424 results about "Soil water" patented technology

The invention belongs to the technical field of intelligent agriculture, and discloses a canopy-atmospheric environment information-based soil water content prediction method and system, and the method comprises the steps: setting a canopy and atmospheric environment monitoring unit in a field, and synchronously collecting the canopy temperature, wind speed, relative humidity, and six-dimensional environment parameters of atmospheric temperature, wind speed and relative humidity; the crown temperature difference is calculated after preprocessing; the method comprises the following steps: constructing a training set by utilizing measured data of soil relative water content, constructing a soil water content prediction model by adopting a random forest algorithm, screening and confirming an optimal prediction model through grid search and cross validation, and screening key features by combining an SHAP value; the water demand is automatically calculated according to a pre-established crop whole-growth-period drought stress threshold table and a dynamic irrigation decision formula, and meanwhile, the adaptation of a general model to a regional specific model is realized by constructing an environmental parameter-actually measured water content database and transfer learning, so that the soil water content state is accurately predicted, and the soil quality is improved. And a scientific basis is provided for precise irrigation.

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

The invention relates to the technical field of resource management, in particular to a seawall ecological protection-oriented vegetation water demand prediction method and system, and the method comprises the steps: collecting the environment monitoring data of a seawall region; establishing a salinity stress factor model based on the soil data, calculating a comprehensive salinity stress coefficient of the monitoring point, and performing multi-factor dynamic correction on the vegetation water demand to obtain a vegetation basic water demand; establishing a multi-dimensional time sequence model of a tidal cycle and a soil moisture supplement relationship considering geologic structure differences, and obtaining resource scheduling weights under different tidal stages and environmental conditions; according to the vegetation data, the underground data and the environmental data of the different seawall regions, based on a multi-region collaborative water resource allocation network, obtaining a deployment optimization scheme based on regional characteristic parameters; and generating a vegetation water demand prediction result and a graded resource scheduling scheme by integrating the vegetation basic water demand, the resource scheduling weight and the allocation optimization scheme, and performing irrigation.

The invention discloses a soil water content inversion method and system based on L-band dual-polarization data and a medium, and belongs to the field of remote sensing technology and environment monitoring. The system comprises an SAR data acquisition module, an optical data processing module, a vegetation scattering separation module, an inversion model construction module, a spatial distribution calculation module and a risk assessment module, wherein the SAR data acquisition module is used for regularly acquiring L-band dual-polarized SAR data of a target area; the optical data processing module assists in identifying and separating vegetation scattering components by calculating vegetation indexes; the vegetation scattering separation module is used for extracting pure soil back scattering signals; the inversion model building module is used for training and optimizing a soil water content inversion model based on the pure soil back scattering signals and field sampling data; the spatial distribution calculation module is used for calculating a spatial distribution diagram of the surface soil water content of the target area; and the risk assessment module is used for carrying out landslide risk grade division and early warning management.

The invention discloses a modularized distributed cold region hydrothermal coupling hydrological model which comprises the following steps: S1, dividing a drainage basin into a plurality of grids, and preparing a file required by a confluence process; s2, preparing driving data and grid feature description data of each grid; s3, calculating an energy balance process of the soil and vegetation surfaces by using an energy balance module; s4, calculating an evapotranspiration process by using an evapotranspiration module; s5, calculating an ice and snow melting process by using an ice and snow melting water module and adopting a degree-day factor method; s6, calculating soil moisture infiltration and an internal hydrothermal transmission process by utilizing a soil hydrothermal transmission and infiltration module; s7, calculating the runoff volume by using a runoff production calculation and confluence module; and S8, evaluating the simulation performance of the model through the model evaluation index. The device has a flexible modular structure, can automatically adjust the time step length, improves the simulation capability of the runoff process in the cold region, and can provide scientific guidance for water resource management in the river source region.

The invention relates to a crop irrigation demand prediction method based on an intelligent integrated prefabricated pump station, and belongs to the technical field of artificial intelligence. The method comprises the following steps: collecting a crop image in a prefabricated pump station irrigation scene, obtaining auxiliary data, calculating a daily accumulated reference evapotranspiration value, and obtaining a time-synchronized multi-modal data set; labeling and dividing the multi-modal data set; constructing an irrigation demand prediction model, wherein the irrigation demand prediction model comprises a spectral reflectivity enhancement module, a double-flow separation convolution module, a cross gating fusion module, a region sensitive pyramid module and a coupling prediction head module; inputting data in the divided training set into the model, and training the model through weighted total loss to obtain a trained model; inputting a to-be-detected image into the trained model to obtain a soil water content and transpiration prediction result; and performing irrigation decision based on the soil water content and transpiration prediction result. The accuracy of irrigation prediction can be improved.

The invention discloses a citrus orchard interplanting pest and disease prevention and control method, which comprises the following steps: installing a drip irrigation facility in a citrus orchard, and uniformly distributing drippers of the drip irrigation facility between each citrus tree and green manure planting rows; soil humidity sensors are buried in multiple positions of the citrus orchard to monitor soil humidity data in real time through the soil humidity sensors; and determining whether to start the drip irrigation facility based on the soil humidity data acquired by the plurality of soil humidity sensors and the position relationship among the plurality of soil humidity sensors. Therefore, whether the irrigation facility is started or not can be automatically decided by monitoring the soil humidity of different positions in the citrus garden in real time, and the intelligent control mode is beneficial to timely discovering and processing the soil water shortage condition; the problems that in a traditional mode, whether irrigation is carried out or not cannot be judged according to the actual humidity condition of soil, and irrigation hysteresis exists are solved, and then a basis is provided for citrus orchard interplanting pest and disease prevention and control management.

The invention belongs to the field of crop growth monitoring, and particularly relates to a crop irrigation decision-making method and system based on crop growth period water demand characteristics, and the method comprises the steps: firstly obtaining a remote sensing satellite image of a to-be-decided crop soil region, and the segmented growth period and transition time period attributes of each crop; secondly, monitoring the soil water content of a crop soil area to be decided by using a temperature vegetation drought index according to a remote sensing satellite image; and finally, combining the soil water content with the related attributes of the crop growth period, and generating a variable irrigation prescription map by means of a preset variable precise irrigation decision model, so as to realize the irrigation decision of the crop soil area to be decided. By comprehensively utilizing the remote sensing data and the crop growth period information, the irrigation scheme can be reasonably planned according to the water demand characteristics of different crops in different growth stages, the water resource utilization efficiency is effectively improved, and the crop growth is guaranteed.

The invention relates to a field water and fertilizer intelligent control method based on the Internet of Things, and the method comprises the steps: carrying out the crop type recognition and growth stage recognition through collecting crop images, soil water and fertilizer data and meteorological prediction data, and generating an initial water and fertilizer control scheme of a current time node; a soil water and fertilizer prediction model is created to predict the soil water and fertilizer change trend in a specified time period, the initial water and fertilizer control scheme of the current time node is adjusted according to the predicted soil water and fertilizer change trend, growth cycle characteristics corresponding to crop types are obtained based on big data, and a dynamic water and fertilizer control scheme is formulated. A water and fertilizer adjusting control scheme is executed at a current time node, a dynamic water and fertilizer control scheme is executed at a specified time node, the field water and fertilizer intelligent control method integrating Internet of Things sensing, intelligent identification, trend prediction, dynamic planning and closed-loop feedback is realized, the whole process intelligence from data acquisition to scheme execution is realized, and the water and fertilizer utilization efficiency is improved. The agricultural production cost is reduced.

The invention discloses a soil water content inversion method based on improved combination roughness, and relates to the field of remote sensing, and the method comprises the steps: synchronously obtaining a Sentinel-1 radar image and a Sentinel-2 optical image, and extracting different polarization backscattering coefficients, local incident angles and normalized water body indexes through preprocessing; the method comprises the following steps: generating a bare soil simulation backscattering coefficient data set, removing vegetation scattering contribution by using a water cloud model, obtaining a real bare soil backscattering coefficient, constructing a training set and a verification set containing actually measured soil water content, constructing a lookup table based on double models, and calculating the water content of the bare soil by minimizing a root-mean-square error between simulation and the real backscattering coefficient. Global search is carried out in a preset parameter space to determine an optimal earth surface root mean square height and a correlation length, a novel polynomial combination roughness is constructed, a physical correlation between the roughness and a backscattering coefficient is established, a dual-polarization empirical equation set is constructed, and simultaneous solution is carried out after parameters are optimized according to a criterion; according to the method, a more reasonable inversion result of the soil water content in a large range can be obtained.

The invention relates to the technical field of ecology and hydrology, in particular to a terrestrial ecosystem water carbon flux estimation method considering water balance constraint, which comprises the following steps: estimating leaf scale photosynthetic rate and stomatal conductance by utilizing a photosynthesis model; the leaf scale photosynthetic rate and the stomatal conductance are upscaled into canopy vegetation total primary productivity and canopy conductance to construct an evapotranspiration model to estimate evapotranspiration data; constructing a water balance model according to the evapotranspiration data so as to estimate the available water amount of the soil; constructing a soil moisture stress function according to the available water amount of the soil so as to constrain the estimation process of the photosynthesis model and the evapotranspiration model; and acquiring historical water-carbon flux data and historical remote sensing observation data of a long sequence daily scale, and calibrating the model to obtain a water-balance-constraint-considered water-carbon flux estimation model of the terrestrial ecosystem capable of estimating the water-carbon flux data. Therefore, the problems that an existing water carbon flux prediction frame is difficult to adapt to climate changes, low in prediction accuracy and the like are solved.

The invention discloses a control system and a control method for water and fertilizer integrated irrigation, and relates to the technical field related to irrigation, and the system comprises a data collection module which is used for collecting soil moisture content data and crop growth stage data; the water and fertilizer demand analysis module is used for performing water and fertilizer demand analysis and outputting soil water and fertilizer demand indexes in a preset irrigation time zone; the water and fertilizer loss compensation module is used for compensating the soil water and fertilizer demand index and outputting an irrigation target and a fertilization target; the mixing completeness optimization module is used for executing analysis and optimization of water and fertilizer mixing completeness; and the irrigation control module is used for controlling the water and fertilizer integrated irrigation device to perform water and fertilizer integrated irrigation on the preset irrigation area. The technical problem that in the prior art, due to the fact that the soil moisture content, the crop growth stage and weather factors cannot be comprehensively considered, the irrigation and fertilization precision and the water and fertilizer resource utilization efficiency are insufficient is solved, and the technical effect of improving the irrigation and fertilization precision and the water and fertilizer resource utilization efficiency is achieved.

The invention discloses seed cultivation and germination accelerating equipment, and relates to the related technical field of agricultural seedling culture. The seedling raising device comprises a seedling raising box, a connecting piece is rotatably arranged in the seedling raising box, a seedling raising box is rotatably mounted on the connecting piece, filter holes are formed in the bottom of the seedling raising box, a water filter film is movably mounted on the inner bottom surface of the seedling raising box, a seedling raising plate is slidably connected into the seedling raising box, and a plurality of seedling raising holes are formed in the seedling raising plate; filter screens are slidably connected to the interiors of the seedling raising holes, a plurality of heating assemblies are fixedly installed in the seedling raising box, a liquid storage box is fixedly installed at the top of the seedling raising box, the liquid storage box is communicated with a flow distribution plate through a guide pipe, and the flow distribution plate is provided with spray heads. The seedling raising box is driven by the connecting piece to rotate, so that seeds are uniformly heated, and good conditions are created for seed growth; meanwhile, the spray head is matched with the splitter plate to uniformly irrigate and fertilize the seeds, and the structures such as the water filtering film and the filtering holes prevent seed rot caused by soil water accumulation, so that the quality and the efficiency of seed cultivation are integrally improved.

The invention relates to the technical field of bamboo forest ecological environment monitoring, in particular to a bamboo forest ecological environment collaborative observation method based on multi-source Internet of Things sensing, and the method comprises the steps: synchronously collecting a multi-source collaborative observation parameter set of a bamboo forest slope through an Internet of Things sensor network, and then constructing a point-surface scale fusion bamboo forest moisture dynamic model; correcting evapotranspiration point cluster data by using a random forest gradient lifting algorithm to generate terrain weighted evapotranspiration, aggregating runoff data according to terrain weight to obtain a total amount of runoff sub-regions, then executing dynamic water balance constraint calculation, optimizing infiltration capacity in combination with a variation assimilation algorithm, redistributing infiltration capacity space proportion according to soil hydraulic conductivity when a residual error exceeds a threshold value, and calculating the total amount of the runoff sub-regions. Finally, according to the residual space distribution thermodynamic diagram, the evapotranspiration point cluster is migrated to the micro-topography representative area, and observation network adaptive optimization is achieved, the problems that in the prior art, the data scale is disjointed, the observation precision is insufficient, and the network is difficult to adapt to change are solved, and the bamboo forest ecological observation precision and reliability are improved.

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

The invention discloses a rain-fed agricultural area drought index construction and application method, equipment and a medium. The rain-fed agricultural area drought index construction and application method comprises the following steps: S1, determining drought influence coefficients of crops in different growth stages; s2, constructing an APSIM model, and carrying out parameter calibration and model verification; s3, the APSIM model dynamically simulates the phenological period, the yield and soil water in the growing season of the crops in the sufficient irrigation and rain culture mode; s4, calculating the crop yield reduction rate; s5, constructing a drought stress index relational expression including the cumulative drought stress degree, the cumulative drought stress index and the standardized cumulative drought stress index; s6, based on the dynamic simulation result of the APSIM model, the crop yield reduction rate and the accumulated drought stress index are calculated in a fitting mode. According to the method, the soil humidity data acquisition problem is considered, the sensitivity difference of different growth stages to drought is also considered, and accurate quantitative evaluation of the drought stress response in the whole crop growth process can be further realized.

The invention discloses an arid region groundwater reserve estimation method and system based on multi-source remote sensing data, and relates to the technical field of groundwater monitoring, and the method comprises the steps: collecting land water reserve data and remote sensing image data of a target arid region, obtaining a land water reserve change sequence according to the land water reserve data, carrying out the region weighting of the target arid region, and obtaining a target arid region groundwater reserve change sequence; obtaining a land water reserve change sequence of the target arid region; based on the remote sensing image data of the target arid region, using a digital elevation model to obtain a surface water reserve change sequence of the target arid region, using a land surface process model to obtain a soil water reserve change sequence of the target arid region, and establishing an underground water reserve model of the target arid region; and performing error analysis on an output result of the groundwater reserve model by using a Monte Carlo simulation method. According to the method, through collaborative inversion of multi-source remote sensing data and integration of the groundwater reserve models, accurate quantification of spatial-temporal dynamic of the groundwater reserves in the arid region is realized.

The invention discloses a garden irrigation intelligent analysis system and method, and relates to the technical field of artificial intelligence, and the system comprises an irrigation area monitoring end, an irrigation mode analysis end, and an irrigation analysis correction end. The irrigation area monitoring end is used for judging whether irrigation reaches the standard or not in real time according to the area of the area and the irrigation flow speed and judging whether the irrigation position deviates or not in real time; the irrigation mode analysis end is used for analyzing the irrigation state of the corresponding time period in real time according to the irrigation position and the soil moisture complement amount and judging whether the irrigation mode can continue to be executed or not in time; the irrigation analysis and correction end is used for timely judging whether garden irrigation meets irrigation requirements or not and automatically adjusting garden irrigation parameters in combination with weather changes and rainwater irrigation early warning information. According to the intelligent garden irrigation analysis system and method, whether the irrigation position is abnormal or not is monitored in real time in the whole process, whether the irrigation mode at the current moment can be executed or not is judged in time according to the irrigation state, irrigation parameters can be automatically regulated and controlled according to weather changes, and water resource waste is avoided.

The invention relates to the technical field of data processing, in particular to an agricultural water efficiency dynamic monitoring and optimizing system based on machine learning, which comprises a processor and a memory, the processor executes a computer program of the memory to realize the following steps: acquiring historical soil water content data and multi-dimensional historical environment data of any to-be-monitored position at each moment in a preset historical time period; according to the data change characteristics of the historical soil water content data and the multi-dimensional historical environment data, obtaining the correlation degree of each environment and the soil water content; when an LSTM prediction algorithm is used for prediction, a prediction time period is obtained, and the weight of each piece of historical soil water content data is obtained according to the correlation degree of each environment and the soil water content and the difference between each piece of multi-dimensional historical environment data in the prediction time period and the multi-dimensional environment data at the current moment; and the soil water content data at the next moment of the current moment is predicted, so that the accuracy of predicting the soil water content is improved.

The invention relates to a power transmission line operation state intelligent identification and analysis method, and belongs to the technical field of power transmission line state analysis. The method comprises the following steps: acquiring soil water content and foundation settlement data in real time, recognizing tower node displacement accumulation and a stress transfer path in combination with environmental factors such as temperature, humidity and wind speed, accurately judging a structural deformation abnormal region, and generating a stress concentration and transfer prediction chart through multi-source data fusion and historical data contrastive analysis. According to the method, the coordinates of potential safety hazard parts are dynamically calibrated, infinitesimal displacement monitoring errors are eliminated, finally, a tower overall structure safety evaluation report and maintenance management measures are output, through full-link data analysis and dynamic calibration, the monitoring precision and early warning capacity of tower stability under geological condition changes are remarkably improved, and safe operation of power transmission facilities is guaranteed.

The invention provides a water resource water-saving valve for a water conservancy dropper, and belongs to the technical field of water-saving valves. Comprising a drip irrigation pipe which is provided with a plurality of drip irrigation holes, and further comprises a valve group. Through the arrangement of the valve shell, the regulation and control assembly and the gravity type valve plate, during drip irrigation, the gravity type valve plate is opened, water in the drip irrigation pipe sequentially passes through the connecting pipe and the valve shell, then enters the permeation mechanism and permeates into soil through the permeation mechanism, and as the content of water in the soil at the permeation mechanism is gradually increased, the amount of water discharged from the permeation mechanism is gradually reduced; then the liquid level in the valve shell gradually rises, the regulating and controlling assembly drives the gravity type valve plate to be closed under the buoyancy action of water in the valve shell, after closing, water flowing out of the drip irrigation pipe does not enter the valve shell any more, the irrigation amount can be automatically adjusted according to the soil water content through the design, different water requirements for growth of multiple plants are met, and the water resource utilization rate is increased; and water resources are saved.

The invention discloses a microbial fertilizer with growth-promoting and yield-increasing effects and a preparation method thereof. The microbial fertilizer is prepared by mixing a hydrogel fertilizer and a microbial agent, the hydrogel fertilizer is formed by taking chitosan as a base material and carrying out cross-linking reaction with 2, 2-dimethyl-5-hexenoic acid, N-(N-butoxymethyl) acrylamide and urea, and the microbial agent is prepared by loading bacteria N9 separated from farmland soil on biochar. The microbial fertilizer has good water retention performance and nutrient slow release effect, the contained microorganisms can effectively promote growth, tests show that the soil water retention rate can be remarkably increased, growth of pakchoi and other crops is promoted, the biological safety is high, and the microbial fertilizer is suitable for achieving crop growth promotion and yield increase in agricultural production.

The invention relates to the technical field of agricultural automation and intelligent irrigation, in particular to a variable irrigation decision-making system, which comprises a meteorological data acquisition module used for acquiring daily meteorological data of a specified farmland in a first time period from the current time period to the future time period; the soil monitoring module is used for monitoring soil data of a specified farmland in real time; the data processing module is used for obtaining the current soil water content of the specified farmland, the evapotranspiration amount of the specified farmland in each day in the first time period in the future and the soil displacement amount of the specified farmland in each day in the first time period in the future based on the meteorological data of the specified farmland in each day from the current time period in the future and the current soil data of the specified farmland; further acquiring the soil water content of the specified farmland after the first preset time period; the control module is used for determining the current irrigation demand quantity of the specified farmland and sending a first irrigation instruction to the irrigation device corresponding to the specified farmland; the first irrigation instruction is an instruction for irrigating the specified farmland according to the irrigation demand quantity of the specified farmland at the current moment.

The invention discloses a star soil water-ice in-situ quantitative analysis system and method, and belongs to the technical field of planetary science and space exploration, the star soil water-ice in-situ quantitative analysis system comprises a detection platform, and a star soil collection unit and a star soil water vapor heating extraction unit are arranged outside a cabin of the detection platform; a first detection unit, a second detection unit, a control unit and a gas distribution unit are arranged in a cabin of the detection platform; the gas distribution unit is used for distributing the water vapor extracted by the star soil water vapor heating and extracting unit to the first detection unit and the second detection unit for analyzing the content and isotope composition of the water vapor of the star soil; the first detection unit and the second detection unit adopt different detection analysis technologies; and the control unit is used for controlling the detection analysis process of the analysis system. According to the system, an in-situ sampling analysis mode of'quantitative sampling of water-containing star soil, low-loss sample transfer, efficient heating extraction and high-precision instrument analysis' is adopted, so that accurate quantitative analysis of star soil water ice is realized.

The invention discloses an efficient regulation and control method and system for water resources in a cold region under soil erosion, and the method comprises the steps: collecting regional weather, soil and crop data, and dividing a space grid unit; constructing a freeze-thaw period water balance model, and simulating day-by-day soil water content and snow melting water dynamics; an irrigation-yield correlation model and an improved soil erosion model are established, and the driving effect of irrigation water quantity on crop growth and soil loss is quantified; designing a yield, water saving amount and erosion amount three-objective optimization function, generating a Pareto optimal solution set by adopting a third-generation non-dominated sorting genetic algorithm (NSGA-III), and optimizing an irrigation scheme in combination with a decision algorithm. The method has the advantages that the limitation of traditional single target regulation and control is broken through multi-model coupling, and accurate quantification of the contribution rate of the irrigation water amount in soil erosion is achieved; the water and soil loss risk of the high-gradient area is effectively reduced based on the differential regulation and control strategy of the space grid, and meanwhile the water demand of crops is guaranteed; the black soil area water resource utilization efficiency is improved, and cultivated land degradation is inhibited.

The invention relates to the field of disaster prediction, in particular to a hydrological data prediction method based on data analysis. The method comprises the following steps: firstly, obtaining the precipitation, soil water content and soil infiltration rate of a to-be-measured area in a preset historical time period every day, obtaining the precipitation intensity every day according to the precipitation and the precipitation of each day in a preset time domain every day, and calculating the precipitation of the to-be-measured area according to the soil water content, the soil infiltration rate and the precipitation intensity every day. The method comprises the following steps: acquiring the soil severity degree of each day, analyzing the similarity of rainfall intensity and the similarity of the soil severity degree between normal days and disaster days, screening out similar normal days from all the normal days, and constructing a disaster prediction model based on the rainfall amount, the soil water content and the soil infiltration rate of the similar normal days and the disaster days. And the debris flow disaster of the current day is predicted. The debris flow disaster prediction accuracy can be 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 relates to the technical field of underground water state evaluation, and discloses an underground water level dynamic evaluation method based on multi-source data mapping and fluid model correction. The invention aims to solve the technical problems of insufficient underground water level monitoring precision and lack of space details caused by low spatial resolution of existing satellite gravity detection data and difficulty in accurately separating a deep underground water change signal from a land total water reserve signal. According to the method, through space-time reference unification of multi-source heterogeneous data, high-resolution surface texture features are utilized to guide a low-resolution gravitational field to carry out space downscaling reconstruction; surface water body and soil water reserve signals are removed layer by layer in combination with spectral characteristics and a thermal inertia principle; and introducing measured data to construct a spatial error correction curved surface of specific yield parameters, and identifying the underground water funnel by using an image connected domain analysis technology.

The embodiment of the invention provides a hydrological disaster early warning method and system based on big data, and relates to the technical field of hydrological disaster early warning, and the method comprises the steps: firstly obtaining a multi-source hydrological monitoring data set which comprises monitoring data including water level, flow, rainfall, soil water content and the like and geographic position coordinate data of each monitoring station; secondly, constructing a hydrological element space-time correlation evolution trajectory, recording dynamic changes of correlation relationships of various data among different sites, then tracking hydrological element space-time anomaly propagation paths, and defining a correlation relationship transmission process exceeding a conventional range; and according to the abnormal propagation path, generating a hydrological disaster dynamic early warning trigger condition including an associated change logic and a space transmission rule, and finally, based on the trigger condition, generating a hydrological disaster early warning instruction including an early warning level identifier and an affected station range, and sending the hydrological disaster early warning instruction to a target terminal, thereby comprehensively and accurately mastering hydrological changes. The accuracy and timeliness of early warning are improved, and disaster loss is effectively reduced.

The present invention discloses a method for agricultural-photovoltaic complementary rainwater harvesting and automatic irrigation on a tableland and relates to the technical field of photovoltaic power generation. The method for agricultural-photovoltaic complementary rainwater harvesting and automatic irrigation on a tableland of the present invention comprises the following steps: S1. Monitoring the soil water content of the tableland and creating a model for the daily water demand Q1 of the tableland crop; S2. Setting a minimum alarm value P1 and a maximum alarm value P2 of the soil water content of the tableland. When the soil water content of the tableland is lower than the minimum alarm value P1, the spray pipe starts spraying, and when the soil water content of the tableland reaches the maximum alarm value P2, the spray pipe stops spraying; S3.Monitoring the water level in the water collection tank and setting a first alarm level H1 and a second alarm level H2. When the water level is lower than H1, the water body is supplied to the water collection tank. When the water level is at H2, the water supply assembly stops supplying the water body to the water collection tank. The method for agricultural-photovoltaic complementary rainwater harvesting and automatic irrigation on a tableland of the present invention can use rainwater to automatically irrigate the tableland, ensure normal crop yield, and achieve win-win complementarity between agriculture and the photovoltaic industry.