Geological disaster risk detection method and device and computer equipment

Abstract

The invention relates to a geological disaster risk detection method and device and computer equipment, and the method comprises the steps: obtaining mountain image data containing a to-be-detected mountain region, carrying out the slope unit division of the mountain image data, and obtaining to-be-detected slope units; based on the first-level static index and the second-level static index, obtaining a static detection result for the to-be-detected slope unit, and screening out an easily-occurring slope unit from the to-be-detected slope unit based on the static detection result; calculating a dynamic detection result based on the static detection result and rainfall working condition data of the to-be-detected slope unit, and screening out a dangerous slope unit from the easily-occurring slope units based on the dynamic detection result; and based on the dynamic detection result and the disaster-bearing body detection data, calculating a geological disaster risk result of the mountain area to be detected. According to the invention, the problem that geological disaster risk detection is difficult to consider accuracy and efficiency at the same time is solved.

Description

Technical Field

[0001] The present application relates to the technical field of engineering geology, and particularly to a method, device and computer equipment for detecting geological disaster risks. Background Art

[0002] Geological disasters are one of the common natural disasters in China, characterized by complex formation mechanisms, wide distribution ranges and great harm. By detecting geological disaster risks and comprehensively analyzing the occurrence probability of geological disasters and the degree of losses caused by disasters, it is possible to help identify and quantify the geological disaster risks faced by specific regions or engineering projects, enabling disaster prevention and mitigation work to be carried out in advance and effectively avoiding risks. However, in the related technologies, due to the complex mountainous terrain and large amounts of data, it is difficult to consume a large amount of computing resources for unified accuracy assessment across the entire region, and it is difficult to balance processing speed and accuracy.

[0003] Currently, there is no effective solution to the problem that it is difficult to balance accuracy and efficiency in detecting geological disaster risks in the related technologies. Summary of the Invention

[0004] Embodiments of the present application provide a method, device and computer equipment for detecting geological disaster risks, so as to at least solve the problem that it is difficult to balance accuracy and efficiency in detecting geological disaster risks in the related technologies.

[0005] In a first aspect, embodiments of the present application provide a method for detecting geological disaster risks, the method comprising:

[0006] Obtain mountain image data including the mountain area to be measured, and perform slope unit division processing on the mountain image data to obtain the slope units to be measured;

[0007] Determine the preset first-level static indicators, and obtain the second-level static indicators determined by dividing the first-level static indicators; based on the first-level static indicators and the second-level static indicators, perform geological disaster susceptibility calculation on the slope units to be measured to obtain a static detection result, and screen out the prone slope units from the slope units to be measured based on the static detection result;

[0008] Detect the precipitation condition data for the prone slope units; based on the static detection result and the precipitation condition data, calculate a dynamic detection result, and screen out the dangerous slope units from the prone slope units based on the dynamic detection result;

[0009] Calculate the disaster-bearing body detection data corresponding to each of the dangerous slope units; based on the dynamic detection result and the disaster-bearing body detection data, calculate the geological disaster risk result of the mountain area to be measured.

[0010] In some of these embodiments, the first - level static indicators at least include topographic and geomorphic indicators and geological environment condition indicators; the second - level static indicators at least include slope gradient indicators and slope shape indicators obtained by dividing the topographic and geomorphic indicators, and overburden thickness indicators obtained by dividing the geological environment condition indicators.

[0011] In some of these embodiments, based on the first - level static indicators and the second - level static indicators, for the slope unit to be measured, a geological hazard susceptibility calculation is performed to obtain a static detection result, including:

[0012] Based on the slope gradient indicators and the slope shape indicators, slope gradient assignment data and slope shape assignment data are calculated for the slope unit to be measured, and based on the overburden thickness indicators, overburden thickness assignment data is calculated for the slope unit to be measured;

[0013] According to the slope gradient assignment data and the slope shape assignment data, first - level assignment data corresponding to the topographic and geomorphic indicators is calculated, and according to the overburden thickness assignment data, second - level assignment data corresponding to the geological environment condition indicators is calculated;

[0014] Based on the first - level assignment data and the second - level assignment data, the static detection result is obtained.

[0015] In some of these embodiments, the calculating the overburden thickness assignment data for the slope unit to be measured based on the overburden thickness indicators includes:

[0016] Obtain a first mapping model corresponding to a first slope shape and a second mapping model corresponding to a second slope shape; the first mapping model is used to indicate the mapping relationship between the slope gradient and the overburden thickness under the first slope shape, and the second mapping model is used to indicate the mapping relationship between the slope gradient and the overburden thickness under the second slope shape;

[0017] Detect the actual slope shape of the slope unit to be measured, and match the actual slope shape with the first slope shape and the second slope shape respectively. According to the matching result, an actual mapping model is selected from the first mapping model and the second mapping model;

[0018] In response to the overburden thickness indicators, using the actual mapping model, calculate the overburden thickness prediction parameter of the slope unit to be measured, and calculate the overburden thickness assignment data according to the overburden thickness prediction parameter.

[0019] In some of these embodiments, the dividing the mountain image data into slope units to obtain slope units to be measured includes:

[0020] Determine the micro-topography disturbance effect area in the mountain image data, and perform depression filling processing on the mountain image data based on the micro-topography disturbance effect area to obtain preprocessed data;

[0021] Calculate the density of slope units according to the preprocessed data, and obtain the slope unit to be measured based on the density of slope units.

[0022] In some embodiments, the obtaining the slope unit to be measured based on the density of slope units includes:

[0023] Perform slope unit division processing on the mountain image data based on the density of slope units to obtain initial slope units;

[0024] Send the mountain image data to a display interface for display, and detect a correction instruction for the mountain image data triggered by the display interface;

[0025] Analyze the detected correction instruction to obtain correction parameters, and perform correction processing on the initial slope units based on the correction parameters to obtain the slope unit to be measured.

[0026] In some embodiments, the calculating the disaster-bearing body detection data corresponding to each of the dangerous slope units includes:

[0027] Match each of the dangerous slope units with the buildings included in the mountain image data to obtain target disaster-bearing body position information;

[0028] Obtain the disaster-bearing body detection data based on the target disaster-bearing body position information.

[0029] In some embodiments, the calculating the geological disaster risk result of the mountain area to be measured based on the dynamic detection result and the disaster-bearing body detection data includes:

[0030] Calculate the risk quantification parameters of each of the dangerous slope units based on the dynamic detection result and the disaster-bearing body detection data;

[0031] Send the risk quantification parameters to a display interface for display, and detect the segmentation boundary values and division strategies for the risk quantification parameters received by the display interface;

[0032] Based on the division strategy, perform risk division processing on each of the dangerous slope units according to the segmentation boundary values and the risk quantification parameters, and obtain the geological disaster risk result.

[0033] In a second aspect, an embodiment of the present application provides a geological disaster risk detection device, including:

[0034] An acquisition module, configured to acquire mountain image data including the mountain area to be measured, and perform slope unit division processing on the mountain image data to obtain slope units to be measured;

[0035] A first-level detection module, configured to determine a preset first-level static index, and acquire a second-level static index determined by dividing the first-level static index; based on the first-level static index and the second-level static index, perform geological disaster susceptibility calculation on the slope units to be measured to obtain a static detection result, and screen out prone slope units from the slope units to be measured based on the static detection result;

[0036] A second-level detection module, configured to detect precipitation condition data for the prone slope units; based on the static detection result and the precipitation condition data, calculate a dynamic detection result, and screen out dangerous slope units from the prone slope units based on the dynamic detection result;

[0037] A risk calculation module, configured to calculate disaster-bearing body detection data corresponding to each of the dangerous slope units; based on the dynamic detection result and the disaster-bearing body detection data, calculate the geological disaster risk result of the mountain area to be measured.

[0038] In a third aspect, an embodiment of the present application provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the geological disaster risk detection method described in the first aspect above is implemented.

[0039] Compared with the related art, the geological disaster risk detection method, device and computer device provided by the embodiments of the present application obtain mountain image data including the mountain area to be measured, and perform slope unit division processing on the mountain image data to obtain slope units to be measured; determine a preset first-level static index, and acquire a second-level static index determined by dividing the first-level static index; based on the first-level static index and the second-level static index, perform geological disaster susceptibility calculation on the slope units to be measured to obtain a static detection result, and screen out prone slope units from the slope units to be measured based on the static detection result; detect precipitation condition data for the prone slope units; based on the static detection result and the precipitation condition data, calculate a dynamic detection result, and screen out dangerous slope units from the prone slope units based on the dynamic detection result; calculate disaster-bearing body detection data corresponding to each of the dangerous slope units; based on the dynamic detection result and the disaster-bearing body detection data, calculate the geological disaster risk result of the mountain area to be measured.

[0040] Based on this, static indicators such as terrain and rock and soil masses are used to quickly identify potential risk units of the slope units to be measured. The calculation range is narrowed through the spatial dimensionality reduction of macroscopic indicators, avoiding redundant calculations of global high-precision modeling. Only real-time precipitation condition data is superimposed on the prone units, and local dynamic modeling is realized in combination with static detection results. Finally, risk quantification is carried out by combining the detection data of disaster-bearing bodies, thereby constructing a progressive mode of global screening, key focus, and precise assessment based on a three-level detection system. It can greatly improve the detection efficiency of large-scale geological disaster risks on the premise of ensuring the accuracy of assessment, realizing both in-depth analysis of high-risk areas and ensuring large-scale monitoring efficiency, thus effectively solving the problem that it is difficult to balance accuracy and efficiency in geological disaster risk detection.

[0041] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more concise and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0043] Figure 1 is a hardware structural block diagram of a terminal of a geological disaster risk detection method according to an embodiment of the present application;

[0044] Figure 2 is a flowchart of a geological disaster risk detection method according to an embodiment of the present application;

[0045] Figure 3 is a schematic diagram of the fitting relationship of different model data of the thickness and slope of a concave slope covering layer according to an embodiment of the present application;

[0046] Figure 4 is a schematic diagram of a grading parameter input display interface according to an embodiment of the present application;

[0047] Figure 5 is a structural block diagram of a geological disaster risk detection device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be described and explained below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments provided in the present application without making creative efforts belong to the scope of protection of the present application. In addition, it can also be understood that although the efforts made in such a development process may be complex and lengthy, for those of ordinary skill in the art related to the content disclosed in the present application, some design, manufacturing or production changes based on the technical content disclosed in the present application are only conventional technical means and should not be understood that the content disclosed in the present application is insufficient.

[0049] Reference to "embodiment" in the present application means that a specific feature, structure or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those of ordinary skill in the art will explicitly and implicitly understand that the embodiments described in the present application may be combined with other embodiments without conflict.

[0050] Unless otherwise defined, the technical terms or scientific terms involved in the present application should have the ordinary meaning understood by those of ordinary skill in the technical field to which the present application belongs. The terms "a", "one", "kind", "the" and other similar words involved in the present application do not indicate a quantity limitation and may represent a single or plural number. The terms "include", "comprise", "have" and any variations thereof involved in the present application are intended to cover non-exclusive inclusion; for example, a process, method, system, product or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may further include unlisted steps or units, or may further include other steps or units inherent to these processes, methods, products or devices. The terms "connected", "coupled" and other similar words involved in the present application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "plurality" involved in the present application means greater than or equal to two. "And / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, "A and / or B" may represent: A exists alone, A and B exist simultaneously, and B exists alone. The terms "first", "second", "third", etc. involved in the present application are only used to distinguish similar objects and do not represent a specific order for the objects.

[0051] The method embodiment provided in this embodiment can be executed on a terminal, a computer or a similar computing device. Taking running on a terminal as an example,Figure 1 It is a hardware structure block diagram of a terminal for a geological disaster risk detection method according to an embodiment of the present application. As Figure 1 shown, the terminal may include one or more ( Figure 1 only one is shown in the figure) processors 102 (the processor 102 may include, but is not limited to, processing devices such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the above terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above terminal. For example, the terminal may further include more or fewer components than Figure 1 shown in the figure, or have a different configuration from Figure 1 shown in the figure.

[0052] The memory 104 can be used to store computer programs. For example, software programs and modules of application software, such as the computer program corresponding to the geological disaster risk detection method in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implements the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories can be connected to the terminal through a network. Examples of the above network include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0053] The transmission device 106 is used to receive or send data via a network. Specific examples of the above network may include a wireless network provided by a communication provider of the terminal. In one instance, the transmission device 106 includes a network adapter (abbreviated as NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one instance, the transmission device 106 may be a radio frequency (abbreviated as RF) module, which is used to communicate with the Internet wirelessly.

[0054] This embodiment provides a geological disaster risk detection method. Figure 2 It is a flowchart of a geological disaster risk detection method according to an embodiment of the present application. As Figure 2 shown, the process includes the following steps:

[0055] Step S210: Obtain the mountain image data containing the mountain area to be measured, and perform slope unit division processing on the mountain image data to obtain the slope units to be measured.

[0056] Among them, multi-source remote sensing data (such as LiDAR point cloud, SRTM 30m DEM) can be combined with the images of the mountain area to be measured taken by an unmanned aerial vehicle to obtain the mountain image data to ensure high-precision terrain modeling. Next, after generating Digital Elevation Model (DEM) data based on the 1:10000 topographic map, methods such as the Hydrological Slope Units (HSU) or curvature segmentation method of the DEM are used, combined with the analysis of terrain undulation to extract valley lines and ridge lines. Finally, the above data is converted into a vectorized slope unit surface file, that is, multiple slope units to be measured are determined by division.

[0057] Step S220: Determine the preset first-level static indicators, and obtain the second-level static indicators determined by dividing the first-level static indicators; based on the first-level static indicators and the second-level static indicators, perform geological disaster susceptibility calculation for the slope units to be measured to obtain a static detection result, and screen out the susceptible slope units from the slope units to be measured based on the static detection result.

[0058] It should be noted that the geological disaster susceptibility evaluation index system for slopes as units mainly consists of two levels of index systems. Among them, there are three first-level static indicators, namely topography, geological environment conditions, and human engineering activities. The second-level static indicators are further subdivisions of the first-level static indicators. For example, for the first-level static indicator of topography, multiple second-level static indicators can be further subdivided, including slope, aspect, elevation difference, and slope shape; for geological environment conditions, it can be subdivided into overburden thickness, rock and soil mass, distance from the structure, average annual precipitation, and earthquake; for human engineering activities, it can be subdivided into cutting height. In addition, for the assignment of cutting height, if support is done after cutting, the assignment should be multiplied by a reduction coefficient α (0 - 1); for better support, α takes a smaller value; for poorer support, α takes a larger value; for no support, α = 1. And the above indicators all belong to the static indicators for aspects such as the mountain geological environment conditions.

[0059] Subsequently, based on the above geological disaster susceptibility evaluation index system and the quantification score situation, the comprehensive value of geological disaster susceptibility of each slope unit is calculated by weighting. And in order to calculate the risk value of each slope unit more accurately, in this step, the comprehensive susceptibility value of each slope unit can also be normalized, as shown in the following formula:

[0060] Y i=(100 / F max )×F i ;

[0061] where F i represents the comprehensive geological disaster susceptibility value of the i-th slope unit; F max represents the maximum value obtained by statistically calculating the comprehensive geological disaster susceptibility values for each slope unit; Y i represents the susceptibility index of the i-th slope unit, and finally a static detection result for indicating the geological disaster susceptibility of each slope unit is obtained.

[0062] Next, according to the susceptibility indices of each slope unit, they are divided into multiple levels according to different threshold criteria, and slope units with high susceptibility are screened out. For example, for slope units with Y≥85, they are divided into high geological disaster prone areas; for slope units with 75≤Y<85, they are divided into medium geological disaster prone areas; for slope units with 40≤Y<75, they are divided into low geological disaster prone areas; for slope units with Y<40, they are divided into non-geological disaster prone areas. After dividing each slope unit according to the degree of susceptibility, the slope units divided into high, medium, and low geological disaster prone areas can be screened out and determined as prone slope units.

[0063] Step S230, detect the precipitation condition data for the prone slope units; based on the static detection result and the precipitation condition data, calculate the dynamic detection result, and screen out the dangerous slope units from the prone slope units.

[0064] Specifically, in this step, four daily precipitation conditions are mainly considered: the daily precipitation (P) is heavy rain (25mm≤P<50mm), the daily precipitation is rainstorm (50mm≤P<100mm), the daily precipitation is heavy rainstorm (100mm≤P<250mm), and the daily precipitation is extremely heavy rainstorm (250mm≤P). Quantitatively assign values to the precipitation indicators as shown in the following formula:

[0065] J k =P k ×100 / P max ;

[0066] In the above formula, P k is the rainfall of different levels. P max is the maximum daily precipitation statistically obtained within the historical time period. For example, the maximum daily precipitation in a certain town in the past 60 years is 267.6mm. J k is the rainfall quantization score of the k-th level.

[0067] Based on the above precipitation index scores and the susceptibility index in the static detection results calculated in the previous steps, calculate the geological hazard risk index for each susceptible slope unit by weighted calculation, as shown in the following formula:

[0068] Q i =W Y Y i +W J J k ;

[0069] Among them, W Y and W J represent the weight values assigned to the susceptibility index and the precipitation index score respectively; for example, the weight value W Y assigned to the susceptibility index is set to 1, and the weight value W J assigned to the precipitation index score is set to 0.2. Q i represents the geological hazard risk index of the i-th susceptible slope unit, that is, the above dynamic detection results are obtained. It should also be understood that, under the allowable circumstances of the embodiment, the geological hazard risk index of each slope unit including susceptible slope units and non-susceptible slope units can also be calculated in the above manner, and the detection results can be generated accordingly. It can be seen that by considering the dynamic precipitation condition data, the dynamic detection results can be further calculated for the susceptible slope units, which helps to improve the accuracy of the subsequent detection steps.

[0070] According to the geological hazard risk indices detected for each susceptible slope unit, divide them into multiple levels according to different threshold criteria, and continue to screen out the slope units with high risk indices from them. For example, for slope units with Q≥85, they are divided into high geological hazard risk areas; for slope units with 75≤Q<85, they are divided into relatively high geological hazard risk areas; for slope units with 40≤Q<75, they are divided into medium geological hazard risk areas; for slope units with Q<40, they are divided into low geological hazard risk areas. After dividing each susceptible slope unit according to the degree of danger, the slope units divided into high geological hazard risk areas and relatively high geological hazard risk areas can be screened out and determined as dangerous slope units.

[0071] Step S240, calculate the disaster-bearing body detection data corresponding to each dangerous slope unit; based on the dynamic detection results and the disaster-bearing body detection data, calculate the geological hazard risk results of the mountain area to be measured.

[0072] Furthermore, on the basis of the risk assessment index system, add the population quantity / property index. Specifically, on the basis of the risk assessment determined through the above steps, calculate the risk value of the disaster-bearing body suffering from geological disasters in each slope threat area according to the following formula:

[0073] Ri =(Q i / Q max )×E i ×V;

[0074] Among them, R i is the risk value of the disaster-bearing body suffering from geological disasters in the threat area of the i-th slope unit obtained by calculation. Q max is the maximum value of the slope hazard index calculated for the whole region; Q i is the hazard index of the i-th slope unit. E i is the number of disaster-bearing bodies (population / property) in the threat area of the i-th slope unit; V is the vulnerability of the disaster-bearing body threatened by geological disasters (according to the maximum risk principle, when the disaster-bearing body is in the threat area, V takes the value of 1). The above disaster-bearing body detection data includes E i and V. Then, according to the risk values of the disaster-bearing bodies in the threat areas of each slope unit, they are divided into multiple levels according to different threshold standards, that is, the geological disaster risk results of the above-mentioned mountain area to be measured are obtained. For example, for slope units with R≥30 people or R≥50 million yuan, they are divided into high-risk areas of geological disasters; for slope units with 10 people ≤ R < 30 people or 10 million yuan ≤ R < 50 million yuan, they are divided into relatively high-risk areas of geological disasters; for slope units with 3 people ≤ R < 10 people or 5 million yuan ≤ R < 10 million yuan, they are divided into medium-risk areas of geological disasters; for slope units with R < 3 people or R < 5 million yuan, they are divided into low-risk areas of geological disasters.

[0075] In addition, in the above steps, since the precipitation condition data and the disaster-bearing body detection data are dynamic index data, in order to ensure the accuracy of risk detection, the precipitation condition data and / or the disaster-bearing body detection data can also be updated according to a preset update cycle. Through the real-time update of the precipitation condition data and the disaster-bearing body detection data, the problem that the traditional static risk detection model is difficult to cope with environmental changes is solved.

[0076] Through the above steps S210 to S240, based on static indicators such as terrain and rock and soil bodies, potential risk units of the slope units to be measured are quickly identified. The calculation range is reduced through the spatial dimensionality reduction of macroscopic indicators, avoiding redundant calculations of high-precision modeling in the whole region, and only superimposing real-time precipitation condition data on the prone units, and realizing local dynamic modeling in combination with static detection results. Finally, the risk of the disaster-bearing body detection data is quantified, so as to construct a progressive mode of global screening, key focus, and precise evaluation based on a three-level detection system, which can greatly improve the efficiency of large-scale geological disaster risk detection on the premise of ensuring the accuracy of evaluation, realizing both in-depth analysis of high-risk areas and ensuring large-scale monitoring efficiency. Therefore, the problem that it is difficult to balance the accuracy and efficiency of geological disaster risk detection is effectively solved.

[0077] In some of these embodiments, the above-mentioned first-level static indicators at least include topographic and geomorphic indicators and geological environment condition indicators; the above-mentioned second-level static indicators at least include a slope gradient indicator and a slope shape indicator obtained by dividing the topographic and geomorphic indicators, and an overburden thickness indicator obtained by dividing the geological environment condition indicators.

[0078] More specifically, the geological hazard susceptibility evaluation index system with slopes as units, which includes the above-mentioned topographic and geomorphic, geological environment condition and other indicators, is shown in Table 1 below:

[0079] Table 1

[0080]

[0081] In some of these embodiments, based on the above-mentioned first-level static indicators and second-level static indicators, when calculating the geological hazard susceptibility for the slope unit to be measured to obtain a static detection result, the following steps may further be included:

[0082] Based on the slope gradient indicator and the slope shape indicator, calculate the slope assignment data and the slope shape assignment data for the slope unit to be measured, and based on the overburden thickness indicator, calculate the overburden thickness assignment data for the slope unit to be measured; according to the slope assignment data and the slope shape assignment data, calculate the first-level assignment data corresponding to the topographic and geomorphic indicators, and according to the overburden thickness assignment data, calculate the second-level assignment data corresponding to the geological environment condition indicators; based on the first-level assignment data and the second-level assignment data, obtain the static detection result.

[0083] Specifically, referring to Table 1 above, the topographic indicators are split into two types of features: slope (angle value) and slope shape (convex / concave shape), and the assignment data for both are calculated through a non-linear assignment model and a morphological classification matrix respectively. For example, for the slope gradient indicator, a slope of 0 - 5° is assigned a value of 4. On this basis, the assigned value increases as the slope increases until the angle range of 25° - 35°, at which point the assigned value is 35; thereafter, the assigned data decreases as the slope increases, and when the slope is greater than 45°, the assigned value is 10. On the other hand, for a convex slope shape, considering that due to the small radius of curvature at the top of the convex slope, significant radial tensile stress and tangential shear stress are generated, resulting in the rock and soil mass being more likely to undergo rotational-slip failure along the arc-shaped slip surface, the assigned value of the convex slope shape is set to be greater than that of the concave slope shape. For example, the assigned value for the concave slope shape is 45, and the assigned value for the convex slope shape is 55. In addition, the topographic and geomorphic features can be further divided into slope aspect indicators and elevation difference indicators, then the higher the slope aspect is concentrated in the due south direction, the higher the assigned value, and the greater the elevation difference of the slope, the higher the assigned value.

[0084] For the overlay thickness index, through the fitted non - linear mapping relationship, the corresponding relationship between the overlay thickness and the assigned value is indicated, where the greater the overlay thickness, the higher the assigned value. In addition, for the geological environment condition index, the rock - soil body index and the distance index from the structure can be further subdivided and added. Different types of rock - soil bodies have different assigned value ranges, and the greater the distance from the structure, the smaller the assigned value. In addition, for the annual average precipitation index and the earthquake index divided from the geological environment condition index, since there are basically no differences in the annual average rainfall and peak ground acceleration of seismic motion within the township area, they are not considered for the time being.

[0085] Subsequently, the assigned value of each of the above - mentioned second - level static indicators is weighted and calculated to obtain the assigned value data of each first - level static indicator, and the assigned value data of each first - level static indicator is comprehensively statistically analyzed to finally obtain the static detection result. It should also be understood that the above - mentioned second - level static indicators can also be directly weighted and calculated together to statistically obtain the final static detection result.

[0086] Through the above steps, in the way of index decoupling - hierarchical quantification - dynamic weight, on the premise of ensuring geological interpretability, the refinement and high - efficiency of the static risk assessment of slope units are realized, providing reliable basic data support for subsequent dynamic risk analysis.

[0087] In addition, based on the above analysis, it can be seen that in the embodiments of the present application, the overlay thickness is an important indicator for detecting geological disaster risks. However, in the related art, it is difficult to directly detect the specific value of the overlay thickness through mountain remote sensing images, thus affecting the accuracy of risk detection.

[0088] To solve the above problems, some embodiments are also provided. Among them, based on the overlay thickness index, calculating the assigned value data of the overlay thickness for the slope unit to be measured may further include the following steps:

[0089] Obtain the first mapping model corresponding to the first slope shape and the second mapping model corresponding to the second slope shape; the first mapping model is used to indicate the mapping relationship between the slope and the overlay thickness under the first slope shape, and the second mapping model is used to indicate the mapping relationship between the slope and the overlay thickness under the second slope shape.

[0090] It should be noted that in the related art, it is difficult to systematically clarify the influencing factors affecting the mountain overlay thickness and the qualitative and quantitative relationships between various influencing factors and the overlay thickness. Therefore, in this embodiment, for the previous accurate process, a large number of measured overlay thickness data of multiple slope units in the geophysical prospecting and drilling work areas, as well as the corresponding geological environment data of the slope units, are also collected. Exemplarily, Table 2 shows a data table of the measured overlay thickness and geological environment data of some slope units:

[0091] Table 2

[0092]

[0093] Based on the above measured data for data analysis, the data analysis results obtained show that the relationship between the thickness of the covering layer and the slope shape and slope is relatively close. Based on this, an attempt was made to establish the quantitative relationship between the thickness of the covering layer and the slope under different slope shapes.

[0094] First, for the concave slope (i.e., the above-mentioned first slope shape), the measured data of the thickness of the covering layer and the slope were fitted to obtain different data fitting relationships, including quadratic curves, cubic curves, composite curves, power curves, etc. For example, please refer to Figure 3 , in the figure, a variety of curves fitted from the measured data are shown, among which the logarithmic curve is represented by a solid line in Figure 3 ; the power curve and the composite curve are two curves with partially overlapping fitting positions and an overall upward trend. The power curve is represented by a dashed line, and the minimum value of the covering layer thickness in the power curve data is less than the minimum value of the covering layer thickness in the composite curve data; the quadratic curve (quadratic parabola) and the cubic curve are two curves with partially overlapping fitting positions, and the minimum slope value in the cubic curve data is less than the minimum slope value in the quadratic curve data, and the maximum slope value in the cubic curve data is greater than the maximum slope value in the cubic curve data. According to the data fitting results of different models, the most suitable fitting curve is selected as the fitting model, and then the quantitative relationship between the thickness of the covering layer and the slope for the concave slope can be obtained, that is, the above-mentioned first mapping model is obtained, as shown in the following formula:

[0095] ;

[0096] In the above formula, D1 represents the thickness of the covering layer of the concave slope, and S1 represents the slope of the concave slope. a1, b1, and c1 respectively represent the fitting coefficients of each polynomial term. For example, through a series of measured data, it is fitted that a1 = -19.1032, b1 = 1.2773, and c1 = -0.0006.

[0097] Similarly, for the convex slope (i.e., the above-mentioned second slope shape), the above method can also be used for fitting to determine the quantitative relationship between the thickness of the covering layer and the slope for the convex slope, that is, the above-mentioned second mapping model is obtained, as shown in the following formula:

[0098] ;

[0099] Among them, D2 represents the thickness of the convex slope covering layer, and S2 represents the slope of the convex slope. a2, b2, and c2 respectively represent the fitting coefficients of each polynomial. For example, through a series of measured data, the fitting results are a2 = -18.3967, b2 = 0.2059, and c2 = -0.0061.

[0100] It can be seen that through the above methods, through a large number of on-site surveys and data analysis of the complex measured data from the on-site surveys, redundant and irrelevant factors are eliminated, and the influencing factors such as slope shape and slope that directly affect the thickness of the mountain covering layer and can be accurately extracted directly from the remote sensing images are determined. Further data analysis is carried out to determine the mapping models under different types of slope shapes, so as to facilitate the subsequent analysis of the covering layer thickness data of the mountain to be measured.

[0101] Next, detect the actual slope shape of the slope unit to be measured, and match the actual slope shape with the first slope shape and the second slope shape respectively. According to the matching results, select the actual mapping model from the first mapping model and the second mapping model. In response to the covering layer thickness index, use the actual mapping model to calculate the covering layer thickness prediction parameter of the slope unit to be measured, and calculate the covering layer thickness assignment data according to the covering layer thickness prediction parameter.

[0102] More specifically, since through the above methods, various mapping models indicating the mapping relationship between the slope and the covering layer thickness under different slope shapes are constructed, when calculating the covering layer thickness of the current slope unit to be measured, the mapping model matching the slope shape of the slope unit to be measured can be selected first. For example, if the slope unit to be measured is a concave slope (i.e., the first slope shape), the first mapping model corresponding to the first slope shape is selected as the actual mapping model for subsequent calculations; if the slope unit to be measured is a convex slope (i.e., the second slope shape), the second mapping model corresponding to the second slope shape is selected as the actual mapping model. Then, substitute the slope value detected based on the image into the determined actual mapping model to calculate the covering layer thickness prediction parameter. Finally, refer to the relationship table shown in Table 1 above, detect the covering layer thickness range where the currently calculated covering layer thickness prediction parameter is located, and determine the preset assignment value corresponding to this covering layer thickness range, so as to obtain the above-mentioned covering layer thickness assignment data.

[0103] Through the above steps, by constructing the mapping models under different slope shapes, the covering layer thickness of the slope unit to be measured is calculated, thus realizing an accurate way to calculate the covering layer thickness based on the influencing factors such as slope shape and slope that can be directly observed or calculated from remote sensing images, solving the problem in the related technology that it is difficult to directly detect the thickness of the mountain covering layer. At the same time, it is no longer necessary for staff to conduct on-site surveys of the covering layer thickness for each mountain to be measured, so the accuracy and efficiency of the covering layer thickness detection are effectively improved, and further the accuracy and efficiency of the geological disaster risk detection are improved.

[0104] In some of these embodiments, the above-mentioned process of dividing the mountain image data into slope units to obtain the slope units to be measured may further include the following steps:

[0105] Determine the micro-topography disturbance effect area in the mountain image data, and based on the micro-topography disturbance effect area, perform depression filling processing on the mountain image data to obtain preprocessed data; according to the preprocessed data, calculate the density of the slope units, and based on the density of the slope units, obtain the slope units to be measured.

[0106] Considering that there are some areas in most slope bodies that are lower than the surrounding ground, when dividing slope units according to the general surface hydrological analysis method, complete slope units will appear and be divided in these areas, resulting in the micro-topography of slope within slope, which is the disturbance effect. Due to the existence of the disturbance effect area, the coincidence degree between the slope division result based on the automated surface hydrological analysis tool and the actual terrain is not high, resulting in an increase in the workload of field actual geological disaster investigation on the one hand, and a decrease in the accuracy of geological disaster risk assessment on the other hand. Therefore, in this embodiment, the hydrological analysis principle is used to eliminate the disturbance effect in the slope body from two aspects: finding the micro-topography and eliminating the micro-topography. Specifically, construct DEM data through the mountain image data, and based on the DEM data, calculate the area where the elevation value is less than the elevation value of the surrounding grid data, and determine it as the micro-topography disturbance effect area. Then, statistically calculate the water flow contribution area of all sinks, and then statistically calculate the lowest elevation of each contribution area by region. Then, calculate the outlet elevation (the minimum elevation of the region boundary) of each contribution area through region filling. Then, use the outlet elevation minus the lowest elevation to calculate the true depression depth, so as to determine the depression filling threshold to fill the micro-topography disturbance area and obtain the filled DEM data, that is, the above-mentioned preprocessed data.

[0107] Next, in order to divide slope units that conform to the actual terrain and meet the work requirements, this embodiment uses the data fitting method, selects a fixed target area, takes the total length of the valley lines in the target area automatically divided by the computer under different flow grid pixel values as sample data, fits the functional relationship between the flow grid pixel value and the valley line, and then obtains the slope units to be measured. Among them, a rectangular area with a relatively high terrain and obvious terrain undulation in the mountain area to be measured can be selected as the standard target area, and the boundary line of the slope unit represented by the valley line in the target area can be outlined. In addition, in order to accurately calculate the flow grid value pixels, in this step, first, according to the collected data, based on the range of the flow grid pixel value, select different pixel values to extract the valley line, and statistically analyze the relationship between different pixel values and the total length of the valley line, fit the power function curve between the two, and obtain the function expression:

[0108] y = k × x n ;

[0109] In the above expression, y represents the total length of the valley line, x represents the flow raster pixel value, and k and n are parameters obtained through fitting calculations. In this way, by substituting the total length value of the currently drawn valley line into the above expression, the flow raster pixel threshold size can be calculated, and the density of the above slope units can be calculated based on this flow raster pixel threshold. Finally, according to the determined density of the slope units, the boundaries of the slope units on the mountain area to be measured are automatically divided by the Geographic Information System (GIS), and then each slope unit divided from the mountain image data is extracted.

[0110] Through the above embodiments, for the micro-geomorphic disturbance effect of the slope body during the automatic pre-division process, the source and sink principles of hydrological analysis are used to find and eliminate the disturbed area, and the slope boundary is divided for the data after filling depressions to eliminate the disturbance effect, thus ensuring the accuracy of automatically extracting each slope unit to be measured from the mountain image data, and further helping to improve the accuracy of geological disaster risk detection.

[0111] In some of these embodiments, obtaining the slope unit to be measured based on the density of the slope units may further include the following steps:

[0112] Based on the density of the slope units, perform slope unit division processing on the mountain image data to obtain an initial slope unit; send the mountain image data to the display interface for display, and detect a correction instruction for the mountain image data triggered by the display interface; parse the detected correction instruction to obtain correction parameters, and perform correction processing on the initial slope unit based on the correction parameters to obtain the slope unit to be measured.

[0113] Among them, considering that if the slope units are simply divided automatically by computer software, the results will still have slope units that violate the actual terrain. Based on this, in this embodiment, a method of correcting and improving unreasonable slope units based on human-computer interaction is also provided. Specifically, combining the remote sensing image (i.e., the above-mentioned mountain image information) and the topographic features shown by the contour lines (such as 2m interval contour lines) generated from the above DEM data, manually circle the boundaries between flat areas, lakes, reservoirs, etc. and the actual slope units, and then generate a boundary correction instruction detected by the display interface, so that the computer program can determine the manually circled boundary based on parsing the instruction and eliminate the flat areas with extremely low probability or no geological disasters. The display interface is an operation interface deployed on the display terminal for interacting with the staff for slope unit adjustment.

[0114] Subsequently, mathematical statistical analysis is performed on the preliminarily generated slope unit areas. 1% of the average area of the slope units in the study area is set as the threshold. Manually merge the slope units with areas smaller than this threshold into the adjacent slope units, generate a unit merging correction instruction, and in response to the detected merging correction instruction, perform a rational adjustment of the slope units to obtain each improved slope unit to be measured. Finally, each extracted slope unit to be measured is divided and overlaid with the DEM data, so that in subsequent steps, with the slope unit as the basic evaluation unit, combined with parameters such as terrain undulation degree and slope generated by the DEM, the corresponding geological disaster risk value can be calculated.

[0115] Through the above embodiments, based on the machine automatic pre-division and human-computer interaction to trim and improve the slope units, the adjustment method of the slope unit boundary for manually removing the areas with extremely low probability of geological disasters or flat areas without geological disasters by professional manual drawing is realized, so that the finally obtained slope units can accurately conform to the actual terrain.

[0116] In some of the embodiments, the above calculation of the disaster-bearing body detection data corresponding to each dangerous slope unit may further include the following steps:

[0117] Match each dangerous slope unit with the buildings included in the mountain image data to obtain the target disaster-bearing body position information; based on the target disaster-bearing body position information, obtain the disaster-bearing body detection data.

[0118] Specifically, through high-resolution optical images (such as WorldView) or LiDAR point cloud data, using an automatic classification algorithm (such as a support vector machine) or manual interactive interpretation, extract the building outlines (surface elements); then perform a spatial intersection operation between the slope unit polygons and the building surface elements, and screen out the buildings located within the dangerous slope units, so as to determine the building positions within each dangerous slope unit, that is, obtain the above-mentioned target disaster-bearing body position information. Then, fuse the matching results with social and economic data (such as population density, building age, and property quantity, etc.) to form a comprehensive disaster-bearing body database, and finally, based on the stored data in the disaster-bearing body database, determine the disaster-bearing body detection data. In addition, when permitted by the embodiments, in order to improve the accuracy of disaster-bearing body matching, an artificial auxiliary detection means can also be added and improved on the basis of the above solution. Through the above embodiments, an algorithm for automatically matching disaster-bearing bodies is provided, which is beneficial to improving the accuracy and efficiency of disaster-bearing body data detection.

[0119] In some of the embodiments, the above calculation of the geological disaster risk result of the mountain area to be measured based on the dynamic detection result and the disaster-bearing body detection data may further include the following steps:

[0120] Based on the dynamic detection results and the disaster-bearing body detection data, calculate the risk quantification parameters for each dangerous slope unit; send the risk quantification parameters to the display interface for display, and detect the segmentation boundary values and division strategies for the risk quantification parameters received by the display interface; based on the division strategy, according to the segmentation boundary values and the risk quantification parameters, perform risk division processing on each dangerous slope unit, and obtain the geological disaster risk results.

[0121] In this embodiment, for the risk detection method with the mountain slope as the dissection unit, a professional detection software is provided, and the display interface of this software is deployed on the display terminal for display. Exemplarily, please refer to Figure 4 , in the "grading parameter input" display interface, the intervals of the comprehensive values of the geological disaster risk assessments of all dangerous slope units automatically calculated and extracted by the software are shown, that is, the above-mentioned risk quantification parameters; for example, the interval of the comprehensive value of the geological disaster risk assessment of the mountain slope unit is from 10.61 to 53.986. And, two division methods for geological disaster risk grading are provided: the 3-point method and the 4-point method, and the staff is required to select one. According to the division method selected by the staff, fill in the corresponding partition boundary values; the software limits the filled boundary values to be between the minimum value and the maximum value, and conform to Y1 < Y2 < Y3. In this way, the display interface can receive the division strategy of the selected 3-point method or 4-point method input by the staff, and the segmentation boundary values under this division strategy. Then, the software calculates the geological disaster risk grading results of each slope unit according to the input information, and generates a report result in the form of a corresponding table, etc., which is finally displayed by the display interface.

[0122] Through the above embodiment, a detection method for risk division by inputting segmentation boundary values through the software display interface is provided. The staff only needs to input the selected division strategy and the boundary values, and the operation is simple.

[0123] It should be noted that the steps shown in the above process or the flowchart of the accompanying drawings Figure 2 can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0124] This embodiment also provides a geological disaster risk detection device, which is used to implement the above embodiment and the preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, terms such as "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0125] Figure 5is a structural block diagram of a geological disaster risk detection device according to an embodiment of the present application. As Figure 5 shown, the device includes: an acquisition module 51, a primary detection module 52, a secondary detection module 53, and a risk calculation module 54; where:

[0126] The acquisition module 51 is configured to acquire mountain image data including the mountain area to be measured, perform slope unit division processing on the mountain image data to obtain the slope units to be measured, and acquire disaster-bearing body data; the primary detection module 52 is configured to determine preset first-level static indicators, and acquire second-level static indicators determined by dividing the first-level static indicators; based on the first-level static indicators and the second-level static indicators, perform geological disaster susceptibility calculation on the slope units to be measured to obtain a static detection result, and screen out the susceptible slope units from the slope units to be measured based on the static detection result; the secondary detection module 53 is configured to detect precipitation condition data for the susceptible slope units; based on the static detection result and the precipitation condition data, calculate a dynamic detection result, and screen out the dangerous slope units from the susceptible slope units based on the dynamic detection result; the risk calculation module 54 is configured to calculate the disaster-bearing body detection data corresponding to each dangerous slope unit; based on the dynamic detection result and the disaster-bearing body detection data, calculate the geological disaster risk result of the mountain area to be measured.

[0127] In some embodiments, the primary detection module 52 is further configured to calculate slope assignment data and slope shape assignment data for the slope units to be measured based on the slope gradient indicator and the slope shape indicator, and calculate overburden thickness assignment data for the slope units to be measured based on the overburden thickness indicator; the primary detection module 52 calculates the first-level assignment data corresponding to the topographic and geomorphic indicators according to the slope assignment data and the slope shape assignment data, and calculates the second-level assignment data corresponding to the geological environment condition indicators according to the overburden thickness assignment data; the primary detection module 52 obtains a static detection result based on the first-level assignment data and the second-level assignment data.

[0128] In some embodiments, the primary detection module 52 is further configured to acquire a first mapping model corresponding to a first slope shape and a second mapping model corresponding to a second slope shape; the first mapping model is used to indicate the mapping relationship between the slope gradient and the overburden thickness under the first slope shape, and the second mapping model is used to indicate the mapping relationship between the slope gradient and the overburden thickness under the second slope shape; the primary detection module 52 is further configured to detect the actual slope shape of the slope units to be measured, match the actual slope shape with the first slope shape and the second slope shape respectively, and screen out the actual mapping model from the first mapping model and the second mapping model according to the matching result; the primary detection module 52 is further configured to, in response to the overburden thickness indicator, use the actual mapping model to calculate the overburden thickness prediction parameter of the slope units to be measured, and calculate the overburden thickness assignment data according to the overburden thickness prediction parameter.

[0129] In some of these embodiments, the above-mentioned acquisition module 51 is further configured to perform slope unit division processing on the mountain body image data based on the density of slope units to obtain initial slope units; send the mountain body image data to a display interface for display, and detect a correction instruction for the mountain body image data triggered by the display interface; parse the detected correction instruction to obtain correction parameters, and perform correction processing on the initial slope units based on the correction parameters to obtain slope units to be measured.

[0130] In some of these embodiments, the above-mentioned risk calculation module 54 is further configured to match each dangerous slope unit with the buildings included in the mountain body image data to obtain target disaster-bearing body location information; based on the target disaster-bearing body location information, obtain disaster-bearing body detection data.

[0131] In some of these embodiments, the above-mentioned risk calculation module 54 is further configured to calculate risk quantification parameters for each dangerous slope unit based on the dynamic detection results and the disaster-bearing body detection data; send the risk quantification parameters to a display interface for display, and detect the segmentation boundary values and division strategies for the risk quantification parameters received by the display interface; based on the division strategy, perform risk division processing on each dangerous slope unit according to the segmentation boundary values and the risk quantification parameters, and obtain geological disaster risk results.

[0132] It should be noted that the above-mentioned each module can be a functional module or a program module, and can be implemented either by software or by hardware. For the modules implemented by hardware, the above-mentioned each module can be located in the same processor; or the above-mentioned each module can also be located in different processors in any combination form. Specific examples in this embodiment can refer to the examples described in the above-mentioned embodiments and optional implementation manners, and will not be elaborated in this embodiment.

[0133] This embodiment also provides a computer device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above-mentioned method embodiments.

[0134] Optionally, the above-mentioned computer device may further include a transmission device and input / output devices. Among them, the transmission device is connected to the above-mentioned processor, and the input / output devices are connected to the above-mentioned processor.

[0135] Optionally, in this embodiment, the above-mentioned processor may be configured to execute the following steps through a computer program:

[0136] S1, obtain mountain body image data including the mountain body area to be measured, and perform slope unit division processing on the mountain body image data to obtain slope units to be measured.

[0137] S2, determine the preset first-level static indicators, and obtain the second-level static indicators determined by dividing the first-level static indicators; based on the first-level static indicators and the second-level static indicators, perform geological disaster susceptibility calculation for the slope unit to be measured to obtain a static detection result, and screen out the prone slope units from the slope units to be measured based on the static detection result.

[0138] S3, detect the precipitation condition data for the prone slope units; based on the static detection result and the precipitation condition data, calculate the dynamic detection result, and screen out the dangerous slope units from the prone slope units based on the dynamic detection result.

[0139] S4, calculate the disaster-bearing body detection data corresponding to each dangerous slope unit; based on the dynamic detection result and the disaster-bearing body detection data, calculate the geological disaster risk result of the mountain area to be measured.

[0140] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation manners, and will not be elaborated here.

[0141] In addition, in combination with the geological disaster risk detection method in the above embodiments, the embodiments of the present application can be implemented by providing a storage medium. A computer program is stored on the storage medium; when the computer program is executed by a processor, any one of the geological disaster risk detection methods in the above embodiments is implemented.

[0142] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties.

[0143] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0144] Those skilled in the art should understand that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0145] The above embodiments only represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A geological disaster risk detection method, characterized in that, The method includes: Obtain mountain image data including the mountain area to be measured, and perform slope unit division processing on the mountain image data to obtain the slope units to be measured; Determine the preset first-level static indicators, and obtain the second-level static indicators determined by dividing the first-level static indicators; based on the first-level static indicators and the second-level static indicators, perform geological disaster susceptibility calculation for the slope units to be measured to obtain a static detection result, and screen out the prone slope units from the slope units to be measured based on the static detection result; Detect the precipitation condition data for the prone slope units, calculate a dynamic detection result based on the static detection result and the precipitation condition data, and screen out the dangerous slope units from the prone slope units based on the dynamic detection result; Calculate the disaster-bearing body detection data corresponding to each of the dangerous slope units; calculate the geological disaster risk result of the mountain area to be measured based on the dynamic detection result and the disaster-bearing body detection data.

2. The geological hazard risk detection method according to claim 1, wherein The first-level static indicators at least include topographic and geomorphic indicators and geological environment condition indicators; the second-level static indicators at least include slope gradient indicators and slope shape indicators obtained by dividing the topographic and geomorphic indicators, and overburden thickness indicators obtained by dividing the geological environment condition indicators.

3. The geological hazard risk detection method according to claim 2, wherein The performing geological disaster susceptibility calculation for the slope units to be measured based on the first-level static indicators and the second-level static indicators to obtain a static detection result includes: Calculate slope assignment data and slope shape assignment data for the slope units to be measured based on the slope gradient indicators and the slope shape indicators, and calculate overburden thickness assignment data for the slope units to be measured based on the overburden thickness indicators; Calculate the first-level assignment data corresponding to the topographic and geomorphic indicators according to the slope assignment data and the slope shape assignment data, and calculate the second-level assignment data corresponding to the geological environment condition indicators according to the overburden thickness assignment data; Obtain the static detection result based on the first-level assignment data and the second-level assignment data.

4. The geological disaster risk detection method according to claim 3, characterized in that, The calculating overburden thickness assignment data for the slope units to be measured based on the overburden thickness indicators includes: Obtain a first mapping model corresponding to a first slope shape and a second mapping model corresponding to a second slope shape; the first mapping model is used to indicate the mapping relationship between the slope gradient and the overburden thickness under the first slope shape, and the second mapping model is used to indicate the mapping relationship between the slope gradient and the overburden thickness under the second slope shape; Detect the actual slope shape of the slope units to be measured, and match the actual slope shape with the first slope shape and the second slope shape respectively, and screen out the actual mapping model from the first mapping model and the second mapping model according to the matching result; In response to the overburden thickness indicator, use the actual mapping model to calculate the overburden thickness prediction parameter of the slope units to be measured, and calculate the overburden thickness assignment data according to the overburden thickness prediction parameter.

5. The geological hazard risk detection method according to claim 1, wherein Dividing the mountain body image data into slope units to obtain the slope units to be measured includes: Determining the microtopography disturbance effect area in the mountain body image data, and performing depression filling processing on the mountain body image data based on the microtopography disturbance effect area to obtain preprocessed data; Calculating the density of slope units according to the preprocessed data, and obtaining the slope units to be measured based on the density of slope units.

6. The geological hazard risk detection method according to claim 5, characterized in that, The obtaining the slope units to be measured based on the density of slope units includes: Performing slope unit division processing on the mountain body image data based on the density of slope units to obtain initial slope units; Sending the mountain body image data to a display interface for display, and detecting a calibration instruction for the mountain body image data triggered by the display interface; Analyzing the detected calibration instruction to obtain calibration parameters, and performing calibration processing on the initial slope units based on the calibration parameters to obtain the slope units to be measured.

7. The geological hazard risk detection method according to claim 1, wherein The calculating the disaster-bearing body detection data corresponding to each of the dangerous slope units includes: Matching each of the dangerous slope units with the buildings included in the mountain body image data to obtain target disaster-bearing body position information; Obtaining the disaster-bearing body detection data based on the target disaster-bearing body position information.

8. The geological disaster risk detection method according to any one of claims 1 to 7, characterized in that, The calculating the geological disaster risk result of the mountain area to be measured based on the dynamic detection result and the disaster-bearing body detection data includes: Calculating the risk quantification parameters of each of the dangerous slope units based on the dynamic detection result and the disaster-bearing body detection data; Sending the risk quantification parameters to a display interface for display, and detecting the segmentation boundary values and division strategies for the risk quantification parameters received by the display interface; Based on the division strategy, performing risk division processing on each of the dangerous slope units according to the segmentation boundary values and the risk quantification parameters, and obtaining the geological disaster risk result.

9. A geological disaster risk detection device, characterized in that, It includes: An acquisition module, configured to acquire mountain body image data including the mountain area to be measured, and perform slope unit division processing on the mountain body image data to obtain slope units to be measured; A primary detection module, configured to determine preset first-level static indicators, and acquire second-level static indicators determined by dividing the first-level static indicators; Performing geological disaster susceptibility calculation on the slope units to be measured based on the first-level static indicators and the second-level static indicators to obtain a static detection result, and screening out prone slope units from the slope units to be measured based on the static detection result; A secondary detection module, configured to detect precipitation condition data for the prone slope units; Calculating a dynamic detection result based on the static detection result and the precipitation condition data, and screening out dangerous slope units from the prone slope units based on the dynamic detection result; A risk calculation module, configured to calculate the disaster-bearing body detection data corresponding to each of the dangerous slope units; calculating the geological disaster risk result of the mountain area to be measured based on the dynamic detection result and the disaster-bearing body detection data.

10. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the geological disaster risk detection method described in any one of claims 1 to 8.

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