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

Abstract

The method comprises the following steps: a double-factor dynamic evaluation framework is constructed, a disaster-bearing body vulnerability index reflecting a geological background and a disaster-causing incubation factor embodying a cumulative effect of recent rainfall are scientifically quantified, and the two are inversely coupled to generate a current rainfall disaster threshold dynamically adjusted in real time for each grid unit. The application effectively solves the problem that the traditional warning cannot adapt to the spatial and temporal differences of the geological environment, resulting in insufficient accuracy of the warning, and realizes precise, dynamic and differentiated evaluation and warning of the geological disaster risk.

Description

Technical Field

[0001] This 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 Technology

[0002] Geological disasters have become one of the major natural disasters threatening people's lives and property. In mountainous areas with complex terrain, geological disasters induced by rainfall are characterized by their suddenness and great harm.

[0003] To accurately assess the impact of rainfall on geological hazards, technicians collect historical rainfall data for specific regions during periods of geological hazard occurrence, as well as extensive rainfall data when no hazard has occurred. Statistical analysis of this massive dataset determines the average probability boundary of hazard occurrence within the region. The early warning system continuously monitors the dynamic changes in the effective rainfall amount (API) and compares it to a fixed disaster-triggered API threshold calculated based on historical hazard data. Once the API value exceeds this threshold, an early warning is activated.

[0004] However, the characteristics of disaster-bearing bodies vary greatly in different locations within the region. Some geological units are inherently highly sensitive, while others are relatively stable. This early warning model, which relies on regional macro-statistics, lacks the ability to respond to individualized risks in specific geological units. It is prone to failing to provide timely warnings before disasters occur at sensitive locations, or generating too many ineffective warnings at stable locations. Summary of the Invention

[0005] This application provides a meteorological-based geological disaster risk early warning method, server, and storage medium to address the difficulty of existing early warning models in adapting to the spatial differences in the geological environment and the dynamic changes in risk status within a region, thereby enhancing the targeting of early warnings while ensuring their accuracy.

[0006] Firstly, this application provides a meteorological-based geological disaster risk early warning method. The method includes: acquiring monitoring data within a target geographic grid cell, which is one of the geographic grid cells within the monitoring area, based on multiple pre-divided geographic grid cells of a pre-defined size within a monitoring area; inputting static and dynamic factors from the monitoring data into a vulnerability index calculation model to obtain a vulnerability index for the target geographic grid cell. The static factors include slope and distance from a fault zone, and the dynamic factors include soil moisture content. The vulnerability index calculation model is a quantitative assessment of the susceptibility to geological disasters of a specific geographic unit. The methodology involves: calculating the disaster-causing factor of the target geographic grid unit based on historical rainfall data determined by a preset optimal number of rainfall days. The disaster-causing factor is a destructive accumulation index obtained based on the impact and continuous accumulation effects of historical rainfall. The vulnerability index of the disaster-bearing body and the disaster-causing factor are coupled and calculated to determine the current rainfall disaster threshold of the target geographic grid unit. The current rainfall disaster threshold is inversely proportional to both the vulnerability index of the disaster-bearing body and the disaster-causing factor. The expected effective rainfall within a preset future time period is obtained, and the warning level of the target geographic grid unit is determined based on the expected effective rainfall and the current rainfall disaster threshold.

[0007] In the above embodiments, a two-factor dynamic assessment framework was constructed. This framework not only calculates the vulnerability index of the geological body, reflecting its inherent properties, using static factors (such as slope) and dynamic factors (such as soil moisture content), but also calculates the disaster-inducing factors, reflecting recent risk accumulation, based on the impact and cumulative effects of historical rainfall. Furthermore, it generates a real-time changing current rainfall disaster threshold for each fine-grained geographic grid unit by applying these two factors. This ensures that areas with poorer geological conditions or greater impact from previous rainfall have lower critical rainfall amounts for triggering disasters. This achieves accurate, dynamic, and differentiated assessment of geological disaster risks, significantly improving the targeting 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 multiple geographic grid cells of preset sizes pre-divided based on 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 to periodically monitor the surface deformation of the monitoring area, obtaining the periodic deformation rate of a preset area within the monitoring area; determining the area within the monitoring area whose periodic deformation rate exceeds a preset anomaly threshold as a key monitoring area; dividing the key monitoring area using a high-density grid, and dividing the general monitoring area defined based on the area outside the key monitoring area using a low-density grid, to obtain the geographic grid cells of the monitoring area.

[0009] In the above embodiments, temporal synthetic aperture radar interferometry (InSAR) technology is used to conduct a comprehensive survey of 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 subdivision of other general areas. This avoids the problems of data redundancy and wasted computing power due to excessively dense grids, or the omission of key risk points due to excessively sparse grids. In this way, the on-demand intelligent allocation of monitoring resources and significant optimization of computing load are achieved. While ensuring refined and high-precision monitoring of high-risk areas, the operating efficiency of the entire early warning system is greatly improved.

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

[0011] In the above embodiments, by clearly defining and integrating key static factors and soil moisture content as the core dynamic factor, and using a standardized mathematical process of maximum-minimum normalization and weighted summation for processing, multiple influencing factors with different dimensions and properties can be scientifically transformed into a single, quantitative disaster-bearing body vulnerability index, thus achieving an objective, refined, and repeatable quantitative evaluation of the inherent disaster-causing risks of each geographical grid unit.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the step of calculating the disaster-causing factor of the target geographic grid unit 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, specifically includes: obtaining historical rainfall data of the target geographic grid unit up to the preset optimal number of rainfall days before the current time; constructing a daily rainfall time series, wherein the preset optimal number of rainfall days is the length of the rainfall period that has the greatest impact on the stability of the current geological body, determined by statistical analysis of the correlation between historical geological disaster events and previous rainfall processes in the monitoring area; and processing the historical rainfall data using a time series analysis algorithm to calculate the daily rainfall intensity. The daily rainfall intensity change rate, continuous rainfall duration, and cumulative effective rainfall distribution are used as input parameters to calculate the impact effect index of rainfall intensity change on the stability of the geological body. Based on the peak characteristics and trend of the daily rainfall intensity change rate, the impact effect index on the stability of the geological body is calculated by integral calculation. The continuous rainfall duration and the cumulative effective rainfall distribution are used as input parameters to a preset rainfall infiltration model to obtain the cumulative time required for the geological body to reach the critical state of failure from the initial stable state, which is used as the duration cumulative effect index. 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. The normalized impact effect index and duration cumulative effect index are weighted and summed according to preset weight coefficients.

[0013] In the above embodiments, a refined rainfall effect analysis model is constructed to decompose the impact of previous rainfall on the stability of geological bodies into two dimensions: "impact effect" and "continuous cumulative effect". Based on historical data analysis, the "optimal number of rainfall days" is determined. Therefore, it is possible to accurately capture the differentiated contributions of different rainfall processes (such as short-term heavy rainfall and continuous rainfall) to the formation of geological disasters, thereby realizing the scientific and dynamic quantification of the risk status of geological bodies at the current moment due to previous rainfall.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the step of coupling the vulnerability index of the disaster-bearing body and the disaster-causing factor to determine the current rainfall disaster threshold of the target geographic grid unit specifically includes: obtaining a preset baseline rainfall disaster threshold for the target geographic grid unit, wherein the baseline rainfall disaster threshold is a standard triggering rainfall amount determined based on the statistical regularity 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 terms, dynamically reducing the baseline rainfall disaster threshold through a preset multiplicative reduction model to obtain a target adjustment threshold; and subjecting the target adjustment threshold to boundary constraints with preset minimum safety threshold and maximum reasonable threshold to obtain the current rainfall disaster threshold of the target geographic grid unit.

[0015] In the above embodiments, by introducing a dynamic threshold reduction mechanism, the static, regional benchmark rainfall disaster threshold is multiplicatively coupled with the real-time changing vulnerability index of the disaster-bearing body and the disaster-causing 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 conjunction with some embodiments of the first aspect, in some embodiments, the step of obtaining the expected effective rainfall within 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 within a preset future time period, wherein the expected effective rainfall includes the total rainfall in the next 24 hours for assessing the long-term cumulative effect and the heavy rainfall in the next hour for assessing the short-term impact effect; calculating 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 taking the larger value as the comprehensive risk index; determining the warning level based on the comprehensive risk index and generating corresponding warning information.

[0017] In the above embodiments, by simultaneously considering two different time scales of rainfall forecasts, namely "total rainfall in the next 24 hours" and "heavy rainfall in the next hour", the dual threat that future rainfall may pose is comprehensively assessed—namely, the continuous infiltration and accumulation effect of long-duration rainfall and the rapid impact effect of short-duration heavy rainfall. This effectively solves the risk that a single rainfall index warning may miss specific disaster triggering modes (such as mudslides caused by short-term rainstorms), ensuring that the warning system can capture the most important disaster-causing threats, and making the determination of warning levels more comprehensive, robust and reliable.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of obtaining the expected effective rainfall within a preset future time period, the expected effective rainfall including the total rainfall over the next 24 hours for assessing the long-term cumulative effect and the heavy rainfall over the next hour for assessing the short-term impact effect, the method further includes: obtaining raw rainfall forecast data and prevailing wind direction information for the preset future time period of the monitoring area, the prevailing wind direction including wind angle and wind speed; based on the slope and aspect of the monitoring area, inputting the raw rainfall forecast data and the prevailing wind direction information into a preset topographic rainfall correction model to obtain a corrected rainfall sequence, the topographic rainfall correction model being used to spatially downscale the raw rainfall forecast data based on the slope and aspect of the monitoring area.

[0019] In the above embodiments, the original, coarse-precision rainfall forecast data is further refined by using a terrain-based rainfall correction model. This can first solve the problem of insufficient spatial representativeness of large-scale weather forecasts in complex mountainous terrain, obtain rainfall data that is closer to the actual situation of geographic grid units, and thus greatly improve the physical authenticity and spatial accuracy of the input data of the early warning model.

[0020] In a second aspect, embodiments of this application provide a server, the server comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, and the one or more processors invoking the computer instructions to cause the server to perform the method as described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a server, cause the server to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a server, cause the server to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, 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 this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in 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:

[0025] 1. Employing a two-factor dynamic assessment framework, this framework not only calculates a vulnerability index reflecting the inherent properties of geological bodies using static factors (such as slope) and dynamic factors (such as soil moisture content), but also calculates a disaster-inducing factor reflecting recent risk accumulation based on the impact and cumulative effects of historical rainfall. Furthermore, it generates a real-time, dynamically changing current rainfall disaster threshold for each finely defined geographic grid unit by applying these two factors. This results in areas with poorer geological conditions or greater prior rainfall impact having lower critical rainfall thresholds for triggering disasters. This achieves accurate, dynamic, and differentiated assessment of geological disaster risks, significantly improving the targeting and reliability of early warnings and making early warning decisions more scientifically grounded.

[0026] 2. By employing two different timescales of rainfall forecasting—"total rainfall in the next 24 hours" and "heavy rainfall in the next hour"—the system comprehensively assesses the dual threats that future rainfall may pose: the continuous infiltration 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 might miss specific disaster triggering patterns (such as mudslides caused by short-duration torrential rain), ensuring that the warning system can capture the most significant disaster threats and making the determination of warning levels more comprehensive, robust, and reliable.

[0027] 3. By employing a topographic rainfall correction model and water balance equation, the original, coarse-precision rainfall forecast data is refined in a secondary manner. This firstly solves the problem of insufficient spatial representativeness of large-scale meteorological forecasts in complex mountainous terrain, obtaining rainfall data that is closer to the actual situation of geographic grid units. Furthermore, it distinguishes the effective rainfall that can actually infiltrate and affect the stability of the soil and rock mass from the total rainfall, thereby greatly improving the physical authenticity and spatial accuracy of the input data of the early warning model. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a meteorological-based geological disaster risk early warning method in an embodiment of this application;

[0029] Figure 2 This is another flowchart illustrating the meteorological-based geological disaster risk early warning method in this application embodiment;

[0030] Figure 3 This is a schematic diagram of the physical device structure of a server in an embodiment of this application. Detailed Implementation

[0031] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0032] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0033] Please see Figure 1 This is a flowchart illustrating a meteorological-based geological disaster risk early warning method in this application embodiment.

[0034] S101. Based on multiple pre-divided geographic grid units of a monitoring area, acquire monitoring data within the target geographic grid unit.

[0035] The monitoring area refers to the specific geographical region requiring geological disaster risk early warning. Monitoring data refers to various geological disaster-related data collected within the target geographic grid unit, including but not limited to meteorological and geological data. Based on the geographical characteristics and early warning needs of the monitoring area, multiple geographic grid units with preset sizes are pre-defined. The division of these grid units comprehensively considers factors such as topography, geological conditions, and monitoring accuracy requirements. For each target geographic grid unit, monitoring data is collected within that unit.

[0036] In some embodiments, a predefined vector boundary of the monitoring area can be loaded, and a grid generation tool of the GIS platform (such as Fishnet) can be invoked to generate a geographic grid cell layer covering the entire monitoring area according to preset dimensions (e.g., 100 meters for key areas and 500 meters for general areas). Then, for each cell in this grid layer, a series of spatial overlay and statistical operations are automatically performed, including: using the "Zonal Statistics" tool to calculate the average slope value from the loaded DEM raster data, with each cell as a range; and using the "Near" or "Spatial Join" tool to calculate the distance from the center point of each cell to the nearest linear feature of the fault zone. For dynamic data, a sensor data point layer collected at a specific time point can be spatially connected to the grid layer, assigning the sensor reading (or the value estimated by a spatial interpolation algorithm) falling within a cell to that cell. After the entire process is completed, all monitoring data are stored as attribute fields in the attribute table of the geographic grid cell layer, and this table can be directly queried later to obtain the monitoring data of any target geographic grid cell.

[0037] In other embodiments, monitoring data can be dynamically acquired based on real-time data streams and service-based queries. When it is necessary to analyze a target geographic grid cell, concurrent requests are initiated to different data service interfaces according to the coordinate range of that cell. For example, a request is sent to a static geographic data service (such as WCS / WFS service) to obtain basic geographic information such as slope and lithology within that coordinate range; simultaneously, a query request is sent to a time-series database (such as InfluxDB) or an IoT platform API to retrieve the latest data, such as soil moisture content, from sensors whose geographic location tags fall within that coordinate range within a specified time window. In the backend, the data returned from various service interfaces is aggregated and aligned in real time to dynamically construct a complete monitoring dataset containing the latest dynamic information for the target geographic grid cell.

[0038] It is understandable that other methods can be used to achieve data acquisition and integration in this step, and no specific method is specified here.

[0039] S102. Input the static and dynamic factors from the monitoring data into the vulnerability index calculation model to obtain the disaster-bearing vulnerability index of the target geographic grid unit.

[0040] Static factors refer to those geological and geographical attributes that remain stable or change extremely slowly over a considerable timescale. Examples include slope determined by topography, distance from fault zones determined by geological structure, and the weathering grade of rock and soil formed by parent material and long-term weathering. These factors constitute the basic environmental conditions for the occurrence of geological disasters. Dynamic factors refer to factors that change rapidly due to short-term environmental changes (especially meteorological factors). For example, soil moisture content, a typical example, changes significantly within hours or days due to processes such as rainfall and evaporation, directly affecting the stability of the soil.

[0041] After acquiring monitoring data for the target geographic grid unit, static and dynamic factors are extracted from the data. Based on the historical maximum and minimum values ​​or preset theoretical extreme values ​​of each factor within the entire monitoring area, the normalized value is linearly mapped to a unified interval using the formula: Normalized value = (Original value - Minimum value) / (Maximum value - Minimum value). After normalization, the vulnerability index calculation model is invoked: a set of weight coefficients pre-set by geological disaster experts or obtained through historical data statistical analysis are loaded, with each factor corresponding 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 summed to obtain the vulnerability index of the target geographic grid unit.

[0042] In some embodiments, a hierarchical model can be constructed, with the vulnerability index of the disaster-bearing body as the top layer and various influencing factors (such as slope, distance from the fault zone, weathering grade of soil and rock, soil moisture content, etc.) as the criterion layers. Based on the pairwise comparisons of each factor in the criterion layers by experts in the field of geological hazards, a 1-9 scaling method is used for quantification to obtain a judgment matrix. Next, the matrix is ​​solved by calculating the largest eigenvalue and its corresponding eigenvector to obtain the weight coefficient of each factor at the current level, and a consistency test is performed (calculating the consistency ratio CR). Finally, the weights of each factor that pass the consistency test are weighted and summed with the normalized factor values ​​to obtain the final vulnerability index of the disaster-bearing body.

[0043] In other embodiments, a large training dataset of historical samples is pre-collected, where each sample corresponds to a geographic grid cell. This dataset includes not only the static and dynamic factor values ​​of the cell at a specific historical moment, but also a label—whether a geological disaster actually occurred in the cell within a certain period after that moment. This labeled dataset is then used to train a regression model: the prepared training dataset (usually divided into training and test sets) is input into the random forest algorithm. The core of the algorithm lies in constructing a "forest" ensemble model composed of hundreds or thousands of independent decision trees. For each decision tree in the forest, its construction process involves two randomization operations: the algorithm uses bootstrap sampling to randomly select a subset of the same size as the original training set from the total training set with replacement, which is then used to train that tree. At each node in the decision tree, when selecting an optimal feature for splitting, the algorithm first randomly selects a preset subset of factors (e.g., out of 10 factors, only 3 are randomly selected as candidates for each split). Then, it searches only within this subset for the optimal splitting feature and splitting point (usually minimizing the sum of the variances of the sample label values ​​(y) within the two child nodes formed after the split). After training, when calculating the vulnerability index of a new target geographic grid unit, the current monitoring data of each factor for that unit is input into this trained model. The model outputs a predicted probability value (the probability of a geological disaster occurring in that unit), which serves as the vulnerability index of that unit.

[0044] It is understandable that other methods can be used to calculate the vulnerability index of disaster-bearing bodies, and no limitation is made here.

[0045] S103. Based on historical rainfall data determined by the preset optimal number of rainfall days, calculate the disaster-causing factor of the target geographic grid unit.

[0046] Based on a preset optimal number of rainfall days (e.g., 20 days), historical rainfall data for the target unit up to the current moment is obtained to construct a daily rainfall time series. The preset optimal number of rainfall days refers to the length of the preceding rainfall period that has the most significant impact on the stability of the current geological body, determined through statistical analysis of historical geological disaster cases in the region. Then, the impact effect of rainfall is quantified by the rainfall peaks (i.e., rainstorm days) and the steepness of their increase in the series, while the cumulative effect of rainfall is quantified by the total duration of continuous rainfall and the distribution of accumulated effective rainfall. The calculated impact effect index and duration cumulative effect index are normalized so that they are all mapped to a unified interval of 0 to 1. These are then input into the destructive accumulation evaluation function, and a weighted sum is performed according to preset weight coefficients to obtain the disaster-causing factor of the target geographic grid unit, i.e., the destructive accumulation index.

[0047] Specifically, the impact of rainfall is quantified by the peak rainfall (i.e., rainstorm days) and the steepness of its increase in the sequence: the daily rainfall time series of the target geographic grid unit is scanned to identify all dates that meet the threshold for rainstorm days; for each identified peak rainfall day, its growth rate or increase (the steepness of the increase) compared to the previous day is calculated; the portion exceeding the rainstorm threshold is multiplied by the increase to calculate the impact contribution of each peak day; an exponential decay model is used to weight and sum the impact contributions of all peak days within the entire time window to obtain the impact effect index.

[0048] Specifically, the cumulative effect of rainfall is quantified by the total duration of continuous rainfall and the distribution of accumulated effective rainfall: Daily rainfall time series are scanned according to preset rules (e.g., the no-rain interval between two consecutive rainy days does not exceed one day) to identify all independent continuous rainfall events; for each identified continuous rainfall event, two core parameters are calculated: the total duration of the event (total number of days from start to end) and the accumulated effective rainfall (by setting a maximum daily infiltration rate, deducting daily rainfall exceeding this threshold as runoff, and then summing the daily effective rainfall within the event); these two parameters are multiplied to calculate the saturation potential index for each event, which comprehensively reflects the duration and depth of rainfall infiltration; finally, the saturation potential indices of all continuous rainfall events within the entire time window are compared, and the maximum value is taken as the final indicator of the cumulative effect.

[0049] In some embodiments, the disaster-causing factor of the target geographic grid cell can also be calculated by establishing a single-layer or multi-layer bucket model: each bucket represents a soil layer with a specific capacity (water holding capacity) and seepage rate (permeability coefficient). Historical daily rainfall time series are used as daily inputs to the model. At each time step (day), the model iteratively calculates the change in water level within the bucket based on the current water volume in the bucket, the daily rainfall, and the seepage rate, using a water balance equation. Finally, at the end of the simulation, the current water storage rate (current water volume / bucket capacity) of the bottommost bucket (representing the key impact layer) is directly used as the disaster-causing factor.

[0050] It is understandable that other methods can be used to calculate the disaster-causing factors, and no specific method is specified here.

[0051] S104. Couple the vulnerability index of the disaster-bearing body and the disaster-causing factor to determine the current rainfall disaster threshold of the target geographic grid unit.

[0052] After obtaining the vulnerability index and disaster-causing factor of the disaster-bearing body, a pre-defined coupled model is invoked, using the normalized vulnerability index and disaster-causing factor as inputs, to calculate the reduction coefficient of the baseline rainfall disaster threshold. This leads to the dynamic current threshold.

[0053] Understandably, the critical rainfall threshold for a geological body is not a fixed constant, but rather dynamically changing. A geological body that is inherently very vulnerable (high vulnerability index) will have a relatively low tolerance for future rainfall, even if it has not recently experienced much rainfall (low disaster-causing factor). Conversely, a geological body that is inherently very stable (low vulnerability index) may have accumulated significant internal damage if it has just experienced a prolonged and intense rainfall event (high disaster-causing factor), resulting in a substantial reduction in its ability to withstand future rainfall. The current rainfall disaster threshold decreases accordingly as the vulnerability index and disaster-causing factor increase.

[0054] In some embodiments, the coupled calculation in this step can be implemented in several 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, representing the disaster-bearing capacity of the geological body under ideal conditions (i.e., without any dynamic factors). Then, the calculated vulnerability index and disaster-causing factor are used as dynamic adjustment terms, and the current rainfall hazard threshold is calculated using a multiplicative reduction model. Optionally, a two-dimensional rainfall threshold query matrix is ​​pre-constructed. The row axis of this matrix represents the vulnerability index, and the column axis represents the disaster-causing factor. Both axes are divided into several levels (e.g., low, medium, high, and extremely high). Each cell in the matrix is ​​filled with a specific rainfall threshold value, which is given by geological disaster 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 disaster-causing 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 ​​then used as the current rainfall hazard threshold for the target geographic grid cell. No restrictions are imposed here.

[0055] S105. Obtain the expected effective rainfall within a preset future time period, and determine the warning level of the target geographic grid unit based on the expected effective rainfall and the current rainfall disaster threshold.

[0056] Quantitative rainfall forecasts for a predetermined time period (e.g., the next 24 hours) for the target geographic grid unit are obtained from meteorological departments and used as the expected effective rainfall. The calculated expected effective rainfall is compared with the current rainfall disaster threshold of the target grid unit, and the warning level is determined from the pre-set warning system.

[0057] In some embodiments, the acquired meteorological data, along with high-precision slope and aspect information of the monitoring area, are input into a preset topographic rainfall correction model. This model simulates the interaction between wind and topography (e.g., increased rainfall on windward slopes and decreased rainfall on leeward slopes) to obtain a high-resolution corrected rainfall sequence. Combining the soil infiltration parameters and vegetation characteristic parameters of the target geographic grid unit, the model calculates and subtracts rainwater losses (e.g., vegetation interception and surface runoff) using water balance equations (e.g., the SCS-CN model or the Horton infiltration model) to obtain the expected effective rainfall sequence that has a real impact on slope stability.

[0058] 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 determining which percentage range the ratio falls into; Optionally, the probability that the expected effective rainfall exceeds the current rainfall disaster threshold can be calculated to obtain the threshold exceedance probability, and the warning level can be determined based on the magnitude of the threshold exceedance probability.

[0059] In the above embodiments, this application constructs a two-factor dynamic evaluation framework to scientifically quantify the vulnerability index of disaster-bearing bodies reflecting the geological background and the disaster-causing factor reflecting the cumulative effect of recent rainfall, and couples the two inversely to generate a real-time dynamically adjusted current rainfall disaster threshold for each grid cell. This method effectively solves the problem that traditional early warning systems cannot adapt to the spatiotemporal differences in the geological environment, resulting in insufficient accuracy of early warnings, and achieves accurate, dynamic, and differentiated assessment and early warning of geological disaster risks. In practical applications, when extending geological disaster early warning methods to wide-area and complex mountainous areas, if a uniform high-density grid is used for undifferentiated fine-grained calculations across the entire region, it will lead to massive data redundancy and huge waste of computing power in a large stable area, resulting in low system operating efficiency.

[0060] In light of the above scenarios, the following is a more detailed description of the process provided in this implementation. Please refer to [link / reference]. Figure 2 This is another flowchart illustrating the meteorological-based geological disaster risk early warning method in this application embodiment.

[0061] S201. Using time-series synthetic aperture radar interferometry, periodic surface deformation monitoring is performed on the monitoring area to obtain the periodic deformation rate of a preset area within the monitoring area.

[0062] The process begins with initiating a time-series InSAR processing workflow. This automatically acquires long-term radar image archives covering the entire monitoring area from the European Space Agency (ESA) or other radar satellite data providers. It then extracts the deformation rate (in millimeters per year) for each ground point from the massive dataset, generating a high-resolution deformation rate field map covering the entire region. Time-series synthetic aperture radar interferometry (InSAR) is an advanced radar remote sensing technique that accurately inverts minute surface displacements between observations by comparing and analyzing two or more radar satellite images of the same area acquired at different times, achieving millimeter-level accuracy.

[0063] S202. Determine the area within the monitoring area where the periodic deformation rate exceeds the preset abnormal threshold as the key monitoring area.

[0064] The deformation rate field map is spatially superimposed and compared with a preset anomaly threshold to obtain pixels exceeding the threshold. The region formed by all pixels exceeding the threshold is delineated and marked as a key monitoring area using image processing algorithms such as connected component analysis.

[0065] In some embodiments, not only is the surface deformation rate of the monitoring area calculated, but also a quadratic function is 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.

[0066] S203. High-density grids are used to divide key monitoring areas, while low-density grids are used to divide general monitoring areas outside the key monitoring areas, resulting in geographic grid units for the monitoring areas.

[0067] Based on a unified coordinate reference system obtained by initializing the entire monitoring area, a high-density grid is used to subdivide the key monitoring areas. Meanwhile, a low-density grid is used to subdivide the vast, low-risk general monitoring areas, ensuring that each geographic grid cell contains its unique spatial index, center point coordinates, and monitoring area identifier.

[0068] In some embodiments, a high-precision risk assessment model is invoked for grid cells within key monitoring areas, while a conventional assessment model is invoked for grid cells within general monitoring areas. The conventional assessment model is mathematically represented as an empirical or semi-empirical formula based on statistical regression of historical data. Its implementation includes: collecting historical disaster data and corresponding rainfall data for the monitoring area or similar geological environments; then, using statistical methods such as logistic regression and information content analysis to establish a macroscopic 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 assessment.

[0069] S204. Based on multiple pre-divided geographic grid units of a pre-defined size obtained from the monitoring area, acquire monitoring data within the target geographic grid unit.

[0070] S205. Input the static and dynamic factors in the monitoring data into the vulnerability index calculation model to obtain the disaster-bearing vulnerability index of the target geographic grid unit.

[0071] S206. Based on historical rainfall data determined by the preset optimal number of rainfall days, calculate the disaster-causing factor of the target geographic grid unit.

[0072] Steps S204 to S206 are similar to steps S101 to S103, and will not be described in detail here.

[0073] S207. Obtain the preset baseline rainfall disaster threshold for the target geographic grid cell.

[0074] Based on an internal parameter database, the database uses the unique spatial index of the target geographic grid cell as the key. Using 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, representing the theoretical upper limit of hazard tolerance under conditions of prolonged drought and complete geological rest.

[0075] In some embodiments, the ID curve method based on historical data statistics can be used. This method first collects detailed data on a large number of historical geological disaster events within the monitoring area, particularly the rainfall intensity and duration at each disaster point. Then, the (I,D) data of these disaster points are plotted on a logarithmic coordinate system, and an ID threshold curve encompassing the vast majority of disaster points is fitted through statistical regression analysis. Finally, based on the geological environmental similarity of each geographic grid unit, corresponding ID curve parameters are matched or interpolated to obtain the appropriate parameters, and a representative threshold (such as a 24-hour cumulative rainfall threshold) is selected from this curve as its baseline rainfall disaster threshold.

[0076] In other embodiments, empirical values ​​generalized from the hydrogeological zoning of the region can be directly used as the baseline rainfall hazard threshold. It is understood that other methods can also be used to set the baseline rainfall hazard threshold, and this is not limited here.

[0077] S208. Using the vulnerability index of the disaster-bearing body and the disaster-causing factor as dynamic reduction terms, the baseline rainfall disaster threshold is dynamically reduced through a preset multiplicative reduction model to obtain the target adjustment threshold.

[0078] The vulnerability index (V) and disaster-causing factor (I) of the target geographic grid unit are retrieved. Then, the three values ​​(baseline rainfall disaster threshold, V, and I) are substituted into a preset multiplicative reduction model for calculation to obtain the target adjustment threshold.

[0079] The multiplicative reduction model transforms the vulnerability index of the disaster-bearing body into a first reduction coefficient using a pre-defined transformation function; it also transforms the disaster-causing factor into a second reduction coefficient. Both reduction coefficients are values ​​less than or equal to 1; the higher the index, the smaller the corresponding reduction coefficient. Then, the baseline rainfall disaster threshold is multiplied by the first reduction coefficient to obtain an initial reduced threshold. Finally, this initial reduced threshold is multiplied by the second reduction coefficient. Through this sequential multiplication, the final target adjustment threshold is obtained.

[0080] In some embodiments, the multiplicative reduction model can be constructed in several ways: Optionally, a linear weighted reduction model can be used. When a dynamic indicator (such as the vulnerability index) is at its minimum value (representing the most stable state), its corresponding reduction coefficient is 1, meaning no reduction is made to the baseline rainfall disaster threshold. As the value of this indicator increases, its corresponding reduction coefficient decreases linearly from 1. The two dynamic indicators generate their respective reduction coefficients through this linear relationship, and then they are sequentially multiplied by 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 resilience is not uniform, but exists in one or more critical intervals, the impact increases sharply within these intervals. When the indicator value is low, the reduction coefficient decreases slowly; while when the indicator value exceeds a certain critical range, the reduction coefficient will decrease rapidly and significantly, which is not limited here.

[0081] S209. Perform boundary constraint processing on the target adjustment threshold and the preset minimum safety threshold and maximum reasonable threshold to obtain the current rainfall disaster threshold of the target geographic grid unit.

[0082] Based on the calculated target adjustment threshold, if the target adjustment threshold is lower than the preset minimum safety threshold, its value is forcibly set to the minimum safety threshold; if the target adjustment threshold is higher than the preset maximum reasonable threshold, its value is forcibly set to the maximum reasonable threshold; if it is between the two, its original value remains unchanged. The minimum safety threshold is directly derived from the minimum protection standard stipulated in national or industry-issued geological disaster prevention and control specifications or engineering design standards, or a very small but non-zero rainfall value used to trigger the monitoring program (e.g., 5 mm / 24 hours). The maximum reasonable threshold is set as the maximum process rainfall that has occurred in the monitored area since meteorological records began, or the theoretical extreme rainfall value of a 100-year or 1000-year return period. Rainfall exceeding this value constitutes a catastrophic event and does not require further model-based judgment.

[0083] S210, Obtain the expected effective rainfall within a preset future time period.

[0084] Quantitative rainfall forecasts for a predetermined time period (e.g., the next 24 hours) are obtained from meteorological departments for target geographic grid units. Based on a digital elevation model (DEM), slope and aspect are calculated. Combining the regional prevailing wind direction forecast by the meteorological department, a topographic precipitation correction model is applied. Based on the angle between the slope aspect and wind direction, and the steepness of the slope, a rainfall adjustment factor is calculated for each fine-grained grid (e.g., 1.2 for windward slopes and 0.8 for leeward slopes). Finally, the rainfall amount in the quantitative rainfall forecast is multiplied by this adjustment factor to obtain the topographically refined rainfall field. Furthermore, water balance analysis (e.g., the SCS-CN model) is used to calculate the portion that actually affects slope stability, which is then used as the expected effective rainfall.

[0085] 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, and take the larger value as the comprehensive risk index.

[0086] Based on the expected effective rainfall within a preset future time period, the cumulative effective rainfall for the next 24 hours is calculated and divided by the current rainfall disaster threshold to obtain the cumulative risk ratio. Next, a one-hour window is slid across the 24-hour forecast to find the largest hourly cumulative effective rainfall (i.e., the strongest short-duration rainfall event in the future), and this is also divided by the current rainfall disaster threshold to obtain the intensity risk ratio. Finally, the two ratios are compared, and the larger one is taken as the final comprehensive risk index.

[0087] S212. Determine the warning level based on the comprehensive risk index and generate corresponding warning information.

[0088] Obtain the preset warning level classification standard, which defines the correspondence 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 an index ≥ 1.0 corresponds to a red warning. Compare the comprehensive risk index with this standard to determine its corresponding warning level. Based on the preset warning template, generate a warning message containing core elements, such as: warning time, warning area (specific to grid unit or corresponding administrative village group), warning level, risk index, main disaster-causing rainfall period, and standardized response measures recommendations for that level (such as strengthening patrols, preparing for evacuation, and immediately organizing evacuation).

[0089] In this embodiment, by using time-series InSAR technology to conduct a comprehensive survey of surface deformation rate and even acceleration, and using this as a scientific basis to perform high / low density differentiated grid subdivision and hierarchical (high-precision / conventional) model management of the monitoring area, 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 operating efficiency is significantly optimized, making early warning decisions more scientific and comprehensively improving the reliability and timeliness of geological disaster prevention and control.

[0090] The server in the embodiments of this invention is described below from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the physical device structure of a server in an embodiment of this application.

[0091] It should be noted that, Figure 3 The server structure shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0092] like Figure 3 As shown, the server includes a CPU 301, which can perform various appropriate actions and processes according to a program stored in ROM 302 or a program loaded into RAM 303 from storage section 308, such as performing 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 interconnected via bus 304. I / O interface 305 is also connected to bus 304.

[0093] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including hard disks, etc.; and communication section 309 including network interface cards such as LAN (Local Area Network) cards, modems, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0094] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by CPU 301, it performs the various functions defined in the present invention.

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0096] Specifically, the server in this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the meteorological-based geological disaster risk early warning method provided in the above embodiment.

[0097] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the server described in the above embodiments; or it may exist independently and not assembled into the server. The storage medium carries one or more computer programs that, when executed by a processor of the server, cause the server to implement the meteorological-based geological disaster risk early warning method provided in the above embodiments.

[0098] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0099] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

Claims

1. A meteorological-based method for early warning of geological disaster risks, characterized in that, The method includes: Based on multiple geographic grid cells of preset sizes obtained by pre-dividing the monitoring area, monitoring data within the target geographic grid cell is obtained, wherein the target geographic grid cell is one of the geographic grid cells within the monitoring area; The static and dynamic factors in the monitoring data are input into the vulnerability index calculation model to obtain the disaster-bearing vulnerability index of the target geographic grid unit. 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 degree of susceptibility to geological disasters of a geographic unit. Based on historical rainfall data determined by a preset optimal number of rainfall days, the disaster-causing factor of the target geographic grid unit is calculated. This disaster-causing factor is a destructive accumulation index obtained based on the impact and continuous cumulative effects of historical rainfall, specifically including: Historical rainfall data of the target geographic grid unit prior to the current time, representing the preset optimal number of rainfall days, are obtained to construct a daily rainfall time series. The preset optimal number of rainfall days is the length of the rainfall period that has the greatest impact on the stability of the current geological body. This is determined by statistical analysis of the correlation between historical geological disaster events and previous rainfall processes in the monitoring area. The daily rainfall time series is determined based on the historical rainfall data. The historical rainfall data were processed using a time-series analysis algorithm to calculate the daily rainfall intensity variation rate, continuous rainfall duration, and cumulative effective rainfall distribution. Based on the peak characteristics and trends of the daily rainfall intensity variation rate, the impact effect index of rainfall intensity variation on the stability of the geological body is calculated by integral calculation. Using the duration of continuous rainfall and the distribution of cumulative effective rainfall as input parameters of a preset rainfall infiltration model, the cumulative time required for a geological body to reach a critical state of failure from its initial stable state is obtained as an index of the cumulative effect of duration. 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. The normalized impact effect index and duration cumulative effect index are weighted and summed according to preset weight coefficients. The vulnerability index of the disaster-bearing body and the disaster-causing factor are coupled and calculated to determine the current rainfall disaster threshold of the target geographic grid unit. The current rainfall disaster threshold is inversely proportional to the vulnerability index of the disaster-bearing body and inversely proportional to the disaster-causing factor. Obtain the expected effective rainfall within a preset future time period, and determine the warning level of the target geographic grid unit based on 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, which is one of the geographic grid cells within the monitoring area, based on multiple pre-divided geographic grid cells of a pre-defined size from the monitoring area, the method further includes: Using time-series synthetic aperture radar interferometry, periodic surface deformation monitoring is performed on the monitoring area to obtain the periodic deformation rate of a preset area within the monitoring area; Areas within the monitoring region whose periodic deformation rate exceeds a preset anomaly threshold are designated as key monitoring areas; The key monitoring area is divided using a high-density grid, while the general monitoring area, defined based on areas outside the key monitoring area, is divided using a low-density grid, resulting in the geographic grid units of the monitoring area.

3. The method according to claim 1, characterized in that, The step of inputting the static and dynamic factors from the monitoring data into the vulnerability index calculation model to obtain the disaster-bearing vulnerability index of the target geographic grid unit specifically includes: Static and dynamic factors within the target geographic grid unit are extracted from the monitoring data. The static factors include the slope extracted from the digital elevation model, the distance to the fault zone calculated through spatial analysis, and the soil weathering grade extracted from the acid weathering potential map. The dynamic factor is the soil moisture content obtained through real-time monitoring by sensors. The original values ​​of the static and dynamic factors are subjected to max-min normalization to obtain factor values ​​mapped to a unified interval. The vulnerability index of the target geographic grid unit is obtained by multiplying the factor values ​​by their corresponding weight coefficients and summing the results. The higher the vulnerability index, the more likely the geographic grid unit is to experience geological disasters under the same external disturbance conditions.

4. The method according to claim 1, characterized in that, The step of coupling the vulnerability index of the disaster-bearing body and the disaster-causing factor to determine the current rainfall disaster threshold of the target geographic grid cell specifically includes: Obtain the preset baseline rainfall disaster threshold for the target geographic grid cell, wherein the baseline rainfall disaster threshold is the standard triggering rainfall amount determined based on the statistical regularity of geological disasters induced by historical rainfall in the monitoring area; The vulnerability index of the disaster-bearing body and the disaster-causing factor are used as dynamic reduction terms. The baseline rainfall disaster threshold is dynamically reduced through a preset multiplicative reduction model to obtain the target adjustment threshold. The target adjustment threshold is subjected to boundary constraint processing with the preset minimum safety threshold and maximum reasonable threshold to obtain the current rainfall disaster threshold of the target geographic grid cell.

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

6. The method according to claim 5, characterized in that, Before the step of obtaining the expected effective rainfall within a preset future time period, wherein the expected effective rainfall includes the total rainfall over the next 24 hours for assessing the long-term cumulative effect and the heavy hourly rainfall over the next hour for assessing the short-term impact effect, the method further includes: Obtain raw 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 angle and wind speed. Based on the slope and aspect of the monitored area, the original rainfall forecast data and the prevailing wind direction information are input into a preset topographic rainfall correction model to obtain a corrected rainfall sequence. The topographic rainfall correction model is used to spatially downscale the original rainfall forecast data based on the slope and aspect of the monitored area.

7. 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 being used to store computer program code, the computer program code including computer instructions, and the one or more processors invoking the computer instructions to cause the server to perform the method as described in any one of claims 1-6.

8. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the server, it causes the server to perform the method as described in any one of claims 1-6.

9. A computer program product, characterized in that, When the computer program product is run on the server, the server performs the method as described in any one of claims 1-6.

Images