1577 results about "Irrigation water" patented technology

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 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 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.

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 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 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 the technical field of greening irrigation equipment, in particular to urban greening green plant irrigation equipment which comprises a supporting plate, a water inlet pipe is fixedly connected to the center of the supporting plate, a grounding support is rotatably mounted on the lower end face of the supporting plate, and a uniform sprinkling irrigation assembly is fixedly mounted at the upper position of the peripheral wall of the water inlet pipe; the uniform sprinkling irrigation assembly comprises two sealing bearings fixedly installed on the peripheral wall of the water inlet pipe, and the outer rings of the two sealing bearings are fixedly connected with an irrigation frame. The spray pipe can perform vertical reciprocating swing spray irrigation during forward and reverse rotation, the irrigation range is greatly widened, the comprehensiveness and sufficiency of the green plant irrigation effect are improved, meanwhile, the rotary spray irrigation rate and the irrigation water flow can be adaptively adjusted according to the air humidity, the green plant irrigation effect is further improved, and the green plant irrigation efficiency is improved. More accurate irrigation operation can be carried out on the green plants, and the water waste condition of the irrigation operation of the green plants can be reduced.

The invention relates to the technical field of intelligent water and fertilizer analysis, in particular to an intelligent water and fertilizer analysis method and system based on farmland regional crop yield increase. The method comprises the following steps: acquiring structure data of a farmland area and extracting the flow direction of irrigation water, deploying a sensor to monitor the farmland state, and generating farmland state data of an irrigation basin; crop form images are collected through electronic monitoring equipment and water flow direction data, the leaf yellowing and curling degree is evaluated, and the soil volume moisture saturation is extracted. According to the soil moisture data, nutrient element content change is extracted, the nutrient leaching loss gradient is quantified, and soil nitrogen circulation abnormity is further simulated; and finally, performing intelligent water and fertilizer dosage controlled release analysis on the leaf yellowing and curling degree based on the nitrogen circulation abnormal data, and sending a result to a terminal to improve the per unit area yield of crops. The water and fertilizer intelligent analysis technology is optimized, so that the water and fertilizer intelligent analysis technology is more perfect.

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 discloses a distributed improvement method and device for farmland crop irrigation water productivity. The method comprises the steps that soil physical indexes and nutrient indexes are obtained; performing management area division on the farmland by using a fuzzy clustering method based on the soil physical indexes and the nutrient indexes; sorting the contribution rate of each soil index to the crop irrigation water productivity in each management partition by using a partial least square discriminant analysis method to obtain a driving factor of the crop irrigation water productivity; using a boundary line analysis method to determine a regulation threshold value of a crop irrigation water productivity driving factor in each management area; calculating actual stage water consumption of crops in each management area, establishing an irrigation system optimization model, and performing multi-objective optimization solution on the irrigation system optimization model by applying a genetic algorithm to obtain an optimized irrigation system; and combining the regulation and control threshold value of the crop irrigation water productivity driving factor with the optimized irrigation system to obtain a farmland distributed crop management scheme, so that the crop irrigation water productivity 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.