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

The invention relates to the technical field of civil engineering, and discloses a road subgrade settlement prediction method and system based on multi-source data. The method comprises the following steps: acquiring and correcting a precipitation sequence and roadbed soil moisture distribution data; analyzing rainfall accumulation characteristics and carrying out penetration risk classification, thereby extracting a penetration depth estimation value, and utilizing finite element analysis to simulate a soil body saturation state change trend; quantifying a soil body supporting capacity reduction range, constructing and calibrating a settlement initiation probability calculation model, and obtaining settlement probability distribution; identifying a high-risk evolution area, integrating path weights and accumulated influence factors, and generating a settlement prediction interval subjected to multi-dimensional verification; and fusing the prediction interval and actual roadbed structure data through a geographic information system to generate a comprehensive evaluation report. According to the method, the whole-process accurate evaluation of the roadbed settlement risk from multi-source perception, mechanism simulation to space prediction is realized, and the accuracy and timeliness of the roadbed settlement risk prediction and the pertinence of engineering maintenance are improved.

The invention is suitable for the technical field of rainfall forecast correction, and provides a regional short-term rainfall quantitative forecast machine learning correction method integrated with weather system information, and the method comprises the steps: determining an initial forecast factor based on meteorological elements in an ECMWF mode, the meteorological elements comprise a precipitation field, ground transformation, convection effective potential energy, UV wind fields of 500hPa, 850hPa and 925hPa, relative humidity, temperature and vertical speed; taking the dichotomy labels corresponding to the 500hPa slot line and the tangent line between 850hPa and 925hPa as newly added forecast factors, and constructing a four-dimensional training set according to the initial forecast factors and the newly added forecast factors; converting the four-dimensional training set into a three-dimensional training set; training a random forest regression model through the three-dimensional training set, and outputting a rainfall forecast field within a preset time; according to the CMASH3 mode rainfall forecast data and a preset judgment logic, dynamically correcting the rainfall forecast field within a preset time, and generating a target rainfall forecast product; and the accuracy of heavy rainfall forecasting is effectively improved.

The invention relates to the technical field of meteorology and hydrology, in particular to a rainfall space reconstruction method, system and device based on terrain and meteorological multi-factor fusion driving and a medium, and the method comprises the steps: obtaining space terrain factors and meteorological observation data of a target area, and employing a BP neural network to progressively interpolate missing measurement values of meteorological driving variables in a layered manner; setting a missing detection elimination rule based on a wet season and a dry season to process precipitation data; constructing a 14-dimensional high-dimensional input feature system fusing a space terrain factor, a time sequence factor and a meteorological driving factor; nonlinear models such as an XGBoost model, a BP neural network model or an LSTM model are used for training, and finally the monthly scale precipitation space reconstruction of the grid is achieved. According to the method, the problems that a traditional interpolation method is poor in adaptability in a complex terrain area and insufficient in multi-factor driving relation description are effectively solved, and the precision and the physical consistency of precipitation space distribution are remarkably improved.

The invention discloses a rainstorm ground waterlogging prediction method, device, equipment and product, and relates to the technical field of urban waterlogging prediction.The rainstorm ground waterlogging prediction method comprises the steps that ephemeris and ground observation station data are obtained, the atmospheric moisture delay amount and the high-altitude water vapor conversion factor are calculated respectively, the historical water vapor content is calculated, and the future atmospheric precipitable water amount is predicted; rainfall time distribution data are obtained through rainstorm intensity decomposition; calculating the rainwater overflow amount of the catchment area in combination with the water discharge amount time distribution data of the urban drainage pipe network; and iterative calculation is carried out by using a digital elevation model grid to obtain the waterlogging depth, and waterlogging early warning information is sent out. By constraining the total rainfall amount, the rainfall amount can be effectively constrained by predicting the precipitable water amount based on the satellite ephemeris; the underground pipe network drainage is restrained, and a water flow condition in a pipe channel is simulated by adopting an SWMM model; and finally, the waterlogging accumulated water volume is calculated, constraint verification is carried out on each link of urban waterlogging, and relatively high precision and relatively long timeliness are ensured.

The embodiment of the invention provides a Beidou water vapor inversion method and system based on a buoy platform. The method is applied to the technical field of data processing, and comprises the following steps: receiving a Beidou dual-frequency signal through a direct antenna to obtain original observation data; the multi-path effect and the platform shake are inhibited by combining buoy motion characteristics obtained by the MEMS sensor to obtain pre-processed data; calculating a troposphere delay estimated value; establishing a delay-water vapor conversion model; suppressing short message interference to obtain effective observation data; and inputting the data into a model to calculate the amount of precipitable water and the relative humidity. According to the method, the key technical problems of sea surface multi-path effect suppression, platform shake compensation, marine atmosphere modeling, short message interference elimination and the like are solved, so that reliable water vapor parameter inversion on the buoy platform is realized.

The invention discloses an electric power facility disaster early warning method and system for tower level identification, and particularly relates to the field of risk assessment of electric power facility disaster early warning, and the method comprises the steps: respectively collecting the data of wind speed, temperature, humidity, precipitation and icing thickness in the range of a target tower and an adjacent tower; and synchronously collecting vibration data and current fluctuation data of the corresponding tower. Point-by-point difference and cross comparison is carried out on various monitoring data, residual changes are extracted, a difference value chain between towers is established, a disaster risk value is calculated step by step in combination with the phase coupling condition between monitoring parameters, and a risk assessment map and a disaster critical state are generated when the risk value reaches a critical level. Therefore, the real risk under the simultaneous action of multiple disaster factors is reflected, the problem that the dangerous synchronous effect is covered as a medium risk by the existing method is avoided, and relatively timely and reliable risk assessment and early warning are realized.

The invention provides a hydrological big data real-time monitoring method and device based on quantum computing and a medium, and belongs to the technical field of hydrological data processing. Hydrological data and change data of a surface water body are collected; determining river course roughness and infiltration coefficient parameters as a hydrological change actual data set by using a quantum annealing process; predicting hydrological change data in the drainage basin based on a random forest regression model; performing comparative analysis on the hydrological change data and a prediction result based on the real-time rainfall data to generate a parameter adjustment instruction; setting a water level change threshold value and a precipitation threshold value; and receiving the precipitation in real time, triggering a historical similar flood event case library retrieval function on the basis of a threshold exceeding condition, and outputting an early warning response record corresponding to a historical event. A quantum walking algorithm is used for simulating flood diffusion, a submerging depth thermodynamic diagram is generated in combination with a digital elevation model, the flood submerging range and depth distribution are rapidly and accurately displayed, and visual and visual support is provided.

The invention provides a sewage flow prediction method and device, electronic equipment and a computer storage medium, is applied to a control center of a sewage plant, and relates to the technical field of urban drainage. The method comprises the following steps: dividing a sewage catchment area corresponding to a sewage plant into a plurality of target areas according to a preset area category; tap water use data and rainfall data of the target area are determined and preprocessed; inputting the preprocessed water flow data into a pre-trained sewage prediction model, and outputting a predicted value of the water inflow of the sewage plant; wherein the sewage prediction model comprises a water consumption time sequence module and a rainfall time sequence module which are arranged in parallel, and a merging layer arranged at the downstream of the water consumption time sequence module and the rainfall time sequence module; according to the method, the sewage catchment area is classified, the water consumption and the precipitation are collected, and the water inflow is predicted by using the wide-deep neural network model, so that the generalization ability and the robustness of the model are improved, and the precision of a predicted value is improved.

The invention discloses an agricultural disaster prevention-oriented high-temperature drought composite event early warning and identification method, which comprises the following steps of: acquiring lattice meteorological data of a target area, including daily maximum temperature, precipitation and environmental parameters; calculating a standardized rainfall evapotranspiration index SPEI based on the meteorological data, and generating a high-temperature drought composite event lattice point judgment matrix in combination with a preset high-temperature threshold value; performing connectivity analysis on the judged high-temperature and drought composite event lattice points through a spatial clustering algorithm, and screening regions with spatial coverage areas exceeding a set threshold value as high-temperature and drought composite event groups; performing time continuity judgment on the high-temperature drought composite event groups in two continuous days by adopting an overlapping area proportion method; counting the duration time, the space coverage area and the event intensity index of the event group, constructing an early warning criterion and generating early warning information; outputting spatial and temporal distribution characteristics and early warning levels of the composite dry heat events; the method provides a scientific basis for climate analysis and disaster prevention and reduction.

The invention relates to a rainfall observation method and device, a computer system and a medium, and solves the problem of high-reliability observation of regional rainfall. The method comprises the following steps: firstly, adopting reanalysis data and earth system mode output data to construct a first deep learning model of satellite inversion rainfall so as to reconstruct a lattice long-series rainfall data set; then, taking the rainfall attenuation information of the historical period as input, obtaining a rainfall simulation series of the corresponding historical period through different rainfall attenuation and rainfall intensity models, determining regional rainfall under the different rainfall attenuation and rainfall intensity models through a weighted link method, and preferably selecting an optimal rainfall attenuation and rainfall intensity model through long-series lattice rainfall data of the historical period; furthermore, a second deep learning model is constructed by adopting satellite inversion data and rainfall data inverted by a rainfall attenuation and rainfall intensity model, real-time correction of regional precipitation is realized, finally, real-time rainfall information of the region is deduced by adopting the optimal rainfall attenuation and rainfall intensity model and taking a real-time mobile signal as input, and the regional average precipitation corrected in real time is obtained.

The present invention relates to a method and system for automatic site construction planning of different types of weather radars, belonging to the field of meteorological radars. The method includes: obtaining the topographic information of the area where the radar is planned to be built and determining the parameters of the weather radar; performing grid processing on the area where the site is planned to be built and determining the radar position movement rules; counting the shielding rate in the area where the radar is located and screening the grid points that meet the shielding rate constraint conditions; counting the average annual precipitation at the grid point positions and obtaining the optimal radar site construction grid points; when networking weather radars, selecting multiple different types of weather radars for networking. The present invention sets the specific position and configuration type of the weather radar station as decision variables, sets the minimization of the radar shielding rate as a constraint condition, and combines the average annual precipitation data statistically by rain gauges, so as to realize the automatic construction of different types of weather radar stations.

The invention provides a quantitative evaluation method for suitability of plot-level water resources, which comprises the following steps: collecting rainfall data and evaporation data of a meteorological station in a research area, and generating rainfall and potential evaporation data of a target grid unit through a spatial interpolation method; calculating the actual evaporation capacity of the target grid unit, and constructing a water supply accessibility evaluation index according to the distance between the target grid unit and the potential water source and the required water diversion distance; determining the water supply convenience of the target grid unit based on the water supply accessibility evaluation index, superposing the influence of rainfall, constructing a water resource suitability evaluation model, and obtaining a water resource suitability quantitative evaluation result of the target grid unit; according to the method, multiple factors such as rainfall, evaporation and irrigation water accessibility are considered, quantitative evaluation of water resource conditions is achieved, and the method can be used for spatial analysis and planning of regional water resource suitability.

The invention discloses a foundation space ionosphere and troposphere integrated joint detection method and device, and belongs to the technical field of satellite navigation, and the method comprises the steps: detecting the vertical total electron content of an ionosphere: extracting the inclined total electron content of the ionosphere at an ionosphere puncture point, and converting the inclined total electron content into the vertical total electron content of the ionosphere; detecting a foundation Beidou ionosphere disturbance index; and detecting an ionosphere phase scintillation index, a troposphere zenith total delay, a troposphere precipitable amount and an ionosphere amplitude scintillation index, and outputting ground-based Beidou space ionosphere and troposphere integrated joint detection parameters. The method effectively improves the reliability, robustness and meteorological perception capability of a navigation positioning system in a complex atmospheric environment, and is especially suitable for special environmental conditions such as severe convection weather, typhoon landing, violent disturbance of an ionized layer and the like.

The invention provides an early warning system and early warning method for extreme climate in the orange orchard soil cultivation process, and relates to the technical field of agricultural meteorological monitoring, and the early warning method specifically comprises the steps: collecting a plurality of climate-related key parameters of an orange orchard soil cultivation region, and calculating a temperature stability index, a precipitation fluctuation index and a temperature and humidity adaptability index; generating an environmental influence index and an ecological adaptability index according to the soil humidity, the illumination intensity, the soil pH value, the soil organic matter content, the temperature stability index and the precipitation fluctuation index after the dimensionless treatment; and combining the environmental influence index, the ecological adaptability index and the temperature and humidity adaptability index to generate a comprehensive evaluation value of the soil cultivation area of the orange garden, comparing the comprehensive evaluation value with a preset climate evaluation threshold value, and formulating corresponding management measures according to a comparison result. The scientific assessment enables farmers to timely identify climate risk levels, and provides a basis for formulating targeted management measures.

The invention provides a flood disaster multi-source information monitoring method and system and electronic equipment, and relates to the technical field of data processing. The method comprises the following steps: determining a precipitation area and accumulated precipitation according to ground observation precipitation data; according to the satellite remote sensing data and the digital elevation model, identifying cities and towns in the rainfall area, and determining a confluence area in the cities and towns and the real-time area and the initial ponding depth of the confluence area; according to the digital elevation model, the rainfall area is divided according to the terrain, and a plurality of monitoring areas and terrain confluence factors of the monitoring areas are obtained; when a preset condition is met, updating the real-time data, and inputting the real-time data and the topographic confluence factor into a water depth prediction model to obtain a predicted ponding depth; and according to the predicted ponding depth and the preset depth, monitoring and early warning are carried out on the ponding condition of the monitoring area. And the predicted ponding depth can be continuously obtained without depending on satellite remote sensing data, so that the monitoring can be continuously carried out in time.

The invention provides an urban meteorological collaborative analysis method based on multi-source data fusion, and the method employs a multi-source data fusion mechanism to combine with rainfall data from GNSS inversion, meteorological data of a ground meteorological station and static geographic information, such as altitude, terrain gradient and the like, and achieves the collaborative analysis of urban meteorological data through the fusion of multi-source data. According to the method, the relation between different meteorological variables can be comprehensively and accurately reflected, namely, the capacity of capturing the complex cooperative relation between the meteorological variables is improved, the precision and response speed of urban meteorological prediction are improved, then extreme weather events such as short and temporary heavy rainfall can be accurately predicted, an early warning signal is sent out in time, and the early warning effect is improved. And important support is provided for urban management and disaster prevention and control.