Meteorological-based geological disaster risk early warning method, server and storage medium

Abstract

The invention discloses a meteorological-based geological disaster risk early warning method, a server and a storage medium, and the method comprises the steps: constructing a two-factor dynamic evaluation framework, scientifically quantifying a disaster-bearing body vulnerability index reflecting a geological background and a disaster-causing inoculation factor reflecting a recent rainfall accumulation effect, and carrying out the inverse coupling of the disaster-bearing body vulnerability index and the disaster-causing inoculation factor, and generating a current rainfall disaster threshold dynamically adjusted in real time for each grid unit. By implementing the method and the device, the problem of insufficient early warning accuracy caused by the fact that traditional early warning cannot adapt to geological environment time-space difference is effectively solved, and accurate, dynamic and differentiated evaluation and early warning of geological disaster risks are realized.

Description

Technical Field

[0001] The present application relates to the field of geological disaster early warning technology, and in particular to a meteorological-based geological disaster risk early warning method, server, and storage medium. Background Art

[0002] As global climate change intensifies and extreme weather events become more frequent, geological disasters have become one of the major natural disasters threatening people's lives and property. In mountainous areas with complex terrain, rainfall-induced geological disasters are characterized by suddenness and severe damage.

[0003] To accurately assess the impact of rainfall on geological hazards, technicians collect historical rainfall data for a specific area, both during periods of geological disasters and when no disasters have occurred. Statistical analysis of this massive data set determines the average probability of a disaster occurring within that area. The early warning system continuously monitors the dynamics of the previous effective rainfall (API) and compares it with a fixed API threshold calculated from historical disaster data. Once the API value exceeds this threshold, an alert is triggered.

[0004] However, the characteristics of hazard-prone structures vary widely across the region. Some geological units are inherently sensitive, while others are relatively stable. This early warning model, which relies on regional macro-statistics, lacks the ability to respond to the individual risks of specific geological units. This can lead to failure to issue early warnings before disasters occur at sensitive locations, or excessive ineffective warnings at stable locations. Summary of the Invention

[0005] This application provides a meteorological-based geological disaster risk warning method, server and storage medium to solve the problem that existing warning models are difficult to adapt to the spatial differences of the geological environment in the region and the dynamic changes in risk status, and to enhance the targeted nature of the warning while ensuring the accuracy of the warning.

[0006] In the first aspect, the present application provides a meteorological-based geological disaster risk warning method, which includes: obtaining monitoring data in a target geographic grid unit based on a plurality of geographic grid units of preset sizes obtained by pre-dividing the monitoring area, wherein the target geographic grid unit is one of the geographic grid units in the monitoring area; inputting static factors and dynamic factors in the monitoring data into a vulnerability index calculation model to obtain a vulnerability index of the disaster-prone body of the target geographic grid unit, wherein the static factors include slope and distance from the fault zone, and the dynamic factors include soil moisture content; and the vulnerability index calculation model is a numerical method for quantitatively evaluating the degree to which a geographic unit itself is prone to geological disasters. A scientific method is adopted; based on historical rainfall data determined by a preset optimal number of rainfall days, a disaster-causing breeding factor of the target geographic grid unit is calculated, wherein the disaster-causing breeding factor is a destructive accumulation index obtained based on the impact effect and continuous cumulative effect of historical rainfall; the vulnerability index of the disaster-bearing body and the disaster-causing breeding factor are coupled and calculated to determine the current rainfall disaster threshold of the target geographic grid unit, wherein the current rainfall disaster threshold is inversely proportional to the vulnerability index of the disaster-bearing body and inversely proportional to the disaster-causing breeding factor; the expected effective rainfall in a preset future time period is obtained, and the warning level of the target geographic grid unit is determined according to the expected effective rainfall and the current rainfall disaster threshold.

[0007] In the above-mentioned embodiment, a dual-factor dynamic assessment framework was constructed. This framework not only calculates a hazard-prone vulnerability index reflecting the inherent properties of geological bodies through static factors (such as slope) and dynamic factors (such as soil moisture), but also calculates a disaster-causing factor reflecting recent risk accumulation based on the impact and cumulative effects of historical rainfall. These two factors are then applied to generate a real-time, current rainfall hazard threshold for each fine-grained geographic grid cell. This results in a lower critical rainfall threshold for triggering disasters in areas with poorer geological conditions or greater impact from previous rainfall. This enables a precise, dynamic, and differentiated assessment of geological hazard risks, significantly improving the pertinence and reliability of early warnings and making early warning decisions more scientifically based.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of acquiring monitoring data within a target geographic grid cell based on a plurality of geographic grid cells of preset sizes obtained by pre-dividing the monitoring area, where the target geographic grid cell is one of the geographic grid cells within the monitoring area, the method further includes: performing periodic surface deformation monitoring of the monitoring area using a time-series synthetic aperture radar interferometry technique to obtain a periodic deformation rate of a preset area within the monitoring area; Determine the area within the monitoring area where the periodic deformation rate exceeds the preset abnormal threshold as the key monitoring area; use high-density grids to divide the key monitoring area, and use low-density grids to divide the general monitoring area based on the area outside the key monitoring area, so as to obtain the geographical grid units of the monitoring area.

[0009] In the above embodiment, time-series synthetic aperture radar interferometry (InSAR) technology is used to survey wide-area surface deformation, and based on this, the risk level of the monitoring area is pre-identified. Therefore, high-density grids can be used for key monitoring areas with significant deformation, while low-density grids can be used for differentiated segmentation in other general areas. This avoids data redundancy and computing power waste due to overly dense grids, or the omission of key risk points due to overly sparse grids, thereby achieving on-demand intelligent allocation of monitoring resources and significant optimization of computing load, while ensuring refined and high-precision monitoring of high-risk areas, and greatly improving the operating efficiency of the entire early warning system.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the step of inputting the static factors and dynamic factors in the monitoring data into the vulnerability index calculation model to obtain the vulnerability index of the disaster-prone body of the target geographic grid unit specifically includes: extracting the static factors and dynamic factors within the target geographic grid unit from the monitoring data, the static factors including the slope extracted from the digital elevation model, the distance from the fault zone obtained by spatial analysis, and the rock and soil weathering grade extracted from the acid weathering potential map, and the dynamic factors are the soil moisture content obtained by real-time monitoring by sensors; performing maximum and minimum normalization processing on the original values ​​of the static factors and the dynamic factors to obtain factor values ​​mapped to a unified interval; multiplying the factor values ​​by their corresponding weight coefficients and then summing them to obtain the vulnerability index of the disaster-prone body of the target geographic grid unit, the higher the value of the vulnerability index of the disaster-prone body, the more likely the geographic grid unit is to suffer geological disasters under the same external disturbance conditions.

[0011] In the above embodiment, by clearly defining and integrating key static factors and the core dynamic factor of soil moisture content, and adopting a standardized mathematical process of maximum and minimum normalization and weighted summation for processing, multiple influencing factors of different dimensions and properties can be scientifically converted into a single, quantitative vulnerability index of the disaster-prone body, thereby achieving an objective, refined and repeatable quantitative evaluation of the inherent disaster risk of each geographic grid unit.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the historical rainfall data determined based on the preset optimal rainfall days is used to calculate the disaster-causing factor of the target geographic grid unit, and the disaster-causing factor is a destructive accumulation index obtained based on the impact effect and continuous cumulative effect of historical rainfall. The steps specifically include: obtaining the historical rainfall data of the target geographic grid unit for the preset optimal rainfall days before the current moment, and constructing a daily rainfall time series, wherein the preset optimal rainfall days are the length of the rainfall period that has the greatest impact on the stability of the current geological body, and the daily rainfall time series is determined based on the historical rainfall data by statistically analyzing the correlation between historical geological disaster events in the monitoring area and the previous rainfall process; and processing the historical rainfall data using a time series analysis algorithm to calculate the daily rainfall intensity. The impact effect index of rainfall intensity change on the stability of the geological body is obtained by integrating and calculating the peak characteristics and change trend of the daily rainfall intensity change rate; the continuous rainfall duration and the cumulative effective rainfall distribution are used as input parameters of a preset rainfall infiltration model to obtain the cumulative time required for the geological body to reach the critical state of destruction from the initial stable state as the duration cumulative effect index; the impact effect index and the duration cumulative effect index are input into a destructive accumulation evaluation function to calculate the disaster-causing factor of the target geographic grid unit. The destructive accumulation evaluation function is a mathematical model for fusing multi-source indicators, and the normalized impact effect index and duration cumulative effect index are weighted and summed according to a preset weight coefficient.

[0013] In the above embodiment, a refined rainfall effect analysis model is constructed to decompose the impact of previous rainfall on the stability of the geological body into two dimensions: "impact effect" and "continuous cumulative effect". The "optimal number of rainfall days" is determined based on historical data analysis. Therefore, the differentiated contributions of different rainfall processes (such as short-term heavy rainfall and continuous rainfall) to the incubation of geological disasters can be accurately captured, thereby achieving scientific and dynamic quantification of the risk status of the geological body at the current moment due to previous rainfall.

[0014] In combination with some embodiments of the first aspect, in some embodiments, the step of coupling the disaster-prone body vulnerability index and the disaster-causing breeding factor to determine the current rainfall disaster threshold of the target geographic grid unit specifically includes: obtaining a preset benchmark rainfall disaster threshold for the target geographic grid unit, the benchmark rainfall disaster threshold being a standard triggering rainfall determined according to the statistical law of historical rainfall-induced geological disasters in the monitoring area; using the disaster-prone body vulnerability index and the disaster-causing breeding factor as dynamic reduction items, and dynamically reducing the benchmark rainfall disaster threshold through a preset multiplication reduction model to obtain a target adjustment threshold; performing boundary constraint processing on the target adjustment threshold, the preset minimum safety threshold and the maximum reasonable threshold to obtain the current rainfall disaster threshold of the target geographic grid unit.

[0015] In the above embodiment, by introducing a dynamic threshold reduction mechanism, the static, regional benchmark rainfall disaster threshold is multiplied and coupled with the real-time changing vulnerability index of the disaster-prone body and the disaster-causing incubation factor. Therefore, the warning threshold of each geographic grid unit can be adjusted in real time and independently according to its own geological health status and the impact of previous rainfall, thereby improving the sensitivity and accuracy of the warning.

[0016] In combination with some embodiments of the first aspect, in some embodiments, the step of obtaining the expected effective rainfall in a preset future time period and determining the warning level of the target geographic grid unit based on the expected effective rainfall and the current rainfall disaster threshold specifically includes: obtaining the expected effective rainfall in a preset future time period, the expected effective rainfall including the total rainfall in the next 24 hours for evaluating the long-term cumulative effect, and the future hourly heavy rainfall for evaluating the short-term impact effect; calculating the ratios of the total rainfall in the next 24 hours and the future hourly heavy rainfall to the current rainfall disaster threshold respectively, and taking the larger value as the comprehensive risk index; determining the warning level according to the comprehensive risk index and generating corresponding warning information.

[0017] In the above embodiment, by simultaneously considering rainfall forecasts at two different time scales, namely, "total rainfall in the next 24 hours" and "heavy rainfall in the next hour", a comprehensive assessment is made of the dual threats that may be posed by future rainfall - namely, the continuous penetration and cumulative effect of long-duration rainfall and the rapid impact effect of short-duration heavy rainfall. This effectively solves the risk that a single rainfall indicator warning may miss specific disaster triggering modes (such as mudslides caused by short-term heavy rains), ensures that the early warning system can capture the most important disaster threats, and makes the determination of the warning level more comprehensive, robust and reliable.

[0018] In combination with some embodiments of the first aspect, in some embodiments, before the step of obtaining the expected effective rainfall in a preset future time period, the expected effective rainfall includes the total rainfall in the next 24 hours for evaluating the long-term cumulative effect, and the future hourly heavy rainfall for evaluating the short-term impact effect, the method also includes: obtaining the original rainfall forecast data and dominant wind direction information of the preset future time period in the monitoring area, the dominant wind direction including the wind direction angle and wind speed; based on the slope and slope direction of the monitoring area, inputting the original rainfall forecast data and the dominant wind direction information into a preset terrain rainfall correction model to obtain a corrected rainfall sequence, and the terrain rainfall correction model is used to perform spatial downscaling correction on the original rainfall forecast data based on the slope and slope direction of the monitoring area.

[0019] In the above embodiment, the original, coarse-precision rainfall forecast data is subjected to secondary refinement processing through the terrain rainfall correction model, so that the problem of insufficient spatial representativeness of large-scale meteorological forecasts in complex mountainous terrain can be first solved, and rainfall that is closer to the actual situation of the geographic grid unit can be obtained, thereby greatly improving the physical authenticity and spatial accuracy of the early warning model input data.

[0020] In a second aspect, an embodiment of the present application provides a server, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions to enable the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when executed on a server, enables the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on a server, the server executes the method described in the first aspect and any possible implementation of the first aspect.

[0023] It is understandable that the server provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding methods and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. Utilizing a dual-factor dynamic assessment framework, the framework not only calculates a vulnerability index reflecting the inherent properties of geological bodies through static factors (such as slope) and dynamic factors (such as soil moisture), but also calculates a disaster-causing factor reflecting recent risk accumulation based on the impact and cumulative effects of historical rainfall. These two factors are then applied to generate a real-time, current rainfall hazard threshold for each fine-grained geographic grid cell. This results in a lower critical rainfall threshold for triggering disasters in areas with poorer geological conditions or greater impact from previous rainfall. This enables precise, dynamic, and differentiated assessment of geological hazard risks, significantly improving the pertinence and reliability of early warnings and making early warning decisions more scientifically based.

[0025] 2. By adopting rainfall forecasts at two different time scales, namely "total rainfall in the next 24 hours" and "heavy rainfall in the next hour", a comprehensive assessment of the dual threats posed by future rainfall is made - the continuous penetration and cumulative effect of long-duration rainfall and the rapid impact effect of short-duration heavy rainfall. This effectively addresses the risk that a single rainfall indicator warning may miss specific disaster triggering modes (such as mudslides caused by short-term heavy rains), ensures that the early warning system can capture the most important disaster threats, and makes the determination of warning levels more comprehensive, robust and reliable.

[0026] 3. By adopting the terrain rainfall correction model and water balance equation, the original, coarse-precision rainfall forecast data is subjected to secondary refinement processing. This can first solve the problem of insufficient spatial representativeness of large-scale meteorological forecasts in complex mountainous terrain, and obtain rainfall that is closer to the actual situation of the geographic grid unit; and further distinguish the effective rainfall in the total rainfall that can actually infiltrate and affect the stability of the rock and soil, thereby greatly improving the physical authenticity and spatial accuracy of the early warning model input data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a meteorological-based geological disaster risk early warning method in an embodiment of the present application; Figure 2 This is another flow chart of the meteorological-based geological disaster risk early warning method in an embodiment of the present application; Figure 3 This is a schematic diagram of the physical device structure of the server in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular expressions "a", "an", "above", "the", and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations of one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0030] See also Figure 1 , which is a flow chart of a meteorological-based geological disaster risk warning method in an embodiment of the present application.

[0031] S101 : Acquire monitoring data within a target geographic grid unit based on a plurality of geographic grid units of preset sizes obtained by pre-dividing a monitoring area.

[0032] The monitoring area refers to the specific geographic area where geological hazard risk warnings are required, and the monitoring data refers to the various geological hazard-related data collected within the target geographic grid cells, including but not limited to meteorological and geological data. Based on the geographic characteristics of the monitoring area and the warning requirements, multiple geographic grid cells of preset sizes are pre-delineated. These grid cells are divided based on factors such as topography, geological conditions, and monitoring accuracy requirements. For each target geographic grid cell, monitoring data is collected within that cell.

[0033] In some embodiments, a predefined monitoring area vector boundary can be loaded, and a GIS platform grid generation tool (such as Fishnet) can be invoked to generate a geographic grid cell layer covering the entire monitoring area based on preset dimensions (e.g., 100 meters for key areas and 500 meters for general areas). Next, a series of spatial overlay and statistical operations are automatically performed on each cell in this grid layer. These operations include: using the "Zonal Statistics" tool to calculate the average slope value from the loaded DEM raster data, with each cell as the range; and using the "Near" or "Spatial Join" tools to calculate the distance from each cell center point to the nearest fault linear feature. For dynamic data, a layer of sensor data points collected at a specific time point can be spatially joined with the grid layer, assigning the sensor reading (or value estimated through spatial interpolation) falling within a cell to that cell. Once the entire process is complete, all monitoring data is stored as attribute fields in the attribute table of the geographic grid cell layer. This table can then be directly queried to obtain monitoring data for any target geographic grid cell.

[0034] In other embodiments, monitoring data can be dynamically acquired based on real-time data streams and service-based queries. When a target geographic grid unit needs to be analyzed, concurrent requests are initiated to different data service interfaces based on the coordinate range of the unit. For example, a request is sent to a static geographic data service (such as a WCS / WFS service) to obtain basic geographic information such as slope and lithology within the coordinate range; at the same time, a query request is sent to a time series database (such as InfluxDB) or an IoT platform API to retrieve the latest data within a specified time window from sensors whose geographic location tags fall within the coordinate range, such as soil moisture content. The data returned from each service interface is aggregated and aligned in real time at the back end to dynamically build a complete monitoring data set containing the latest dynamic information for the target geographic grid unit.

[0035] It is understandable that other methods may be used to implement data acquisition and integration in this step, which are not limited here.

[0036] S102. Input the static factors and dynamic factors in the monitoring data into the vulnerability index calculation model to obtain the vulnerability index of the disaster-prone body of the target geographic grid unit.

[0037] Static factors represent those geological and geographical attributes that remain stable or change very slowly over relatively long timescales. Examples include slope determined by topography, distance from fault zones determined by geological structure, and the grade of rock and soil weathering formed by rock parent material and long-term weathering. These factors constitute the fundamental environmental conditions for the occurrence of geological hazards. Dynamic factors are those that change rapidly due to short-term environmental changes (especially meteorological factors). For example, soil moisture content, a typical example, can change significantly over hours or days with processes such as rainfall and evaporation, directly affecting the stability of the soil.

[0038] After obtaining the monitoring data of the target geographic grid unit, static factors and dynamic factors are extracted from the monitoring data; based on the historical maximum and minimum values ​​of each factor in the entire monitoring area or the preset theoretical extreme values, the values ​​of all factors are linearly mapped to a unified interval through the formula: normalized value = (original value - minimum value) / (maximum value - minimum value). After normalization, the vulnerability index calculation model is called: a set of weight coefficients pre-set by geological disaster experts or obtained through statistical analysis of historical data is loaded. Each factor corresponds to a weight, and the sum of all weights is usually 1. Finally, each normalized factor value is multiplied by its corresponding weight coefficient, and all products are added together to obtain the hazard-bearing body vulnerability index of the target geographic grid unit.

[0039] In some embodiments, a hierarchical model can be constructed, with the hazard-prone body vulnerability index as the top layer and various influencing factors (such as slope, distance from the fault zone, rock and soil weathering grade, soil moisture content, etc.) as the criterion layer. Based on the results of pairwise comparisons of the factors in the criterion layer by experts in the field of geological hazards, the factors are quantified using a 1-9 scaling method to produce a judgment matrix. Next, the judgment matrix is ​​solved by calculating the maximum eigenvalue and its corresponding eigenvector to obtain the weight coefficient of each factor at the current level. A consistency check is then performed (calculating the consistency ratio CR). Finally, the weights of each factor that pass the consistency check are weighted and summed with the normalized specific value of the factor to obtain the final hazard-prone body vulnerability index.

[0040] In other embodiments, a training data set of a large number of historical samples is collected in advance, where each sample corresponds to a geographic grid unit and contains not only the static and dynamic factor values ​​of the unit at a certain historical moment, but also a label - that is, whether a geological disaster actually occurred in the unit within a period of time after that moment. This labeled data set is then used to train the regression model: the prepared training data set (usually divided into a training set and a test set) is input into the random forest algorithm. The core of the algorithm is to construct a "forest" ensemble model composed of hundreds or thousands of independent decision trees. For each decision tree in the forest, its construction process includes two randomization operations: the algorithm uses a bootstrap sampling method to randomly extract a subset of the same size as the original training set from the total training set with replacement to train the tree. When constructing each node of this decision tree and selecting an optimal feature for splitting, the algorithm first randomly extracts a preset subset of factors (for example, out of a total of 10 factors, only 3 are randomly selected as candidates for each split). The algorithm then searches for the optimal splitting feature and split point within this subset (typically minimizing the sum of the variances of the sample label values ​​(y) within the two child nodes formed after the split). After training, when the vulnerability index of a new target geographic grid cell needs to be calculated, the current monitoring data for each factor in that cell is input into the trained model. The model then outputs a predicted probability value (the probability of a geological disaster occurring in that cell) as the vulnerability index of the cell's hazard-bearing structure.

[0041] It is understandable that other methods may be used to calculate the vulnerability index of the disaster-prone body, which is not limited here.

[0042] S103. Calculate the disaster-causing factor of the target geographic grid cell based on historical rainfall data determined by a preset optimal number of rainfall days.

[0043] Based on a preset optimal number of rainfall days (e.g., 20 days), historical rainfall data for the target unit prior to the current moment is obtained to construct a daily rainfall time series. The preset optimal rainfall number refers to the length of a preceding rainfall period that has the most significant impact on the stability of the current geological body, as determined through statistical analysis of historical regional geological disaster cases. The impact effect of rainfall is then quantified by the occurrence of rainfall peaks (i.e., rainstorm days) in the sequence and the steepness of their increase. The continuous cumulative effect of rainfall is quantified by the total duration of continuous rainfall and the cumulative effective rainfall distribution. The calculated impact effect index and duration cumulative effect index are normalized so that they are both mapped to a uniform range of 0 to 1. These are then input into a destructive accumulation evaluation function and weighted summed according to preset weight coefficients to obtain the disaster-causing factor for the target geographic grid unit, namely the destructive accumulation index.

[0044] Specifically, the impact effect of rainfall is quantified by the rainfall peaks (i.e., rainstorm days) that appear in the sequence and the steepness of their growth: the daily rainfall time series of the target geographic grid unit is scanned to identify all dates that meet the judgment threshold of rainstorm days; for each identified rainfall peak day, its rainfall growth rate or growth amount (steepness of growth) compared with the previous day is calculated; the part exceeding the rainstorm threshold is multiplied by the growth amount to calculate the impact contribution of each peak day; an exponential decay model is used to perform a weighted summation of the impact contribution scores of all peak days in the entire time window to obtain an impact effect index.

[0045] Specifically, the continuous cumulative effect of rainfall is quantified by the total duration of continuous rainfall and the cumulative effective rainfall distribution: the daily rainfall time series is scanned according to preset rules (for example, the rain-free interval between two consecutive rainfall days does not exceed one day) to identify all independent continuous rainfall events; for each identified continuous rainfall event, two core parameters are calculated separately, including the total duration of the event (the total number of days from the beginning to the end) and the cumulative effective rainfall of the event (by setting a daily maximum infiltration amount, treating the daily rainfall exceeding the threshold as runoff deduction, and then summing the daily effective rainfall within the event); these two parameters are multiplied together to calculate the saturation potential index of each event, which comprehensively reflects the duration and water depth of rainfall soaking; finally, the saturation potential indices of all continuous rainfall events in the entire time window are compared, and the maximum value is taken as the final continuous cumulative effect indicator.

[0046] In some embodiments, a single- or multi-layer bucket model can be used to calculate the disaster-proneness factor for a target geographic grid cell: each bucket represents a soil layer with a specific capacity (water holding capacity) and seepage rate (permeability coefficient). A time series of historical daily rainfall is used as the model's daily input. At each time step (day), the model iteratively calculates the change in water level within the bucket using the water balance equation based on the current water volume in the bucket, the daily rainfall, and the seepage rate. Finally, at the end of the simulation, the current water storage rate (current water volume / bucket capacity) of the bucket in the bottommost layer (representing the critical impact layer) is directly used as the disaster-proneness factor.

[0047] It is understandable that other methods may be used to calculate the disaster-causing factors, which are not limited here.

[0048] S104: Couple the vulnerability index of the disaster-prone body and the disaster-causing factor to determine the current rainfall disaster threshold of the target geographic grid unit.

[0049] After obtaining the vulnerability index and disaster incubation factor of the hazard-bearing body, the preset coupling model is called, using the normalized vulnerability index and disaster incubation factor as input to calculate the reduction coefficient of the baseline rainfall disaster threshold, thereby obtaining the dynamic current threshold.

[0050] It's understandable that the critical rainfall threshold for a geological body's disaster risk isn't a fixed constant but rather changes dynamically. For an inherently fragile geological body (high fragility index), even if it hasn't experienced much recent rainfall (low hazard incubation factor), its ability to withstand future rainfall will be relatively low. Conversely, an inherently stable geological body (low fragility index) that has just experienced a prolonged and intense rainfall event (high hazard incubation factor) may have accumulated significant internal damage, significantly reducing its ability to withstand future rainfall. As the fragility index and hazard incubation factor increase, the output rainfall threshold decreases accordingly.

[0051] In some embodiments, the coupled calculation in this step can be implemented in a variety of ways: Optionally, a baseline rainfall threshold can be set for each geographic grid cell. This baseline rainfall hazard threshold is determined solely by the static geological conditions of the cell and represents the hazard-bearing capacity of the geological body under optimal conditions (i.e., unaffected by any dynamic factors). The calculated vulnerability index and hazard incubation factor of the hazard-bearing body are then used as dynamic adjustment factors to calculate the current rainfall hazard threshold using a multiplication-reduction model. Optionally, a two-dimensional rainfall threshold query matrix is ​​pre-constructed. The row axis of this matrix represents the vulnerability index of the hazard-bearing body, and the column axis represents the hazard incubation factor. Both axes are divided into several levels (e.g., low, medium, high, and very high). Each cell in the matrix is ​​populated with a specific rainfall threshold value, which is determined by geological hazard experts based on experience or derived through statistical analysis of a large number of historical disaster cases. When the current threshold needs to be calculated, the real-time calculated vulnerability index and hazard incubation factor values ​​are mapped to the corresponding level intervals in the matrix. The preset threshold at the intersection of the corresponding row and column in the matrix is ​​searched and used as the current rainfall hazard threshold for the target geographic grid cell. No limitation is given here.

[0052] S105: Obtain the expected effective rainfall in a preset future time period, and determine the warning level of the target geographic grid unit according to the expected effective rainfall and the current rainfall disaster threshold.

[0053] Obtain a quantitative rainfall forecast for a predetermined time period (e.g., the next 24 hours) for the target geographic grid cell from the meteorological department as the expected effective rainfall. This calculated expected effective rainfall is compared with the target grid cell's current rainfall hazard threshold to determine the warning level within the pre-set warning system.

[0054] In some embodiments, acquired meteorological data, along with high-precision slope and aspect information for the monitored area, are fed into a pre-set terrain-precipitation correction model. This model simulates the interaction between wind and terrain (e.g., increasing rainfall on windward slopes and reducing rainfall on leeward slopes), generating a high-resolution corrected rainfall sequence. Combined with soil infiltration parameters and vegetation characteristic parameters for the target geographic grid cells, this model uses a water balance equation (e.g., the SCS-CN model or the Horton infiltration model) to calculate and deduct rainwater losses (e.g., vegetation interception and surface runoff) to produce an expected effective rainfall sequence that effectively impacts slope stability.

[0055] In some embodiments, the warning level can be determined in a variety of ways: optionally, the warning level can be directly determined by calculating the ratio of the expected effective rainfall to the current rainfall disaster threshold, and according to which pre-set percentage interval the ratio falls into; optionally, the probability of the expected effective rainfall exceeding the current rainfall disaster threshold can be calculated to obtain the over-threshold probability, and the warning level can be determined according to the size of the over-threshold probability.

[0056] In the above embodiment, this application scientifically quantifies the vulnerability index of the disaster-bearing body reflecting the geological background and the disaster-causing factor reflecting the cumulative effect of recent rainfall by constructing a dual-factor dynamic assessment framework, and inversely couples the two to generate a real-time dynamically adjusted current rainfall disaster threshold for each grid unit. This method effectively solves the problem that traditional early warning cannot adapt to the temporal and spatial differences of the geological environment, resulting in insufficient accuracy of early warning, and realizes accurate, dynamic, and differentiated assessment and early warning of geological disaster risks. In practical applications, when the geological disaster early warning method is extended to wide-area and complex mountainous areas, if a unified high-density grid is used for indifferent and refined calculations for the entire area, it will lead to massive data redundancy and huge waste of computing power in the vast stable area, making the system operation inefficient.

[0057] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the meteorological-based geological disaster risk warning method in an embodiment of the present application.

[0058] S201: Use time-series synthetic aperture radar interferometry technology to perform periodic surface deformation monitoring on the monitoring area to obtain a periodic deformation rate of a preset area within the monitoring area.

[0059] First, the time-series InSAR processing process is initiated. It automatically acquires a long-term radar image archive covering the entire monitoring area from the European Space Agency (ESA) or other radar satellite data providers. From this massive amount of data, it extracts the deformation rate of each ground point in millimeters per year, generating a high-resolution deformation rate field map covering the entire area. Time-series synthetic aperture radar interferometry (InSAR) is an advanced radar remote sensing technology that compares and analyzes two or more radar satellite images of the same area acquired at different times to accurately invert the tiny surface displacements that occurred between the two observations, with millimeter-level accuracy.

[0060] S202: Determine an area in the monitoring area where the periodic deformation rate exceeds a preset abnormal threshold as a key monitoring area.

[0061] The deformation rate field map is spatially overlaid and compared with the preset anomaly threshold to obtain pixels exceeding the threshold. The area formed by all pixels exceeding the threshold is then delineated and marked as a key monitoring area using image processing algorithms such as connected domain analysis.

[0062] In some embodiments, not only is the surface deformation rate of the monitored area calculated, but a quadratic function is also fitted to the deformation time series of each pixel to calculate its deformation acceleration. Only areas where both the rate and acceleration are within a stable range are considered non-key monitoring areas.

[0063] S203. Divide the key monitoring area into high-density grids, and divide the general monitoring area defined as an area outside the key monitoring area into low-density grids to obtain geographic grid units of the monitoring area.

[0064] Based on the unified coordinate reference system initialized for the entire monitoring area, a high-density grid is used to divide the key monitoring areas. Meanwhile, a low-density grid is used to divide the larger, lower-risk general monitoring areas. Each geographic grid cell contains its unique spatial index, center point coordinates, and monitoring area identifier.

[0065] In some embodiments, a high-precision risk assessment model is used for grid cells within key monitoring areas, while a conventional assessment model is used for grid cells within general monitoring areas. Mathematically, the conventional assessment model is represented by an empirical or semi-empirical formula based on statistical regression of historical data. Its implementation involves collecting historical disaster and corresponding rainfall data from the monitoring area or similar geological environments; then, using statistical methods such as logistic regression and information volume, establishing a macro-response relationship between rainfall indicators such as previous cumulative effective rainfall and hourly rainfall intensity and the probability of disaster occurrence; finally, solidifying this statistical relationship into a calculation function for direct evaluation.

[0066] S204: Acquire monitoring data within a target geographic grid unit based on a plurality of geographic grid units of preset sizes obtained by pre-dividing the monitoring area.

[0067] S205: Input the static factors and dynamic factors in the monitoring data into a vulnerability index calculation model to obtain the vulnerability index of the disaster-prone body of the target geographic grid unit.

[0068] S206. Calculate the disaster-causing factor of the target geographic grid cell based on historical rainfall data determined by the preset optimal rainfall days.

[0069] Steps S204 to S206 are similar to steps S101 to S103 and are not described in detail here.

[0070] S207: Obtain a preset benchmark rainfall disaster threshold for the target geographic grid cell.

[0071] Based on an internal parameter database, the unique spatial index of the target geographic grid cell is used as the key. Based on this index, the database retrieves and returns the baseline rainfall hazard threshold associated with that cell. This baseline rainfall hazard threshold is calculated and assigned to each grid cell during the initialization phase. It represents the theoretical upper limit of the hazard tolerance under conditions of long-term rainfall absence and complete geological recovery.

[0072] In some embodiments, an ID curve method based on historical data statistics can be used. This method first collects detailed information on a large number of historical geological disaster events in the monitoring area, especially the rainfall intensity (Intensity) and rainfall duration (Duration) at each disaster point. Then, the (I, D) data of these disaster points are plotted in a logarithmic coordinate system, and through statistical regression analysis, an ID threshold curve that can envelop the vast majority of disaster points is fitted. Finally, based on the geological environment similarity of each geographic grid unit, the corresponding ID curve parameters are matched or interpolated for it, and a representative threshold (such as the 24-hour cumulative rainfall threshold) is selected from the curve as its baseline rainfall disaster threshold.

[0073] In other embodiments, the empirical value of the generalized hydrogeological zoning of the region can be directly used as the baseline rainfall disaster threshold. It is understandable that other methods can also be used to achieve the setting of the baseline rainfall disaster threshold, which is not limited here.

[0074] S208: Using the vulnerability index of the disaster-prone body and the disaster-causing factor as dynamic reduction items, dynamically reduce the baseline rainfall disaster threshold through a preset multiplication reduction model to obtain a target adjustment threshold.

[0075] The vulnerability index (V) and disaster incubation factor (I) of the target geographic grid cell are retrieved. These three values ​​(baseline rainfall disaster threshold, V, and I) are then substituted into a pre-set multiplication reduction model to calculate the target adjustment threshold.

[0076] The multiplication reduction model uses a preset conversion function to convert the vulnerability index of the hazard-prone body into a first reduction coefficient; the disaster-prone factor is converted into a second reduction coefficient. Both reduction coefficients are values ​​less than or equal to 1, with higher indexes corresponding to smaller reduction coefficients. The baseline rainfall disaster threshold is then multiplied by the first reduction coefficient to obtain a preliminary lowered threshold. Finally, this preliminary lowered threshold is multiplied by the second reduction coefficient. This sequential multiplication method ultimately yields the target adjusted threshold.

[0077] In some embodiments, the construction of a multiplication reduction model can be implemented in a variety of ways: Optionally, a linear weight reduction model can be used. When a dynamic indicator (such as the vulnerability index of the disaster-bearing body) is at its minimum value (indicating the most stable state), its corresponding reduction coefficient is 1, that is, no reduction is generated for the baseline rainfall disaster threshold. As the value of the indicator increases, its corresponding reduction coefficient decreases linearly from 1. The two dynamic indicators each generate their own reduction coefficients through this linear relationship, and then sequentially multiply the baseline rainfall disaster threshold to obtain the final result. Optionally, a nonlinear exponential reduction model can be used. Since the impact of dynamic indicators on disaster-bearing capacity is not uniform, but there are one or more critical intervals within which the impact increases sharply. When the indicator value is low, the reduction coefficient decreases slowly; when the indicator value exceeds a certain critical range, the reduction coefficient will drop rapidly and significantly, which is not limited here.

[0078] S209: Perform boundary constraint processing on the target adjustment threshold, the preset minimum safety threshold, and the maximum reasonable threshold to obtain the current rainfall disaster threshold of the target geographic grid unit.

[0079] Based on the calculated target adjustment threshold, if the target adjustment threshold is lower than the preset minimum safety threshold, it is forcibly set to the minimum safety threshold; if it is higher than the preset maximum reasonable threshold, it is forcibly set to the maximum reasonable threshold; if it is between the two, it remains unchanged. The minimum safety threshold is directly derived from the minimum defense standard specified in the national or industry geological disaster prevention and control regulations or engineering design standards, or a very small but non-zero rainfall value (such as 5 mm / 24 hours) used to trigger the attention procedure. The maximum reasonable threshold is set as the maximum rainfall process ever recorded in the monitoring area or the theoretical extreme rainfall of once in a century or once in a thousand years. Rainfall exceeding this value constitutes a catastrophic disaster in itself, eliminating the need to rely on model judgment.

[0080] S210: Obtain the expected effective rainfall in a preset future time period.

[0081] Obtain a quantitative rainfall forecast for the target geographic grid cell for a preset time period (e.g., the next 24 hours) from the meteorological department. Calculate the slope and aspect based on the digital elevation model (DEM). Combined with the regional prevailing wind direction forecast by the meteorological department, apply the orographic precipitation correction model. Based on the angle between the slope aspect and the wind direction, as well as the steepness of the slope, a rainfall adjustment factor is calculated for each fine-grained grid cell (e.g., 1.2 for windward slopes and 0.8 for leeward slopes). Finally, multiply the rainfall in the quantitative rainfall forecast by this adjustment factor to obtain a rainfall field that has been refined based on the terrain. Based on this, a water balance analysis (e.g., the SCS-CN model) is used to calculate the portion of rainfall that actually affects slope stability and serves as the expected effective rainfall.

[0082] S211. Calculate the ratios of the total rainfall in the next 24 hours and the heavy rainfall in the next hour to the current rainfall disaster threshold respectively, and take the larger value as the comprehensive risk index.

[0083] Based on the expected effective rainfall over a preset future time period, the cumulative effective rainfall over the next 24 hours is calculated and divided by the current rainfall hazard threshold to obtain the cumulative risk ratio. Next, a one-hour window is applied within the 24-hour forecast to find the maximum hourly cumulative effective rainfall (i.e., the strongest short-term rainfall event in the future). This is also divided by the current rainfall hazard threshold to obtain the intensity risk ratio. Finally, the two ratios are compared, and the larger of the two ratios is used as the final comprehensive risk index.

[0084] S212. Determine the warning level based on the comprehensive risk index and generate corresponding warning information.

[0085] Obtain the preset warning level classification standard, which defines the relationship between the comprehensive risk index and different warning levels. For example, 0.4 ≤ index < 0.6 corresponds to a blue warning, 0.6 ≤ index < 0.8 corresponds to a yellow warning, 0.8 ≤ index < 1.0 corresponds to an orange warning, and index ≥ 1.0 corresponds to a red warning. Compare the comprehensive risk index to this standard to determine the warning level it belongs to. Based on the pre-set warning template, generate a warning information document containing core elements such as the warning time, warning area (specifically, grid cells or corresponding administrative village groups), warning level, risk index, main disaster-prone rainfall period, and standardized response measures for that level (such as strengthening inspections, preparing for evacuation, and organizing immediate evacuation).

[0086] In the embodiment of the present application, the time-series InSAR technology is used to survey the surface deformation rate and even the acceleration, and based on this, the monitoring area is divided into high / low density differentiated grids and graded (high-precision / conventional) model management is carried out. Combined with the future effective rainfall forecast after terrain correction, a current rainfall disaster threshold is generated for each fine geographic grid unit, which is adjusted in real time and independently according to its own geological health status and previous risk accumulation. While ensuring fine monitoring of high-risk areas, the system operation efficiency is significantly optimized, making the early warning decision more scientific and comprehensively improving the reliability and timeliness of geological disaster prevention and control.

[0087] The following describes the server in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of a server in an embodiment of the present application.

[0088] It should be noted that Figure 3 The structure of the server shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0089] like Figure 3 As shown, the server includes a CPU 301, which can perform various appropriate actions and processes based on programs stored in ROM 302 or programs loaded from storage unit 308 into RAM 303, such as executing the methods described in the above embodiments. RAM 303 also stores various programs and data required for system operation. CPU 301, ROM 302, and RAM 303 are connected to each other via bus 304. I / O interface 305 is also connected to bus 304.

[0090] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, push button switches, and the like; an output section 307 including a liquid crystal display (LCD), an audio output device, indicator lights, and the like; a storage section 308 including a hard disk and the like; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. Removable media 311, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 310 as needed, so that computer programs read from the removable media can be installed in the storage section 308 as needed.

[0091] In particular, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present invention includes a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 309 and / or installed from the removable medium 311. When the computer program is executed by the CPU 301, the various functions defined in the present invention are performed.

[0092] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings.

[0093] Specifically, the server of this embodiment includes a processor and a memory, and a computer program is stored in the memory. When the computer program is executed by the processor, the meteorological-based geological disaster risk warning method provided in the above embodiment is implemented.

[0094] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the server described in the above embodiments, or may exist independently and not incorporated into the server. The storage medium carries one or more computer programs, which, when executed by a processor of the server, enable the server to implement the meteorological-based geological hazard risk early warning method provided in the above embodiments.

[0095] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0096] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

Claims

1. A meteorological-based geological disaster risk early warning method, characterized in that: The method comprises: Acquiring monitoring data within a target geographic grid cell based on a plurality of geographic grid cells of preset sizes obtained by pre-dividing the monitoring area, wherein the target geographic grid cell is one of the geographic grid cells within the monitoring area; Inputting static factors and dynamic factors in the monitoring data into a vulnerability index calculation model to obtain a vulnerability index for the hazard-prone body of the target geographic grid unit, wherein the static factors include slope and distance from the fault zone, and the dynamic factors include soil moisture content. The vulnerability index calculation model is a mathematical method for quantitatively assessing the susceptibility of a geographic unit to geological hazards. Calculating the disaster-causing factor of the target geographic grid cell based on historical rainfall data determined by a preset optimal number of rainfall days, wherein the disaster-causing factor is a destructive accumulation index obtained based on the impact effect and continuous cumulative effect of historical rainfall; Couple the vulnerability index of the hazard-bearing body and the disaster-causing breeding factor to determine a current rainfall disaster threshold of the target geographic grid unit, where the current rainfall disaster threshold is inversely proportional to the vulnerability index of the hazard-bearing body and inversely proportional to the disaster-causing breeding factor; The expected effective rainfall in a preset future time period is obtained, and the warning level of the target geographic grid unit is determined according to the expected effective rainfall and the current rainfall disaster threshold.

2. The method according to claim 1, characterized in that Before the step of acquiring monitoring data within a target geographic grid cell based on a plurality of geographic grid cells of preset sizes obtained by pre-dividing the monitoring area, wherein the target geographic grid cell is one of the geographic grid cells within the monitoring area, the method further includes: Using time-series synthetic aperture radar interferometry technology, periodic surface deformation monitoring is performed on the monitoring area to obtain the periodic deformation rate of a preset area within the monitoring area; Determine the area within the monitoring area where the periodic deformation rate exceeds a preset abnormal threshold as a key monitoring area; The key monitoring area is divided into high-density grids, and the general monitoring area defined as an area outside the key monitoring area is divided into low-density grids to obtain geographical grid units of the monitoring area.

3. The method according to claim 1, characterized in that The step of inputting the static factors and dynamic factors in the monitoring data into a vulnerability index calculation model to obtain the vulnerability index of the disaster-prone body of the target geographic grid unit specifically includes: Extracting static factors and dynamic factors within the target geographic grid cell from the monitoring data, the static factors including slope extracted from a digital elevation model, distance to a fault zone calculated through spatial analysis, and rock and soil weathering grade extracted from an acid weathering potential map, and the dynamic factor being soil moisture content obtained through real-time monitoring by sensors; Performing maximum and minimum normalization processing on the original values ​​of the static factor and the dynamic factor to obtain factor values ​​mapped to a unified interval; The factor values ​​are multiplied by their corresponding weight coefficients and then summed to obtain the disaster-prone body vulnerability index of the target geographic grid unit. The higher the disaster-prone body vulnerability index value, the more likely the geographic grid unit is to be prone to geological disasters under the same external disturbance conditions.

4. The method according to any one of claims 1 to 3, characterized in that: The step of calculating the disaster-causing factor of the target geographic grid unit based on the historical rainfall data determined based on the preset optimal rainfall days, wherein the disaster-causing factor is a destructive accumulation index obtained based on the impact effect and continuous cumulative effect of historical rainfall, specifically includes: Acquire historical rainfall data of the target geographic grid cell for the preset optimal rainfall days before the current moment, and construct a daily rainfall time series, wherein the preset optimal rainfall days are the length of the rainfall period that has the greatest impact on the stability of the current geological body, and the daily rainfall time series is determined by statistically analyzing the correlation between historical geological disaster events and previous rainfall processes in the monitoring area, and is determined based on the historical rainfall data; The historical rainfall data are processed using a time series analysis algorithm to calculate the daily rainfall intensity change rate, continuous rainfall duration, and cumulative effective rainfall distribution; Based on the peak characteristics and change trends of the daily rainfall intensity change rate, an impact effect index of the rainfall intensity change on the stability of the geological body is obtained by integral calculation; The continuous rainfall duration and the cumulative effective rainfall distribution are used as input parameters of a preset rainfall infiltration model, and the cumulative time required for the geological body to reach the critical state of destruction from the initial stable state is obtained as a duration cumulative effect indicator; The impact effect index and the duration cumulative effect index are input into the destructive accumulation evaluation function to calculate the disaster-causing factor of the target geographic grid unit. The destructive accumulation evaluation function is a mathematical model for fusing multi-source indicators, and the normalized impact effect index and duration cumulative effect index are weighted and summed according to a preset weight coefficient.

5. The method according to claim 1, wherein The step of coupling the vulnerability index of the disaster-prone body and the disaster-causing factor to determine the current rainfall disaster threshold of the target geographic grid unit specifically includes: Obtaining a preset benchmark rainfall disaster threshold for the target geographic grid cell, where the benchmark rainfall disaster threshold is a standard triggering rainfall amount determined based on the statistical law of historical rainfall-induced geological disasters in the monitoring area; Using the vulnerability index of the disaster-bearing body and the disaster-causing factor as dynamic reduction items, the baseline rainfall disaster threshold is dynamically reduced through a preset multiplication reduction model to obtain a target adjustment threshold; The target adjustment threshold is subjected to boundary constraint processing with a preset minimum safety threshold and a maximum reasonable threshold to obtain a current rainfall disaster threshold of the target geographic grid unit.

6. The method according to claim 1, characterized in that The step of obtaining the expected effective rainfall in a preset future time period and determining the warning level of the target geographic grid unit according to the expected effective rainfall and the current rainfall disaster threshold specifically includes: Obtaining the expected effective rainfall in a preset future time period, wherein the expected effective rainfall includes the total rainfall in the next 24 hours for evaluating long-term cumulative effects, and the future hourly heavy rainfall for evaluating short-term impact effects; Calculate the ratios of the total rainfall in the next 24 hours and the heavy rainfall in the next hour to the current rainfall disaster threshold respectively, and take the larger value as the comprehensive risk index; The warning level is determined based on the comprehensive risk index and corresponding warning information is generated.

7. The method according to claim 6, characterized in that Before the step of obtaining the expected effective rainfall in a preset future time period, wherein the expected effective rainfall includes the total rainfall in the next 24 hours for evaluating the long-term cumulative effect and the future hourly heavy rainfall for evaluating the short-term impact effect, the method further includes: Obtaining original rainfall forecast data and prevailing wind direction information for the preset future time period in the monitoring area, wherein the prevailing wind direction includes wind direction angle and wind speed; Based on the slope and aspect of the monitoring area, the original rainfall forecast data and the dominant wind direction information are input into a preset terrain rainfall correction model to obtain a corrected rainfall sequence. The terrain rainfall correction model is used to perform spatial downscaling correction on the original rainfall forecast data based on the slope and aspect of the monitoring area.

8. A server, characterized in that: The server includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the server to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a server, the server is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a server, the server is caused to perform the method according to any one of claims 1 to 7.

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