed water flow can accurately reach a water needing position.

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 relates to the technical field of agriculture, in particular to a greenhouse energy-saving water consumption monitoring system which is used for solving the problems that in the prior art, historical data and real-time data cannot be combined to accurately predict irrigation water consumption, scientificity and rationality of an irrigation strategy cannot be guaranteed, and the irrigation cost is low. And different environmental conditions and crop growth stages cannot be dynamically adapted. Historical data and real-time data are combined through the irrigation water consumption adjusting module, the irrigation water consumption is accurately predicted through a machine learning model, automatic adjustment is achieved to improve efficiency, multiple factors are comprehensively considered, scientificity and reasonability of an irrigation strategy are guaranteed, different environment conditions and crop growth stages are dynamically adapted, water resource utilization is optimized, and the irrigation efficiency is improved. Cost is reduced, personal errors are reduced through automatic operation, and various crops and complex growth environments are supported.

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.

A method of increasing delivery of irrigation water and optionally a fertilizer to a predetermined depth in soil containing agricultural crops is disclosed. The method includes introducing formic acid to irrigation water to form a formate-enhanced irrigation water having a formate concentration of from about 10 ppm to about 10,000 ppm, and distributing the formate-enhanced irrigation water to the soil in an amount sufficient to (i) increase a flow rate of the irrigation water through the soil, (ii) reduce a compaction of the soil, and / or (iii) reduce a calcium concentration in the soil.

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

The invention relates to the technical field of agricultural water conservancy, and discloses a dynamic regulation and control method for a farmland-irrigation area-drainage basin water conservation and drainage system in a black soil area, which comprises the following steps: S1, collecting basic data including meteorological and hydrological data, irrigation data, environmental data and monitored soil data; s2, analyzing water retention threshold values and soil fitting parameters of the four kinds of soil by applying a naive Bayes method; s3, calculating a function relationship between the water demand and the yield; the soil water retention threshold value serves as input and is input into a management file of a DSSAT model, and the DSSAT model is used for simulating the growth conditions of the four kinds of soil under the condition that multiple crops are planted on the farmland scale; and S4, constructing a dynamic regulation and control framework of a water retention and drainage system of soil-farmland-irrigation area-drainage basin. According to the method, the minimum water retention threshold value and the effective water threshold value of different soil types in the black soil area are quantified, the crop water consumption amount based on the crop growth process is estimated, and a response relation function among soil water, crop water demand and irrigation water is obtained.

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 present application relates to the technical field of heavy-metal pollution remediation, and relates in particular to a soil heavy‐metal accumulation predicting method employing an emission inventory and a receptor model. The method comprises: using atmospheric dust deposition, irrigation water, chemical fertilizers and pesticides as input flux sources of heavy metals to farmland soil, using surface runoff and crops as output fluxes, and constructing an input-output flux inventory of heavy-metal pollutants to clarify a dynamic balance relationship of soil heavy-metal accumulation; and continuously collecting and monitoring of soil samples. The invention provides crucial assistance in determining the transport balance of heavy metals in farmland areas impacted by non-ferrous metal smelting and beneficiation slag sites and in identifying sources of soil heavy-metal pollution, and offers theoretical guidance for subsequent soil heavy-metal remediation and targeted control.

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 relates to the field of agricultural water resource management, in particular to a method, device and equipment for estimating irrigation water consumption and canal system water utilization efficiency, and the method comprises the steps: screening extreme meteorological and hydrological data meeting extreme rainfall scene conditions from historical meteorological data and hydrological data, calculating label data representing moisture storage and discharge capacity of an underground water change zone by combining net exchange flux at a deep soil boundary in an extreme rainfall scene, and constructing a space-time variable specific yield estimation model by combining a machine learning algorithm based on dynamic nonlinear influence and spatial heterogeneity of the extreme rainfall scene on soil moisture; training a specific yield estimation model by using the data set carrying the label data; and based on the dynamic similar characteristics of the soil moisture in the extreme rainfall scene and the irrigation scene, migrating the trained specific yield estimation model to the irrigation scene so as to estimate the irrigation water consumption and the canal system water utilization efficiency. Therefore, the problem of how to improve the parameter accuracy of the arid region is solved.