Mountain slope collapse treatment scheme generation method and device and related equipment

Abstract

The invention relates to the technical field of geological disaster prevention and control, and discloses a mountain slope collapse treatment scheme generation method and device and related equipment. The method comprises the following steps: acquiring mountain slope historical visual data of a mountain slope collapse site; processing the historical visual data through a visual analysis model to obtain first collapse factor data and first collapse factor proportion data; matching a first collapse treatment scheme according to the first collapse factor data and the first collapse factor proportion data; sending the first collapse factor data, the first collapse factor proportion data and the first collapse treatment scheme to the trained mountain slope collapse treatment scheme analysis model to obtain corresponding comprehensive effect data; and if the comprehensive effect data is greater than preset comprehensive effect threshold data, setting the first collapse treatment scheme as a target mountain slope collapse treatment scheme corresponding to the mountain slope collapse site. In this way, the efficiency and reliability of generating the mountain slope collapse treatment scheme are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of geological disaster prevention and control, and specifically relates to a method, device, equipment and storage medium for generating a mountain slope collapse control plan. Background Art

[0002] Mountain slope collapse refers to the phenomenon that the slope or foot of a mountain is destroyed, displaced or collapsed due to the influence of multiple factors such as geology, climate, and human factors, resulting in the downward movement of soil and gravel on the slope. This phenomenon is common in areas with complex geological structures and changeable climates, such as mountainous areas and hilly areas. Mountain slope collapse poses a serious threat to the surrounding environment, human life and property safety. Mountain slope collapse can bring various serious hazards. Mountain slope collapse may cause casualties, especially in densely populated areas such as mountain settlements and tourist attractions. Once a collapse accident occurs, there are often heavy casualties. Mud and rock flows, landslides and other phenomena caused by collapse will damage infrastructure, houses, farmland, etc., resulting in huge property losses. Mountain slope collapse can also cause soil erosion, vegetation destruction, soil and water loss and other problems, thereby destroying the ecological balance and affecting the stability and sustainable development of the ecological environment. In addition, mountain slope collapse often leads to the interruption of roads and railways, affecting transportation, and bringing inconvenience to local economic development and personnel exchanges. Moreover, a large amount of earth and stone materials after the collapse will be lost with rainwater, causing soil erosion, which will further affect the fertility and productivity of the land. In the process of generating traditional mountain slope collapse control plans, it is necessary to collect relevant data of the corresponding collapse site in advance, and then analyze the relevant data based on previous manual control experience, and then formulate corresponding preliminary control measures. On the one hand, the efficiency of analyzing the corresponding collapse-related data based on manual control experience is low. On the other hand, the preliminary control plan issued in the process of mountain slope collapse control also usually relies on manual experience. Due to human subjectivity, the accuracy of the control plan for slope collapse accidents obtained by relying on manual experience cannot be effectively guaranteed. Therefore, it is necessary to evaluate the effect of the control plan. At present, the existing technology does not have a reliable comprehensive evaluation, so it is impossible to obtain a more reliable control plan. Summary of the invention

[0003] The main technical problem solved by the implementation method of the present application is that the traditional method for generating a mountain slope collapse control plan is inefficient in generating a control plan, and the reliability of the control plan obtained is low.

[0004] To solve the above technical problems, the first technical solution adopted in the implementation mode of the present application is: to provide a method for generating a mountain slope collapse control plan, including: obtaining historical visual data of the mountain slope at the mountain slope collapse site; processing the mountain slope historical visual data through a pre-trained visual analysis model to obtain first collapse factor data corresponding to the mountain slope collapse site, and first collapse factor ratio data corresponding to the first collapse factor data; matching the corresponding first collapse control plan according to the first collapse factor data and the first collapse factor ratio data; sending the first collapse factor data, the first collapse factor ratio data and the first collapse control plan to the trained mountain slope collapse control plan analysis model to obtain comprehensive effect data corresponding to the first collapse control plan; if the comprehensive effect data is greater than the preset comprehensive effect threshold data, setting the first collapse control plan as the target mountain slope collapse control plan corresponding to the mountain slope collapse site.

[0005] Optionally, the training step of the mountain slope collapse control solution analysis model includes: obtaining historical data of mountain slope collapse accidents, parsing the historical data of mountain slope collapse to obtain second collapse factor data, and first collapse factor associated data corresponding to the second collapse factor data; obtaining historical data of mountain slope collapse control solutions, parsing the historical data of mountain slope collapse control solutions to obtain first collapse control factor data, and first collapse control effect data corresponding to the first collapse control factor data; obtaining first collapse control factor associated effect data corresponding to the first collapse control factor data, and establishing the first collapse control factor. The control effect association relationship between the associated action data and the first collapse control effect data; sending the second collapse factor data, the first collapse factor associated data, the first collapse control factor data, the first collapse control effect data, the first collapse control factor associated action data and the control effect association relationship to a pre-constructed initial mountain slope collapse control solution analysis model for model training to obtain a trained mountain slope collapse control solution analysis model, wherein the initial mountain slope collapse control solution analysis model is constructed through the mountain slope collapse accident historical data and the mountain slope collapse control solution historical data.

[0006] Optionally, the step of processing the historical visual data of the mountain slope through a pre-trained visual analysis model to obtain first collapse factor data corresponding to the mountain slope collapse site, and first collapse factor ratio data corresponding to the first collapse factor data, includes: obtaining historical visual data of the mountain slope at the historical mountain slope collapse site, wherein the historical visual data includes historical video data and historical picture data; parsing the historical video data and historical picture data to obtain different collapse factor sample data corresponding to the historical video data and historical picture data, and different collapse factor ratio sample data corresponding to the collapse factor sample data; training a pre-created initial visual analysis model through the collapse factor sample data and the collapse factor ratio sample data to obtain the trained visual analysis model; and inputting the historical visual data of the mountain slope into the trained visual analysis model to obtain the first collapse factor data and the first collapse factor ratio data.

[0007] Optionally, the step of sending the first collapse factor data, the first collapse factor weight data and the first collapse control plan to the trained mountain slope collapse control plan analysis model to obtain comprehensive effect data corresponding to the first collapse control plan includes: obtaining second collapse factor associated data of the mountain slope collapse location according to the first collapse factor data; sending the first collapse factor data, the first collapse factor weight data, the second collapse factor associated data and the first collapse control plan to the trained mountain slope collapse control plan analysis model; and receiving the comprehensive control effect data returned by the mountain slope collapse control plan analysis model.

[0008] Optionally, after the step of receiving the comprehensive control effect data returned by the mountain slope collapse control plan analysis model, it also includes: judging whether each control effect data in the comprehensive control effect data is within a preset control effect data threshold range; if there is at least one target control effect data in the comprehensive control effect data that is not within the corresponding control effect data threshold range, optimizing at least one of the first collapse factor data corresponding to the target control effect data, and the first collapse factor ratio data corresponding to the first collapse factor data; updating the second collapse factor-related data and the first collapse control plan according to the optimized first collapse factor data and the corresponding first collapse factor ratio data; re-executing the steps of sending the first collapse factor data, the first collapse factor ratio data, the second collapse factor-related data and the first collapse control plan to the trained mountain slope collapse control plan analysis model to updating the second collapse factor-related data and the first collapse control plan according to the optimized first collapse factor data and the corresponding first collapse factor ratio data, until whether each control effect data in the comprehensive control effect data corresponding to the optimized first collapse control plan is within the preset control effect data threshold range.

[0009] Optionally, the step of matching the corresponding first collapse control plan according to the first collapse factor data and the first collapse factor ratio data includes: obtaining historical mountain slope collapse control plan data, parsing the historical mountain slope collapse control plan data to obtain corresponding second collapse control factor data, and second collapse control effect data corresponding to the second collapse control factor data; combining the first collapse factor data and the first collapse factor ratio data into a corresponding collapse factor data pair, and combining the second collapse control factor data and the second collapse control effect data into a corresponding collapse control factor data pair; according to a preset collapse factor data and collapse control factor correspondence table, calculating the data matching percentage between the collapse factor data pair and the collapse control factor data pair; if the data matching percentage is within a preset data matching percentage threshold range, setting the historical mountain slope collapse control plan data corresponding to the maximum data matching percentage as the first collapse control plan.

[0010] Optionally, after the step of setting the first collapse control plan as the target mountain slope collapse control plan corresponding to the mountain slope collapse site, it also includes: obtaining actual collapse control effect data after the implementation of the target mountain slope collapse control plan; optimizing the mountain slope collapse control plan analysis model and the collapse factor data and the collapse control factor correspondence table through the target mountain slope collapse control plan and the corresponding actual collapse control effect data.

[0011] To solve the above technical problems, the second technical solution adopted in the implementation mode of the present application is: to provide a device for generating a mountain slope collapse control plan, including: a historical visual data module, used to obtain the mountain slope historical visual data of the mountain slope collapse site; a collapse factor data module, used to process the mountain slope historical visual data through a pre-trained visual analysis model to obtain the first collapse factor data corresponding to the mountain slope collapse site, and the first collapse factor ratio data corresponding to the first collapse factor data; a control plan matching module, used to match the corresponding first collapse control plan according to the first collapse factor data and the first collapse factor ratio data; a comprehensive effect data module, used to send the first collapse factor data, the first collapse factor ratio data and the first collapse control plan to the trained mountain slope collapse control plan analysis model to obtain the comprehensive effect data corresponding to the first collapse control plan; a collapse control plan module, used to set the first collapse control plan as the target mountain slope collapse control plan corresponding to the mountain slope collapse site if the comprehensive effect data is greater than the preset comprehensive effect threshold data.

[0012] To solve the above technical problems, the third technical solution adopted in the implementation mode of the present application is: to provide an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for generating a mountain slope collapse control plan as described above.

[0013] In order to solve the above technical problems, the third technical solution adopted in the implementation mode of the present application is: to provide a non-volatile computer-readable storage medium, characterized in that the non-volatile computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by an electronic device, the electronic device executes the method for generating a mountain slope collapse control plan as described above.

[0014] Different from the related art, the mountain slope collapse control plan generation method of the present application obtains the historical visual data of the mountain slope collapse site, and through the pre-trained visual analysis model processing, it can accurately obtain the first collapse factor data and its proportion data, which helps to quickly and comprehensively identify the key factors and their relative importance that cause the mountain slope collapse from an intuitive visual perspective. Matching the corresponding first collapse control plan based on accurate collapse factor data and proportion data greatly improves the fit between the control plan and the actual collapse situation compared to the traditional empirical or general control plan selection, saving the time and cost of screening and formulating the control plan. Sending the collapse factor data, proportion data and control plan to the trained analysis model to obtain comprehensive effect data can scientifically and quantitatively predict and evaluate the implementation effect of the control plan, so that decision makers can understand the possible results of the plan before actually implementing the control plan, and prepare for response and adjustment in advance. The preset comprehensive effect threshold data is set, and the comprehensive effect data output by the model is compared with it. Only when the comprehensive effect data is greater than the threshold, it is determined as the target governance plan, which ensures that the final selected governance plan has high effectiveness and reliability, can effectively solve the problem of mountain slope collapse, and ensure the safety of the surrounding environment and personnel. The entire generation method process continuously accumulates data and experience. As more historical visual data of mountain slopes are included in the analysis, and the model is continuously fed back and adjusted in actual application, it can continuously optimize the collapse factor analysis, governance plan matching and effect evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] One or more embodiments are exemplarily described by corresponding drawings, which do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and the figures in the drawings do not constitute proportional limitations unless otherwise stated.

[0016] Figure 1 It is a schematic diagram of the operating environment of the method for generating a mountain slope collapse control plan provided in an embodiment of the present application.

[0017] Figure 2 It is a schematic diagram of the execution flow of the method for generating a mountain slope collapse control plan provided in an embodiment of the present application.

[0018] Figure 3 It is a schematic diagram of the execution flow of obtaining the collapse factor in the method for generating a mountain slope collapse control plan provided in an embodiment of the present application.

[0019] Figure 4 It is a schematic diagram of the execution process of matching the corresponding first collapse control plan in the mountain slope collapse control plan generation method provided in the embodiment of the present application.

[0020] Figure 5It is a schematic diagram of the execution flow of obtaining the comprehensive effect data corresponding to the first collapse control plan in the mountain slope collapse control plan generation method provided in the embodiment of the present application.

[0021] Figure 6 It is a schematic diagram of the execution process of the training process of the mountain slope collapse control solution analysis model in the mountain slope collapse control solution generation method provided in the embodiment of the present application.

[0022] Figure 7 A schematic diagram of the system structure of a device for generating a mountain slope collapse control solution provided in an embodiment of the present application.

[0023] Figure 8 It is a hardware structure diagram of an electronic device for executing a method for generating a mountain slope collapse control plan provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail 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.

[0025] It should be noted that, if there is no conflict, the various features in the embodiments of the present application can be combined with each other, all within the scope of protection of the present application. In addition, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a different order than the module division in the device schematic diagram or the order in the flow chart.

[0026] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "and / or" used in this specification includes any and all combinations of one or more of the related listed items.

[0027] To facilitate understanding of this embodiment, firstly, a method for generating a mountain slope collapse control solution disclosed in the embodiment of this application is described in detail. Figure 1 , Figure 1 Schematic diagram of the operating environment of the method for generating a mountain slope collapse control solution provided in the embodiment of the present application. Figure 1As shown, the execution subject of the method for generating a mountain slope collapse treatment plan provided in the embodiment of the present application is generally an electronic device with certain computing capabilities, such as a computer device. In some possible implementations, the method for generating a mountain slope collapse treatment plan can be implemented by a processor calling a computer-readable instruction stored in a memory. Figure 1 The computer device in the term can be a server. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. It can be understood that Figure 1 The number of computer devices in the figure is only for reference and can be expanded to any number according to actual needs.

[0028] Please continue reading Figure 2 , Figure 2 Schematic diagram of the execution flow of the method for generating a mountain slope collapse control solution provided in the embodiment of the present application. Figure 2 As shown, the following steps are included: S1. Obtain historical visual data of the mountain slope at the location of the mountain slope collapse.

[0029] Among them, from the perspective of data processing type classification, the historical visual data of mountain slopes can be divided into historical image data of mountain slopes and historical video data of mountain slopes. On the one hand, historical images of mountain slopes and historical video data of mountain slopes can provide intuitive changes in geological information and clearly show the changes in the landforms of mountain slopes over the past period of time. For example, by comparing photos taken many years ago with recent videos, we can find the increase or decrease of vegetation coverage on the surface of the mountain, the change in the degree of weathering of rocks, etc. These intuitive information helps to determine whether the mountain slope is in a slow natural erosion process or has undergone rapid changes due to recent human activities or special geological events (such as earthquakes, heavy rains, etc.). On the other hand, historical images of mountain slopes and historical video data of mountain slopes can assist in analyzing the causes of collapse (such as natural factors and human factors), and historical videos and image data can record the response of mountain slopes under different climatic conditions. For example, by viewing videos of heavy rains over the years, we can observe the degree of scouring of mountain slopes by rainwater, whether there are signs of debris flow formation, etc., and historical images may show geological structural information such as faults and folds inside the mountain. For example, a historical geological exploration image shows that there is a hidden fault under the mountain slope. Over time, the rocks around the fault gradually deform under the action of gravity. The impact of human activities on the stability of mountain slopes can also be analyzed through historical data. For example, historical images and videos can record engineering construction activities near the mountain slope, such as road construction, house construction, mining, etc. If you see in the video that new cracks and loose rocks appear in the mountain after large-scale blasting operations near the mountain slope, then it can be determined that blasting is a human factor that causes the stability of the mountain slope to decrease.

[0030] S2. Processing the historical visual data of the mountain slope through the pre-trained visual analysis model to obtain the first collapse factor data corresponding to the collapse location of the mountain slope and the first collapse factor proportion data corresponding to the first collapse factor data.

[0031] It should be noted that when the corresponding collapse factor cannot be obtained from the above historical visual data, collapse-related data in other data forms (such as text data of collapse accident investigation reports on the collapse site, conference audio data on collapse accidents, etc.) can also be obtained to extract collapse factor data.

[0032] As an optional implementation, please continue to refer to Figure 3 , Figure 3 It is a schematic diagram of the execution flow of obtaining the collapse factor in the method for generating a mountain slope collapse control plan provided in an embodiment of the present application, which specifically includes the following steps S21 to S24.

[0033] S21. Obtain historical visual data of the mountain slope at the historical mountain slope collapse site, wherein the historical visual data includes historical video data and historical picture data.

[0034] S22, parsing the historical video data and the historical picture data to obtain sample data of different collapse factors corresponding to the historical video data and the historical picture data, and sample data of different collapse factor proportions corresponding to the collapse factor sample data.

[0035] Among them, there are many types of factors that affect the collapse of various types of mountain slopes, such as geological factors, topographic factors, gas phase factors, hydrological factors and vegetation factors, etc. There are different factors under various factors, such as rock and soil factors, geological structural factors, seismic activity factors, etc. under geological factors, slope and height factors, slope morphology factors under topographic factors, rainfall factors, ice and snow factors, etc. under meteorological factors, river scouring factors, groundwater activity factors, etc. under hydrological factors, and vegetation coverage factors, vegetation root system factors, etc. under vegetation factors.

[0036] S23. Train the pre-created initial visual analysis model using the collapse factor sample data and the collapse factor weight sample data to obtain a trained visual analysis model.

[0037] S24, inputting historical visual data of the mountain slope into the trained visual analysis model to obtain first collapse factor data and first collapse factor proportion data.

[0038] Among them, the collapse factor data and specific gravity data provide a quantitative basis for the risk assessment of mountain slope collapse. The collapse factor specific gravity data can guide the formulation of the governance plan and make the governance measures more targeted. For the collapse factor with a large specific gravity, the corresponding governance technology should be adopted. For example, if the geotechnical type factor has a high specific gravity and the rock and soil of the mountain slope is loose sand, the governance plan can focus on the use of geotextile reinforcement, grouting and other measures to improve the strength and stability of the rock and soil. In addition, in the governance process, the collapse factor data and specific gravity data can be used to predict the effect of the governance measures. By comparing the changes in the collapse factor data before and after the governance and considering the adjustment of the specific gravity of each factor, it is evaluated whether the governance measures have effectively reduced the risk of mountain slope collapse. For example, after drainage governance of a mountain slope that is greatly affected by groundwater activity (high specific gravity), by monitoring the changes in the groundwater level (collapse factor data) and re-evaluating the specific gravity of the groundwater factor on the collapse, if the groundwater level drops significantly and its specific gravity has a reduced impact on the collapse, it can be preliminarily judged that the governance measures are effective.

[0039] S3. Matching the corresponding first collapse control plan according to the first collapse factor data and the first collapse factor ratio data.

[0040] As an optional implementation, please continue to refer to Figure 4 , Figure 4 It is a schematic diagram of the execution process of matching the corresponding first collapse control plan in the mountain slope collapse control plan generation method provided in the embodiment of the present application, which specifically includes the following steps S31 to S34.

[0041] S31. Acquire historical mountain slope collapse control plan data, analyze the historical mountain slope collapse control plan data to obtain corresponding second collapse control factor data, and second collapse control effect data corresponding to the second collapse control factor data.

[0042] For example, the control factor can be an engineering measure factor, such as anchoring factor, retaining wall factor, etc., or a vegetation restoration factor, such as plant species factor, planting density factor, etc., or a drainage factor, such as surface drainage factor, underground drainage factor, etc. The collapse control effect data corresponding to the collapse control factor can be stability index data, such as displacement data and anti-sliding stability coefficient of the mountain slope, or environmental index data, such as vegetation coverage data and soil erosion data. In a slope control project of a large hydropower project, a combination of multiple control measures was adopted. The displacement rate of the slope was fast before the control, reaching 5-8 mm per month. After the control, the displacement rate gradually decreased and stabilized at 0.5-1 mm per month after half a year, indicating that the slope stability has been greatly improved. For another example, in the control of some soil slopes, the anti-sliding stability coefficient was 1.05 before the control, which was in a critical stable state. After the implementation of control measures (such as reinforced soil technology and drainage measures), the anti-sliding stability coefficient increased to 1.30, which met the slope stability requirements, proving that the control measures effectively improved the anti-sliding ability of the slope.

[0043] S32. Combining the first collapse factor data and the first collapse factor ratio data into a corresponding collapse factor data pair, and combining the second collapse control factor data and the second collapse control effect data into a corresponding collapse control factor data pair.

[0044] Among them, the collapse factors with different proportions need to correspond to the collapse control factors in the actual implementation process, because the collapse control effects of different collapse control factors are not exactly the same (implementation costs, implementation difficulties, etc. also need to be considered). For example, in the management of mountain slopes where rainfall is the main collapse factor (accounting for 40%), it is crucial to improve the drainage system. For example, in the management of the slope of a certain mountain road in the south, due to the large and concentrated annual rainfall, rainwater erosion and infiltration are the main causes of slope collapse. The management plan focuses on the construction of surface and underground drainage systems, including setting up intercepting ditches on the top of the slope, and setting up drainage holes and drainage blind ditches on the slope. The construction cost of the drainage system is relatively moderate, and the main cost lies in material procurement and labor costs. In terms of implementation difficulty, the construction of the surface drainage system is relatively simple, but it is necessary to ensure that the drainage slope is reasonable and the drainage is smooth. The construction of the underground drainage system is difficult, and the location and depth of the drainage holes and blind ditches need to be accurately determined to avoid excessive disturbance to the slope rock mass. In terms of the treatment effect, during the rainy season, the slope runoff was reduced by about 40% for slopes with a complete drainage system, which effectively reduced the erosion of the slope by rainwater. The slope displacement was also significantly reduced, ensuring the stability of the slope.

[0045] S33. According to the preset correspondence table between collapse factor data and collapse control factor, the data matching percentage between the collapse factor data pair and the collapse control factor data pair is calculated.

[0046] S34. If the data matching percentage is within a preset data matching percentage threshold range, the historical mountain slope collapse control plan data corresponding to the maximum data matching percentage is set as the first collapse control plan.

[0047] It should be noted that if the corresponding first landslide control plan cannot be obtained according to the data matching percentage threshold range, the historical mountain slope landslide control plan data with a higher matching degree can also be used as a control plan template. On the basis of the control plan template, the control factor data in the template is adjusted so that the newly generated first landslide control plan can meet the requirements of the above-mentioned data matching percentage threshold.

[0048] For example, after conducting a landslide risk assessment on a mountain slope, it was found that the existing data matching degree could not directly correspond to the existing first landslide control plan. After analysis, the slope of the slope is 55°, the rock and soil type is silty clay, the annual rainfall is about 1000mm, and the vegetation coverage is only 15%. When referring to the data of historical mountain slope collapse control plans, a historical case similar to the current slope situation was found, and its control plan is as follows: the slope of the historical case is 60°, the rock and soil is loose sand, the annual rainfall is 800mm, and the vegetation coverage is 10%. The control plan mainly adopts slope cutting and load reduction (reducing the slope to 45°), grouting reinforcement for sandy soil, and a small amount of vegetation restoration (imported soil spraying) measures. On the basis of the historical mountain slope collapse control plan, a new first collapse control plan is generated, which is to reduce the slope from 55° to 50°, rationally plan the excavation sequence and method, ensure construction safety, properly handle the excavated earth and stone, and transport it to the designated location for stacking or use it for other filling projects. Grouting reinforcement: For silty clay, cement slurry with special admixtures is used for grouting. The grouting pressure is controlled within a suitable range (such as 0.3-0.5MPa), and the appropriate grouting volume is determined according to calculation (the grouting volume per cubic meter of soil is about 0.2-0.3 cubic meters), ensuring that the slurry fully fills the soil pores and improves the cohesion and internal friction angle of the soil. Vegetation restoration: Adopt the soil spraying technology, select plant seeds suitable for humid environment, reduce the use of water retaining agent, strengthen maintenance and management after spraying, regularly water and fertilize to ensure the normal growth of vegetation, and the goal is to increase the vegetation coverage to more than 50% within one year.

[0049] S4. Send the first collapse factor data, the first collapse factor weight data and the first collapse control plan to the trained mountain slope collapse control plan analysis model to obtain the comprehensive effect data corresponding to the first collapse control plan.

[0050] As a preferred implementation, please continue to refer to Figure 5 , Figure 5 It is a schematic diagram of the execution process of obtaining the comprehensive effect data corresponding to the first collapse control plan in the mountain slope collapse control plan generation method provided in the embodiment of the present application, which specifically includes the following steps S41 to S43.

[0051] S41. Acquire second collapse factor associated data of the mountain slope collapse location according to the first collapse factor data.

[0052] For example, in terms of geological-related collapse factors, rock and soil mechanical parameter data and geological structure data can be used as collapse factor-related data; in terms of terrain-related collapse factors, slope geometric size data and terrain and landform relative position data can be used as collapse factor-related data; in terms of meteorological and hydrological-related collapse factors, rainfall and rainfall intensity data and vegetation-related collapse factor-related data can be used as collapse factor-related data. For example, in terms of rock and soil mechanical parameter data, parameters such as cohesion, internal friction angle, and elastic modulus vary greatly for different rock and soil types. Hard and intact rocks have higher cohesion and internal friction angle, while loose sand has lower cohesion and a relatively smaller internal friction angle. These data can be obtained through indoor geotechnical tests and on-site in-situ tests to quantitatively evaluate the stability of rock and soil, and then determine the possibility of collapse under gravity and external forces.

[0053] S42, sending the first collapse factor data, the first collapse factor weight data, the second collapse factor associated data and the first collapse control plan to the trained mountain slope collapse control plan analysis model.

[0054] S43. Receive the comprehensive management effect data returned by the mountain slope collapse management program analysis model.

[0055] As another preferred implementation, after the above step S43, the following steps S44 to S48 may also be included.

[0056] S44. Determine whether each governance effect data in the comprehensive governance effect data is within a preset governance effect data threshold range.

[0057] S45. If there is at least one target governance effect data in the comprehensive governance effect data that is not within the corresponding governance effect data threshold range, optimize at least one first collapse factor data corresponding to the target governance effect data, and the first collapse factor proportion data corresponding to the first collapse factor data.

[0058] S46. According to the optimized first collapse factor data and the corresponding first collapse factor weight data, the second collapse factor associated data and the first collapse control plan are updated accordingly.

[0059] S47, re-execute the above steps from S42 to S46 until each item of governance effect data in the comprehensive governance effect data corresponding to the optimized first collapse governance plan is within a preset governance effect data threshold range.

[0060] Among them, the threshold range of the control effect data is designed to ensure that the generated slope collapse control plan can achieve the desired control effect, such as indicating that the control measures are effectively implemented, ensuring the stability of the slope, and providing a basis for long-term maintenance. Specifically, the various control effect data are within the preset threshold range, which most directly indicates that the control measures are effectively implemented as planned and have achieved the expected initial effect, which means that there are no major mistakes in engineering construction, technology application, etc., and the implementation of the control plan is reliable. The preset threshold is usually set based on the previous risk assessment. When the control effect data meets the threshold range, it verifies that the methods, models and factors considered in the previous risk assessment are reasonable and accurate, providing a reliable reference for the subsequent risk assessment of similar projects.

[0061] S5. If the comprehensive effect data is greater than the preset comprehensive effect threshold data, the first landslide control plan is set as the target mountain slope landslide control plan corresponding to the mountain slope landslide location.

[0062] As a preferred implementation, after the above-mentioned step S5, actual collapse control effect data after the implementation of the target mountain slope collapse control plan can also be obtained; through the target mountain slope collapse control plan and the corresponding actual collapse control effect data, the mountain slope collapse control plan analysis model and the collapse factor data and the collapse control factor correspondence table are optimized.

[0063] As another preferred embodiment, please continue to refer to Figure 6 , Figure 6 It is a schematic diagram of the execution process of the training process of the mountain slope collapse control solution analysis model in the mountain slope collapse control solution generation method provided in the embodiment of the present application, which specifically includes the following steps S61 to S64.

[0064] S61, obtaining historical data of mountain slope collapse accidents, parsing the historical data of mountain slope collapse to obtain second collapse factor data, and first collapse factor associated data corresponding to the second collapse factor data.

[0065] S62, obtaining historical data of mountain slope collapse control solutions, parsing the historical data of mountain slope collapse control solutions to obtain first collapse control factor data, and first collapse control effect data corresponding to the first collapse control factor data.

[0066] S63, obtaining the first collapse control factor correlation data corresponding to the first collapse control factor data, and establishing a control effect correlation relationship between the first collapse control factor correlation data and the first collapse control effect data.

[0067] S64, sending the second collapse factor data, the first collapse factor associated data, the first collapse control factor data, the first collapse control effect data, the first collapse control factor associated action data and the control effect associated relationship to the pre-built initial mountain slope collapse control solution analysis model for model training, and obtaining a trained mountain slope collapse control solution analysis model. The initial mountain slope collapse control solution analysis model is constructed by using the historical data of mountain slope collapse accidents and the historical data of mountain slope collapse control solutions.

[0068] Among them, the mountain slope collapse control solution analysis model obtained through the training process of the above-mentioned mountain slope collapse control solution analysis model can not only deeply understand the cause of collapse, optimize the formulation of control solutions, and clarify the mechanism of factor action, but also improve the prediction accuracy of the analysis model and promote the improvement of control effects and technologies. For example, through the analysis of a large amount of historical data and model training, the shortcomings of existing control solutions and technologies can be found, thereby providing direction for the innovation of control technology. New control factors or combinations of control factors may be discovered, promoting the continuous progress of mountain slope collapse control technology. Specifically, in the process of analyzing historical data, it was found that traditional vegetation slope protection technology is not effective under certain complex geological conditions. Based on this, researchers can explore innovative control methods that combine new materials or biotechnology, such as using plant varieties with special root systems or developing new soil conditioners to improve the effect of vegetation slope protection.

[0069] The method for generating a mountain slope collapse control plan provided in the embodiment of the present application obtains historical visual data of the mountain slope collapse site, and through processing with a pre-trained visual analysis model, can accurately obtain the first collapse factor data and its proportion data, which helps to quickly and comprehensively identify the key factors and their relative importance that cause the mountain slope collapse from an intuitive visual perspective. Matching the corresponding first collapse control plan based on accurate collapse factor data and proportion data greatly improves the fit between the control plan and the actual collapse situation compared to the traditional empirical or general control plan selection, saving the time and cost of screening and formulating the control plan. Sending the collapse factor data, proportion data and control plan to the trained analysis model to obtain comprehensive effect data can scientifically and quantitatively predict and evaluate the implementation effect of the control plan, so that decision makers can understand the possible results of the plan before actually implementing the control plan, and prepare for response and adjustment in advance. The preset comprehensive effect threshold data is set, and the comprehensive effect data output by the model is compared with it. Only when the comprehensive effect data is greater than the threshold, it is determined as the target governance plan, which ensures that the final selected governance plan has high effectiveness and reliability, can effectively solve the problem of mountain slope collapse, and ensure the safety of the surrounding environment and personnel. The entire generation method process continuously accumulates data and experience. As more historical visual data of mountain slopes are included in the analysis, and the model is continuously fed back and adjusted in actual application, it can continuously optimize the collapse factor analysis, governance plan matching and effect evaluation.

[0070] Please continue reading Figure 7 , Figure 7 The system structure diagram of the device for generating a mountain slope collapse treatment plan provided in the embodiment of the present application is as follows: Figure 7 As shown, the mountain slope collapse control plan generating device 70 includes: a historical visual data module 71, a collapse factor data module 72, a control plan matching module 73, a comprehensive effect data module 74 and a collapse control plan module 75.

[0071] The historical visual data module 71 is used to obtain the historical visual data of the mountain slope at the location of the mountain slope collapse.

[0072] The collapse factor data module 72 is used to process the historical visual data of the mountain slope through a pre-trained visual analysis model to obtain first collapse factor data corresponding to the mountain slope collapse location and first collapse factor proportion data corresponding to the first collapse factor data.

[0073] The control scheme matching module 73 is used to match the corresponding first collapse control scheme according to the first collapse factor data and the first collapse factor weight data.

[0074] The comprehensive effect data module 74 is used to send the first collapse factor data, the first collapse factor weight data and the first collapse control plan to the trained mountain slope collapse control plan analysis model to obtain the comprehensive effect data corresponding to the first collapse control plan.

[0075] The collapse control scheme module 75 is used to set the first collapse control scheme as the target mountain slope collapse control scheme corresponding to the mountain slope collapse location if the comprehensive effect data is greater than the preset comprehensive effect threshold data.

[0076] As an optional implementation, the mountain slope collapse control program generation device 70 also includes an analysis model training module, which is specifically used to obtain historical data of mountain slope collapse accidents, parse the historical data of mountain slope collapse to obtain second collapse factor data, and first collapse factor associated data corresponding to the second collapse factor data; obtain historical data of mountain slope collapse control programs, parse the historical data of mountain slope collapse control programs to obtain first collapse control factor data, and first collapse control effect data corresponding to the first collapse control factor data; obtain first collapse control factor associated effect data corresponding to the first collapse control factor data, And establish a governance effect association relationship between the first collapse control factor association data and the first collapse control effect data; send the second collapse factor data, the first collapse factor association data, the first collapse control factor data, the first collapse control effect data, the first collapse control factor association data and the governance effect association relationship to a pre-constructed initial mountain slope collapse control solution analysis model for model training to obtain a trained mountain slope collapse control solution analysis model, wherein the initial mountain slope collapse control solution analysis model is constructed through the mountain slope collapse accident historical data and the mountain slope collapse control solution historical data.

[0077] As an optional implementation, the collapse factor data module 72 is specifically used to obtain historical visual data of mountain slopes at historical mountain slope collapse sites, wherein the historical visual data includes historical video data and historical picture data; parse the historical video data and historical picture data to obtain different collapse factor sample data corresponding to the historical video data and historical picture data, and different collapse factor proportion sample data corresponding to the collapse factor sample data; train a pre-created initial visual analysis model through the collapse factor sample data and the collapse factor proportion sample data to obtain the trained visual analysis model; input the historical visual data of the mountain slope into the trained visual analysis model to obtain the first collapse factor data and the first collapse factor proportion data.

[0078] As an optional implementation, the comprehensive effect data module 74 is specifically used to obtain the second collapse factor related data of the mountain slope collapse site based on the first collapse factor data; send the first collapse factor data, the first collapse factor proportion data, the second collapse factor related data and the first collapse control plan to the trained mountain slope collapse control plan analysis model; receive the comprehensive control effect data returned by the mountain slope collapse control plan analysis model.

[0079] As an optional implementation, the comprehensive effect data module 74 is also specifically used to determine whether each governance effect data in the comprehensive governance effect data is within a preset governance effect data threshold range; if there is at least one target governance effect data in the comprehensive governance effect data that is not within the corresponding governance effect data threshold range, optimize at least one of the first collapse factor data corresponding to the target governance effect data, and the first collapse factor ratio data corresponding to the first collapse factor data; update the second collapse factor-related data and the first collapse governance plan according to the optimized first collapse factor data and the corresponding first collapse factor ratio data; re-execute the steps of sending the first collapse factor data, the first collapse factor ratio data, the second collapse factor-related data and the first collapse governance plan to the trained mountain slope collapse governance plan analysis model to updating the second collapse factor-related data and the first collapse governance plan according to the optimized first collapse factor data and the corresponding first collapse factor ratio data, until whether each governance effect data in the comprehensive governance effect data corresponding to the optimized first collapse factor plan is within the preset governance effect data threshold range.

[0080] As an optional implementation mode, the control plan matching module 73 is specifically used to obtain historical mountain slope collapse control plan data, parse the historical mountain slope collapse control plan data to obtain corresponding second collapse control factor data, and second collapse control effect data corresponding to the second collapse control factor data; combine the first collapse factor data and the first collapse factor proportion data into a corresponding collapse factor data pair, and combine the second collapse control factor data and the second collapse control effect data into a corresponding collapse control factor data pair; calculate the data matching percentage between the collapse factor data pair and the collapse control factor data pair according to a preset collapse factor data and collapse control factor correspondence table; if the data matching percentage is within a preset data matching percentage threshold range, set the historical mountain slope collapse control plan data corresponding to the maximum data matching percentage as the first collapse control plan.

[0081] As an optional implementation, the collapse control plan module 75 is also used to obtain actual collapse control effect data after the implementation of the target mountain slope collapse control plan; through the target mountain slope collapse control plan and the corresponding actual collapse control effect data, the mountain slope collapse control plan analysis model and the collapse factor data and collapse control factor correspondence table are optimized.

[0082] It should be noted that the above-mentioned device for generating a mountain slope collapse treatment plan can execute the method for generating a mountain slope collapse treatment plan provided in the embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not fully described in the embodiment of the device for generating a mountain slope collapse treatment plan, please refer to the method for generating a mountain slope collapse treatment plan provided in the embodiment of the present application.

[0083] Please continue reading Figure 8 , Figure 8 is a hardware structure diagram of an electronic device 800 for executing a method for generating a mountain slope collapse control plan provided in an embodiment of the present application, such as Figure 8 As shown, the electronic device 800 includes: One or more processors 810 and memory 820, Figure 8 A processor 810 is taken as an example.

[0084] The processor 810 and the memory 820 may be connected via a bus or other means. Figure 8 The example of connecting through bus is taken in the following.

[0085] The memory 820, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the method for generating a mountain slope collapse treatment plan in the embodiment of the present application. The processor 810 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions and modules stored in the memory 820, that is, the method for generating a mountain slope collapse treatment plan in the above method embodiment is implemented.

[0086] The memory 820 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function; the data storage area may store data created according to the use of the device for generating a plan for managing a landslide. In addition, the memory 820 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 820 may optionally include a memory remotely arranged relative to the processor 810, and these remote memories may be connected to the device for generating a plan for managing a landslide via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0087] The one or more modules are stored in the memory 820, and when executed by the one or more processors 810, the method for generating a mountain slope collapse control plan in any of the above method embodiments is executed, for example, the method described above is executed. Figure 2 Steps S1 to S5 of the method, Figure 3 Steps S21 to S24 of the method, Figure 4 Steps S31 to S34 of the method, Figure 5 Steps S41 to S43 of the method, Figure 6 Steps S61 to S64 of the method are implemented Figure 7 The functions of modules 71-75 in.

[0088] The above-mentioned product can execute the method provided in the embodiment of the present application, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in this embodiment, please refer to the method provided in the embodiment of the present application.

[0089] The embodiment of the present application provides a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are executed by one or more processors, such as Figure 8 A processor 810 in the embodiment may enable the one or more processors to execute the method for generating a mountain slope collapse control plan in any of the above method embodiments, for example, executing the above described Figure 2 Steps S1 to S5 of the method, Figure 3 Steps S21 to S24 of the method, Figure 4 Steps S31 to S34 of the method, Figure 5 Steps S41 to S43 of the method, Figure 6 Steps S61 to S64 of the method are implemented Figure 7 The functions of modules 71-75 in.

[0090] The present application provides a computer program product, which includes a computer program stored on a non-volatile computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by the electronic device, the electronic device is enabled to execute the method for generating a mountain slope collapse control plan in any of the above method embodiments, for example, executing the above described Figure 2 Steps S1 to S5 of the method, Figure 3 Steps S21 to S24 of the method, Figure 4 Steps S31 to S34 of the method, Figure 5 Steps S41 to S43 of the method, Figure 6 Steps S61 to S64 of the method are implemented Figure 7 The functions of modules 71-75 in.

[0091] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0092] Through the description of the above embodiments, it can be clearly understood by those skilled in the art that each embodiment can be implemented by means of software plus a general hardware platform, or by hardware. It can be understood by those skilled in the art that all or part of the processes in the above embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Under the concept of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes in different aspects of the present application as described above, which are not provided in detail for the sake of simplicity. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for generating a mountain slope collapse control plan, characterized in that: include: Obtain historical visual data of mountain slopes at the locations of mountain slope collapses; Processing the historical visual data of the mountain slope through a pre-trained visual analysis model to obtain first collapse factor data corresponding to the collapse location of the mountain slope and first collapse factor proportion data corresponding to the first collapse factor data; Matching a corresponding first collapse control plan according to the first collapse factor data and the first collapse factor weight data; Sending the first collapse factor data, the first collapse factor weight data and the first collapse control plan to the trained mountain slope collapse control plan analysis model to obtain comprehensive effect data corresponding to the first collapse control plan; If the comprehensive effect data is greater than the preset comprehensive effect threshold data, the first landslide control plan is set as the target mountain slope landslide control plan corresponding to the mountain slope landslide location.

2. The method for generating a mountain slope collapse control plan according to claim 1, characterized in that: The training steps of the mountain slope collapse control solution analysis model include: Acquire historical data of mountain slope collapse accidents, and analyze the historical data of mountain slope collapse to obtain second collapse factor data and first collapse factor associated data corresponding to the second collapse factor data; Acquire historical data of a mountain slope collapse control program, and analyze the historical data of the mountain slope collapse control program to obtain first collapse control factor data and first collapse control effect data corresponding to the first collapse control factor data; Acquire the first collapse governance factor correlation data corresponding to the first collapse governance factor data, and establish a governance effect correlation relationship between the first collapse governance factor correlation data and the first collapse governance effect data; The second collapse factor data, the first collapse factor associated data, the first collapse control factor data, the first collapse control effect data, the first collapse control factor associated action data and the control effect associated relationship are sent to a pre-constructed initial mountain slope collapse control solution analysis model for model training to obtain a trained mountain slope collapse control solution analysis model, wherein the initial mountain slope collapse control solution analysis model is constructed by using the mountain slope collapse accident historical data and the mountain slope collapse control solution historical data.

3. The method for generating a mountain slope collapse control plan according to claim 1, characterized in that: The step of processing the historical visual data of the mountain slope by a pre-trained visual analysis model to obtain first collapse factor data corresponding to the collapse location of the mountain slope and first collapse factor weight data corresponding to the first collapse factor data includes: Acquire historical visual data of the mountain slope at the historical mountain slope collapse site, wherein the historical visual data includes historical video data and historical picture data; Parsing the historical video data and the historical picture data to obtain different collapse factor sample data corresponding to the historical video data and the historical picture data, and different collapse factor weight sample data corresponding to the collapse factor sample data; The pre-created initial visual analysis model is trained by using the collapse factor sample data and the collapse factor weight sample data to obtain the trained visual analysis model; The historical visual data of the mountain slope is input into the trained visual analysis model to obtain the first collapse factor data and the first collapse factor proportion data.

4. The method for generating a mountain slope collapse control plan according to claim 1, characterized in that: The step of sending the first collapse factor data, the first collapse factor weight data and the first collapse control plan to the trained mountain slope collapse control plan analysis model to obtain comprehensive effect data corresponding to the first collapse control plan includes: According to the first collapse factor data, obtaining second collapse factor associated data of the mountain slope collapse location; Sending the first collapse factor data, the first collapse factor weight data, the second collapse factor associated data and the first collapse control plan to the trained mountain slope collapse control plan analysis model; Receive the comprehensive management effect data returned by the mountain slope collapse management program analysis model.

5. The method for generating a mountain slope collapse control plan according to claim 4, characterized in that: After the step of receiving the comprehensive management effect data returned by the mountain slope collapse management solution analysis model, the method further includes: Determine whether each item of governance effect data in the comprehensive governance effect data is within a preset governance effect data threshold range; If there is at least one target governance effect data in the comprehensive governance effect data that is not within the corresponding governance effect data threshold range, optimizing at least one of the first collapse factor data corresponding to the target governance effect data and the first collapse factor proportion data corresponding to the first collapse factor data; According to the optimized first collapse factor data and the corresponding first collapse factor weight data, the second collapse factor associated data and the first collapse management plan are updated accordingly; Re-execute the steps of sending the first collapse factor data, the first collapse factor weight data, the second collapse factor associated data and the first collapse control plan to the trained mountain slope collapse control plan analysis model to updating the second collapse factor associated data and the first collapse control plan according to the optimized first collapse factor data and the corresponding first collapse factor weight data, until each control effect data in the comprehensive control effect data corresponding to the optimized first collapse control plan is within the preset control effect data threshold range.

6. The method for generating a mountain slope collapse control plan according to claim 1, characterized in that: The step of matching the first collapse control solution corresponding to the first collapse factor data and the first collapse factor weight data comprises: Acquire historical mountain slope collapse control program data, analyze the historical mountain slope collapse control program data to obtain corresponding second collapse control factor data, and second collapse control effect data corresponding to the second collapse control factor data; Combining the first collapse factor data and the first collapse factor weight data into a corresponding collapse factor data pair, and combining the second collapse management factor data and the second collapse management effect data into a corresponding collapse management factor data pair; According to the preset table of correspondence between collapse factor data and collapse governance factors, the percentage of data matching between the collapse factor data pair and the collapse governance factor data pair is calculated; If the data matching percentage is within a preset data matching percentage threshold range, the historical mountain slope collapse control plan data corresponding to the maximum data matching percentage is set as the first collapse control plan.

7. The method for generating a mountain slope collapse control plan according to claim 6, characterized in that: After the step of setting the first collapse control scheme as the target mountain slope collapse control scheme corresponding to the mountain slope collapse location, the method further includes: Obtaining actual landslide control effect data after the implementation of the target mountain slope landslide control plan; The target mountain slope collapse control plan and the corresponding actual collapse control effect data are used to optimize the mountain slope collapse control plan analysis model and the collapse factor data and collapse control factor correspondence table.

8. A device for generating a mountain slope collapse control plan, characterized in that: include: A historical visual data module is used to obtain the historical visual data of the mountain slope at the location of the mountain slope collapse; A collapse factor data module, used for processing the historical visual data of the mountain slope through a pre-trained visual analysis model to obtain first collapse factor data corresponding to the collapse location of the mountain slope and first collapse factor proportion data corresponding to the first collapse factor data; A governance scheme matching module, used for matching a corresponding first collapse governance scheme according to the first collapse factor data and the first collapse factor weight data; A comprehensive effect data module, used for sending the first collapse factor data, the first collapse factor weight data and the first collapse control plan to the trained mountain slope collapse control plan analysis model to obtain the comprehensive effect data corresponding to the first collapse control plan; The landslide control scheme module is used to set the first landslide control scheme as the target mountain slope landslide control scheme corresponding to the mountain slope landslide location if the comprehensive effect data is greater than the preset comprehensive effect threshold data.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for generating a mountain slope collapse control plan as described in any one of claims 1-7.

10. A non-volatile computer-readable storage medium, characterized in that: The non-volatile computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by an electronic device, the electronic device executes the method for generating a mountain slope collapse control plan as described in any one of claims 1-7.

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