222 results about "Precipitationshed" patented technology

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 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 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 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 high-speed rail operation environment safety intelligent sensing early warning method and system, and the method comprises the steps: carrying out the time-space alignment of the wind speed, wind direction and rainfall data issued by a meteorological part and the data collected by a device at a preset position of a railway perimeter based on reverse distance weight interpolation; based on the pre-trained strong wind prediction model and precipitation prediction model, outputting kilometer-level and hour-level refined wind and rain prediction results; the millimeter-wave radar, the laser radar and the high-definition video are jointly calibrated in a unified world coordinate system, personnel and foreign matters are detected by a target detection model, and a second-level intrusion behavior recognition result is realized. By means of remote sensing and aircraft inspection, hidden danger objects, external environment abnormity and infrastructure abnormity are efficiently recognized. And in combination with the multi-factor detection result related to the risk, efficient, automatic and accurate analysis and early warning are performed according to the preset grading early warning rule, so that the operation risk of the high-speed rail is remarkably reduced.

The invention discloses a forest fire danger early warning system and method based on meteorological and vegetation data analysis, and relates to the technical field of fire risk prediction and control. According to the forest fire danger early warning system and method based on meteorological and vegetation data analysis, fine grid division is carried out on a target forest area through a high-resolution DEM and a multispectral or hyperspectral image, and vegetation type identification is carried out on each monitoring subunit; calculating a wet-dry cycle index WDI, a monsoon effect index MFI and a dry thunderstorm risk factor DRF by combining hourly wind speed, wind direction, relative humidity, precipitation and vapor pressure difference meteorological data; through extracting canopy height, canopy density, under-forest shrub and grass layer thickness and high VOC tree species coverage rate, fuel continuity coefficient FCI and volatile matter flammability index VEI are calculated, and accurate quantification of continuity and flammability of vertical ladder fuel and horizontal canopy is realized. And a comprehensive fire danger index FFI is constructed and subjected to hierarchical management after judgment, so that the dynamism, the hierarchical property and the pertinence of fire danger early warning are realized, and the forest fire danger prevention and control precision and the patrol resource utilization efficiency are improved.

The invention relates to the technical field of meteorology and artificial intelligence, in particular to a short-time rainfall nowcasting method based on multi-source meteorological data and a neural network, and the method comprises the steps: collecting the multi-source meteorological data of a target, and building a historical data set; respectively expanding the training set and the verification set based on a sliding window; fusing the radar data and the multi-source meteorological variable data by using a multi-head self-attention mechanism; training a rainfall nowcasting neural network driven by the multi-source meteorological data by using the historical data set; and on the basis of the trained rainfall nowcasting neural network, selecting parameters meeting a preset optimal condition, performing quantitative evaluation on the test set, generating an evaluation result of short-time rainfall nowcasting, and forecasting the short-time rainfall amount of the target period according to the real-time radar rainfall and the multi-meteorological variable data on the basis of the evaluation result. Therefore, the problems that an existing rainfall nowcasting method is single in driving data source, low in rainfall forecasting precision, difficult to forecast a complex rainfall process and the like are solved.

The invention provides a weather forecasting method based on big Beidou three-dimensional water vapor and weather model cooperative driving, and the method comprises the steps: carrying out the point-by-point registration of the obtained prediction data of a GraphCast model and ERA5 measured data, obtaining residual field data, taking the residual field data as the input of a deep learning residual correction network, and obtaining predicted residual field data; a terrain adaptive algorithm is adopted, predicted ERA5 meteorological parameters corresponding to the predicted residual field data are converted into a three-dimensional grid structure under height coordinates, and zenith troposphere delay is determined through an integration method; based on the dynamic correction term, converting the zenith troposphere delay into a precipitable water amount through a space-time inversion network; inputting the predicted ERA5 meteorological parameters and the rainfall data into a parameter efficient adapter and a big ancient meteorological model to obtain meteorological forecast factors output by the big ancient meteorological model; through the above technical scheme, real-time positioning enhancement and gradual improvement of meteorological element forecasting capability can be realized.

The application discloses a hydrological station hour precipitation comprehensive quality control method and device based on multi-source data cross verification. The control method comprises the following steps: step (1), counting and analyzing the problem list of the hydrological station hour precipitation data; step (2), determining and giving the station information table of all hydrological stations, including the latitude, longitude, altitude data observation start and end time and precipitation observation accuracy; step (3), climatology limit value test, time consistency test and space consistency test; step (4), analyzing the integrity, quality condition and correctness of the hydrological precipitation data after quality control. The control device comprises a cloud PaaS unit, a cloud IaaS unit and a computing resource unit. The hour precipitation data of the Chinese hydrological rainfall station obtained by using the method or device of the application is fused with the meteorological data, so that the monitoring and identification ability of the extreme precipitation event is improved.

The application belongs to the technical field of deep learning and meteorological prediction, and particularly relates to a daily precipitation grade classification method based on GA-XGBoost. The method realizes autonomous learning of time sequence characteristics of precipitation, reduces the non-stability of precipitation data, and accurately classifies and predicts daily precipitation. The method comprises the following steps: preprocessing original precipitation data, including data screening, data cleaning, data classification and smote method balanced dataset; establishing an XGBoost model and initializing hyperparameters; using a genetic algorithm to optimize network parameters; inputting each subsequence into the model for training and prediction and comparing and analyzing the results.

The invention provides a plateau rainfall prediction method, system and device based on intelligent similarity correction, and relates to the technical field of short-term climate prediction.The method comprises the steps that mode rainfall data of a month to be predicted, multivariable historical factor data corresponding to the mode rainfall data and global historical annual rainfall error data are obtained, determining standard grid point data of the wind field, the height field and the air temperature and effective historical annual rainfall error data; inputting the standard grid point data and the effective historical annual rainfall error data into a pre-trained error prediction model to obtain a prediction error field output by the error prediction model; and based on the prediction error field, correcting the mode precipitation data to obtain the final predicted precipitation. According to the method, multivariable information is integrated, the nonlinear synergistic effect relationship is mined more deeply, the description capability of the rainfall anomaly driving mechanism of the to-be-predicted region is enhanced, and compared with a traditional single-variable or linear scheme, the accuracy of similar selection and rainfall season prediction is effectively improved.

The application relates to an ARMS-based satellite microwave humidity data all-weather assimilation method, which comprises the following steps: collecting YH4DVAR global background field data; calculating the effective particle radius of water condensate; establishing a symmetric error model for ARMS scattering characteristics according to the forecast deviation of cloud and precipitation intensity and spatial position, wherein the symmetric error model expands the observation error into a segmented function of cloud amount or precipitation amount in the mode and observation, and assigns an assimilation weight to the microwave observation data under all-weather conditions; and using the model to assimilate and forecast the all-weather satellite microwave humidity data. The application has good all-weather microwave radiation simulation capability, provides a new observation operator for YH4DVAR all-weather assimilation, and has a wide application prospect in the field of satellite microwave data assimilation.

A collapsible rain gauge for detecting precipitation gradients with connection to a PC assembly includes a mounting frame, a measuring part and a recording mechanism, whereby: – the mounting frame includes a table base (1) with legs, on which a mounting rod (2) is attached, on which a metal eyelet (3) is attached, next to the table base (1) is a support column (10) with a holder (9) of a funnel (8); – the measuring part contains a spring (4) with hooks, which is fixed on one side in the metal eyelet (3) of the fixing rod (2) and on the other end of the spring (4) a cable (13) is fixed, on the opposite end of which a container (5) for collecting precipitation with a bottom lined with soft suction material (7) is suspended by a hook (6) and a funnel (8) for collecting rainwater is fixed to the container (5) through a tube (11) which is fixed in an S-shape using fixing cables (16);– the recording mechanism includes a rotary optical encoder (12) with an axis that is connected to the converter (14) via cables (15), which is connected to the computer assembly (17) via cables (15); the measuring part being equipped with protective walls: side walls (18a) and top wall (18b) for protection against rain. The method of measuring precipitation with this device is described below.;

The present application discloses a wetland vegetation carbon sequestration potential prediction method and system under the influence of future climate change, belongs to the technical field of wetland vegetation carbon sequestration potential prediction, and comprises the following steps: obtaining the net primary productivity of vegetation, the vegetation coverage, the monthly temperature, the precipitation and the atmospheric carbon dioxide concentration in the historical period and the future period; calculating the annual average net primary productivity of vegetation, the coverage and the multi-year average vegetation coverage; calculating the annual average temperature, the precipitation and the carbon dioxide concentration; extracting the wetland vegetation pixel distribution as the research area range; calculating the ratio of the annual average net primary productivity to the vegetation coverage to obtain the wetland vegetation carbon sequestration potential value in the historical period; adopting the multiple stepwise regression analysis method to build a per-pixel wetland vegetation carbon sequestration potential estimation model; and utilizing the annual temperature, the precipitation and the carbon dioxide concentration in the future period to realize the accurate prediction of the wetland vegetation carbon sequestration potential in the research area range in the future period.

The application discloses a kind of ground and satellite-borne measurement precipitation data fusion algorithm under bayesian framework, comprising the following steps: selecting time and space matching satellite-borne measurement precipitation and ground radar measurement precipitation data, when matching, set certain space window and time window, time window is within ±6min with the time difference that ground radar starts once body scanning time that satellite-borne measurement sweeps matching space window, space window is the region that with ground radar as center, the circular region of r radius and the region that satellite-borne radar survey strip is intersected, the ground and satellite-borne measurement precipitation data fusion algorithm under bayesian framework of the application, based on hierarchical bayesian method, the fusion of ground radar and satellite-borne measurement precipitation data, improve satellite-borne measurement instantaneous precipitation precision, obtain the high-precision, high-resolution precipitation estimation result of comprehensive multi-source precipitation observation, and can quantitatively give the uncertainty size that fusion result contains, to better be applied to hydrological model.

The application provides a drought risk identification and early warning method based on multi-source satellite fusion NDVI and machine learning, acquires NDVI time series data corresponding to a research area, and extracts feature information of a disaster-bearing body corresponding to the research area based on the NDVI time series data; acquires water resource supply data corresponding to the research area, and extracts water resource supply features corresponding to the water resource supply data; through a machine learning model obtained by pre-training, a disaster-bearing body NDVI prediction value of the research area at a future time is predicted based on the disaster-bearing body feature information and the water resource supply features; the disaster-bearing body NDVI prediction value is used for identifying the drought risk of the research area in combination with precipitation forecast data, or dynamically adjusting the anti-drought irrigation strategy data. The application can significantly improve the accuracy of drought risk prediction and effectively improve the dynamic adjustment effect of the anti-drought irrigation strategy.

The invention belongs to the technical field of resource optimization scheduling, and provides a drainage basin water volume accurate scheduling method and system based on precipitation real-time monitoring, and the method comprises the following steps: collecting hydrological data, early-stage influence rainfall and soil water content of each hydropower station in a target drainage basin; the earlier-stage influence rainfall is corrected through the collected hydrological data, and the corrected earlier-stage influence rainfall is obtained; in combination with hydrological data and the corrected early-stage influence rainfall, carrying out runoff forecasting in a short-term period of the drainage basin and middle-and-long-term end-of-period water level forecasting in the drainage basin, and obtaining end-of-period water level information; determining a flood prevention early warning level and a drought resistance early warning level according to the end-of-period water level information; and a scheduling decision is made according to the flood prevention early warning level and the drought resistance early warning level. According to the scheduling method provided by the embodiment of the invention, accurate scheduling of the water volume of the drainage basin can be realized.

The application discloses a lattice rainstorm prediction method, and the method adopts circulation background field, U\V prediction field, rainfall prediction field, model adjustment field and data of a ground meteorological station, and constructs a training sample set; an XGBoost method is used to establish a prediction model based on a mapping relationship of a circulation background field of an EC model and prediction products and future 0-24h precipitation; the training sample set is trained through the prediction model, so that the prediction model can perform medium and short-term precipitation prediction. The prediction ability (rainstorm TS) of the method for precipitation above rainstorm is improved by more than 17% compared with the EC model prediction, the prediction precision of medium and short-term precipitation can be effectively improved, and the method has a good application prospect and provides more accurate prediction services for disaster prevention and reduction.

The invention discloses a method and a device for calculating drainage basin space surface rainfall by using radar combined reflectivity, and a medium. The method comprises the following steps: calculating a radar combined reflectivity space area in a set range over a drainage basin; calculating the projection area of the radar combined reflectivity space area in the watershed range; according to the intensity value of each radar combined reflectivity unit in the spatial range corresponding to the projection area, calculating the rainfall intensity of each radar combined reflectivity unit by using a radar quantitative rainfall estimation relation; calculating precipitation qt in unit time t; calculating the total space rainfall amount of the time period; according to the total space rainfall amount of the time period, the projection of the radar combined reflectivity space area and the drainage basin geographic area, the drainage basin space surface rainfall in the T time period is calculated. The problems of rainfall intensity distribution and rainfall intensity center distribution of traditional area rainfall are solved, and the time precision, the space precision and the rainfall distribution precision of the area rainfall of the drainage basin are improved.

The application provides a short-time precipitation nowcasting method fusing quantum computation and a deep learning model, and belongs to the field of short-time precipitation nowcasting, and steps are as follows: firstly, long-term historical data of precipitation and meteorological elements of a target area are extracted; then, the precipitation at a time of tentative prediction is taken as a prediction target quantity, and meteorological elements and precipitation of M hours before the time of tentative prediction are taken as model input characteristic quantities; then, a quantum computation method is used to change the meteorological elements and precipitation of M hours before the time of tentative prediction into high-dimensional quantum probability characteristics about the precipitation at the time of tentative prediction; finally, quantum circuit precipitation probability information is taken as input, and the precipitation of 1 hour in the future is taken as output, a short-time precipitation nowcasting model is constructed and trained, and the application uses quantum circuits and quantum superposition states to map classical data to a high-dimensional quantum Hilbert space, can avoid a local optimal solution trap, significantly reduces a calculation burden, and realizes short-time precipitation prediction under the condition of massive high-dimensional meteorological element inputs.

The present application relates to the technical field of precipitation forecast, solves the problem that the prior art is prone to underestimate the peak value of precipitation or produce false alarm under strong convective weather, and particularly relates to a short-impending precipitation forecast method fusing GNSS and water conservancy rain measurement radar, GNSS observation data and water conservancy rain measurement radar base data in a monitoring area are acquired, pretreated, corresponding atmospheric precipitable water distribution field and radar reflectivity factor distribution field are generated, based on a preset time sliding window, time sequence characteristics of the atmospheric precipitable water distribution field and the radar reflectivity factor distribution field are extracted, and the two are superimposed in channel dimension to construct a multi-channel spatio-temporal fusion input tensor. The present application overcomes the defect that the traditional extrapolation method rapidly decays with the prolongation of the forecast time, significantly improves the prediction accuracy of the rainstorm center intensity and the falling area, reduces the missed report and underestimation of the precipitation event, and solves the problem of low prediction accuracy of the traditional linear extrapolation algorithm under strong convective weather.

The invention discloses a debris flow susceptibility evaluation method considering a whole starting process condition. The debris flow susceptibility evaluation method comprises the following steps: acquiring a remote sensing satellite image of a research area; the excess terrain volume, the InSAR erosion volume and the historical landslide source volume of the area are calculated firstly, and then a comprehensive source supply index is calculated in a fusion mode; calculating a river power index SPI and a daily maximum precipitation amount of the research area, and performing normalization processing on grids of the river power index and the daily maximum precipitation amount by a minimum value and maximum value synthesis method; performing weighted stacking on the two normalized factor layers to obtain a rainfall trigger index; calculating a transportation connectivity index; and carrying out three-dimensional joint probability modeling and susceptibility index calculation. According to the method, objective and accurate evaluation of the debris flow susceptibility is realized through quantitative description of the debris flow forming process and non-parametric probability modeling.

The invention relates to the technical field of agricultural remote sensing, in particular to a harvesting monitoring method and device based on multi-source remote sensing data. If the NDVI data is the optical remote sensing data, calculating a cloud coverage ratio by using the cloud wave band data, and if the cloud coverage ratio is low, comparing each pixel value of the NDVI data with an optical harvesting judgment threshold to obtain a latest harvesting ratio; if the rainfall data is radar remote sensing data, calculating daily average rainfall by using the rainfall data; if the average daily precipitation is low, comparing each pixel value of the vh polarization data with a radar harvesting judgment threshold value to obtain a harvesting proportion; updating the effective harvesting proportion according to the harvesting proportion. According to the technical scheme, the optical remote sensing data and the radar remote sensing data are combined to jointly judge the effective harvesting proportion of the land parcel, meanwhile, the influence of weather on the remote sensing data is overcome to a certain extent, the requirements for timeliness and accuracy are met, and the probability of harvesting misjudgment caused by data noise is reduced.

This invention relates to the field of security technology, specifically to a control module and method for a comprehensive energy security system. It includes an analysis and prediction unit, an evaluation and calculation unit, a variable consumption unit, and a control strategy unit. The demand prediction module of this invention receives user periodic electricity consumption, temperature deviation, and precipitation deviation from the analysis and analysis module. It calculates a baseline energy demand based on the user periodic electricity consumption, and then calculates the impact of temperature deviation on user energy demand under extreme weather conditions. By combining the baseline energy demand and the impact of extreme weather conditions, it predicts the user's energy demand under extreme weather conditions. This combination allows for more accurate prediction of energy demand under extreme weather conditions, enabling advance adjustments to power generation plans and power dispatch, avoiding energy waste caused by supply-demand imbalances or excessive microgrid generation, optimizing resource allocation, and improving resource utilization efficiency.

The invention provides a new energy power station planning method and system, and the method comprises the steps: obtaining the static characteristics of a plurality of candidate points, the static characteristics including the seismic activity frequency, the altitude, the gradient and the underground water burial depth; calculating a static abnormal score of each candidate point based on the static features of the candidate points, and performing a first round of screening on all the candidate points according to the static abnormal scores to obtain remaining candidate points; obtaining dynamic characteristics of each residual candidate point, wherein the dynamic characteristics comprise wind speed, sunlight intensity, environment temperature, environment humidity and precipitation; and performing a second round of screening on all the remaining candidate points based on the dynamic feature of each remaining candidate point and the static abnormal score of each candidate point to obtain a final point location. According to the method, multi-dimensional quantitative evaluation of new energy power station site selection is realized through a static and dynamic dual screening and clustering optimization mechanism, and site selection scientificity and engineering reliability are remarkably improved.

The utility model relates to the technical field of water level measurement, and discloses a precipitation observation device. The precipitation observation device comprises a rain collecting tank, a water storage tank and a water outlet pipe, wherein a water accumulation cavity is formed in the rain collecting tank; the measuring piece is movably installed in the water accumulation cavity, and a height indication mark is arranged on the peripheral wall of the measuring piece; the positioning assembly is arranged on the peripheral side of the measuring piece in a sleeving mode and fixedly connected with the inner circumferential wall of the rainwater collecting tank, the positioning assembly is provided with a current identification face, the current identification face is used for determining the current relative height position between the measuring piece and the rainwater collecting tank, and the initial identification assembly is provided with an initial identification face. And the initial identification surface is used for determining an initial relative height position between the measuring piece and the rainwater collecting tank. Therefore, the precipitation amount of the mine area within the preset time is observed by using the precipitation amount observation device so as to analyze the correlation between the water inflow of the mine and the precipitation amount, and the calculation error during analysis can be reduced, so that water damage in the mine can be effectively prevented.

This invention relates to the field of sponge city pavement layout technology, and provides a GIS-based method and system for sponge city building facility layout. The method includes: using the terrain information of the area to be constructed obtained by the GIS system to construct a surface model corresponding to the slope, and dividing multiple first surface regions with the same slope; calculating the amount of precipitation borne by each region through the first type of surface integral and precipitation, calculating the proportion of precipitation that can be absorbed by the steep slope of the region, and determining the flow direction of precipitation based on the height difference between adjacent surface regions, calculating the final amount of rainwater that each region needs to bear, and setting the pavement parameters of the building facilities accordingly so that the permeability is consistent with the rainwater bearing capacity, so that urban construction can have better adaptability to rainwater in sloping areas and improve the city's ability to bear rainwater.

The invention discloses a photovoltaic electric field sand and dust meteorological gradient three-dimensional monitoring and early warning system. According to the system, a plurality of gradient micro-meteorological station networks are arranged in a peripheral area, an edge area and a central area of a photovoltaic electric field, wind speed, wind direction, air temperature, humidity, air pressure, precipitation and radiation are measured at different heights, and soil temperature, humidity and conductivity sensors are buried; piezoelectric wind erosion sensors with different heights from the ground are arranged on the ground surface, and the sensors have particle counting, kinetic energy and single particle energy output and are provided with adjustable gains. The system is also equipped with a multi-rotor unmanned aerial vehicle to measure meteorological and dust concentration data at different heights above the photovoltaic panel. All data are recorded in a unified mode through the collecting unit and processed through a fusion algorithm, the intelligent early warning processing unit determines and calculates the dust damage degree through machine learning dynamic weight according to an improved damage index, and early warning is triggered when the index exceeds a threshold value. According to the invention, three-dimensional monitoring and intelligent early warning of sand and dust activities of the photovoltaic electric field can be provided.

The invention relates to the technical field of ecological data monitoring and analysis, and particularly discloses an ecological monitoring data integrated analysis method and system. Soil moisture content and pH value data of each sub-region are collected in real time through a capacitive soil humidity sensor and an electrode pH value sensor, a soil moisture content abnormal index and a pH value abnormal index are calculated by using fast Fourier transform and a K-Means clustering algorithm, and a soil degradation coefficient is obtained by combining weighted normalization processing; and evaluating the soil health condition of each sub-region, constructing a machine learning model based on the data features to predict the future soil degradation level, further analyzing the relationship between precipitation and soil degradation for the region predicted to be seriously degraded, and starting a dynamic precipitation treatment scheme if it is found that precipitation increase can prevent soil degradation.