339 results about "Precipitationshed" patented technology

The invention provides a method of predicting and accessing level of a wildfire-caused trip risk in power transmission lines. Based on the power transmission line corridor fire risk level as an indicator and factors such as daily precipitation, temperature, wind speed, relative humidity, slope, sloping direction, road network density, population density, conventional solar terms and vegetation water content, influence factors to the wildfire occurrence risk are quantitatively assessed so as to establish a wildfire risk assessment model. During occurrence of a wildfire, based on the line trip risk level as an indicator and flame height bridge ratio, vegetation variety and vegetation distribution, the influence factors to the power transmission line trip risk level is quantitatively assessed in the case of the wildfire so as to establish a line trip assessment model. The method is applied to predicting and accessing the level of the wildfire-caused trip risk in the power transmission lines by integrating the two models. The method according to the technical scheme has the advantages that the trip risk in the power transmission lines can be subjectively predicted and assessed on the basis of real-time data, targeted preventive measures are taken in advance, wildfire-caused trip accidents of the power transmission lines are maximally decreased, and the method is significant to the improvement of safety and running reliability of the power transmission lines.

The invention provides a method for grading flood risk according to a plurality of space scales. Under each space scale, the method comprises the following steps of: S1, calculating a flood risk index by taking a precipitation amount index, a river-lake index and a terrain index as risk evaluating factors of flood; S2, calculating a vulnerability index after calculating vulnerability evaluating factors of the flood by taking a population density index and an economic GDP (Gross Domestic Product) density index as evaluating factors; and S3, generating a flood risk index by synthesizing the risk index and the vulnerability index, and further grading the flood risk index to generate a flood risk level. The method is implemented in a sequence from larger space scales to smaller space scales. Due to the adoption of the method for grading the flood risk, areas with social and economic damages possibly caused by flood disaster phenomena and risk levels can be analyzed on different space scales, and the monitoring frequency of highly-risky areas is increased.

The invention discloses an urban hydrological simulation system and a simulation method, which relate to the technical field of urban precipitation management. The invention includes a rainfall floodmanagement model, the simulation system includes a time-varying precipitation unit, a surface water evaporation unit, a snow cover and melt unit, a depression interception unit, a soil seepage unit, agroundwater recharge unit, a groundwater and drainage pipe exchange unit, a slope catchment unit, a LID control unit, a region editing unit that are associated with the rainfall flood management model, and the simulation system further comprise a storage module, an aquifer editing module and a groundwater flow editing module. The invention has comprehensive simulation function and can provide comprehensive and representative analysis data through a complete functional system unit structure. The influence of DCIA and TIA setting on runoff generation in urbanization region is analyzed under thepremise of comprehensive consideration of parameters. More objective and accurate reference and opinions are provided for urban planning.

The invention discloses a dynamic-modification-combined storm rainfall fine alarming method for a power grid zone. The method includes an S1 step of forecasting local precipitation amount, generating forecasting information and extracting forecast values of precipitation at tower positions of a power grid; an S2 step of extracting position information of towers of the power grid meeting condition for rainstorms and storm rainfall information; an S3 step of determining correcting indexes by utilizing history observational data of a meteorological station and corresponding forecast information, correcting the forecast values of precipitation at the tower positions of the power grid of the current date according to the correcting indexes and acquiring the final forecast result; and an S4 step of making priory forecast of power equipment according to the final forecast result and publishing rainstorm disaster early warning information for the towers of the power grid. By adopting the method provided by the invention, accuracy of the storm rainfall forecast value of the current date is improved effectively, so that an effective information guarantee is made for rainstorm disaster early warning.

The invention relates to a high-resolution satellite data-based TRMM rainfall data downscaling method. The method comprises the following steps of: firstly obtaining multiple pieces of satellite meteorological data through an official way, and correcting TRMM remote sensing rainfall data through a Leave-one-out cross validation method; and selecting a variable through step regression according tofactors influencing the rainfall data, considering hysteresis quality, for vegetation, of rainfall and spatial correlation of the vegetation, establishing a multi-spatial scale model, and selecting anoptimum model to downscale the rainfall data. The method is simple in process and correct in rainfall prediction.

The invention discloses a method for quantitatively forecasting extreme rainfall. The method comprises the following steps that: I, an input module (100) reads raw data of a hydrometric station from a database or an Excel file, performing abnormal detection and washing on the read data, and preprocessing the data to obtain standardized hydrometric time sequence data; II, a modeling module (200) constructs a model consisting of a first forecasting model (210), a second forecasting model (220) and a combined BP neural network (230) according to the hydrometric time sequence data in the input module (100); III, a correction module (300) performs parameter adjustment on the forecasting models constructed in the modeling module (200) according to the quality of the forecast data in the step II; and IV, an output module (400) stores an extreme rainfall forecast value in the next year into the database or the file, so that a user can check and analyze the data. By the method, a yearly average extreme rainfall amount can be modeled and forecast from a data excavation angle; and the method is high in applicability and relatively high in accuracy.

The invention discloses a regional carbon reserve space pattern monitoring system and method based on remote sensing data. The system comprises a remote sensing data processing module, an actual measurement data processing module and a data analysis and output module. The output ends of the remote sensing data processing module and the actual measurement data processing module are connected with the input end of the data analysis and output module; satellite remote sensing data, precipitation, vegetation coverage rate, surface temperature, land utilization data, light data, DEM data and meteorological data are used to establish a model to monitor the spatial-temporal change of regional carbon reserves, and the defect of long research period of large-scale regional carbon reserves is overcome. The monitoring system also has the advantages of easily available source data, high computer operation efficiency, low cost, strong timeliness and the like.

The invention discloses a probability distribution based short-time rainfall forecast method and system, and belongs to the technical field of rainfall forecast. The system comprises a cloud atlas collection module, a meteorology data acquisition module, a data transmission module, a master control module, a rainfall probability generation module, a precipitation estimation module, a rainfall characteristic determination module and a data display module. The precipitation estimation module can obtain rainfall data of higher precision and confidence by using advantages of multi-source data; therainfall characteristic determination module uses a rainfall rain type characteristic matrix and time characteristic matrix of a rainfall station to describe the rainfall and movement condition of the rainstorm center in the aspects of time and space; and a mathematic matrix is used to describe the time-space distribution characteristic of rainfall, the rainfall characteristic is researched lateron the basis of the mathematic matrix, and the accuracy and comprehensiveness of rainfall characteristic research are improved.

The invention discloses a system for the real-time monitoring and prediction of soil moisture content, and a detection method for the soil moisture content. The system comprises a monitoring module and a prediction module. The monitoring module comprises a detection system, a data collector, a wireless transmitter, and a data receiver. The detection system comprises at least two detectors, and the detectors comprise a soil moisture content detector disposed at a to-be-detected point, a precipitation detector, a ground surface wind speed detector, a ground surface temperature detector, a ground temperature detector, and a ground surface air moisture detector. The data collector is connected with the detection system, and the detection system enables the detected data to be transmitted to the data collector and to be transmitted to the data receiver through the wireless transmitter. The prediction module comprises a comprehensive cloud computing platform and an information publishing and self-determining inquiry platform. The comprehensive cloud computing platform is connected with the data receiver, and receives the detection data transmitted by the data receiver. After processing, the detection data is transmitted to the information publishing and self-determining inquiry platform. The system is simple in operation, is stable in performance, is safe in data, is diversified in function, and is high in adaptability.

The invention provides a satellite remote sensing data-based near-surface air temperature estimation method under cloud conditions. The method comprises the following steps: (1) acquiring historical data of a meteorological station in a research area; (2) preprocessing the historical data of the atmospheric precipitation, the relative humidity near the ground, the cloud top temperature, the cloudtop height and the cloud optical thickness to obtain a spatio-temporal matching data set; (3) taking the temperature of the instrument shelter as the output of a neural network, taking the data set asthe input of the neural network, and constructing the neural network; (4) obtaining a data set of an area to be subjected to air temperature estimation; (5) carrying out time and space interpolationby utilizing a cubic spline interpolation method according to the latitude, longitude and time information of the meteorological satellite to obtain a data set matched with the space-time of the meteorological satellite; And (6) inputting the data set matched with the space-time space of the meteorological satellite into the constructed neural network to estimate the near-surface air temperature under the cloud day condition. The method is simple and easy to implement and high in precision.

The invention provides an urban inland inundation prediction method and device. The method comprises the steps of performing hydrological analysis on a to-be-predicted city to determine a first mapping relationship between the ponding amount of each low-lying point in the to-be-predicted city and the corresponding ponding depth, and the percentage of the area of each low-lying point in the sum ofthe areas of all the low-lying points; performing big data analysis on the historical water circulation monitoring data of the to-be-predicted city to determine a second mapping relationship between the regional waterlogging water accumulation amount of the to-be-predicted city and the corresponding regional precipitation amount; predicting the regional waterlogging water accumulation amount of the rainfall according to the second mapping relationship; and according to the respective corresponding percentage of each low-lying point, distributing the waterlogging water accumulation amount in the rainfall area to each low-lying point, and according to the respective corresponding first mapping relationship of each low-lying point, determining the respective water accumulation depth of each low-lying point in the rainfall area. The rainfall is monitored to a certain extent, and the possibility of causing urban inland inundation is predicted according to the rainfall.

The invention provides an irrigation method and device based on the Internet of Things, and is used for solving the technical problems that an existing irrigation method is high in labor input, and water resources are wasted. The method comprises the steps that a server receiving current soil water content uploaded by a soil moisture sensor and meteorological information uploaded by a meteorological sensor, and determines precipitation amount in a preset time period based on the meteorological information; receiving the image data of the crop area uploaded by the image acquisition device, andinputting the image data into the crop type identification model to obtain a crop type corresponding to the crop area; based on the crop variety and the current soil water content, determining the actual water demand of the crop variety within a preset time period; calculating a first difference value between the precipitation and the actual water demand, and then determining the water drainage states of the multiple irrigation devices in the crop area. According to the irrigation method provided by the invention, a targeted irrigation scheme can be automatically provided, and irrigation wateris effectively saved.

The invention relates to the technical field of meteorological and hydrological forecasting and meteorological information processing, in particular to a regional atmospheric and hydrological coupling early warning decision-making system. The system comprises a regional data acquisition module for acquiring the regional data in real time; an artificial intelligence module which is used for obtaining a rainfall analysis model in the region; a rainfall forecast analysis module which is used for obtaining the rainfall amount and the rainfall path forecast in the region; a correction module which is used for obtaining the corrected precipitation amount and precipitation path forecast and storing the corrected precipitation amount and precipitation path forecast in a database; a hydrological data acquisition module which is used for acquiring the surface runoff data; a hydrological forecasting module which is used for providing water level forecasting, early warning and pre-discharging decisions in the region; a response demonstration module which is used for simulating the rainfall runoff trend in the region in several hours in the future and the convergence of rainfall to a low position on the ground so as to realize the runoff early warning and prediction; and an early-warning decision-making module which is used for determining an early-warning decision-making scheme according to a preset runoff monitoring point threshold value.

Based on dual-polarization radar data, raindrop spectrum data and rain gauge data, a neural network and a Logistic multiple regression method are utilized to realize the adaptive optimization inversion of the dual-polarization radar precipitation, and the precipitation inversion formula can be optimally selected to obtain high-precision radar precipitation data, so that the method can be well applied to disastrous weather monitoring and early warning forecasting. Besides, the method is good in adaptability, parameter setting does not need to be changed according to the weather process, and dual-polarization radar precipitation optimization inversion can be achieved through raindrop spectrum data, dual-polarization radar observation data and rain gauge data in the radar coverage range. Moreover, the method is less affected by rainfall system examples and data measurement precision, can be applied to any detection position of the ground-based radar, and has high rainfall estimation precision and good universality and robustness.

The invention discloses a precipitation monitoring system and method, a storage medium and a processor. The system comprises precipitation monitoring equipment which is arranged in a transformer substation and used for collecting precipitation of the transformer substation in a first preset time period and sending first precipitation data corresponding to the precipitation to meteorological data center equipment; a meteorological station equipment which is used for sending second precipitation data corresponding to precipitation, observed by the meteorological station equipment, of the transformer substation in a first preset time period to the meteorological data center equipment; a Doppler weather radar which is used for sending third precipitation data corresponding to precipitation, observed by the Doppler weather radar, of the transformer substation in a first preset time period to meteorological data center equipment; and the meteorological data center equipment which is used fordetermining the actual precipitation of the transformer substation according to the first precipitation data, the second precipitation data and the third precipitation data. The technical problem that an existing meteorological observation network cannot give specific precipitation according to the position of the transformer substation is solved.

Provided is an environmental factor prediction device that includes a predictor and predicting means. The predictor uses, as explanatory variables: water quality data in a plurality of layers in water, the data including a value corresponding to a biochrome level or a bioluminescence level (e.g. chlorophyll concentration), water temperature, salt concentration, dissolved oxygen, turbidity and flow rate; and meteorological data including atmospheric temperature, precipitation and sunshine duration, and outputs an estimated value of each item of the explanatory variables at a unit time later, based on time series data of the explanatory variables. The predicting means predicts the water quality data up to an N unit time later by repeating prediction using the estimated value acquired by the predictor as input of the predictor again. According to the present invention, the environmental factors that cause generation of red tide, blue tide or water bloom, diseases of fish, and the like, can be predicted, on a long term basis and at high accuracy.

The invention relates to a device and method for predicting river water flow generated by rainfall runoff in a region. The method comprises the following steps: segmenting the region into a plurality of grids; obtaining rainfall data, a vegetation interception water amount and a soil infiltration water amount in the grids; determining the rainfall allowance of each grid according to the rainfall data, the vegetation interception water amount and the soil infiltration water amount; determining surface runoff in the region according to the rainfall allowance of the plurality of grids in the region; and determining the river water flow according to the surface runoff and the river characteristics in the region. According to the method disclosed by the invention, the absorption of vegetation and soil in the region on precipitation and the surface runoff from each grid to the adjacent grid are accurately considered, and finally, the water flow of the river channel is calculated. According to the method disclosed by the invention, each geographical factor influencing the river water flow in the to-be-predicted region is comprehensively considered, so that the calculated river water flow is more accurate.

The invention discloses a reservoir scheduling early warning method based on meteorological numerical forecasting, which comprises the following steps: calculating the interceptable water storage capacity in a basin according to the acquired hydrological information of each hydropower station in the basin of a target reservoir, and correcting the early-stage influence rainfall Pa according to theinterceptable water storage capacity; determining a runoff coefficient rho according to the hydrological information and the meteorological information, calculating the capacity storage purified wateramount and the capacity storage precipitation amount of the reservoir based on the runoff coefficient rho, and carrying out capacity storage precipitation amount early warning; according to the hydrological information, the drainage basin information, the early-stage influence rainfall Pa, the runoff coefficient rho and the meteorological information, short-term period runoff prediction of the reservoir is conducted, and the medium-term and long-term end water level is calculated; and determining a flood prevention early warning level and a drought resistance early warning level according tothe hydrological information, the meteorological information and the end-of-period water level information, and performing reservoir scheduling. The reservoir scheduling early warning method has the advantages that the reservoir scheduling risk early warning capacity is improved, and life and property safety of reservoir dams and downstream people is ensured.

The invention discloses a quantitative rainfall prediction method based on radar reflectivity extrapolation and relates to the technical field of atmospheric science. The method comprises the steps: S1, collecting radar base data for prediction and generating a radar echo reflectivity image through a reflectivity factor in the radar base data; S2, solving a pre-constructed optical flow equation based on the radar echo reflectivity image to obtain a motion field of radar echo; S3, identifying a strong convection region in the motion field of the radar echo by using a pre-constructed identification model and tracking and positioning a strong convection region; S4, constructing a radar echo extrapolation model, training the radar echo extrapolation model by using the strong convection region,and predicting radar echo data; and S5, converting the radar echo data by using a trained quantitative precipitation neural network prediction model to obtain predicted precipitation. The method hasthe advantages of simplicity and high prediction accuracy.

The invention provides a forest fire early warning model. The architecture comprises an adjacent component, a periodic component and an external component, the construction process comprises the steps: S1, converting the air temperature, humidity, wind speed and precipitation of preprocessed meteorological data in each time intervalinto a four-channel approximate image matrix, and then dividing atime axis into two segments which represent the relation of proximity and period similarity of all positions in a time flow; s2, respectively inputting the image matrix in each time slice into an adjacent component and a periodic component for modeling; s3, endowing results of different components with different weights through the parameter matrix, and fusing output results of the adjacent component and the periodic component into a residual component XRes; and S4, mapping an XRes and XExt integration result to [-1, 1] through a Tanh function. According to the invention, a neural network structure with a deep residual network as a core is used to construct a forest fire early warning model.

Aiming at a defect that the insufficient consideration to the particularity of a local environment causes a problem that predicted data is liable to have an error in a conventional precipitation prediction method, the invention provides a precipitation meteorological message data error correction method, and the method is used for correcting the precipitation message data Ym, issued by a meteorological station to the public, of a prediction day on a prediction start day, and obtaining a precipitation correction value Yj of the prediction data. The invention also provides a glaze landscape prediction method which is implemented through the above method, and the glaze landscape prediction method is used for predicting on the prediction start day the probability that glaze happens on the prediction day, and correcting a temperature value in the prediction method. The glaze landscape prediction method provided by the invention can be used in a tourism attraction of the Mount Emei. The methods provided by the invention take the meteorological prediction products issued by the meteorological station as the data basis, achieve the prediction of the probability of a meteorological landscape in a given region through the meteorological prediction products issued by the meteorological station, can achieve the effective use of the public service data, and are higher in practicality.

The invention relates to artificial influence weather operation effect evaluation, in particular to an artificial influence weather operation effect evaluation method. The method comprises the following steps: S1, dividing a manual operation influence area; s2, determining a comparison area; s3, carrying out manual operation; s4, setting the average precipitation of the manual operation influencearea, comparing the average precipitation of each comparison area with the average precipitation of the manual operation influence area, and obtaining the increment of the average precipitation of each comparison area corresponding to the manual operation influence area; and interpreting the increment of the average rainfall, and judging the artificial influence effect of the artificial operationinfluence area. The method is suitable for testing the effects of artificial precipitation (snow) increasing operation, artificial precipitation (snow) eliminating operation, artificial haze eliminating and the like.