Geological disaster early warning method and system triggered by geological deep and shallow layer parameter classification

Abstract

The present application relates to geological disaster early warning method and system triggered by geological deep and shallow layer parameter grading. The first function only needing shallow layer parameter and the second function combining deep and shallow layer parameter are constructed; the direct monitoring area provided with monitoring device and the indirect monitoring area not provided with monitoring device are divided; the direct monitoring area is preliminarily screened based on shallow layer parameter and the first function, the risk value is calculated based on deep and shallow layer parameter and the second function after the secondary early warning area is determined; for the indirect monitoring area, the interpolation is firstly carried out by using the shallow layer parameter of the direct monitoring area, the secondary interpolation area is locked by combining the first function, then the interpolation is directly carried out by using the existing deep layer parameter of the surrounding secondary early warning area, and the final risk value is obtained by combining the second function. The mechanism of shallow layer preliminary screening and deep layer calculation is adopted, the frequency of deep layer parameter participation is reduced, the data acquisition and uploading frequency of deep buried detection device is effectively reduced, and the risk blind area of the area without monitoring device is effectively eliminated at low cost.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of geological disaster early warning technology, specifically to a geological disaster early warning method and system that is triggered by graded geological parameters at both shallow and deep layers. Background Technology

[0002] Early warning and prevention of geological disasters (such as landslides, debris flows, and ground subsidence) are crucial for ensuring the safety of life and property. During the evolution of geological disasters, shallow geological parameters typically reflect early signs (such as soil moisture content, surface fissure displacement, shallow ground temperature, and rainfall), providing sensitive indicators of the initial development of the disaster. Deep geological parameters, on the other hand, reflect the deeper evolution of the disaster (such as deep soil moisture content, deep pore water pressure, and deep earth pressure), serving as decisive evidence for determining whether a disaster is imminent and its scale.

[0003] To achieve accurate early warning, related technologies typically require the simultaneous acquisition of both shallow and deep geological parameters for comprehensive risk calculation. However, acquiring deep geological parameters usually necessitates the use of detection devices buried deep underground. In practical applications, these buried detection devices face high costs for maintenance, calibration, and replacement. Therefore, to reduce the maintenance costs of buried detection devices, the frequency of data acquisition and uploading should be reduced to minimize wear and tear. Furthermore, currently, to lower the cost of geological disaster early warning, parameter monitoring devices are not installed in every area.

[0004] Therefore, under the existing circumstances, how to set up a scheme that balances the accuracy of early warning, the low loss of deeply buried detection devices, and the ability to conduct geological disaster early warning for the entire area based on monitoring devices with limited parameters has become an urgent technical problem to be solved. Summary of the Invention

[0005] This specification provides a geological disaster early warning method and system based on the graded triggering of geological parameters at both shallow and deep layers. It offers a solution that balances early warning accuracy, low loss of deeply buried detection devices, and the ability to provide geological disaster early warning for the entire area based on a limited number of parameter monitoring devices.

[0006] The technical solution is as follows: Firstly, the embodiments of this specification provide a geological disaster early warning method triggered by graded geological parameters at both shallow and deep layers, including: Construct a first function for calculating disaster risk value based on shallow geological parameters, and a second function for calculating disaster risk value based on both shallow and deep geological parameters; The warning area is evenly divided into multiple warning zones, and direct monitoring zones with parameter monitoring devices and indirect monitoring zones without parameter monitoring devices are determined in the multiple warning zones. The parameter monitoring devices can monitor shallow geological parameters and deep geological parameters. Based on the shallow geological parameters and the first function corresponding to each of the multiple direct monitoring areas, the first disaster risk value corresponding to each of the multiple direct monitoring areas is obtained, and then the secondary early warning area is determined from the multiple direct monitoring areas. Based on the shallow geological parameters, deep geological parameters, and the second function corresponding to each secondary warning area, the second disaster risk value corresponding to each secondary warning area is obtained. Based on the shallow geological parameters corresponding to each of the multiple directly monitored areas, interpolation is used to obtain the shallow geological parameters corresponding to each of the multiple indirectly monitored areas. Based on the shallow geological parameters and the first function corresponding to each of the multiple indirect monitoring areas, the first disaster risk value corresponding to each of the multiple indirect monitoring areas is obtained, and then the secondary interpolation area is determined from the multiple indirect monitoring areas. Based on the geological deep parameters corresponding to each secondary early warning area, interpolation is performed to obtain the geological deep parameters corresponding to each secondary interpolation area. Based on the shallow geological parameters, deep geological parameters, and the second function corresponding to each quadratic interpolation area, the second disaster risk value corresponding to each quadratic interpolation area is obtained.

[0007] As a preferred approach, the interpolation of shallow geological parameters for each indirect monitoring area includes: Based on the area distances between each of the multiple direct monitoring areas surrounding the indirect monitoring area and the shallow geological reference degree of the area, the interpolation weights corresponding to each of the multiple direct monitoring areas surrounding the indirect monitoring area are obtained. Based on the interpolation weights and shallow geological parameters of each of the multiple direct monitoring areas surrounding the indirect monitoring area, the shallow geological parameters of the indirect monitoring area are obtained by interpolation. The shallow geological reference degree of the area is obtained based on the similarity of geological static parameters and the difference in altitude between the areas.

[0008] As a preferred embodiment, the step of obtaining the interpolation weights corresponding to each of the multiple directly monitored areas surrounding the indirect monitoring area based on the area distances between each of the multiple directly monitored areas and the indirect monitoring area, and the shallow geological reference degree of the area, includes: Obtain the proportional coefficients corresponding to the distance between areas and the shallow geological reference degree of the area; For each directly monitored area, a comprehensive score is obtained based on the area distance between the directly monitored area and the indirectly monitored area, the shallow geological reference degree of the area, and the proportional coefficients corresponding to the area distance and the shallow geological reference degree of the area. Based on the comprehensive scores corresponding to each of the multiple directly monitored areas, the comprehensive scores corresponding to each of the multiple directly monitored areas are normalized to obtain the interpolation weights corresponding to each of the multiple directly monitored areas surrounding the indirect monitoring area.

[0009] As a preferred approach, the interpolation of deep geological parameters for each quadratic interpolation area includes: Determine the interpolation reference area corresponding to the secondary interpolation area from the secondary early warning area; Based on the superior and inferior solution distance method, the area distance between each interpolation reference area and the secondary interpolation area, and the deep geological reference degree of the area, the first relative proximity degree corresponding to each interpolation reference area is obtained. Based on the first relative proximity of each interpolation reference region, the interpolation weight corresponding to each interpolation reference region is obtained. Based on the interpolation weights and geological deep parameters corresponding to each interpolation reference area, the geological deep parameters corresponding to the secondary interpolation area are obtained through interpolation. The deep geological reference degree of the region is obtained based on the similarity of geological static parameters between regions, the difference in altitude, and the deep connectivity of the region.

[0010] As a preferred embodiment, the acquisition of the deep geological reference degree of each interpolation reference area relative to the secondary interpolation area includes: Based on the superior-inferiority solution distance method, the similarity of geological static parameters, altitude difference, and deep connectivity of each interpolation reference area with the secondary interpolation area, the second relative proximity of each interpolation reference area is obtained. Based on the second relative proximity of each interpolation reference area, the deep geological reference degree of each interpolation reference area and the secondary interpolation area is obtained.

[0011] As a preferred option, the shallow geological parameters include one or more of the following: shallow soil moisture content, surface fissure displacement, shallow ground temperature, and rainfall. Geological parameters include one or more of the following: deep soil moisture content, deep pore water pressure, and deep earth pressure.

[0012] As a preferred option, the first function calculates the disaster risk value based on shallow geological parameters and static geological parameters; The second function calculates the disaster risk value based on shallow geological parameters, deep geological parameters, and static geological parameters. Geological static parameters include one or more of the following: geological hardness, topographic slope, and vegetation cover.

[0013] As a preferred embodiment, determining the direct monitoring areas equipped with parameter monitoring devices and the indirect monitoring areas without parameter monitoring devices among multiple early warning areas includes: Obtain the geological static parameters, altitude, and location information corresponding to each of the multiple early warning areas; Based on the geological static parameters, altitude, location information, and multi-objective solution algorithm corresponding to each of the multiple early warning areas, the direct monitoring areas with parameter monitoring devices and the indirect monitoring areas without parameter monitoring devices are determined among the multiple early warning areas. The fitness function used in the multi-objective solution algorithm is calculated based on the shallow interpolation reliability, deep interpolation reliability, and the number of direct monitoring areas corresponding to each of the determined indirect monitoring areas. The shallow interpolation reliability of the indirect monitoring area is obtained based on the number of direct monitoring areas within the first radius centered on the indirect monitoring area, as well as the area distance, geological static parameter similarity, and altitude difference between each of the multiple direct monitoring areas and the indirect monitoring area. The reliability of deep interpolation in the indirect monitoring area is obtained based on the number of direct monitoring areas within the second radius centered on the indirect monitoring area, as well as the area distance, similarity of geological static parameters, altitude difference, and deep connectivity of the area between each of the multiple direct monitoring areas and the indirect monitoring area.

[0014] As a preferred option, the second radius is larger than the first radius.

[0015] Secondly, embodiments of this specification provide a geological disaster early warning system triggered by graded geological parameters at varying depths, and a geological disaster early warning method triggered by graded geological parameters at varying depths as described in the first aspect of the above embodiments, comprising: The function construction module constructs a first function for calculating disaster risk values ​​based on shallow geological parameters, and a second function for calculating disaster risk values ​​based on both shallow and deep geological parameters. The block division module evenly divides the warning block into multiple warning zones, and determines the direct monitoring zone with parameter monitoring device and the indirect monitoring zone without parameter monitoring device in the multiple warning zones. The parameter monitoring device can monitor shallow geological parameters and deep geological parameters. The first acquisition module obtains the first disaster risk value corresponding to each of the multiple direct monitoring areas based on the shallow geological parameters and the first function, and then determines the secondary early warning area from the multiple direct monitoring areas. The second acquisition module obtains the second disaster risk value corresponding to each secondary warning area based on the shallow geological parameters, deep geological parameters, and the second function corresponding to each secondary warning area. The first interpolation module interpolates the shallow geological parameters corresponding to each of the multiple directly monitored areas to obtain the shallow geological parameters corresponding to each of the multiple indirectly monitored areas. The third acquisition module obtains the first disaster risk value corresponding to each of the multiple indirect monitoring areas based on the geological shallow parameters and the first function, and then determines the secondary interpolation area from the multiple indirect monitoring areas. The second interpolation module interpolates the geological deep parameters corresponding to each secondary early warning area based on the geological deep parameters corresponding to each secondary interpolation area. The fourth acquisition module obtains the second disaster risk value corresponding to each quadratic interpolation area based on the shallow geological parameters, deep geological parameters, and the second function.

[0016] Thirdly, embodiments of this specification provide an electronic device, including a processor and a memory; the processor is connected to the memory; the memory is used to store executable program code; the processor reads the executable program code stored in the memory to run a program corresponding to the executable program code, so as to perform the steps described in the first aspect of the above embodiments.

[0017] Fourthly, embodiments of this specification provide a computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the steps described in the first aspect of the above embodiments.

[0018] The beneficial effects of the technical solutions provided in some embodiments of this specification include at least the following: This invention changes the traditional, crude method of directly calling deep geological parameters for calculation regardless of risk level by constructing a "first function" that only requires shallow geological parameters and a "second function" that combines shallow and deep geological parameters. For directly monitored areas, the second function is only activated to call deep geological parameters when the first disaster risk value calculated by the first function reaches the warning threshold (i.e., it is identified as a secondary warning area). This "shallow screening first, deep calculation later" mechanism fundamentally and significantly reduces the frequency of deep geological parameters, thereby effectively reducing the data acquisition and uploading frequency of deeply buried detection devices, significantly extending equipment life, and reducing later maintenance, calibration, and replacement costs.

[0019] For indirect monitoring areas without parameter monitoring devices, this invention cleverly utilizes shallow geological parameters of the directly monitored areas for spatial interpolation, and combines this with a first function to complete preliminary risk screening to identify secondary interpolation areas. Subsequently, it directly uses deep geological parameters of surrounding high-risk secondary warning areas to perform secondary interpolation on the secondary interpolation areas (Note: Directly using deep geological parameters of surrounding high-risk secondary warning areas is because, since the secondary interpolation area is determined to be shallowly high-risk, the deep evolution characteristics of adjacent and equally high-risk secondary warning areas already have high practical reference value for the secondary interpolation area; therefore, this invention no longer uses deep geological parameters corresponding to unconfirmed high-risk direct monitoring areas surrounding the secondary interpolation area for interpolation, thereby ensuring the accuracy of deep geological parameter interpolation for the secondary interpolation area while further reducing the data acquisition and uploading frequency of the buried detection device). Finally, it combines the second function to obtain the second disaster risk value of the secondary interpolation area. Based on the setting of limited parameter monitoring devices, this invention uses spatial extrapolation and transmission of data to accurately fill the risk blind spots in areas without parameter monitoring devices. In this process, the data acquisition and uploading frequency of the deep-buried detection device is also kept low. This is because the geological deep risk extrapolation of the secondary interpolation area is entirely based on the existing geological deep data of the secondary early warning area that has been identified as high-risk, without the need to additionally trigger or wake up the deep-buried detection devices in other directly monitored areas in the surrounding low-risk state.

[0020] For the interpolation and extrapolation process of deep geological parameters in secondary interpolation areas, this invention introduces a dynamic weight allocation mechanism based on the superior-inferior solution distance method. Since the deep risk extrapolation of secondary interpolation areas is entirely dependent on the deep geological parameters of surrounding areas already identified as high-risk secondary early warning areas, the reference weights of the deep geological parameters provided by these limited interpolation reference areas directly determine the accuracy of the final interpolation result. Therefore, the setting of interpolation weights for interpolation reference areas is particularly important. Based on this, this invention does not adopt a simple averaging or a coarse allocation method based solely on inverse distance. Instead, it comprehensively considers the spatial distance and geological reference degree between areas, calculating the first relative proximity of each interpolation reference area using the superior-inferior solution distance method, thereby ensuring that the interpolation weights corresponding to each interpolation reference area are obtained as reasonably as possible (Note: Calculating the interpolation weights using the superior-inferior solution distance method achieves an objective fusion and balance of the two dimensions of spatial proximity and geological reference degree, ensuring the rationality of the interpolation weight allocation). Furthermore, based on the geological deep parameters provided by the limited interpolation reference area, the actual geological deep parameters of the secondary interpolation area were restored to the greatest extent possible, laying a solid data foundation for the accurate calculation of the subsequent second disaster risk value. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a geological disaster early warning method based on the graded triggering of geological depth and shallow layer parameters according to some embodiments of this disclosure is shown.

[0023] Figure 2 A schematic diagram of the structure of a geological disaster early warning system triggered by geological depth and shallow layer parameter classification according to some embodiments of the present disclosure is shown.

[0024] Figure 3 A schematic block diagram of an electronic device according to some embodiments of the present disclosure is shown. Detailed Implementation

[0025] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings.

[0026] The terms "first," "second," "third," etc., in the description, claims, and accompanying drawings are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such processes, methods, products, or apparatus.

[0027] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of this specification. Various processes or components may be appropriately omitted, substituted, or added to the examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0028] Figure 1 The flowchart illustrates a geological disaster early warning method based on the graded triggering of geological depth and shallow layer parameters according to some embodiments of this disclosure. It should be understood that the numbers in the flowchart do not indicate the order in which these steps are executed; some or all of these steps can be executed in parallel, or their execution order can be interchanged, and this disclosure does not limit this. Furthermore, Figure 1 The methods described may also include additional steps not shown and / or the steps shown may be omitted, and the scope of this disclosure is not limited in this respect.

[0029] like Figure 1 As shown, a geological disaster early warning method triggered by geological parameters at different depths can include at least the following: Step 102: Construct a first function for calculating disaster risk value based on shallow geological parameters, and a second function for calculating disaster risk value based on shallow geological parameters and deep geological parameters; Step 104: Divide the warning area into multiple warning zones evenly, and determine the direct monitoring zone with parameter monitoring device and the indirect monitoring zone without parameter monitoring device in the multiple warning zones. The parameter monitoring device can monitor shallow geological parameters and deep geological parameters. Step 106: Based on the shallow geological parameters and the first function corresponding to each of the multiple direct monitoring areas, obtain the first disaster risk value corresponding to each of the multiple direct monitoring areas, and then determine the secondary early warning area from the multiple direct monitoring areas. Step 108: Based on the shallow geological parameters, deep geological parameters, and the second function corresponding to each secondary early warning area, obtain the second disaster risk value corresponding to each secondary early warning area. Step 110: Based on the shallow geological parameters corresponding to each of the multiple directly monitored areas, interpolation is performed to obtain the shallow geological parameters corresponding to each of the multiple indirectly monitored areas. Step 112: Based on the shallow geological parameters and the first function corresponding to each of the multiple indirect monitoring areas, obtain the first disaster risk value corresponding to each of the multiple indirect monitoring areas, and then determine the secondary interpolation area from the multiple indirect monitoring areas. Step 114: Based on the geological deep parameters corresponding to each secondary early warning area, interpolate to obtain the geological deep parameters corresponding to each secondary interpolation area. Step 116: Based on the shallow geological parameters, deep geological parameters, and the second function corresponding to each quadratic interpolation area, obtain the second disaster risk value corresponding to each quadratic interpolation area.

[0030] In the embodiments described in this specification, by constructing a "first function" that only requires shallow geological parameters and a "second function" that combines shallow and deep geological parameters, the traditional, crude method of directly calling deep parameters for calculation regardless of risk level is changed. For directly monitored areas, the second function is only activated to call deep geological parameters when the first disaster risk value calculated by the first function reaches the warning threshold (i.e., it is identified as a secondary warning area). This mechanism of "shallow screening first, then deep calculation" fundamentally and significantly reduces the frequency of deep geological parameters, thereby effectively reducing the data acquisition and uploading frequency of the deep-buried detection device, significantly extending the equipment life, and reducing the costs of later maintenance, calibration, and replacement.

[0031] For indirect monitoring areas without parameter monitoring devices, this invention cleverly utilizes shallow geological parameters of the directly monitored areas for spatial interpolation, and combines this with a first function to complete preliminary risk screening to identify secondary interpolation areas. Subsequently, it directly uses deep geological parameters of surrounding high-risk secondary warning areas to perform secondary interpolation on the secondary interpolation areas (Note: Directly using deep geological parameters of surrounding high-risk secondary warning areas is because, since the secondary interpolation area is determined to be shallowly high-risk, the deep evolution characteristics of adjacent and equally high-risk secondary warning areas already have high practical reference value for the secondary interpolation area; therefore, this invention no longer uses deep geological parameters corresponding to unconfirmed high-risk direct monitoring areas surrounding the secondary interpolation area for interpolation, thereby ensuring the accuracy of deep geological parameter interpolation for the secondary interpolation area while further reducing the data acquisition and uploading frequency of the buried detection device). Finally, it combines the second function to obtain the second disaster risk value of the secondary interpolation area. Based on the setting of limited parameter monitoring devices, this invention uses spatial extrapolation and transmission of data to accurately fill the risk blind spots in areas without parameter monitoring devices. In this process, the data acquisition and uploading frequency of the deep-buried detection device is also kept low. This is because the geological deep risk extrapolation of the secondary interpolation area is entirely based on the existing geological deep data of the secondary early warning area that has been identified as high-risk, without the need to additionally trigger or wake up the deep-buried detection devices in other directly monitored areas in the surrounding low-risk state.

[0032] It should be noted that the first and second functions can be constructed using the methods for multiple regression functions. A multiple regression function is a statistical model used to describe the relationship between two or more independent variables (explanatory variables) and a dependent variable (response variable). Mathematically, multiple regression function models include multiple linear regression function models and multiple nonlinear regression function models. The multiple linear regression function model can be written in the form of the following equation: ; in, The dependent variable represents the variable we want to predict or explain. These are independent variables, and they are explanatory variables that affect the dependent variable; It is the intercept, which is the expected value of the dependent variable when all independent variables are 0; These are the coefficients of each independent variable, representing the expected change in the dependent variable when the corresponding independent variable changes by one unit. This is the error term, representing the random variation that the model failed to explain.

[0033] In multiple regression, our goal is to find the optimal coefficients. This is to enable accurate prediction of the dependent variable. The least squares method is typically used to estimate the coefficients when solving a multiple linear regression model.

[0034] In the embodiments of this specification, the shallow geological parameters are the independent variables and the disaster risk value is the dependent variable in the first function. In the second function, the shallow geological parameters and deep geological parameters are the independent variables and the disaster risk value is the dependent variable.

[0035] When solving the first function, a dataset containing multiple data samples is required, each including corresponding shallow geological parameters and hazard risk values. Similarly, when solving the second function, a dataset containing multiple data samples is needed, each including corresponding shallow geological parameters, deep geological parameters, and hazard risk values. Understandably, the hazard risk values ​​in the data samples used to solve the first and second functions can be obtained based on historical geological conditions and expert experience. It should be noted that, based on the 'shallow-to-deep' evolution mechanism of geological hazards and the initial hazard screening of the first function, the hazard risk values ​​in the data samples used to solve the first function need to be set to the maximum hazard risk value corresponding to the shallow geological parameter in historical geological conditions. This forces the first function (initial screening function) to maintain the highest sensitivity, achieving zero omission of geological risks.

[0036] Furthermore, it's understandable that if there's no obvious linear relationship between the independent and dependent variables, a multiple nonlinear regression model is needed. Similar to the multiple linear regression model, the difference lies in that it allows the relationship between the independent and dependent variables to be represented by a nonlinear equation. This means that the relationship between one or more independent variables and the dependent variable in the model is not linear, but follows some kind of nonlinear function, which can be, but is not limited to, exponential, logarithmic, or power functions. Solving a multiple nonlinear regression model is usually more complex than solving a linear model because it involves nonlinear optimization problems. In practical applications, computer algorithms such as the Newton-Raphson method, gradient descent, and genetic algorithms are typically used to estimate the model parameters.

[0037] In some embodiments of this specification, the interpolation of shallow geological parameters for each indirect monitoring area includes: Based on the area distances between each of the multiple direct monitoring areas surrounding the indirect monitoring area and the shallow geological reference degree of the area, the interpolation weights corresponding to each of the multiple direct monitoring areas surrounding the indirect monitoring area are obtained. Based on the interpolation weights and shallow geological parameters of each of the multiple direct monitoring areas surrounding the indirect monitoring area, the shallow geological parameters of the indirect monitoring area are obtained by interpolation. The shallow geological reference degree of the area is obtained based on the similarity of geological static parameters and the difference in altitude between the areas (Note: The shallow geological reference degree of the area can be calculated directly based on the similarity of geological static parameters and the difference in altitude between the areas using a linear weighted calculation method).

[0038] The method involves obtaining interpolation weights for each of the multiple directly monitored areas surrounding the indirect monitoring area, based on the area distances between each of the multiple directly monitored areas and the indirect monitoring area, and the shallow geological reference degree of the area. This includes: Obtain the proportional coefficients corresponding to the distance between areas and the shallow geological reference degree of the area; For each directly monitored area, a comprehensive score is obtained based on the area distance between the directly monitored area and the indirectly monitored area, the shallow geological reference degree of the area, and the proportional coefficients corresponding to the area distance and the shallow geological reference degree of the area. Based on the comprehensive scores corresponding to each of the multiple directly monitored areas, the comprehensive scores corresponding to each of the multiple directly monitored areas are normalized to obtain the interpolation weights corresponding to each of the multiple directly monitored areas surrounding the indirect monitoring area.

[0039] It should be noted that, since the shallow geological parameters of the directly monitored area are obtained in full in the embodiments of this specification, a simple linear weighted normalization method is used to obtain the interpolation weights corresponding to the multiple directly monitored areas surrounding the indirect monitoring area, in order to reduce the computational workload of obtaining the interpolation weights during the interpolation of shallow geological parameters. The specific calculation process is as follows: Step 1: Assign preset proportional coefficients to the distance between areas and the shallow geological reference degree of the area. and , ; Step 2: Calculate the overall score through linear weighted summation: ; in, Indicates the area surrounding the indirect monitoring zone. The overall score of each directly monitored area Indicates the area surrounding the indirect monitoring zone. The distance between directly monitored areas and indirectly monitored areas Indicates the area surrounding the indirect monitoring zone. Shallow geological reference degree between direct and indirect monitoring areas A proportionality coefficient representing the distance between areas. A proportionality coefficient representing the shallow geological reference level of the area; Step 3: Based on the comprehensive scores corresponding to each of the multiple directly monitored areas, normalize the comprehensive scores corresponding to each of the multiple directly monitored areas: ; in, Indicates the area surrounding the indirect monitoring zone. Interpolation weights corresponding to each directly monitored area This represents the total number of directly monitored areas surrounding an indirect monitoring area. It should be noted that the total number of directly monitored areas surrounding an indirect monitoring area can be defined as the number of directly monitored areas within a first radius centered on the indirect monitoring area.

[0040] In some embodiments of this specification, the interpolation of deep geological parameters for each quadratic interpolation area includes: Determine the interpolation reference area corresponding to the secondary interpolation area from the secondary early warning area; Based on the superior and inferior solution distance method, the area distance between each interpolation reference area and the secondary interpolation area, and the deep geological reference degree of the area, the first relative proximity degree corresponding to each interpolation reference area is obtained. Based on the first relative proximity of each interpolation reference region, the interpolation weight corresponding to each interpolation reference region is obtained. Based on the interpolation weights and geological deep parameters corresponding to each interpolation reference area, the geological deep parameters corresponding to the secondary interpolation area are obtained through interpolation. The deep geological reference degree of the region is obtained based on the similarity of geological static parameters between regions, the difference in altitude, and the deep connectivity of the region.

[0041] Understandably, in the embodiments of this specification, the deep geological parameters of the secondary interpolation area are directly interpolated using the geological parameters of the surrounding areas that have been confirmed as high-risk secondary early warning areas. This is an inference based on a limited sample. If a simple linear weighted normalization method is used, it is easy for the limited data to produce a large error amplification effect after interpolation. Therefore, it is necessary to use the superior-inferiority distance method to finely set the interpolation weights. Through multi-dimensional relative proximity calculation, the weights are truly focused on the core areas with the most interpolation reference value, thereby minimizing the interpolation error to the greatest extent.

[0042] The calculation of the first relative closeness is as follows: ; Indicates the first The first relative proximity of the interpolation reference area Indicates the first The distance between each interpolated reference area and the secondary interpolated area, and the distance of the deep geological reference degree of the area relative to the negative ideal solution obtained from the distances between each interpolated reference area and the secondary interpolated area, and the deep geological reference degree of the area. Indicates the first The distance between each interpolated reference area and the secondary interpolated area, and the distance of the deep geological reference degree of the area relative to the positive ideal solution obtained from the distance between each interpolated reference area and the secondary interpolated area, and the distance of the deep geological reference degree of the area.

[0043] Among them: the negative ideal solution includes the area distance between each interpolated reference area and the secondary interpolated area, the maximum area distance and the minimum area deep geological reference degree in the area; the positive ideal solution includes the area distance between each interpolated reference area and the secondary interpolated area, the minimum area distance and the maximum area deep geological reference degree in the area.

[0044] ; ; in, This indicates the weight used in calculating the first relative proximity of the regions. This indicates the weighting of the first relative proximity calculation corresponding to the deep geological reference degree of the area. Indicates the first The distance between the interpolation reference region and the secondary interpolation region. This represents the region distance in the negative ideal solution. Indicates the first The deep geological reference degree of the interpolation reference area and the secondary interpolation area is corresponding to the area of ​​deep geological reference. This represents the deep geological reference level of the region in the negative ideal solution. This represents the region distance in the ideal solution. This represents the deep geological reference degree of the region in the positive ideal solution.

[0045] Furthermore, the interpolation weights for each interpolation reference region can be obtained as follows: ; in, Indicates the first Interpolation weights corresponding to each interpolation reference region This represents the total number of interpolation reference areas corresponding to the secondary interpolation areas determined from the secondary early warning areas. It should be noted that the total number of interpolation reference areas can be defined as the number of secondary early warning areas within a second radius centered on the secondary interpolation area (Note: Because the determination of secondary early warning areas is uncertain, the distance between secondary early warning areas and secondary interpolation areas may be relatively large, so the second radius should be larger than the aforementioned first radius).

[0046] It should be noted that in certain rare cases, there may be no secondary warning area within the second radius centered on the secondary interpolation area. In this case: Obtain the corresponding deep geological parameters of each of the multiple direct monitoring areas within the first radius centered on the quadratic interpolation area (i.e., activate the deep-buried detection devices in the multiple direct monitoring areas within this range to detect the deep geological parameters). Based on the geological deep parameters corresponding to each of the multiple directly monitored areas within the first radius centered on the secondary interpolation area, the geological deep parameters of the secondary interpolation area are obtained by interpolation.

[0047] In some embodiments of this specification, the acquisition of the deep geological reference degree of each interpolation reference area and the secondary interpolation area includes: Based on the superior-inferiority distance method, the similarity of geological static parameters (i.e., the similarity between parameters such as geological hardness, terrain slope, and vegetation cover) between each interpolation reference area and the secondary interpolation area, the altitude difference, and the deep connectivity of the area (i.e., the connectivity in the deep geological layers, which refers to whether there are physical barriers such as aquitards and faults between deep rock structures and the degree of such barriers, reflecting the degree of connectivity of the area in the deep space), the second relative proximity of each interpolation reference area is obtained. Based on the second relative proximity of each interpolation reference area, the deep geological reference degree of each interpolation reference area and the secondary interpolation area is obtained.

[0048] Understandably, to further ensure the accuracy of the interpolation of deep geological parameters in the secondary interpolation area, in the embodiments of this specification, the deep geological reference degree between the interpolation reference area and the secondary interpolation area is also obtained based on the relative proximity calculated by the superior-inferiority solution distance method. Furthermore, the calculation method for the second relative proximity degree here is similar to that for the first relative proximity degree described above, and therefore will not be elaborated upon further.

[0049] In some embodiments of this specification, the shallow geological parameters include one or more of the following: shallow soil moisture content, surface fissure displacement, shallow ground temperature, and rainfall. Geological parameters include one or more of the following: deep soil moisture content, deep pore water pressure, and deep earth pressure.

[0050] In some embodiments of this specification, the first function calculates the disaster risk value based on shallow geological parameters and static geological parameters; The second function calculates the disaster risk value based on shallow geological parameters, deep geological parameters, and static geological parameters. Geological static parameters include one or more of the following: geological hardness, topographic slope, and vegetation cover.

[0051] Understandably, geological static parameters are also particularly important for early warning of geological risks. Therefore, geological static parameters were introduced as independent variables for both the first and second functions.

[0052] In some embodiments of this specification, determining the direct monitoring areas where parameter monitoring devices are installed and the indirect monitoring areas where parameter monitoring devices are not installed among multiple early warning areas includes: Obtain the geological static parameters, altitude, and location information corresponding to each of the multiple early warning areas; Based on the geological static parameters, altitude, location information, and multi-objective solution algorithm corresponding to each of the multiple early warning areas, the direct monitoring areas with parameter monitoring devices and the indirect monitoring areas without parameter monitoring devices are determined among the multiple early warning areas. Among them, the fitness function used by the multi-objective solution algorithm (note: it may be, but is not limited to, using a genetic algorithm) is calculated based on the shallow interpolation reliability, deep interpolation reliability, and number of direct monitoring areas corresponding to each of the determined indirect monitoring areas; The shallow interpolation reliability of the indirect monitoring area is obtained based on the number of direct monitoring areas within the first radius centered on the indirect monitoring area, as well as the area distance, geological static parameter similarity, and altitude difference between each of the multiple direct monitoring areas and the indirect monitoring area. The reliability of deep interpolation in the indirect monitoring area is obtained based on the number of direct monitoring areas within the second radius centered on the indirect monitoring area, as well as the area distance, similarity of geological static parameters, altitude difference, and deep connectivity of the area between each of the multiple direct monitoring areas and the indirect monitoring area.

[0053] Similarly, the second radius should be larger than the first radius.

[0054] Understandably, this area setting scheme, based on the framework of the geological disaster early warning scheme triggered by the classification of geological depth and shallow layer parameters in the embodiments of this specification, can achieve a reasonable setting of direct monitoring areas and indirect monitoring areas, so as to optimize the overall combination of the deployment cost of parameter monitoring devices, the interpolation effect of geological shallow layer parameters in indirect monitoring areas, and the interpolation effect of geological deep layer parameters in indirect monitoring areas.

[0055] The formula for calculating the fitness function value can be, but is not limited to, the following: ; ; ; in, This represents the fitness function value. , , , This represents the sum of the reliability of deep interpolation. This represents the sum of the shallow interpolation reliability. Indicates the number of directly monitored areas. Indicates the first The reliability of deep interpolation corresponding to each indirect monitoring area No. The reliability of shallow interpolation corresponding to each indirect monitoring area This indicates the number of indirect monitoring areas (Note: This refers to the total number of directly monitored areas with parameter monitoring devices set up among multiple early warning areas).

[0056] ; Indicates the first The number of direct monitoring areas within a second radius centered on the indirect monitoring area. Indicates the first The average distance between each of the multiple directly monitored areas and the indirect monitored area within a second radius centered on the indirect monitored area. Indicates the first The average of the absolute values ​​of the altitude differences between each of the multiple direct monitoring areas and the indirect monitoring areas within a second radius centered on the indirect monitoring area. Indicates the first The average value of the similarity of geological static parameters between each of the multiple directly monitored areas and the indirect monitored area within a second radius centered on the indirect monitored area. Indicates the first The average value of the deep connectivity between each of the multiple directly monitored areas and the indirect monitored areas within a second radius centered on an indirect monitored area.

[0057] ; Indicates the first The number of direct monitoring areas within the first radius centered on the indirect monitoring area. Indicates the first The average distance between each of the multiple directly monitored areas and the indirect monitored area within a first radius centered on the indirect monitored area. Indicates the first The average of the absolute values ​​of the altitude differences between each of the multiple directly monitored areas and the indirect monitored areas within a first radius centered on an indirect monitored area. Indicates the first The average value of the similarity of the geological static parameters between each of the multiple direct monitoring areas and the indirect monitoring area within the first radius centered on the indirect monitoring area.

[0058] Figure 2 This document illustrates a schematic diagram of a geological disaster early warning system triggered by graded geological depth parameters, representing some embodiments of this disclosure. The various embodiments in this specification are described in a progressive manner; similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are largely similar to the method embodiments, and therefore the descriptions are relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0059] like Figure 2 As shown, a geological disaster early warning system may include at least: The function construction module constructs a first function for calculating disaster risk values ​​based on shallow geological parameters, and a second function for calculating disaster risk values ​​based on both shallow and deep geological parameters. The block division module evenly divides the warning block into multiple warning zones, and determines the direct monitoring zone with parameter monitoring device and the indirect monitoring zone without parameter monitoring device in the multiple warning zones. The parameter monitoring device can monitor shallow geological parameters and deep geological parameters. The first acquisition module obtains the first disaster risk value corresponding to each of the multiple direct monitoring areas based on the shallow geological parameters and the first function, and then determines the secondary early warning area from the multiple direct monitoring areas. The second acquisition module obtains the second disaster risk value corresponding to each secondary warning area based on the shallow geological parameters, deep geological parameters, and the second function corresponding to each secondary warning area. The first interpolation module interpolates the shallow geological parameters corresponding to each of the multiple directly monitored areas to obtain the shallow geological parameters corresponding to each of the multiple indirectly monitored areas. The third acquisition module obtains the first disaster risk value corresponding to each of the multiple indirect monitoring areas based on the geological shallow parameters and the first function, and then determines the secondary interpolation area from the multiple indirect monitoring areas. The second interpolation module interpolates the geological deep parameters corresponding to each secondary early warning area based on the geological deep parameters corresponding to each secondary interpolation area. The fourth acquisition module obtains the second disaster risk value corresponding to each quadratic interpolation area based on the shallow geological parameters, deep geological parameters, and the second function.

[0060] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0061] Figure 3 A block diagram of an electronic device 300 that can implement various embodiments of the present disclosure is shown. For example... Figure 3As shown, the electronic device 300 includes a processor 310, a disk drive 320, an input / output interface 330, a network interface 340, and a memory 350. The processor 310, disk drive 320, input / output interface 330, network interface 340, and memory 350 can communicate with each other via a communication bus 360.

[0062] The processor 310 can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs in order to implement the technical solution provided in this application.

[0063] The memory 350 can be implemented in the form of ROM (Read Only Memory), RAM (Read Access Memory), static memory, dynamic storage devices, etc. The memory 350 can store the operating system 351 for controlling the operation of the electronic device 300, and the Basic Input / Output System 352 for controlling the low-level operations of the electronic device 300. Additionally, it can store a web browser 353, a data storage management system 354, etc. In summary, when implementing the technical solution provided in this application through software or firmware, the relevant program code is stored in the memory 350 and is called and executed by the processor 310.

[0064] The input / output interface 330 is used to connect input / output devices to enable information input and output. Input / output devices can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0065] Network interface 340 is used to connect network devices (not shown in the figure) to enable network communication between the device and other devices. The network devices can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0066] Bus 360 includes a pathway for transmitting information between various components of the device, such as processor 310, disk drive 320, input / output interface 330, network interface 340, and memory 350.

[0067] It should be noted that although the above-described device only shows the processor 310, disk drive 320, input / output interface 330, network interface 340, memory 350, bus 360, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the method of this application, and does not necessarily include all the components shown in the figures.

[0068] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0069] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. Furthermore, although operations are depicted in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the foregoing discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0070] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A geological disaster early warning method triggered by graded geological parameters at both shallow and deep layers, characterized in that, include: Construct a first function for calculating disaster risk value based on shallow geological parameters, and a second function for calculating disaster risk value based on both shallow and deep geological parameters; The warning area is evenly divided into multiple warning zones, and direct monitoring zones with parameter monitoring devices and indirect monitoring zones without parameter monitoring devices are determined in the multiple warning zones. The parameter monitoring devices can monitor shallow geological parameters and deep geological parameters. Based on the shallow geological parameters and the first function corresponding to each of the multiple direct monitoring areas, the first disaster risk value corresponding to each of the multiple direct monitoring areas is obtained, and then the secondary early warning area is determined from the multiple direct monitoring areas. Based on the shallow geological parameters, deep geological parameters, and the second function corresponding to each secondary warning area, the second disaster risk value corresponding to each secondary warning area is obtained. Based on the shallow geological parameters corresponding to each of the multiple directly monitored areas, interpolation is used to obtain the shallow geological parameters corresponding to each of the multiple indirectly monitored areas. Based on the shallow geological parameters and the first function corresponding to each of the multiple indirect monitoring areas, the first disaster risk value corresponding to each of the multiple indirect monitoring areas is obtained, and then the secondary interpolation area is determined from the multiple indirect monitoring areas. Based on the geological deep parameters corresponding to each secondary early warning area, interpolation is performed to obtain the geological deep parameters corresponding to each secondary interpolation area. Based on the shallow geological parameters, deep geological parameters, and the second function corresponding to each quadratic interpolation area, the second disaster risk value corresponding to each quadratic interpolation area is obtained.

2. The geological disaster early warning method based on the graded triggering of geological depth and shallow layer parameters according to claim 1, characterized in that, For each indirect monitoring area, the interpolation of shallow geological parameters includes: Based on the area distances between each of the multiple direct monitoring areas surrounding the indirect monitoring area and the shallow geological reference degree of the area, the interpolation weights corresponding to each of the multiple direct monitoring areas surrounding the indirect monitoring area are obtained. Based on the interpolation weights and shallow geological parameters of each of the multiple direct monitoring areas surrounding the indirect monitoring area, the shallow geological parameters of the indirect monitoring area are obtained by interpolation. The shallow geological reference degree of the area is obtained based on the similarity of geological static parameters and the difference in altitude between the areas.

3. The geological disaster early warning method based on graded triggering of geological depth and shallow layer parameters according to claim 2, characterized in that, The method involves obtaining interpolation weights for each of the multiple directly monitored areas surrounding the indirect monitoring area, based on the area distances between each of the multiple directly monitored areas and the indirect monitoring area, and the shallow geological reference degree of the area. This includes: Obtain the proportional coefficients corresponding to the distance between areas and the shallow geological reference degree of the area; For each directly monitored area, a comprehensive score is obtained based on the area distance between the directly monitored area and the indirectly monitored area, the shallow geological reference degree of the area, and the proportional coefficients corresponding to the area distance and the shallow geological reference degree of the area. Based on the comprehensive scores corresponding to each of the multiple directly monitored areas, the comprehensive scores corresponding to each of the multiple directly monitored areas are normalized to obtain the interpolation weights corresponding to each of the multiple directly monitored areas surrounding the indirect monitoring area.

4. The geological disaster early warning method based on the graded triggering of geological depth and shallow layer parameters according to claim 1, characterized in that, For each quadratic interpolation area, the deep geological parameter interpolation includes: Determine the interpolation reference area corresponding to the secondary interpolation area from the secondary early warning area; Based on the superior and inferior solution distance method, the area distance between each interpolation reference area and the secondary interpolation area, and the deep geological reference degree of the area, the first relative proximity degree corresponding to each interpolation reference area is obtained. Based on the first relative proximity of each interpolation reference region, the interpolation weight corresponding to each interpolation reference region is obtained. Based on the interpolation weights and geological deep parameters corresponding to each interpolation reference area, the geological deep parameters corresponding to the secondary interpolation area are obtained through interpolation. The deep geological reference degree of the region is obtained based on the similarity of geological static parameters between regions, the difference in altitude, and the deep connectivity of the region.

5. The geological disaster early warning method based on the graded triggering of geological depth and shallow layer parameters according to claim 4, characterized in that, The acquisition of the deep geological reference degree of each interpolation reference area and the corresponding secondary interpolation area includes: Based on the superior-inferiority solution distance method, the similarity of geological static parameters, altitude difference, and deep connectivity of each interpolation reference area with the secondary interpolation area, the second relative proximity of each interpolation reference area is obtained. Based on the second relative proximity of each interpolation reference area, the deep geological reference degree of each interpolation reference area and the secondary interpolation area is obtained.

6. The geological disaster early warning method based on graded triggering of geological depth and shallow layer parameters according to claim 1, characterized in that, The shallow geological parameters include one or more of the following: shallow soil moisture content, surface fissure displacement, shallow ground temperature, and rainfall. Geological parameters include one or more of the following: deep soil moisture content, deep pore water pressure, and deep earth pressure.

7. The geological disaster early warning method based on the graded triggering of geological depth and shallow layer parameters according to claim 1 or 6, characterized in that, The first function calculates the disaster risk value based on shallow geological parameters and static geological parameters. The second function calculates the disaster risk value based on shallow geological parameters, deep geological parameters, and static geological parameters. Geological static parameters include one or more of the following: geological hardness, topographic slope, and vegetation cover.

8. The geological disaster early warning method based on graded triggering of geological depth and shallow layer parameters according to claim 1, characterized in that, The process of identifying direct monitoring areas with parameter monitoring devices and indirect monitoring areas without parameter monitoring devices among multiple early warning areas includes: Obtain the geological static parameters, altitude, and location information corresponding to each of the multiple early warning areas; Based on the geological static parameters, altitude, location information, and multi-objective solution algorithm corresponding to each of the multiple early warning areas, the direct monitoring areas with parameter monitoring devices and the indirect monitoring areas without parameter monitoring devices are determined among the multiple early warning areas. The fitness function used in the multi-objective solution algorithm is calculated based on the shallow interpolation reliability, deep interpolation reliability, and the number of direct monitoring areas corresponding to each of the determined indirect monitoring areas. The shallow interpolation reliability of the indirect monitoring area is obtained based on the number of direct monitoring areas within the first radius centered on the indirect monitoring area, as well as the area distance, geological static parameter similarity, and altitude difference between each of the multiple direct monitoring areas and the indirect monitoring area. The reliability of deep interpolation in the indirect monitoring area is obtained based on the number of direct monitoring areas within the second radius centered on the indirect monitoring area, as well as the area distance, similarity of geological static parameters, altitude difference, and deep connectivity of the area between each of the multiple direct monitoring areas and the indirect monitoring area.

9. The geological disaster early warning method based on the graded triggering of geological depth and shallow layer parameters according to claim 8, characterized in that, The second radius is greater than the first radius.

10. A geological disaster early warning system triggered by graded geological parameters at varying depths, based on the geological disaster early warning method triggered by graded geological parameters at varying depths as described in any one of claims 1 to 9, characterized in that... include: The function construction module constructs a first function for calculating disaster risk values ​​based on shallow geological parameters, and a second function for calculating disaster risk values ​​based on both shallow and deep geological parameters. The block division module evenly divides the warning block into multiple warning zones, and determines the direct monitoring zone with parameter monitoring device and the indirect monitoring zone without parameter monitoring device in the multiple warning zones. The parameter monitoring device can monitor shallow geological parameters and deep geological parameters. The first acquisition module obtains the first disaster risk value corresponding to each of the multiple direct monitoring areas based on the shallow geological parameters and the first function, and then determines the secondary early warning area from the multiple direct monitoring areas. The second acquisition module obtains the second disaster risk value corresponding to each secondary warning area based on the shallow geological parameters, deep geological parameters, and the second function corresponding to each secondary warning area. The first interpolation module interpolates the shallow geological parameters corresponding to each of the multiple directly monitored areas to obtain the shallow geological parameters corresponding to each of the multiple indirectly monitored areas. The third acquisition module obtains the first disaster risk value corresponding to each of the multiple indirect monitoring areas based on the geological shallow parameters and the first function, and then determines the secondary interpolation area from the multiple indirect monitoring areas. The second interpolation module interpolates the geological deep parameters corresponding to each secondary early warning area based on the geological deep parameters corresponding to each secondary interpolation area. The fourth acquisition module obtains the second disaster risk value corresponding to each quadratic interpolation area based on the shallow geological parameters, deep geological parameters, and the second function.

Images