56 results about "Irrigation management" patented technology

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 belongs to the technical field of equipment planning, and discloses an automatic planning method for intelligent agricultural Internet of Things equipment, which comprises the following steps: dividing a target region into grid units, collecting soil moisture parameters and nutrient parameters of each grid, and establishing a regional digital twinborn model; obtaining a target crop of each grid, obtaining growth information from the crop knowledge base, and executing time sequence simulation to generate a water and fertilizer demand table of each grid; executing a water-fertilizer balance strategy based on the water-fertilizer demand table, and generating a water-fertilizer supply plan of each grid; analyzing spatial distribution characteristics of supply plans, and clustering adjacent grids with similar plans into irrigation management partitions; and generating a pipe network topological structure and an equipment planning scheme based on the partition layout. According to the method, a traditional uniformly distributed area is scientifically divided into management subareas with consistent internal requirements, so that unified management of the same subarea is realized, and the problem of supply and demand mismatching caused by neglect of spatial heterogeneity in a traditional method is solved.

The invention provides a greenhouse crop habitat optimization method, system and device and a storage medium, and belongs to the technical field of intelligent agriculture, and the method comprises the steps: continuously obtaining greenhouse internal environment multi-element data, greenhouse external meteorological data, crop root zone current soil moisture data and device state data; predicting greenhouse internal environment multi-element data and soil moisture content of different soil layers in a future time period based on the data; predicting photosynthetic physiological habitat conditions of crops in the greenhouse in the future time period based on the multi-element data of the internal environment of the greenhouse in the future time period and soil moisture contents of different soil layers in the crop root zone; and taking the predicted habitat condition as an optimization target, performing optimization decision on current irrigation and environment multi-element regulation and control, generating a control scheme, further generating a regulation and control signal, and controlling greenhouse terminal equipment to operate according to the regulation and control signal. According to the invention, the accuracy, real-time performance, collaboration and intelligent level of greenhouse environment regulation and irrigation management are obviously improved, and the crop yield, quality and resource utilization efficiency are further improved.

The invention provides a cold region greenhouse irrigation method and system, and belongs to the technical field of environment control. The method comprises the steps that soil temperature data and humidity data of different depths in all partitions of a greenhouse are acquired in real time; carrying out preprocessing and spatial-temporal feature enhancement on the collected data, inputting the data into a soil temperature-humidity dynamic coupling monitoring model, and calculating to obtain a root retting risk index of the current soil environment; if the root retting risk index exceeds a dynamic risk threshold, generating an irrigation strategy for inhibiting the root retting risk; if the root retting risk index does not exceed a dynamic risk threshold, generating a conventional irrigation strategy based on a multi-objective optimization algorithm; according to the generated irrigation strategy, driving the irrigation execution mechanism of the corresponding subarea to perform independent action, and realizing subarea accurate irrigation; the problem of root retting caused by low temperature and high humidity in a cold region greenhouse environment is solved, and precision, intelligence and high efficiency of irrigation management are achieved.

This invention discloses a smart irrigation management system based on soil and crop moisture status identification, belonging to the field of crop moisture status monitoring technology. The system includes: acquiring leaf electrophysiological signal sequences and multi-depth water potential parameters of the rhizosphere microdomain of the target crop; determining the onset time of water stress and its corresponding first characteristic frequency based on the leaf electrophysiological signal sequences; determining the active water-uptake layer and its corresponding second characteristic water potential based on the multi-depth water potential parameters; performing a coupled comparison based on the first characteristic frequency and the second characteristic water potential, and determining whether irrigation triggering conditions are met based on the comparison results. If the irrigation triggering conditions are met, a differentiated irrigation quota is determined for different irrigation areas. This invention determines irrigation triggering conditions by comparing the consistency and degree of deviation between the first and second deviation directions, avoiding premature or late irrigation caused by misjudgment based on a single indicator.

The invention belongs to the technical field of agricultural intelligent control and irrigation management, particularly relates to a self-adaptive irrigation decision-making system oriented to crop water demand response, and aims to solve the problems that a traditional irrigation system is lagged in response, extensive in water distribution and difficult to match actual physiological water demand of crops. The system collects crop physiological and environmental parameters through a multi-source sensor, inverts a water stress index in real time in combination with an edge computing node, dynamically calls a basic water demand curve based on a crop growth stage, and generates an instantaneous water demand deviation. A double-level decision-making mechanism is adopted, wherein the first layer triggers emergency irrigation according to a dynamic warning threshold value; and the second layer realizes irrigation plan feed-forward adjustment through trend analysis. The system also integrates a closed-loop feedback verification and federated learning optimization mechanism, and continuously improves the decision precision. By fusing physiological feedback and self-adaptive modeling, the conversion from watering on day to water supply on demand is realized, the water utilization efficiency is remarkably improved, and the stress risk is reduced.

The invention relates to the technical field of management systems, in particular to an intelligent spray irrigation management system under forestry plant maintenance, which comprises a soil layering model construction unit for collecting the types of planted saplings and topographic data of a planting area, extracting soil layering structure data of the planting area and a three-dimensional topographic model, and constructing a three-dimensional model; performing fusion processing to obtain a soil layering model; determining the distribution positions of planted saplings on the soil layering model, marking the position corresponding to each sapling on the soil layering model as a planting point position, constructing a sapling root growth model based on the type of the sapling and the soil layering structure of the position, and forming a sapling soil layering model together with the soil layering model; and the irrigation network construction unit is used for setting an irrigation point position for each sapling root growth model based on the sapling soil layering model, connecting a plurality of drip irrigation point positions to form a pipeline trend straight line, erecting branch pipes along the pipeline trend straight line, erecting a main pipe and connecting a plurality of branch pipes to form an irrigation network.

The invention relates to the technical field of agriculture, and discloses a fresh corn efficient harvesting and storage method based on ecological planting, which comprises the following steps: ecological planting management, harvesting opportunity determination, efficient harvesting operation and storage condition control. According to the ecological planting management, scientific fertilization and irrigation management are implemented, nutrient and water supply is dynamically adjusted according to the growth stage of the fresh corn, a balanced nutrition environment is provided for growth of the fresh corn, and therefore balanced development of quality indexes of the fresh corn is promoted; the harvesting opportunity is determined based on quality index monitoring and a harvesting prediction model, accurate judgment of the mature state of the fresh corn is achieved, it is guaranteed that harvesting is conducted at the optimal harvesting time, a foundation is laid for obtaining high-quality fresh corn, efficient harvesting operation reduces mechanical damage to the fresh corn by optimizing harvesting parameters and the harvesting environment, and the yield of the fresh corn is improved. And immediately transferring to a shading environment, and slowing down the physiological deterioration process after harvesting.

The present application relates to the technical field of agricultural drought monitoring and precision irrigation, and particularly relates to a crop water stress diagnosis method and system based on sunlight-induced chlorophyll fluorescence, which collects crop canopy oxygen absorption band spectral data through a hyperspectral sensor, and calculates sunlight-induced chlorophyll fluorescence intensity SIF values by using a fluorescence extraction algorithm. The SIF value of a non-water-stressed crop canopy is taken as a reference canopy SIF value, the difference between the SIF value of a to-be-measured crop canopy and the reference canopy SIF value is determined, then a relative SIF water deficit index SIFWDI is generated by combining a preset formula, a regression analysis model is used to establish a mapping relationship between the SIFWDI and a soil water deficit index to output a diagnosis report, a variable irrigation decision and early warning are triggered according to a stress level, and model parameter adaptive optimization is realized through a feedback mechanism. The system realizes non-destructive, real-time and high-precision water stress monitoring and irrigation management.

The application relates to the irrigation technical field and discloses a smart energy-saving irrigation management system and method based on environment monitoring, which acquires the daily water requirement of crops in farmland through a crop growth monitoring module, sets the water flow of irrigation according to the daily water requirement, divides the farmland into several regions, then monitors the soil humidity, temperature and light intensity environmental parameters of each region in real time through an environment monitoring module, then processes and analyzes the environmental parameters through a data processing module to obtain the current irrigation indexes of each region, judges whether the set water flow of irrigation meets the irrigation requirements of each region based on the current irrigation indexes of each region, if some region does not meet the requirements, the region is watered through a sprinkling irrigation mode, then the water flow of the water supplement is calculated based on the environmental parameters, the water content of each region of the farmland is ensured to be consistent, and therefore the growth of crops in each region is ensured to be consistent.

The present application relates to the field of water resource management, and particularly relates to a zero-carbon water resource comprehensive management system for agricultural irrigation areas, comprising: an irrigation area basic data calculation module which calculates soil latent heat flux and crop basic evapotranspiration of the irrigation area respectively according to detected air temperature, air pressure, solar radiation and wind speed; an irrigation area data prediction module which generates predicted water storage and utilization amount of the irrigation area through an irrigation area prediction model based on a ConvLSTM architecture; a water demand fusion prediction module which generates comprehensive predicted water demand of the irrigation area through a fusion mapping model based on a gating mechanism and multi-scale convolution; and a zero-carbon water resource management module which calculates predicted net irrigation amount of the irrigation area based on the comprehensive predicted water demand to perform irrigation management on photovoltaic water pumps and photovoltaic valves of the irrigation area. The present application improves the precision of water resource management of the irrigation area, optimizes the scheduling energy consumption of the zero-carbon photovoltaic water pumps and photovoltaic valves, and realizes the optimization of the zero-carbon transformation of the agricultural irrigation area from the head of irrigation energy consumption.

The present invention provides a system, a method, and an apparatus for providing an irrigation scheduling module including a graphical user interface to provide irrigation scheduling data for a given field location. According to a preferred embodiment, the irrigation scheduling module is configured to calculate and display an irrigation recommendation for a given set of forecast data. According to another preferred embodiment, the irrigation recommendation includes a representative form in the shape of a circle that changes from a complete circle to an increasing percentage of the complete circle based on the field moisture status.

The invention discloses a planting method for preventing and treating yellowing and regreening of tree leaves under the condition of reclaimed water irrigation in drought saline-alkali soil, which can prevent the yellowing of transplanted nursery stock leaves through the measures of backfilling planting soil in planting holes, raising soil ball height (shallow planting), heavy pruning and the like. The survival rate of transplanted trees under the condition of high-salt reclaimed water irrigation in arid and saline-alkali areas is increased, the survival rate can reach 90% or above, and the method is suitable for reclaimed water irrigation management.

This application relates to a method for planting sugarcane in sandy yellow soil, comprising: preparing the selected sandy yellow soil and applying base fertilizer; selecting sugarcane varieties suitable for the sandy yellow soil; treating and planting the selected sugarcane varieties; dividing the selected planting area into basic unit zones to facilitate field management; conducting field management of the sugarcane planted in the sandy yellow soil, including integrated water and fertilizer irrigation management; harvesting the mature sugarcane; the sugarcane is harvested twice a year, with the second harvested sugarcane being cultivated from the sugarcane roots after the first harvest. The technical solution of this application effectively solves the problem of poor sugarcane yield in existing technologies for planting sugarcane in sandy yellow soil.

The application discloses a winter wheat multi-target irrigation dynamic optimization method based on a crop model and artificial intelligence, and belongs to the field of agricultural intelligent irrigation optimization and decision-making. The method comprises the following steps: preprocessing meteorological data, soil data, crop parameter data and irrigation management data to obtain standardized input data; performing coupling processing on an AquaCrop model and a PyFAO56 model based on the standardized input data to obtain final output of the coupling model; based on the final output of the coupling model, adopting an NSGA-2 multi-target optimization algorithm to optimize decision variables to obtain a Pareto optimal solution set; and combining a fusion strategy of an entropy weight method and a random forest algorithm to sort candidate schemes in the Pareto optimal solution set to obtain a final irrigation strategy. The application provides a new type of precise irrigation scheme by giving consideration to yield and water efficiency through multi-target optimization, objective weighting and machine learning intelligent evaluation.

The utility model discloses an agricultural internet of things data acquisition control system, including main control cabinet, solar generating mechanism, monitoring mechanism and spraying irrigation mechanism. This agricultural internet of things data acquisition control system utilizes each bottom monitoring branch to monitor the area respectively, monitors carbon dioxide concentration, temperature and soil humidity etc. data in field, helps remote control center real -time monitoring the data in each area, and remote control center judges whether need irrigation according to the plant humidity demand in the area and the data in the corresponding area, when need irrigation, then through main control unit control corresponding irrigation branch watering, thereby according to the plant difference of each area, carries out corresponding irrigation management and data acquisition to each area, realizes the fine management, adapts the field that has different crops to plant, utilizes water storage branch to collect rainwater to reach the purpose of comprehensive utilization rainwater resources and water saving.

The invention relates to the technical field of agricultural production, and discloses an irrigation management system for agricultural production, which comprises a multi-source data acquisition module, a preprocessing and water distribution decision module, a management and execution module, a farmer cooperation module and a data transmission and management module. Through the multi-source sensing data fusion and intelligent decision technology, all dimensions of agricultural irrigation management can be comprehensively covered, traditional water quantity distribution is concerned, factors such as soil moisture content, a crop growth period water demand rule, personnel operation quality and farmer participation degree are also included, a dynamic water distribution and whole-process closed-loop management model is deeply constructed, and the management efficiency is improved. The irrigation decision can be more accurately matched with the actual demand of the farmland, and a more scientific basis is provided for efficient utilization and fine management of agricultural water resources.

The invention relates to the technical field of water resource information management, in particular to a Yellow River well and canal combined irrigation area water distribution information processing method and system, and the method comprises the following steps: processing Yellow River well and canal multi-source data to generate a supply and demand matching set, analyzing the coupling strength to screen and optimize a water distribution area, and evaluating the ratio of dependence to the total irrigation period number to adjust and optimize distribution. The core units are classified according to crop and salt control coordination, water distribution group priorities are sorted according to water source coverage efficiency, and an irrigation area water quantity distribution execution table is generated. Dynamic adaptation and accurate distribution of Yellow River diversion and underground well water dispatching are achieved by collecting and analyzing multi-source hydrological monitoring data; the matching relation between the water source spatial and temporal distribution and the irrigation units is optimized, the intelligent level of irrigation management is improved, the coupling strength of the water source and the irrigation units and the crop water demand dynamic state are analyzed, the real-time response and accurate adjustment of the irrigation demand are enhanced, flexible water distribution and efficient scheduling are achieved, and the intelligence and real-time performance of water source scheduling are remarkably improved.

This invention discloses an integrated water and fertilizer drip irrigation device and its application method for orchards, comprising a drip irrigation mechanism, at least two sets thereof; the drip irrigation mechanism includes drip irrigation pipes and drip irrigation components, each drip irrigation component having a first structure and a second structure, the first structure for implementing above-ground drip irrigation and the second structure for implementing underground drip irrigation; a water delivery mechanism for delivering water and fertilizer to the drip irrigation mechanism to achieve above-ground and / or underground drip irrigation operations; this integrated water and fertilizer drip irrigation device and its application method for orchards, by setting two sets of drip irrigation mechanisms on both sides of the same fruit tree trunk at different soil depths, allows irrigation managers to flexibly choose above-ground drip irrigation only, underground drip irrigation only, or both simultaneously, depending on the fruit tree's growth stage, soil moisture, or weather conditions; through the alternating above-ground and underground drip irrigation method, it can induce a balanced distribution of roots in the soil profile, promoting the fruit tree to develop compensatory water and fertilizer absorption capacity in different soil layers.

The present application relates to the technical field of management system, and specifically relates to a forestry plant maintenance intelligent spraying irrigation management system, which comprises a soil layering model construction unit, collects the type of planted seedlings and topographic data of the planting area, extracts soil layering structure data and a three-dimensional terrain model of the planting area, and obtains a soil layering model through fusion processing; the distribution position of the planted seedlings is determined on the soil layering model, the position corresponding to each seedling on the soil layering model is recorded as a planting point, a seedling root growth model is constructed based on the type of the seedling and the soil layering structure of the position, and the soil layering model and the seedling root growth model jointly constitute a seedling soil layering model; an irrigation network construction unit sets an irrigation point for each seedling root growth model based on the seedling soil layering model, connects multiple drip irrigation points to form a pipeline direction straight line, erects branch pipes along the pipeline direction straight line, erects a main pipe and connects multiple branch pipes to form an irrigation network.

The application provides an irrigation management and control system based on artificial intelligence, and relates to the technical field of drip irrigation management and control.The system comprises a leaf height information identification module for collecting first crop images and identifying multiple leaf height information; a growth vigor information identification module for collecting second crop images and identifying multiple growth vigor information; a root stem water constraint construction module for analyzing the water demand of root stems and the disease probability of leaves, and constructing root stem water constraints and leaf humidity constraints; and an optimal drip irrigation scheme acquisition module for acquiring an optimal drip irrigation scheme, conducting drip irrigation management and control, and optimizing the process by combining leaf height information to predict leaf humidity. The application can solve the technical problem of low drip irrigation accuracy caused by poor combination of drip irrigation control and actual crop conditions in the prior art, achieve the technical goal of improving plant irrigation accuracy and intelligence, and achieve the technical effect of improving plant crop growth quality.

The invention discloses an arid and semi-arid region irrigation water-deficient area rapid identification and diagnosis method, system, equipment and medium by coupling channel network hydraulics and multi-source remote sensing, and belongs to the technical field of agricultural irrigation management, and the method comprises the steps: obtaining multi-source remote sensing data of a to-be-irrigated area of an arid and semi-arid region, and constructing a spatial data set; remote sensing data correction processing is executed through the spatial data set, and a corrected remote sensing image data set is generated; and constructing a boundary model of the region to be irrigated by combining the corrected remote sensing image data set with auxiliary geographic information. The method comprises the following steps: acquiring and correcting multi-source remote sensing data of a to-be-irrigated area in an arid and semi-arid region to generate an image data set; the method comprises the following steps: establishing a boundary model in combination with geographic information to define a range, calculating a crop water stress index to establish a distribution model to identify a water shortage area, establishing a water delivery constraint model in combination with the boundary model to supply limitation, superposing and integrating multiple models and meteorological data, and constructing a water shortage cause diagnosis model, thereby providing a basis for precise irrigation and water resource optimization.

The present application relates to the technical field of cotton cultivation and water-saving irrigation management, in particular to a kind of cultivation method of high yield water-saving of cotton in arid region, comprising the following steps: step one, land preparation and planting: the land for planting is directly sowed and planted, and drip irrigation belt is laid along the planting row and covered with mulch, and after sowing on the mulch, it is covered with soil, so that mulching, laying drip irrigation belt, punching, sowing and covering soil are completed at one time.The present application obtains different soil layer moisture regime, cotton plant growth and future 72h weather information through information collection, and forms root zone effective water supply value, surface layer wetness difference and group balance state through state generation, and then based on the above state, it carries out controlled irrigation from seedling stage to present budding stage, compensatory irrigation and water-fertilizer linkage from initial flowering to full boll stage, and withdrawal control after boll opening, so as to solve the problem that in the process of cotton cultivation in arid region, irrigation trigger basis is single, early stage is prone to misirrigation, middle stage is prone to excessive growth and boll shedding, and timing of withdrawal in later stage is not accurate, which leads to difficult to balance water-saving effect and yield level.

The invention discloses a balanced water and fertilizer management method for greenhouse tomato yield and quality, which is characterized in that a group benefit value, an individual regret value and a compromise evaluation value of each test water and fertilizer scheme are obtained by adopting a CRITIC-VIKOR comprehensive evaluation model, and then a wormcast irrigation management strategy meeting compromise requirements is obtained by comparing the group benefit value, the individual regret value and the compromise evaluation value. Compared with the prior art, the method has the advantages that the CRITIC-VIKOR comprehensive evaluation model has remarkable advantages in greenhouse drip irrigation tomato water and fertilizer management optimization, the core advantage of the method is that compromise thoughts are fused, all indexes are comprehensively analyzed and evaluated in subjective and objective aspects, the CRITIC method is mainly used for objective weighting, and the method has the advantages of being high in evaluation accuracy and high in evaluation efficiency. The comparison strength and conflict between indexes are comprehensively considered, and the group utility maximization and the individual regret minimization are comprehensively considered by the VIKOR model; in addition, compromise thoughts can be integrated according to preferences of decision makers, and higher sorting stability and credibility are achieved.

The application provides a salt-alkali soil improvement comprehensive benefit evaluation method and system based on a watershed water salt balance, and belongs to the technical field of agricultural ecological management. The method comprises the following steps: collecting geographical zoning data, soil hydrological data, crop salt tolerance data and irrigation management data, and preprocessing; a salt migration-yield coupling model is constructed, which comprises the following steps: a salt migration dynamic equation is established, and the preprocessed data is substituted into the salt migration dynamic equation to calculate the downstream salt concentration; a yield response model is established, and the downstream salt concentration and the preprocessed data are substituted into the yield response model to calculate the yield increase of the upstream management area and the yield decrease of the downstream; and the net benefit value of the salt-alkali soil improvement is calculated according to the yield increase of the upstream management area and the yield decrease of the downstream. The method not only considers the salt increment of the soil in the management area exceeding the salt tolerance threshold, but also systematically evaluates the yield changes of the upstream and downstream, thereby providing reliable guidance for the selection and optimization of irrigation measures.

The invention relates to the technical field of agricultural intelligent irrigation, in particular to an irrigation decision optimization system and method based on complementation of soil moisture content and meteorological data. Comprising a soil moisture content monitoring unit used for collecting layered soil moisture content and temperature in real time; the meteorological data acquisition unit is used for acquiring local actually measured meteorological parameters and integrating third-party remote sensing rainfall and evapotranspiration prediction data; the data fusion and processing unit is used for modeling the multi-dimensional data by using a machine learning model and extracting a field evapotranspiration feature vector; the decision optimization unit is used for analyzing the deviation degree of the actually measured moisture reduction rate and a theoretical predicted value based on a machine learning algorithm, dynamically correcting the crop coefficient in the current growth period and generating a precise irrigation instruction in combination with a water balance model; and the execution control unit is used for realizing closed-loop irrigation management and checking the infiltration effect through the intelligent ecological gateway. According to the invention, through deep fusion of the actually measured soil data and the weather forecast data, the defect of crop coefficient staticization in a traditional irrigation decision is corrected.

The invention relates to system integration of monitoring technologies of river water level, flow, sand content, water temperature, precipitation, evaporation capacity, wind speed, wind direction, soil moisture content and the like, and belongs to the technical field of hydrological element monitoring in water conservancy and hydrological industries. A station-building-free total-factor hydrometric station monitoring device comprises a first support and a second support, a fixing structure is arranged on the first support and the second support, a travelling crane and a radar travelling crane are arranged on the fixing structure, a hydrometric station monitoring device is arranged on the first support, the second support, the travelling crane and the radar travelling crane, and local automatic intelligent monitoring of a multifunctional hydrometric station is achieved through the structure. The system can remotely control automatic intelligent monitoring or real-time interrogation operation, and is widely applied to hydrological monitoring, water conservation monitoring, environment-friendly water quality monitoring, reservoir dispatching management, irrigation management dispatching of irrigation areas, traffic monitoring and other industries.

The invention relates to the technical field of water conservancy data management, in particular to an irrigation area water consumption prediction method based on LSTM network and multi-modal data fusion. The invention specifically relates to application of artificial intelligence in agricultural irrigation management of an irrigation area, in particular to a water consumption prediction method combining deep learning and multi-source data fusion. Comprising the steps of data preprocessing, feature extraction, model construction, training optimization and prediction evaluation. The method has higher prediction precision and better generalization ability, and can reflect the change trend of the actual water demand more accurately. Meanwhile, through real-time monitoring and dynamic adjustment of the irrigation plan, the utilization efficiency of water resources is improved, and the phenomenon of water resource waste is reduced.

The application relates to a digital twin visualized irrigation area management platform, which comprises an irrigation area partition module, a digital twin model establishment module and a data anomaly monitoring module, is used for monitoring data anomalies of a sub-digital twin area, marking a sub-digital twin area with data anomalies, simultaneously sending associated data of the sub-digital twin area marked as having data anomalies to an administrator IP and a visualized display module. In one aspect, the target irrigation area is partitioned to obtain a plurality of different sub-irrigation areas, a sub-digital twin model is established for the sub-irrigation areas, the model can be improved in pertinence, and the model establishment can be accelerated to a certain extent; in addition, the data anomaly detection module is arranged, can monitor data anomalies of the sub-digital twin area, is beneficial to the management personnel to master the abnormal data and then perform targeted processing; the visualized display module is further arranged, can digitally display the irrigation area, and is convenient for the management personnel to digitally manage the irrigation area.

The utility model relates to the technical field of farmland irrigation, and disclose high -standard farmland irrigation management device, including water storage tank, the top of water storage tank is equipped with the water storage warehouse, the bottom of water storage warehouse is fixed with water pump, and the water outlet of water pump is connected with main water pipe, and the irrigation department is equipped on main water pipe, and the irrigation department includes the water distribution pipe, and the side away from main water pipe of water distribution pipe is installed with electric control valve, and the wall of water storage tank outside installs the management department, and the management department includes electric control box, and the inside of electric control box is equipped with programmable controller, and electric control valve, water pump and programmable controller electric connection, and the inside of electric control box still is equipped with the power supply end of the power supply of electric control valve, water pump, through programmable controller to the control of electric control valve, the instruction of remote sending of farmland irrigation is convenient, the management of farmland irrigation is convenient, and the control of different electric control valves is convenient for accurate efficient irrigation, and support piece supports main water pipe.