713 results about "Precipitationshed" patented technology

The invention provides a water resource scheduling optimization method and system based on artificial intelligence. The method comprises the following steps: acquiring water resource multi-source heterogeneous data of a target area, generating a dynamic balance strategy according to a precipitation gradient change trend of precipitation distribution characteristic data and a consumption rate association relationship in user side water demand fluctuation mode data, and determining the water demand fluctuation mode of the user side on the basis of node pressure distribution characteristics of dynamic load data of a water delivery pipe network. Outputting a multi-target water delivery path optimization scheme in combination with a priority weight in a dynamic balance strategy, and converting the multi-target water delivery path optimization scheme into a scheduling instruction set for controlling a pump station valve in the geographic space topology; according to the technical scheme provided by the invention, the defects of non-uniform resource allocation, poor real-time response capability and the like of a traditional static scheduling strategy are overcome, the utilization rate of water resources is also improved, and the flexibility and the response speed of a water supply system are enhanced.

The invention discloses a precipitation estimation method and system based on territorial rainfall multi-data fusion and application, and relates to the technical field of weather forecast, by fusing radar, satellite and ground observation data, the accuracy and reliability of precipitation prediction are remarkably improved, the fusion of multi-source data can make up for the deficiency of a single data source, and the prediction accuracy and reliability are improved. Through space-time registration and a weighted average fusion algorithm, a macroscopic rainfall background is provided by introducing territorial rainfall data, and radar, satellite and ground observation rainfall data are combined, so that rainfall estimation is more comprehensive, ground observation stations are supplemented and fused, effective data of the stations are increased, the real time of rainfall can be reflected more accurately, and the rainfall estimation accuracy is improved. In addition, the fusion of multi-source data can make up for the deficiency of a single data source, and the advantages of different data sources can be effectively integrated through the space-time registration and weighted average fusion algorithm.

The invention provides a power equipment meteorological monitoring and early warning system based on artificial intelligence. The power equipment meteorological monitoring and early warning system based on artificial intelligence comprises a data acquisition module, a data preprocessing module, a spatio-temporal feature fusion module and a meteorological disaster prediction model, the meteorological disaster prediction model adopts a deep reinforcement learning framework, inputs a multi-dimensional spatio-temporal feature matrix, and carries out meteorological disaster prediction on the multi-dimensional spatio-temporal feature matrix. And outputting meteorological disaster risk levels and key parameter predicted values in a future preset time period, including a wind speed, precipitation, temperature anomaly and tropical cyclone path probability, a dynamic early warning threshold generation module, an early warning decision module and a model optimization module. The power equipment meteorological monitoring and early warning system based on artificial intelligence provided by the invention has the advantages that the data interpolation precision of a complex terrain region can be improved, high-precision prediction of a typhoon path, short-time strong wind and an icing risk can be realized, and the early warning accuracy and defense response efficiency of a power system to meteorological disasters can be comprehensively improved.

The invention relates to the technical field of data analysis, in particular to an intelligent hydrological data analysis method and system, and the method comprises the following steps: collecting water level, flow velocity and precipitation data, calculating the change rate of adjacent moments, marking hydrological parameter abrupt change points, carrying out the statistics of time intervals, predicting the duration of extreme events, and obtaining the distribution characteristics of the hydrological abrupt change points. According to the method, by analyzing the time interval and the duration time of the data points, the distribution characteristics of hydrological abrupt change can be mastered in the early stage, response can be made in advance, the propagation sequence and the diffusion rate of the abrupt change points in different river reaches can be analyzed, and accurate division of the influence range and the risk level of hydrological events is enhanced; effective management of hydrological event space risks is achieved, understanding of hydrological key influence factors is deepened by screening key variables and evaluating the evolution relation of the variables along with time, accuracy of short-term and long-term hydrological risk prediction is enhanced, and a scientific basis is provided for dynamic early warning of hydrological abnormal events.

The invention discloses a water level monitoring and management method based on multi-source information fusion, and relates to the technical field of water level monitoring and management. Comprising the following steps: acquiring a water level and water level related data by adopting a sensor, and acquiring surface feature information and meteorological data through GIS data; performing data cleaning and standardization on the collected data; a convolutional neural network is adopted to analyze infrared cloud picture data obtained by satellites to predict precipitation, a weighted average method is adopted to carry out multi-source information fusion, and water level prediction is carried out through a hydrological model; and performing real-time water level monitoring and water level management decision according to accurate water level data obtained by data fusion. Through the above steps, the system can provide a more comprehensive view angle for water level management by fusing multi-source information, and can more accurately predict the water level change trend, thereby optimizing the water resource management strategy and flood control measures.

The invention relates to an electric power meteorological disaster risk early warning method and system based on artificial intelligence. The method comprises the following steps: cleaning and preprocessing basic meteorological data including temperature, humidity, wind speed and precipitation to obtain a cleaned basic meteorological data set; on the basis of satellite cloud picture or radar image data, meteorological changes are modeled through a neural network model, assimilation features are obtained, and the basic meteorological data and the assimilation features are spliced to form a joint feature vector; and constructing a comprehensive disaster risk assessment model based on the meteorological factors, the power grid factors and the geographic information, carrying out training and multi-objective optimization on the comprehensive disaster risk assessment model, and carrying out real-time electric power meteorological disaster risk early warning by using the optimized comprehensive disaster risk assessment model. According to the invention, meteorological data, power grid equipment states and geographic information can be monitored in real time, meteorological disasters possibly suffered by power equipment can be accurately warned, and the safety and stability of a power system are improved.

The invention discloses a method and system for estimating irrigation water consumption and water utilization efficiency, and relates to the technical field of water resource management, and the method comprises the steps: collecting the plant stem flow rate, canopy temperature and soil profile water content data of a target area through a multi-source sensing device, and obtaining a gradient and permeability distribution diagram of a geographic information system; dividing the farmland of the target area into sub-areas with different irrigation priorities based on a soil conductivity mutation point detection algorithm; constructing a dynamic water demand model fused with the plant transpiration hysteresis effect, performing space-time correlation analysis on the stem flow rate data and the soil moisture content gradient, and calculating the real-time water demand intensity of each sub-region; the method comprises the following steps: accessing short-time rainfall prediction data of a meteorological radar, dynamically correcting a future irrigation decision by adopting a sliding time window mechanism, and triggering a gradient delay irrigation instruction when the predicted rainfall amount exceeds an irrigation threshold value; and calculating the actual moisture utilization efficiency based on the inversion data of the moisture content of the irrigated soil and the unmanned aerial vehicle multispectral image.

The invention relates to the technical field of waterlogging and waterlogging disasters, in particular to a remote sensing monitoring system and method for oilseed rape waterlogging and waterlogging disasters, which couple a new generation of earth observation satellite observation means, apply historical disaster data and observation data, identify key sensitive parameters of crop waterlogging and waterlogging disasters, screen oilseed rape waterlogging and waterlogging disaster monitoring and evaluation indexes and determine index parameters. Constructing a waterlogging disaster monitoring and evaluating model; respective advantages of ground observation data and satellite remote sensing data are fully utilized, advantages of microwave satellite remote sensing data and optical and thermal infrared satellite remote sensing data are utilized, and soil moisture content and precipitation data products with higher time and space resolution are formed through fusion; the problems that in the prior art, a traditional waterlogging disaster monitoring and evaluating means is difficult to match with new changes of current complex underlying surface conditions, and new requirements for monitoring and evaluating accuracy and timeliness at the present stage are difficult to meet are solved.

The invention relates to the technical field of hydrological forecasting, in particular to a deep learning-based short-term rainfall and flood forecasting method and system, and the method comprises the following steps: constructing a deep learning-based radar echo epitaxial prediction model and a rainfall and flood forecasting model, training and testing the two constructed models by adopting the first data set and the second data set respectively; inputting the rainfall radar echo data of the target watershed in a certain period of time into the radar echo epitaxial prediction model, and predicting the change trend of the rainfall radar echo in a future period of time; and inputting the change trend of the rainfall radar echo in the future period of time into the rainfall and flood forecasting model, and forecasting the rainfall and runoff of the target drainage basin in the future period of time. According to the method, the precipitation and the runoff of the target drainage basin can be predicted more accurately in a short time, and the prediction capability and the decision level of regional water resource management are effectively improved.

The invention relates to the technical field of internet-of-things control, in particular to a wheat-corn annual water nitrogen content adjusting system, which comprises a data sensing module for acquiring soil humidity and nitrogen content data in a wheat-corn growth cycle, filtering the soil humidity and nitrogen content data and generating processed soil data; based on the processed soil data, a set standard threshold value is compared, and a soil state result is obtained. According to the method, the adjustment strategy is dynamically generated by further combining water and nitrogen demand standards of different growth stages, instant weather forecast data is synchronously analyzed when the strategy is generated, multi-dimensional environmental factors such as precipitation, wind speed, temperature and air humidity are considered, and an optimization adjustment scheme which better conforms to real-time demands of crops and environmental changes is generated. In the execution process, an instruction received by the irrigation and fertilization machinery is not a static single execution instruction any more, but is iteratively executed based on real-time feedback and environmental dynamics.

The invention discloses a river water ecological health monitoring method and a river water ecological health monitoring system, relates to the technical field of ecological health monitoring, and aims to solve the problem that a traditional water quality monitoring method mainly depends on water sample collection and laboratory analysis, and judges the water quality by measuring indexes such as dissolved oxygen, pH value, ammonia nitrogen, total phosphorus, heavy metal and dissolved organic matters in water; the river runoff is calculated from the precipitation and the drainage basin area, and the monitoring points are set in combination with the environmental information, so that an accurate data basis can be provided for subsequent water quality health assessment. The water quality condition is predicted by utilizing the water flow speed and the pollution point discharge amount and then compared with an actual monitoring result, so that potential problems can be found in time and a monitoring strategy can be optimized.

The invention discloses an inland typhoon long-distance extreme rainfall prediction method based on deep learning and integrated with an IDII index, a medium and a program product, and the method comprises the steps: constructing a whole-layer dry intrusion index, and comprehensively considering the overall effect of dry and cold air at different heights; historical TRP rainfall events, typhoons and reanalysis meteorological data are obtained, preprocessing is carried out, IDII indexes are fused, and a training data set is constructed; constructing a prediction model comprising a feature extraction layer, an IDII feature fusion layer and a TRP prediction output layer by adopting a deep learning technology; training, verifying and testing the model by using a TRP sample data set; and predicting a future TRP event by using the trained model, and outputting prediction results such as TRP precipitation, occurrence time, place and duration. According to the method, the dry intrusion physical quantity IDII and the deep learning technology are fused, the prediction precision of the TRP event is improved, and technical support is provided for forecasting of inland extreme rainfall and disaster prevention and reduction.

The invention relates to the technical field of building construction, in particular to a foundation pit dewatering prediction method based on a digital twinborn technology, which comprises the following steps of: 1, establishing a multi-source data fusion layer; 2, establishing a digital twin modeling layer; 3, establishing an intelligent prediction algorithm layer; 4, establishing a dynamic optimization decision layer, and generating a precipitation well optimization scheme based on a prediction result; according to the method, engineering geological data of a geological survey report before excavation of the building foundation pit, historical meteorology of a building location and hydrogeological data are utilized, digital twinning is carried out on precipitation during the whole construction period of the proposed building foundation pit on the basis of a digital twinning technology, the precipitation period, daily precipitation and influence of precipitation on foundation pit supporting are predicted, and the foundation pit supporting effect is improved. According to the method, the influence of settlement deformation of surrounding buildings is reduced, digital simulation is provided for smooth construction of a whole project, and measurement and prediction are provided for the project and the surrounding environment, so that the construction cost is greatly reduced.

The invention relates to the technical field of salt tide upstream prediction, in particular to a salt tide upstream prediction method and system based on artificial intelligence. The method comprises the following steps: performing river upstream and downstream fall correlation analysis on an estuary to obtain width change fall correlation data; non-flood season and flood season precipitation extraction is carried out on the historical environment data of the estuary, and downstream runoff change analysis is carried out on the width change fall associated data to obtain non-flood season / flood season downstream runoff change data; according to the non-flood season / flood season downstream runoff change data, carrying out tide superimposed effect analysis and non-flood season / flood season salt tide upstream action analysis to obtain non-flood season / flood season salt tide upstream action data; and based on a recurrent neural network algorithm, constructing a salt tide upstream prediction model for the non-flood season / flood season salt tide upstream action data, and sending the model to a data cloud platform to execute salt tide upstream prediction. According to the method, the salt tide upstream prediction technology is optimized, so that the salt tide upstream prediction technology is more accurate.

The invention provides an atmospheric precipitable water quantity inversion method and system based on multi-source satellite remote sensing data, and relates to the technical field of remote sensing data. After clear sky pixels are screened, radiation brightness temperature data, digital elevation model data and spatio-temporal feature coding data are processed through three feature extraction branches, obtained feature representations are spliced and input into a feature fusion network, a spatial attention module is introduced in the feature fusion stage, finally, a composite loss function is adopted for training, and the feature fusion network is constructed. And high-precision atmospheric precipitable water quantity inversion is realized. According to the method, the multi-source data characteristics are fully utilized, and the inversion precision and the space continuity are improved.

The invention provides a regional ecological environment quality evaluation method and system based on remote sensing data, and relates to the technical field of environment remote sensing and ecological monitoring, and the method comprises the steps: extracting red light and near-infrared reflectivity at the peak of a growing season by using Landsat8 satellite data, and calculating a mixed vegetation index; synchronously measuring vegetation indexes of pure vegetation and bare soil on site to obtain a vegetation coverage rate, and obtaining water transparency, suspended solid concentration and chlorophyll a concentration through on-site measurement on the basis of green light, red light, blue light and near-infrared reflectivity in a heavy rainfall period and a dry season; the method comprises the following steps: constructing a linear regression model of reflectivity and water quality parameters through a least square method, calculating a water quality index, constructing an extreme weather influence factor based on ten-year extreme weather data, and finally fusing a vegetation coverage rate, water quality, the extreme weather influence factor, annual precipitation, annual average temperature, optimal regional vegetation temperature and historical rainfall extremum. And constructing an ecological quality index, and dividing ecological environment quality grades.

The invention provides an intelligent path planning method and system based on dynamic road condition prediction. The method comprises the steps of firstly obtaining multi-source dynamic data of a target area; then, constructing a weather influence prediction model to predict weather influence parameters in a future time period; secondly, constructing a weather-traffic coupling model, and respectively establishing correlation models of corresponding precipitation, traffic flow density and average vehicle speed according to road types through historical data analysis, so as to estimate the traffic efficiency of each road section under a dynamic weather condition; and finally, generating a plurality of candidate paths according to the passing efficiency, screening out an alternative path set meeting a multi-target optimization condition from the candidate paths, performing simulation evaluation on the alternative path set, and determining an optimal path according to a simulation result. Compared with a traditional static path planning method, the method has the advantages that the responsiveness of a traffic system to meteorological disasters is remarkably improved, and predictable navigation service is provided for intelligent network connection vehicles.

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 an optimized dispatching water supply method for reclaimed water and conventional water under a drought condition, which comprises the following steps: evaluating a drought grade through meteorological data, precipitation prediction and reservoir level factors, dynamically adjusting the water supply amount of a conventional water source according to the drought grade, and preferentially guaranteeing life and ecological water; according to the production type and the water quality requirement of the enterprise, the reclaimed water demand and the water quality grade are evaluated and divided, and water sources are optimally distributed; the total water deficit and the water consumption cost are minimized by optimizing the scheduling model, and the reclaimed water and the conventional water are reasonably scheduled; and setting constraint conditions to ensure that the conventional water source water supply capacity, the reclaimed water treatment capacity and the pipe network distribution capacity are not in overload operation. Reasonable supply of a conventional water source and reclaimed water is ensured through accurate drought grade evaluation and water source dynamic adjustment; reclaimed water with matched water quality grades is provided according to different water quality requirements of enterprises, and excessive treatment and water quality waste are effectively reduced.

The invention discloses a crop flood disaster large-area quantitative evaluation method and system, and the method comprises the steps: obtaining flood disaster data and SAR time sequence remote sensing data, obtaining a flood disaster submerging space range based on a deep learning image semantic segmentation model according to an SAR remote sensing image, and obtaining a crop flood disaster flood area based on landform and river water level observation data. Estimating the flood submerging depth of the DEM pixel scale by using a submerging depth model; recognizing a flood disaster area by combining SAR (Synthetic Aperture Radar) time sequence remote sensing data, the precipitation observed on the ground and the water level change of a river channel; core evaluation indexes of crop flood submerging range, submerging duration and submerging depth are calculated, flood disaster events are associated with crop periods, and a flood hazard degree equation is constructed by taking flood stress sensitivity as a basis. According to the method, local crop characteristics of a research area are fused, a flood disaster event is associated with a crop period, and the flood hazard degree equation is constructed by taking the flood stress sensitivity as one of parameters, so that quantitative calculation of the flood hazard degree is realized.

The invention provides a watershed artificial precipitation enhancement operation effect evaluation method based on hydrological simulation, and belongs to the technical field of artificial influence weather, and the method comprises the steps: obtaining first data and second data; based on the first data, settling a rainfall-runoff process, selecting a preset hydrological model to carry out a drainage basin hydrological batch simulation test, calibrating hydrological parameters, and constructing a drainage basin hydrological simulation forecast model; based on the second data, calculating the natural precipitation of the non-operation area and the operation precipitation of the operation area, and constructing a natural precipitation-operation precipitation data sequence; based on the natural precipitation-operation precipitation data sequence, inputting the natural precipitation into the watershed hydrological simulation forecasting model to obtain runoff before precipitation enhancement operation, and inputting the operation precipitation into the watershed hydrological simulation forecasting model to obtain runoff after precipitation enhancement operation; and based on the runoff before the rainfall enhancement operation and the runoff after the rainfall enhancement operation, carrying out quantitative evaluation on the artificial rainfall enhancement operation effect of the drainage basin. Scientific quantitative evaluation of the artificial precipitation enhancement operation effect of the watershed is realized.

The invention discloses a drought and flood sudden change event process risk quantification method and device. The method comprises the following steps: acquiring precipitation evapotranspiration data of a plurality of grid points in each month in a rasterized drainage basin; determining a standardized rainfall evapotranspiration index of each grid point in each month according to the rainfall evapotranspiration data of each grid point in each month; determining a plurality of target months and a plurality of target drought and flood patches based on the standardized rainfall evapotranspiration index of each grid point in each month and each grid point; according to each target month and each target drought and flood patch, determining a plurality of drought and flood sudden change events, performing three-dimensional drought and flood feature extraction on each drought and flood sudden change event, and outputting a plurality of feature attributes of each drought and flood sudden change event; generating a risk quantification result of each drought and flood sudden change event according to the plurality of feature attributes of each drought and flood sudden change event; the technical problem that an existing drought and flood sudden turning event analysis method cannot integrally reflect the overall risk condition of drought and flood sudden turning disasters on drainage basin water resource safety is solved.

The invention provides a method for improving flood inundation simulation precision based on fusion of VGI data and optimization of DEM, and the method comprises the steps: selecting HEC-RAS as a flood inundation simulation model, and inputting data including DEM data, river flow data, precipitation data and land coverage type data; according to a Manning coefficient range table provided by the land coverage database, setting corresponding Manning coefficients for different coverage types, and preliminarily setting the Manning coefficients as the median of each coverage type; the Manning coefficient setting is iteratively adjusted by comparing the difference between the simulation result and the actual water depth data; establishing a relation chain between the extracted water depth information and the submerged simulated water depth; height measurement data of a land observation satellite is combined with the COPDEM to estimate a residual error between the COPDEM at a water depth point position and a real elevation, and a conversion relation is established between extracted water depth and simulated water depth of a relatively optimized DEM according to an elevation difference value between the DEM and the COPDEM. Based on the technical scheme of the invention, the simulation precision is obviously improved, the VGI data is successfully combined with the optimized DEM, and the accurate correction of the Manning coefficient is realized.

The invention provides a multi-dimensional flood disaster analysis method. The method comprises disaster space-time analysis, disaster semantic analysis, response adequacy analysis and population exposure degree analysis. According to the disaster situation space-time analysis, disaster situation related information extracted from a text is associated with a time attribute and a space coordinate attribute; analyzing the disaster information from the angles of time sequence and space distribution; the time sequence analysis adopts a quantitative statistics mode, and key nodes of the flood event are analyzed from the precipitation trend, the flood discharge trend, the microblog number trend, the ponding point number trend, the help seeking information number trend and the taken measure number trend; according to disaster semantic analysis, subject analysis is carried out on the text of each help-seeking point, and semantic information of disaster manifestation in space is mined; according to the response measure sufficiency analysis, the disaster response sufficiency is obtained by analyzing the correlation between the number of response measures and the number of help seeking information.

The invention provides hydrological information monitoring equipment for a hydropower station, and relates to the technical field of water quality detection.The hydrological information monitoring equipment comprises a control box, the control box is installed on a main body assembly, and the main body assembly drives a rotating hollow rod to rotate through a rotating gear; the main body assembly is sequentially provided with a rainfall monitoring assembly, an energy supply assembly, a camera and a water level flow monitoring assembly from top to bottom; the rainfall monitoring assembly is used for metering rainfall and snowfall based on the metering structure and the weighing structure, the energy supply assembly achieves automatic tracking of the sun through a first hydraulic telescopic rod and a second hydraulic telescopic rod, and the control box is electrically connected with the rainfall monitoring assembly, the energy supply assembly, the camera and the water level flow monitoring assembly. The water quality monitoring assembly is used for monitoring the water quality of a water body in the hydropower station. The invention solves the technical problems that the hydrological information monitoring equipment adopts a fixed structure, the maintenance of operators is difficult, the power generation efficiency is low, the precipitation statistics is single and only the precipitation can be counted by adopting a fixed photovoltaic panel for power generation.

The invention discloses a diffusion model iteration correction precipitation prediction method based on space-time conditions, which improves the precision and timeliness of precipitation prediction, and is especially suitable for prediction of extreme weather events. The method comprises the following steps: firstly, receiving multi-modal input data including a radar echo image, a time identifier, earth surface remote sensing data and multichannel data of a meteorological satellite, and extracting characteristics of various input data by utilizing a plurality of encoders; in order to solve the problem that radar image rainfall distribution does not conform to a probability model, after an encoder is adopted to extract features, M frames of radar images are classified into different categories through a k-means clustering algorithm, so that corresponding prediction models are selected according to the categories, and multi-task prediction is carried out. A diffusion model predicts precipitation in a hidden space in a step-by-step generation mode, and iterative correction is introduced in each time period of a prediction result so as to reduce error accumulation. And finally, outputting a high-precision long-time sequence rainfall prediction result by the model. According to the method, multi-modal information, time conditions and an iterative correction structure are combined, the rainfall prediction capacity under complex terrains and extreme weather is remarkably improved, and the method has high practical value and application prospects.

The invention discloses a short temporary rainfall intensity prediction and identification method based on space-time composite attention, and the method comprises the following steps: obtaining an IMERG satellite rainfall data set, and carrying out the data construction and marking of a target region; establishing a space-time composite attention network, accessing an MFGA module and a TSMA module layer by layer, and performing non-negative activation on output to ensure physical interpretability; proposing a C-DWABP loss function, performing direction asymmetrical penalty aiming at a pixel-level prediction error, and introducing a grading weight mapped according to a real rainfall intensity grade at the same time; based on the output, intensity classification is carried out through a microthresholding ordered layer, and learnable threshold stability training is set; generating a forecast product, and obtaining an hour-by-hour precipitation grid in future 1-6 hours and a corresponding intensity identification result; according to the method, the precision of short temporary rainfall intensity prediction is improved, the false alarm rate is reduced, and a heavy rainfall event is effectively identified.

The invention belongs to the technical field of ecological information, and particularly relates to a Qinghai-Tibet Plateau land-gas coupling strength prediction method and system based on a CMIP6 model and emergence constraints. The method comprises the following steps: collecting monthly total precipitation and soil humidity data of a historical period and a future period in nine earth system models in a CMIP6 model, and constructing a data set; performing data preprocessing on the data set through spatial resolution unification and multi-source data fusion; constructing an emergence constraint model based on the preprocessed data; and calculating a land-gas coupling strength index according to a coupling measurement method based on the emergence constraint model. According to the method, by means of emerging constraint, the uncertainty of the model is reduced, the standard deviation of the land-gas coupling strength after constraint is reduced by 65.12%, the prediction precision of the space-time trend is improved, and the problems that a CMIP6 model is underestimated to the northeast and southwest of the Qinghai-Tibet Plateau and is overestimated to the west and northwest of the Qinghai-Tibet Plateau are solved.

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 discloses a rainwater collection device based on a sponge city and a collection method thereof, and particularly relates to the technical field of rainwater collection, the rainwater collection device comprises a reservoir and a precipitation cylinder, the reservoir is internally provided with an inspection well and a recycling well, the bottom of the precipitation cylinder is provided with a first pollution discharge hole, and the first pollution discharge hole is internally provided with a first sealing element; the first sealing piece can change the sealing state according to the amount of accumulated water in the precipitation barrel, a stirring piece is arranged at the inner bottom of the precipitation barrel, and the stirring piece can stir sediment at the first pollution discharge hole when the first pollution discharge hole is opened; the sewage draining exit is opened in advance according to the precipitation amount and the water storage amount in the water storage pool, meanwhile, accumulated water in the water storage pool is kept at a certain water level, the water storage pool has a certain buffer space, and therefore it is guaranteed that when the precipitation amount is large due to rainstorm or long-time raining, the water storage pool can not be blocked; the rainwater collecting device can quickly discharge flood to prevent urban waterlogging.