39 results about "Crop coefficient" patented technology

The present disclosure provides a decision-making method and apparatus for water and fertilizer stress of crops, and a mobile phone terminal, and belongs to the technical field of intelligent agriculture. The method includes: determining a current crop coefficient and canopy temperature of a crop to be identified on the basis of a first crop image and a second crop image; determining a water stress state and a nutrient stress state on the basis of the current crop coefficient and canopy temperature of the crop to be identified; and processing the water stress state and the nutrient stress state with a fuzzy control algorithm, and determining a water and fertilizer stress decision result of the crop to be identified.

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

The invention discloses a crop growth water demand calculation method and an irrigation system formulated according to the same, and relates to the field of agricultural irrigation. The method comprises the following calculation steps: selecting reference ET0, selecting and confirming the length of a growth stage, selecting a crop coefficient, adjusting the value of Kc ini to reflect the soil wetting frequency, adjusting the values of Kc mid and Kc end to adapt to regional climate conditions, calculating ETc, determining the effective water consumption and calculating the water balance of a soil root system layer. According to the irrigation water distribution system disclosed by the invention, a water demand calculation result in actual agricultural production can be reasonably adjusted and optimized in combination with a local water resource condition and an irrigation system capability, and during fertilization or pesticide spraying, the amount can be more accurately calculated by utilizing the calculated water amount, so that the irrigation efficiency is improved. And meanwhile, selective operation can be carried out according to a fertilization mode required by crops.

The invention discloses a precise irrigation decision-making method and system based on a crop growth model and weather forecast, and relates to the field of agricultural irrigation automation control. The method comprises the following steps: calculating reference crop evapotranspiration corresponding to each growth period of crops in an irrigation decision-making area by adopting an FAO56PM formula according to meteorological data, and calculating a theoretical growth period water demand and a water demand proportion of an ith growth period in a whole growth period according to a growth period crop coefficient; the optimal growth period water demand of the crops in the whole growth period in the typical hydrological year is calculated through optimization of a crop growth model, the daily precipitation serves as a reference value of the effective precipitation, the effective precipitation is estimated according to weather forecast, and by comparing the quantitative relation between the effective precipitation and the optimal water demand of the ith growth period, the optimal growth period water demand of the crops in the ith growth period is calculated. Judging whether the irrigation decision-making area needs to be irrigated or not, and when the irrigation decision-making area needs to be irrigated, calculating and determining the optimal irrigation amount of the crops in the irrigation decision-making area in the ith growth period according to a formula.

The invention discloses an intelligent irrigation dynamic decision-making method based on reference crop evapotranspiration estimation. The method comprises the following steps: firstly, acquiring historical day-by-day weather data of a farmland area, calculating the evapotranspiration of a reference crop, and preprocessing the evapotranspiration; secondly, weather factors are screened to serve as input features, reference crop evapotranspiration serves as a target variable, and a reference crop evapotranspiration estimation model is constructed and trained; and then, through a reference crop evapotranspiration estimation model, obtaining reference crop evapotranspiration estimation results of the current day and the future n days. And finally, according to an evapotranspiration estimation result in combination with a crop coefficient corresponding to the growth stage of the rice, evapotranspiration of the current day and n days in the future is calculated, an irrigation decision is made, and the water storage capacity of the reservoir is adjusted based on the irrigation water consumption of the current day and the irrigation water consumption of the n days in the future. The invention provides an efficient, economic and sustainable technical scheme for intelligent irrigation management of large-scale farmland, and has important value for promoting agricultural precision and ecological development.

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

The invention relates to the technical field of agricultural information, in particular to a crop yield prediction method, a verification method, a computer system and a readable storage medium. Standardized moisture productivity, a crop coefficient before canopy aging, a harvest index, a maximum canopy coverage degree, a canopy growth coefficient, a canopy attenuation coefficient, a minimum rooting length and a maximum rooting length are selected as analysis parameters, and the main effect of crop model parameters is quantified through a first-order sensitivity index. The interaction effect of crop model parameters is quantified through the global sensitivity index, and the complexity of a parameter system is reduced; a differentiation calibration strategy is constructed based on sensitivity grading, a traditional all-parameter parameter adjustment mode is replaced, and the complexity of a parameter system is remarkably reduced. The objective of the invention is to solve the problem of how to improve the crop yield prediction precision of a crop model in a climate-variable region.

The invention provides an intelligent irrigation method based on a leaf area index and environmental parameters, and the method comprises the steps: calculating a crop coefficient through obtaining the leaf area index of a crop, then measuring the growth environmental parameters of the crop in a breeding shed, and further calculating the transpiration of the crop, and enabling the ratio of the actual water demand of the crop to the transpiration of the crop to be the crop coefficient. The actual water demand of the crops can be obtained through the product of the crop coefficient and the transpiration of the crops, the transpiration of the crops is influenced by factors such as solar radiation, environment temperature and wind speed, and the minimum value and the maximum value of the actual water demand of the crops are obtained through calculation. By comparing the actual irrigation amount with the minimum value of the actual water demand of the crops and comparing the actual soil humidity with the preset soil humidity of the crops, the actual irrigation amount can be controlled to achieve precise irrigation of the crops in different growth periods, and meanwhile the purpose of saving agricultural water is achieved.

The invention discloses a plant growth monitoring system, and relates to the technical field of plant growth monitoring, and multi-parameter monitoring equipment in the system comprises a main body, a supporting rod, a data processing module, a fixed sensor group and a sensor expansion module interface, atmospheric temperature, atmospheric humidity, atmospheric pressure, carbon dioxide content, illumination intensity, rainfall, wind speed, wind direction, soil temperature, soil humidity, soil conductivity, short-wave radiation flux, long-wave radiation flux, photosynthetically active radiation flux, multiband solar spectrum, plant reflection spectrum, dynamic crop coefficient, canopy temperature, latent heat flux and net radiation flux can be obtained; according to the method, various plant growth monitoring parameters such as the vegetation index, the chlorophyll index and the effective accumulated temperature are normalized, so that more plant growth parameters can be obtained, and more accurate plant growth information is provided.

The invention discloses an irrigation water demand measurement and calculation and dominant driving factor identification method, and relates to the field of agricultural irrigation, and the method specifically comprises the following steps: S1, data preprocessing; s2, calculating evapotranspiration of a reference crop; s3, correcting the crop coefficient of each stage; s4, calculating the actual water demand of the crops in the growth period; s5, calculating a crop net irrigation quota; s6, calculating the irrigation water demand; s7, constructing an enhanced regression tree model; and S8, identifying main control factors influencing the irrigation water demand. The method provides a reference basis for adjustment of a planting structure and improvement of agricultural water efficiency, provides a new view angle and decision support for agricultural management, and has important guiding significance for water resource saving and modern agricultural production development.

The present application relates to a kind of water culture crop biomass prediction methods based on stage-specific feedback evapotranspiration calculation, comprising the following steps: according to growth day or accumulated temperature, water culture crop growth is divided into three stages;For current growth stage, calculate stress factor and regulate the weight corresponding to each stage stress factor;Multiply basic crop coefficient, canopy coverage, reference evapotranspiration and stress factor and corresponding weight, calculate stage-specific evapotranspiration;Multiply water productivity adjustment value, the ratio of stage-specific evapotranspiration and reference evapotranspiration and environmental stress factor and corresponding weight, predict biomass increment, obtain water culture crop biomass prediction value;Using ensemble Kalman filter, integrated assimilation canopy coverage and biomass prediction value, carry out sustained biomass prediction in the whole growth cycle of water culture crop.Compared with prior art, the present application has the advantages of biomass prediction accuracy.

The application discloses an irrigation system for adjusting soil moisture by means of meteorological data information, and relates to the technical field of agricultural irrigation. The system comprises a historical meteorological data processing module, a crop water requirement model optimization module, a soil moisture balance model optimization module, an irrigation decision model optimization module, a multi-modal fusion model optimization module, an execution control module and an irrigation device. The system calculates the ET0 value by the Penman-Monteith formula, optimizes the soil moisture model in combination with the precipitation intensity duration correction factor, dynamically correlates the crop coefficient and meteorological elements, constructs the irrigation water function based on the meteorological forecast and real-time monitoring data, introduces the disaster early warning mechanism to adjust the irrigation timing, adopts the historical meteorological feature fusion neural network, reduces the prediction error through cross validation, and realizes the accurate matching of soil moisture and crop water requirement. The application integrates multiple source meteorological parameters, optimizes the simulation precision of water cycle, improves the irrigation efficiency, and improves the efficiency of maintenance of the sprinkler equipment by replacing the sprinkler equipment without shutdown.

The invention provides a water and fertilizer control system and method based on artificial intelligence, and the method comprises the steps: extracting a chlorophyll absorption index of a crop in a current growth stage and a vegetation index of a growth environment from a canopy multispectral image of the crop, and carrying out the nutrition diagnosis of the growth environment of the crop based on the chlorophyll absorption index and the vegetation index. Obtaining the nutrition stress state of each nutrient element, and determining the target fertilizer solution concentration of each nutrient element according to each nutrition stress state; determining the crop coefficient of the crop in the current growth stage, and performing moisture balance on the crop growth environment according to the crop coefficient, the moisture distribution characteristic in the soil moisture content data and the rainfall evaporation characteristic in the meteorological information of the crop growth environment to obtain the net water demand of the crop in the current growth stage; and generating a fertilization control instruction for directly driving the water and fertilizer integrated equipment based on each target fertilizer solution concentration and the net water demand. Based on the scheme, collaborative decision-making of water and fertilizer supply and demand in a crop growth environment can be realized.

The invention relates to the technical field of agricultural irrigation, in particular to a drip irrigation water and fertilizer configuration, calculation and selection integrated system and method, and the method comprises the following steps: 1, calculating the water demand of crops; step 2, calculating irrigation water consumption; 3, designing an irrigation period and an irrigation quota; 4, calculating the flow of the water pump; 5, selecting the type of the filter; and step 6, system integration and verification. The system comprises a data acquisition module, a data calculation module, a decision suggestion module and a system integration module. The meteorological data and the crop coefficient Kc are collected in real time, and the digital twinborn model is constructed, so that the irrigation decision can be dynamically adjusted based on the current environmental condition, the possibility of resource waste and system failure is reduced, and the operation stability of the system is improved.

The invention discloses a method and a device for accurately acquiring water demand of cut-flower chrysanthemum. The method comprises the following steps: calculating a daily crop coefficient of the cut-flower chrysanthemum after field planting according to a field planting date and a formula; acquiring the anti-interference evaporation capacity Epan, namely acquiring the water level height Ht of an inner cylinder of the evaporator according to a set frequency, detecting a water splashing event and an evaporator water replenishing event in real time by adopting a dynamic threshold method, and finally outputting the evaporation capacity Epan according to the water level difference value in a continuous time period and the water level net increment of the water splashing event and the water replenishing event; calculating the evapotranspiration ETc of the cut-flower chrysanthemum in unit area: calculating the evapotranspiration of the cut-flower chrysanthemum according to a formula ETc = Kc * Epan; according to the field planting area A and the formula Q = (ETc-sigma delta Hsplah) * A, the water demand Q of the cut-flower chrysanthemum in the area is calculated, and sigma delta Hsplah is the sum of net increment of water splashing events in adjacent fixed time. According to the invention, based on the time-driven cut-flower chrysanthemum crop coefficient Kc model, the accurate dynamic management of moisture in the whole growth period of cut-flower chrysanthemum is significantly improved.

This invention relates to the field of water demand prediction technology, specifically to an intelligent water demand prediction system and method for rural areas. By quantifying the crop growth process into continuous growth state parameters and constructing a dynamic crop coefficient model based on a sine function, it solves the problems of discontinuous water demand calculation and physiological distortion caused by traditional step-type models during growth stage transitions. It corrects root depth and combines measured soil moisture content with preset field capacity to dynamically constrain effective precipitation, overcoming the shortcomings of existing technologies that ignore basic soil moisture conditions and overestimate the contribution of natural precipitation. By constructing a priority score based on water depletion time and growth sensitivity, it achieves rational scheduling in scenarios with limited water resources across multiple plots, ensuring the continuity of crop physiological water demand and improving the precision of irrigation decisions and water resource utilization efficiency.

The invention belongs to the technical field of intelligent agriculture, and particularly relates to a farmland multi-source data fusion precise water-saving intelligent irrigation decision-making method, which comprises the following steps: acquiring farmland multi-source monitoring data; the method comprises the following steps: dividing a farmland into rasterized spatial infinitesimal elements, constructing a farmland hydrothermal migration heterogeneous graph model, and outputting a whole-field soil moisture content spatial-temporal distribution prediction result; crop growth period parameters and an improved double-crop coefficient method are combined, and crop water demand and water shortage stress levels of different grid units are quantified; an improved self-adaptive weight particle swarm optimization algorithm is adopted to generate a grid-level differential irrigation decision scheme, and meanwhile, an irrigation early warning threshold is self-calibrated through Bayesian optimization; the method is issued to a field edge control node through the low-power-consumption wide-area Internet of Things, an intelligent irrigation execution mechanism is linked to complete precise irrigation, and model parameters and a decision strategy library are updated. Therefore, the problems of insufficient multi-source data integration efficiency, inaccurate farmland hydrothermal heterogeneity description and the like in the prior art are solved.

The invention belongs to the field of irrigation control methods, particularly relates to a vegetable water consumption digital management system, and aims to solve the problems that underground water overdraft is serious in facility vegetable irrigation in water resource-deficient areas such as the Hebei Province, and water consumption is low-efficiency and waste is caused by extensive traditional management modes. According to the system, a correlation model of a vegetable crop coefficient Kc and an effective accumulated temperature Ta is constructed, and in combination with a re-established crop coefficient calculation formula (Kc = ETc / ET0), a relation model of a greenhouse vegetable evapotranspiration estimated value ETc and the effective accumulated temperature Ta is deduced; the crop daily evapotranspiration is estimated by means of data of a single environment collector, the problem that water supply and demand are unbalanced in traditional irrigation decision-making methods such as experience and time sequence is solved through the model method, and irrigation water is converted from quantitative supply to on-demand supply. And a powerful technical support is provided for pressure mining of regional underground water and sustainable development of the vegetable industry.

The invention discloses an intelligent precise irrigation method for matrix cultivation based on environmental parameter optimization. Firstly, the theoretical evapotranspiration is calculated based on the total irrigation amount, the irrigation discharge amount and the total weight of a crop matrix; then, calculating reference evapotranspiration based on a Penman correction formula, and calculating the reference evapotranspiration in different growth stages by utilizing crop coefficients; finally, by taking the reference evapotranspiration of different growth stages as a contrast, optimizing the theoretical evapotranspiration by using the daily average temperature, the daily average photosynthetically active radiation quantity and the daily average relative humidity to obtain a difference function between the reference evapotranspiration and the theoretical evapotranspiration; compensating the theoretical evapotranspiration by using a difference value between the reference evapotranspiration and the theoretical evapotranspiration to obtain an actual evapotranspiration; when the actual evapotranspiration is larger than the allowable water deficit value of the matrix, irrigation is started, and the irrigation amount is the actual evapotranspiration. According to the method, the quantitative relation among the irrigation time, the irrigation amount and the actual evapotranspiration amount is obtained, intelligent and accurate irrigation is achieved, and the problems that the irrigation accuracy is poor, and irrigation is not timely are solved.

The invention discloses an irrigation system for deploying soil moisture content by means of meteorological data information, and relates to the technical field of agricultural irrigation. Comprising a historical meteorological data processing module, a crop water demand model optimization module, a soil moisture balance model optimization module, an irrigation decision model optimization module, a multi-modal fusion model optimization module, an execution control module and irrigation equipment, the system calculates an ET0 value through a Penman-Monteith formula, a soil moisture content model is optimized in combination with a rainfall intensity duration correction factor, and the irrigation efficiency is improved. The method comprises the following steps: dynamically associating crop coefficients and meteorological elements, constructing an irrigation water quantity function based on weather forecast and real-time monitoring data, introducing a disaster early warning mechanism to adjust an irrigation time sequence, adopting a historical meteorological feature fusion neural network, reducing prediction errors through cross validation, and realizing accurate matching of the soil moisture content and the crop water demand. The multi-source meteorological parameters are integrated, the water circulation simulation precision is optimized, the irrigation efficiency is improved, and the maintenance efficiency of the spray irrigation equipment is improved by replacing the spray irrigation equipment without shutdown.

The present invention discloses a method for calculating a drought index based on crop water deficit, comprising the following steps: S1, obtaining meteorological data and irrigation water availability data; S2, obtaining crop coefficients for typical crops in a study area; S3, calculating reference evapotranspiration based on the Penman formula; S4, calculating actual evapotranspiration and effective rainfall; S5, calculating crop water requirements; S6, calculating irrigation water deficit based on a water supply-demand balance; S7, determining an optimal fitting distribution function for an irrigation water deficit time series; and S8, calculating a new agricultural drought index based on an inverse normal transformation based on the optimal distribution. The present invention takes into account both the crop's inherent water demand and the irrigation's role in replenishing crop water. It directly utilizes water deficit information, which is most directly related to crop drought stress, to characterize agricultural drought, achieving direct quantification of agricultural drought with the advantage of low error.

The present invention discloses a greenhouse water-saving irrigation method based on multi-information fusion, which is applied to an irrigation system. The greenhouse water-saving irrigation method based on multi-information fusion includes: collecting data on light intensity, air temperature, air humidity, and soil water content in the greenhouse at fixed intervals. Using weighted normalization, multi-objective centroid method and average silhouette coefficient to optimize clustering for the growth characteristics of greenhouse crops, and obtaining the change in soil water content corresponding to the centroid of each cluster, that is, the water demand of crops in different environments in the greenhouse; establishing a soil water balance model suitable for the greenhouse, determining crop coefficients in different periods according to different crop growth cycles, calculating more accurately the water lost due to crop evapotranspiration, and using the results obtained by optimal clustering as the reference input of the model predictive controller to avoid irrigation waste caused by relying on experience.

The invention belongs to the technical field of agricultural water conservancy and water-saving irrigation, and provides a winter wheat daily crop coefficient calculation method. According to the method, key meteorological factors influencing the evapotranspiration of typical crops in each growth period of the Huaibei plain are extracted through a geographic detector method, a winter wheat growth period crop coefficient model based on meteorology and underground water burial depth is constructed, and the crop evapotranspiration process under different underground water burial depths is simulated. According to the technical scheme provided by the invention, the crop evapotranspiration simulation precision can be improved, a scientific basis is provided for efficient utilization of regional agricultural water resources and daily evapotranspiration simulation, and meanwhile, the blank of considering the underground water level burial depth influence in an existing crop evapotranspiration empirical model is filled.

The invention provides a crop whole-growth-period drought monitoring and early warning method based on an adaptive water shortage index. The method comprises the following specific steps: establishing a regression relationship between a crop coefficient and a leaf area index; calculating the daily potential evapotranspiration; establishing an effective rainfall experience calculation formula, and calculating day-by-day effective rainfall according to the rainfall observed by the meteorological station; constructing adaptive day-by-day-scale crop water shortage indexes considering different crop types and drought-enduring characteristics in different growth stages; setting a crop drought grade division standard based on the adaptive water shortage index, carrying out crop drought identification and grade research and judgment, and achieving the monitoring and early warning of the whole process of crop drought occurrence, development and ending. According to the method, multi-source data such as remote sensing data, meteorological data and crop characteristic data are fused, the problem of water shortage sensitivity difference of different crop types and different growth stages is considered, the adaptive day-by-day scale crop water shortage index is constructed, and the whole drought process of the crop growth stage can be monitored.

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 application discloses a crop irrigation quota determination method and relates to the field of water resource management.The method comprises the following steps: obtaining basic data; the basic data comprises meteorological data, soil physicochemical property data and crop characteristic data; calculating a leaf area index curve based on the meteorological data, the soil physicochemical property data and the crop characteristic data; wherein the leaf area index curve is used for representing the relationship between a crop growth period cumulative heat unit fraction and a leaf area index; the crop growth period cumulative heat unit fraction is related to temperature; calculating a dynamic crop coefficient based on the meteorological data and the leaf area index curve; calculating soil water content based on the dynamic crop coefficient, the meteorological data and the soil physicochemical property data; determining irrigation water quantity of each time based on the principle of ensuring that the soil water content is in a preset range, accumulating the irrigation water quantity of each time and determining a crop irrigation quota.

The application provides a facility tomato water and fertilizer intelligent management method and device fusing agricultural experience knowledge, and the method comprises the following steps: inputting a to-be-recognized target parameter into a crop coefficient prediction model to obtain a crop coefficient output by the crop coefficient prediction model; the crop coefficient prediction model is obtained by training an extreme learning machine model based on a training set and a knowledge function; the knowledge function is determined based on a loss function and a conflict function; the conflict function is determined based on an output result and domain experience knowledge; and water and fertilizer management is performed on the facility tomato based on the crop coefficient. The facility tomato water and fertilizer intelligent management method fusing agricultural experience knowledge provided by the application determines a knowledge function by combining a target parameter and domain experience knowledge, trains an extreme learning machine model, thereby obtaining a crop coefficient prediction model, and performs water and fertilizer management on the facility tomato according to the crop coefficient output by the crop coefficient prediction model, so that the irrigation precision is improved.

The application discloses a kind of irrigation water requirement calculation method considering crop fine distribution, electronic equipment and storage medium, to accurately calculate the crop irrigation water requirement and its spatial distribution in target area by remote sensing technology, meteorological data and soil moisture balance model.The method comprises the following steps: collecting remote sensing crop classification, meteorological and soil parameter data;Calculate the reference crop evapotranspiration at the grid scale;Configure crop parameters;Calculate the root zone soil moisture change based on the water balance model;Calculate the crop water requirement and irrigation water requirement using the FAO-56 crop coefficient method.The application provides high-resolution irrigation water requirement distribution information by fine crop distribution and dynamic soil moisture simulation, and provides a scientific basis for agricultural water resource management.

The invention discloses a water resource allocation scheduling method. The method comprises the following steps: mapping a sub-basin-plaque water demand grid of a distributed water demand model into a water demand boundary vector consistent with a pipe network node-segmental arc topology through a double-topology isomorphic mapper; a double-rate Runge-Kutta asynchronous coupler is embedded in a time axis, the macroscopic water demand trend and the microcosmic hydraulic fluctuation are alternately integrated, and an asynchronous flow driving sequence synchronous with the valve beat is generated; the sequence is input into a pipe network hydraulic solution kernel to calculate pressure, water age and water quality, and real-time calibration is carried out; and reversely correcting the crop coefficient and the soil water content through the adjoint sensitivity matrix, carrying out closed-loop iteration until error convergence, and finally outputting a valve regulation and control instruction to realize water demand-hydraulic heterogeneous closed-loop scheduling. According to the invention, efficient, accurate and closed-loop linkage between distributed day-by-day water demand and centralized real-time hydraulic power can be realized.

The invention discloses an urban green land intelligent monitoring system and method based on the Internet of Things, and relates to the field of green land monitoring, and the method comprises the steps: dividing a plant growth cycle through setting time windows, collecting growth data of each window to generate a record table, and carrying out the comparison and extraction of difference parameters; on the basis of the difference parameters, screening positive correlation data as evaluation indexes, calculating growth evaluation scores after standardizing the evaluation indexes, determining a difference threshold by combining historical data, and dividing growth stages according to the difference threshold; acquiring data through a weather station, calculating evapotranspiration, calculating water demand by combining crop coefficients, and drawing a time-varying curve of the water demand; after curve data are preprocessed, a polynomial algorithm is used for curve fitting, and a water demand prediction model is constructed; in combination with real-time weather and growth stages, the irrigation amount is determined according to the water demand predicted by the model, the result is evaluated after irrigation, the model is corrected if the result is unqualified, and urban green land plant growth monitoring and precise irrigation management are achieved.