573 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 discloses an intelligent irrigation control method and system based on meteorological information, and relates to the technical field of irrigation control, an area to be irrigated is divided into a plurality of sub-areas, and future rainfall (first rainfall) is predicted through a preset meteorological early warning model; acquiring water absorption data of each sub-region, calculating a water absorption coefficient, and determining absorbable rainfall (second rainfall) in combination with the first rainfall; and judging the water consumption of each sub-region according to the crop growth data and the historical data, and comparing the water consumption with the second rainfall. If the water consumption is greater than the second rainfall, the sub-region needs to be irrigated; according to the method, the soil water content of each sub-region can be finely judged, and whether rainfall meets the water demand or not is evaluated, so that whether irrigation is needed or not is judged, excessive irrigation and water resource waste are avoided, meanwhile, it is ensured that crops obtain enough water, water resource management is optimized, and the crop growth effect and agricultural production efficiency are improved.

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 relates to the technical field of drought stress, and discloses a method for identifying a vegetation water stress dominant factor and determining a drought threshold value based on a Copula function. The method comprises the following steps: acquiring vegetation productivity, soil water content and atmospheric water deficit data of a research area in a research time period; respectively constructing marginal distribution functions of the vegetation productivity, the soil water content and the atmospheric water deficit by using a univariate parameter estimation method; respectively constructing a joint probability distribution function of the soil water content and the vegetation productivity and a joint probability distribution function of the atmospheric water deficit and the vegetation productivity based on the marginal distribution function by utilizing a Copula function; respectively calculating the maximum conditional probability of vegetation productivity loss caused by soil water content reduction and atmospheric water deficit rise by utilizing the joint probability distribution function, and determining a vegetation productivity water stress dominant factor; and calculating a drought threshold value of the vegetation productivity water stress dominant factor.

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 discloses a non-linear production and confluence characteristic analysis method for a valley terrain extra-small watershed. The method comprises the steps that S1, valley terrain-hydrological data are acquired, and hydrological grid units are spatially divided; s2, identifying hydrological behavior characteristics of the hydrological grid units to form independent hydrological response units; s3, analyzing a first influence degree formed by rainfall infiltration, a second influence degree formed by surface runoff and a third influence degree formed by dynamic change of soil moisture in the hydrological response unit, and constructing a valley dynamic fluid model; s4, combining the valley dynamic fluid model with the underground water flow model and the surface water flow model to form a hydrological-hydrodynamic simulation framework; and S5, simulating various rainfall scenes by using a hydrological-hydrodynamic simulation framework, performing analysis in combination with valley microtopography factors, calculating flow change rates of different rainfall scenes, and identifying nonlinear features. According to the method, high-precision simulation and prediction of the valley terrain extra-small watershed are realized, and the accuracy and timeliness of productivity and confluence analysis are improved.

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 water conservation amount calculation simulation method based on an RCCC-WBM hydrological model, and the method comprises the steps: collecting hydro meteorological data in a research basin, calculating the average rainfall, air temperature and water surface evaporation amount of a lunar surface, and inputting the average rainfall, air temperature and water surface evaporation amount into the model; calculating and researching the average soil water content, the actual evapotranspiration, the surface runoff, the snow melting runoff and the underground runoff of the watershed for many years by adopting an RCCC-WBM model; according to the monthly-scale water source conservation amount calculation formula and the annual-scale water source conservation amount calculation formula, the monthly-scale water source conservation amount and the annual-scale water source conservation amount of the drainage basin are calculated respectively. The water conservation amount calculation formula provided by the invention can effectively avoid the negative value problem in water conservation evaluation, and the dynamic process of the basin water conservation amount can be calculated through the state variable and the intermediate output result of the RCCC-WBM model. The water conservation amount calculation method provided by the invention has less requirements on input data and parameters, and has a better simulation effect on a hydrological process.

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 discloses a soil water content estimation method fusing multi-source time sequence remote sensing data, and relates to the field of optical detection, and the method comprises the following steps: 1, obtaining an SAR remote sensing image of Sentinel-1 and an optical remote sensing image of Sentinel-2 in a designated area, carrying out the image fusion through a PCA algorithm, carrying out the feature set extraction of the fused images, and carrying out the feature set extraction of the feature set; a minimum analysis unit is generated through image segmentation, geometric shape features of the minimum analysis unit are extracted, a crop planting area recognition model is constructed through a convolutional neural network algorithm, and a crop coverage area is obtained; 2, introducing an MIMICS microwave scattering model, and constructing a crop multi-growth-period microwave scattering model; the SAR image and the optical image are fused through the PCA algorithm, the crop planting area recognition model based on multi-source remote sensing image fusion and the convolutional neural network algorithm is constructed, and the accuracy of crop planting area recognition is improved by utilizing complementary advantages of remote sensing images.

The invention discloses a salinization dynamic monitoring method based on water-salt migration characteristics and a related device, and relates to the technical field of agricultural informationization, the method comprises the following steps: processing microwave remote sensing data into characteristic data, and using a soil salt content inversion model to obtain remote sensing inversion soil salt content, and assimilating microwave remote sensing data by using a water-salt migration model in combination with daily evaporation capacity, soil water content and underground water data to obtain soil salt content changing along with time in a target period. The microwave remote sensing data and the water-salt migration model are combined, the defects of the water-salt migration model in the aspect of soil salinization monitoring can be overcome, the limitation that common optical remote sensing data are greatly restricted by weather factors such as cloud and mist can be overcome, the method can be suitable for soil salinization monitoring in a regional range, and the application prospect is wide. The dynamic change of the salt content of the saline soil can be accurately monitored in a large range for a long time.

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 ratooning rice planting, and particularly discloses a ratooning rice high-yield harvester and a planting method thereof.The method comprises the steps that the two ends of a rice field are filled with soil to form bed surface heightening areas; rice seedling planting is conducted through the modified wide-narrow row rice transplanter, and rice seedlings are transplanted in the mode that seven rows are planted at the same time; drying the field before the first season rice is harvested until the soil water content of the middle bed surface of the rice field is reduced to 40-45% and the soil water content of the bed surface heightening area is reduced to 25-30%, and then starting machine harvesting; a ratooning rice high-yield harvester is adopted for machine harvesting, the telescopic crawler belt keeps the maximum grounding length during straight running, and the telescopic crawler belt retracts to the minimum grounding length when the machine turns around in a bed surface heightening area; according to the planting method, on the premise that excessive field drying and machine tool sinking are avoided, the rolling area of the crawler belt on rice stubbles in the machine tool turning-around process is minimum, and the yield of ratooning rice is increased.

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 relates to the technical field of soil water seepage detection, in particular to a soil water seepage detection device for geotechnical engineering, and provides the soil water seepage detection device for geotechnical engineering aiming at solving the problem that an existing soil water seepage detection device cannot adapt to slope section detection and the like. A transverse frame capable of moving up and down is arranged at the upper end of the base, a ball pin capable of rotating is arranged in the middle of the transverse frame, a testing cover is arranged at the lower end of the ball pin, a measuring cylinder is arranged at the upper end of the ball pin, clamping pads matched with the ball pin are arranged on the inner walls of the two sides of the transverse frame respectively, reinforcing piles are arranged at the four end corners of the base respectively, and a plurality of expanding and supporting plates are arranged on the inner walls of the reinforcing piles respectively. When the transverse frame moves downwards, a structure that the clamping pads move towards the outer side and then move towards the inner side can be formed, and when the transverse frame moves downwards, a structure that the multiple expanding and supporting plates synchronously move towards the outer side can also be formed. The slope surface can be always kept vertical during testing, the detection accuracy is improved, the bottom is firmly fixed, and the slope surface is prevented from moving during working.

The invention provides soybean saline-alkaline tolerance simulation experiment equipment and method.The soybean saline-alkaline tolerance simulation experiment equipment comprises a sowing test bed, a closed box is connected to the top face of the sowing test bed, a sowing soil layer is arranged on the inner side of the closed box, a driving gear ring is arranged on the outer side of the sowing soil layer, and the driving gear ring is rotationally connected to the inner side of the top face of the sowing test bed in a limited mode; a saline-alkali water spraying tank is arranged on one side of the driving gear ring, the driving gear ring is in transmission connection with the saline-alkali water spraying tank, a sunlight simulation lamp set is arranged over the sowing soil layer and is in transmission connection with the driving gear ring, and a soil water conveying tank is arranged on the side, adjacent to the side connected with the saline-alkali water spraying tank, of the driving gear ring; according to the device, the saline-alkali water spraying tank automatically and intermittently sprays atomized saline-alkali water to the upper part of the sowing soil layer, meanwhile, the sowing soil layer can automatically feed saline-alkali water into the interior and the upper part of the sowing soil layer in the day or at night, and the precision of a soybean saline-alkali tolerant sowing experiment result is improved.

The invention discloses an automatic dynamic measurement method and device for long-term forest eco-hydrology water volume in the technical field of eco-hydrology water volume measurement, and the method comprises the specific steps: 1, selecting a measurement point, building a sample plot according to a standard, and selecting a plurality of experiment points meeting conditions in the sample plot; 2, installing a water quantity monitoring device at the experiment point meeting the condition; 3, measuring the trunk runoff, the penetration precipitation, the water content of a surface fallen leaf layer and the soil water content of the forest in a preset time period by using a water quantity monitoring device, and recording a drainage time point; and 4, calculating the flow velocity, the total flow and the forest water conservation amount according to the measured data. The device is convenient and practical, penetrating rainfall is intercepted and measured through the plastic film, stem flow is intercepted and detected through the runoff ring, the fixing claws are continuously pushed to enter a tree trunk through the telescopic connecting rods by means of the gravity of water in the water measuring cylinder to fix the whole device, and the data quality stability of long-term hydrological water volume measurement and the safety of the device 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 karst area foundation pit water inrush early warning system and method, and relates to the technical field of artificial intelligence. Data of groundwater dynamic, geological stress and soil water content of a karst area are monitored in real time through a data acquisition module, and data cleaning, noise filtering and complementing are performed in a data processing module; a multi-dimensional interpolation algorithm is used for compensating data missing, and the data integrity and accuracy are improved; in the region division and analysis module, a karst region is divided into independent karst sub-regions, geological characteristic data is monitored in real time, and the fracture expansion trend and the water level change are analyzed through a model of the chain reaction early warning module, so that the water inrush time and region are predicted, and the dependence of the system on a historical data mode is reduced; and the feedback control module evaluates the associated risk index Tsyz, so that the water inrush risk of adjacent areas is prevented in advance, and the real-time response and construction safety of the system are ensured.

The invention discloses a frozen soil active layer thickness inversion method, medium and equipment, and relates to the field of radar remote sensing image processing, and the frozen soil active layer thickness inversion method mainly comprises the steps: obtaining deformation information according to single-view complex orbit descending data, digital elevation model data and atmospheric delay data; according to the deformation information, obtaining to-be-selected long-term deformation and to-be-selected seasonal deformation of the permafrost by using multiple decomposition algorithms; evaluating the to-be-selected long-term deformation and the to-be-selected seasonal deformation, and determining long-term deformation and seasonal deformation; and obtaining the thickness of the active layer of the research area according to the seasonal deformation and the layered soil water content data. By implementing the frozen soil active layer thickness inversion method, the medium and the equipment provided by the invention, the accuracy of frozen soil active layer thickness inversion can be improved.

The invention provides a method, medium and equipment for evaluating stability evolution of an unsaturated soil slope in a cold region, and establishes a framework combining a soil-atmosphere interaction model and a cold region unsaturated soil water-steam-heat coupling transmission model. A finite element transient simulation program for simulating the water-heat state response of the unsaturated soil body in the cold region under the action of long-term and short-term climates is developed, and the technical problem that when a traditional numerical model simulates the sand freezing and thawing phenomenon, the latent heat release amount is prone to losing due to the narrow phase change temperature interval is solved. According to a limit analysis upper limit theorem, a slope stability evaluation method is established by adopting a discretization recursion technology, and a novel iterative solution scheme combining a dichotomy and a meta-heuristic optimization algorithm is innovatively provided. A finite element transient analysis model-stability evaluation coupling method is provided, so that the stability evolution evaluation of the unsaturated soil slope in the cold region driven by climate data can be carried out.

The present invention discloses a method for improving the load estimation of the output-into-river coefficient model in a large watershed. A nested sub-basin is set for the measured watershed, a Latin hypercube sampling method is used to initialize the population, and a genetic algorithm is used in combination with relevant data of the nested watershed to solve the optimized watershed pollution equation to calculate the land use output coefficient of different sub-basins; the surface runoff factor, soil water leaching factor, landscape interception factor and soil erosion factor of the corresponding watershed are coupled to calibrate the output coefficient of pollution sources in different spatial units. At the same time, the ratio of the product of the rainfall erosivity factor and the terrain influence factor to the product of the cost distance factor and the vegetation coverage factor is used to calibrate the river inlet coefficient of pollution sources in different spatial units; a large watershed non-point source pollution load estimation model is constructed based on the calibrated output coefficient and river inlet coefficient, and the pollutant output and river inlet under various land use types are calculated to improve the accuracy of the estimation of non-point source pollution in large-scale watersheds.

The invention discloses a green three-dimensional planting system for promoting agricultural economic development in an arid region. The green three-dimensional planting system comprises a climate adaptation module; the weather prediction module is responsible for providing weather change prediction; the crop selection and crop rotation module is responsible for selecting suitable drought-enduring crops and implementing scientific crop rotation; the three-dimensional planting structure module is responsible for designing a reasonable three-dimensional planting framework according to the selected drought-enduring crops; the soil improvement module is responsible for adding an organic fertilizer and a modifier and improving the soil moisture retention capability; according to the invention, through weather prediction information provided by the climate adaptation module, the system can predictively adjust an irrigation strategy, and insufficient or excessive irrigation caused by sudden weather change is avoided. The intelligent irrigation module is combined with a neural network algorithm, the irrigation amount and time are accurately calculated and automatically adjusted according to the real-time meteorological data, the drought-enduring crop characteristics, the three-dimensional planting architecture and the soil improvement condition, accurate control over irrigation is achieved, and the utilization efficiency of water resources is remarkably improved.