A method and system for predicting the scope of harm of a landslip

Abstract

The present application relates to the technical field of geological disaster prevention and mitigation, in particular to a method and system for predicting the damage range of a rockslide slope, the method comprising the following steps: obtaining geological environment information of the rockslide slope, selecting key impact indicators of the damage range of the rockslide slope according to the geological environment information of the rockslide slope; establishing a quantitative determination method for the key impact indicators, obtaining quantitative results of different key impact indicators according to the quantitative determination method; constructing a quantitative change relationship between the damage range of the rockslide slope and the key impact indicators based on test data and the quantitative results; determining a prediction model for the damage range of the rockslide slope according to the quantitative change relationship, and analyzing and predicting the damage range of the rockslide slope by using the prediction model. The present application sets the key impact indicators and mathematical models to reduce the engineering cost, improve the work efficiency and result accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological disaster prevention and mitigation, in particular to a method and system for predicting the damage range of a rock slide slope. BACKGROUND

[0002] Rock slide slopes are mainly formed in the ice edge zone of arid, semi-arid and high-cold high-altitude regions, and are a typical type of geological disaster. The surface of a rock slide slope is covered with relatively uniform rock particles, and the height can reach tens of meters or even hundreds of meters, and the rock slide slope is linearly distributed along the traffic line in the form of a cone or a column.

[0003] At present, rock slide slopes are mostly found in high mountain and high-cold regions, earthquake-prone areas, and semi-arid and arid regions, especially along the highway from Karakoram to the Himalayas, such as the Suistu (K716) to Bidakhshin (K792) section of the China-Pakistan Highway. Rock slide slopes have become the main engineering geological problem of this section. Rock particles often roll down from the cliff at the top of the slope, damaging the road surface and squeezing into the roadbed. In extreme cases, rock slide slopes can even block the highway, causing traffic disruptions and traffic accidents. However, there is a lack of research on the damage range of rock slide slopes, and the understanding of the damage range of rock slide slopes is very limited. The existing technology cannot meet the actual needs of regional engineering construction and disaster prevention. SUMMARY

[0004] In view of the defects of the existing method and the deficiencies in practical application, and due to the limitations of the existing technology in the evaluation of the damage range of rock slide slopes and the deficiencies in practical application, in order to fill the gap in this technical field, a method for predicting the damage range of rock slide slopes is proposed, which aims to quickly and accurately define the area that may be affected by rock slide slope disasters. In a first aspect, the present application provides a method for predicting the damage range of a rock slide slope, the method comprising the following steps: obtaining rock slide slope disaster geological environment information, selecting key influence indicators of the damage range of the rock slide slope disaster according to the rock slide slope disaster geological environment information; establishing a quantitative determination method for the key influence indicators, obtaining quantitative results of different key influence indicators according to the quantitative determination method; constructing a quantitative change relationship between the damage range of the rock slide slope disaster and the key influence indicators based on the test data and the quantitative results; determining a rock slide slope disaster damage range prediction model according to the quantitative change relationship, and analyzing and predicting the damage range of the rock slide slope disaster using the rock slide slope disaster damage range prediction model. The present application comprehensively considers various prediction reference indicators and analysis functions, which can more accurately predict the damage range of rock slide slope disasters, help to reduce prediction errors, and improve the reliability of disaster warning.

[0005] Optionally, the selecting the key influence indexes of the landslide hazard range according to the landslide geological environment information comprises: selecting a first key influence index, a second key influence index, a third key influence index and a fourth key influence index of the landslide hazard range according to the landslide geological environment information; the first key influence index comprises: content of particle size of 10-40 mm in the landslide particle size distribution, content ratio of particle size of 10-20 mm and 20-40 mm in the landslide particle size distribution, and content ratio of particle size less than 10 mm and greater than 40 mm in the landslide particle size distribution; the second key influence index comprises: topographic slope of the landslide sliding area; the third key influence index comprises: topographic slope of the landslide accumulation area; and the fourth key influence index comprises: potential source quality of the landslide source area causing the landslide disaster. The present application comprehensively considers multiple key influence indexes to more comprehensively capture various influence factors affecting the landslide hazard range, significantly improves the accuracy of the prediction method, and makes the prediction result more close to the actual situation.

[0006] Optionally, the establishing the quantitative determination method of the key influence indexes comprises: establishing a first key influence index calculation method, a second key influence index calculation method, a third key influence index calculation method and a fourth key influence index calculation method based on the key influence indexes. The key influence index calculation method of the present application can ensure that each index is accurately and comprehensively evaluated, so as to more accurately capture the changes and trends of different indexes.

[0007] Optionally, the first key influence index calculation method satisfies the following relationship:

[0008]

[0009] wherein, represents the i-th particle size distribution content of the landslide, represents the i-th particle size distribution quality of the landslide, represents the total mass of the landslide sample in the particle analysis test;

[0010] The second key influence index calculation method satisfies the following relationship:

[0011]

[0012] wherein, represents the topographic slope of the landslide sliding area, represents the vertical height of the landslide sliding area, represents the horizontal distance of the landslide sliding area;

[0013] The third key influence index calculation method satisfies the following relationship:

[0014]

[0015] wherein, denotes the topographic slope of the rock avalanche accumulation zone, denotes the vertical height of the rock avalanche accumulation zone, denotes the horizontal distance of the rock avalanche accumulation zone;

[0016] The fourth key influence index calculation method satisfies the following relationship:

[0017]

[0018] wherein, denotes the potential source quality of the rock avalanche source area leading to rock avalanche disasters, 2900 denotes the average density of the rock avalanche source area source, denotes the area of the strong medium weathering area of the rock avalanche source area, denotes the stratum thickness of the strong medium weathering area of the rock avalanche source area.

[0019] The key influence index calculation method of the present application makes the prediction result can be verified and calibrated by actual observation data, which helps to continuously optimize the key influence index calculation method and improve its analysis ability and accuracy.

[0020] Optionally, the quantitative results of different key influence indexes obtained according to the quantitative determination method include: obtaining different particle size distribution contents of the rock avalanche by using the first key influence index calculation method, wherein the different particle size distribution contents of the rock avalanche include the content of the particle size of 10-40mm in the rock avalanche particle size distribution, the content ratio of the particle size of 10-20mm and 20-40mm in the rock avalanche particle size distribution, and the content ratio of the particle size less than 10mm and greater than 40mm in the rock avalanche particle size distribution; obtaining the topographic slope of the rock avalanche sliding zone according to the second key influence index calculation method; obtaining the topographic slope of the rock avalanche accumulation zone according to the third key influence index calculation method; obtaining the potential source quality of the rock avalanche source area leading to rock avalanche disasters according to the fourth key influence index calculation method; and obtaining the quantitative results of different key influence indexes by combining the content of the particle size of 10-40mm in the rock avalanche particle size distribution, the content ratio of the particle size of 10-20mm and 20-40mm in the rock avalanche particle size distribution, the content ratio of the particle size less than 10mm and greater than 40mm in the rock avalanche particle size distribution, the topographic slope of the rock avalanche sliding zone, the topographic slope of the rock avalanche accumulation zone, and the potential source quality of the rock avalanche source area leading to rock avalanche disasters.

[0021] Optionally, the constructing the quantitative change relation between the rockslide hazard range and the key influence index based on the test data and the quantitative result comprises: combining the content of the particle size of 10-40mm in the rockslide particle size distribution, the content ratio of the particle size of 10-20mm and 20-40mm in the rockslide particle size distribution, the content ratio of the particle size less than 10mm and greater than 40mm in the rockslide particle size distribution, the topographic slope of the rockslide sliding area, the topographic slope of the rockslide accumulation area, the potential source quality of the rockslide source area causing the rockslide disaster and the rockslide hazard range test data to establish the quantitative change relation between the rockslide hazard range and the key influence index.

[0022] The present application can establish a more accurate quantitative change relation by combining the test data and the key influence index, and can more accurately reflect the internal relation between the rockslide hazard range and various influence factors, thereby improving the accuracy of disaster prediction.

[0023] Optionally, the quantitative change relation satisfies the following relation:

[0024] ,

[0025] wherein, represents the rockslide hazard range, represents the content of the particle size of 10-40mm in the rockslide particle size distribution, represents the content ratio of the particle size of 10-20mm and 20-40mm in the rockslide particle size distribution, represents the content ratio of the particle size less than 10mm and greater than 40mm in the rockslide particle size distribution, represents the topographic slope of the rockslide sliding area, represents the topographic slope of the rockslide accumulation area, represents the potential source quality of the rockslide source area causing the rockslide disaster. The present application can more accurately analyze the relation between the rockslide hazard range and the key influence index by the quantitative change relation, and more accurately predict the hazard degree and the influence range of the rockslide disaster, thereby providing strong support for disaster warning and emergency response.

[0026] Optionally, the rockslide hazard range prediction method further comprises: introducing a linear model, a logarithmic model, an exponential model and a power function model based on a theoretical analysis model; and determining a rockslide hazard range prediction model in combination with the quantitative change relation, the linear model, the logarithmic model, the exponential model and the power function model. The present application introduces various mathematical models to enrich the types of prediction models, makes the prediction method more flexible and diverse, and makes the present application applicable to different situations and data characteristics, thereby improving the adaptability of the prediction model.

[0027] Optionally, determining the rockslide hazard range prediction model according to the quantitative change relationship comprises:

[0028] The rockslide hazard range prediction model satisfies the following relationship:

[0029]

[0030] wherein, represents the rockslide hazard range, represents the content of the particle size of 10-40 mm in the rockslide particle size distribution, represents the content ratio of the particle size of 10-20 mm to 20-40 mm in the rockslide particle size distribution, represents the content ratio of the particle size less than 10 mm to the particle size greater than 40 mm in the rockslide particle size distribution, represents the topographic slope of the rockslide sliding area, represents the topographic slope of the rockslide accumulation area, represents the potential source quality of the rockslide source area causing the rockslide disaster; a prediction result of the rockslide hazard range is obtained based on the rockslide hazard range prediction model; the rockslide hazard range is comprehensively analyzed in combination with the prediction result, the key influence index, and the rockslide geological environment information. The prediction model of the present application integrates various mathematical models and quantitative change relationships, can more comprehensively consider various key factors influencing the rockslide hazard range, and can accurately reflect the physical mechanism and influencing factors of the disaster.

[0031] In a second aspect, the present application further provides a rockslide hazard range prediction system capable of efficiently performing the rockslide hazard range prediction method provided by the present application. The system comprises an input device, a processor, an output device, and a memory, wherein the input device, the processor, the output device, and the memory are connected to each other. The memory comprises a computer readable storage medium as described in the first aspect of the present application. The memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to invoke the program instructions. The rockslide hazard range prediction system provided by the present application has compact structure, strong applicability, and greatly improved operation efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The flowchart of the rockslide hazard range prediction method of the present application;

[0033] Figure 2 The structural schematic diagram of the rockslide hazard range prediction system of the present application. DETAILED DESCRIPTION

[0034] ​Specific embodiments of the present application will now be described in detail with reference to the drawings, which are by way of example only and shall not be taken in a limiting sense. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. It will be apparent, however, to one skilled in the art, that the present application can be practiced without

[0035] Reference throughout this specification to "one embodiment", "an embodiment", "one example", or "an example", means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the application. Thus, the appearances of the phrases "in one embodiment", "in an embodiment", "one example", or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics can be combined in any suitable

[0036] Reference throughout this specification to "one embodiment", "an embodiment", "one example", or "an example", means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the application. Thus, the appearances of the phrases "in one embodiment", "in an embodiment", "one example", or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics can be combined in any suitable Figure 1 Due to the limitations and deficiencies of the analysis technology of the harm range of rockslide, a method for predicting the harm range of rockslide is provided, which can quickly and accurately define the range of the area that may be affected by the rockslide disaster, so as to effectively cope with the disaster risk. The present application provides a method for predicting the harm range of rockslide, and the method comprises the following steps:

[0037] S1, obtaining rockslide disaster geological environment information, and selecting key influence indexes of the harm range of rockslide disaster according to the rockslide disaster geological environment information, the implementation steps and specific contents of which are as follows:

[0038] Firstly, the rockslide disaster geological environment information is obtained.

[0039] The rockslide disaster geological environment information is collected and sorted systematically, and the data mainly covers the key influence index information of the harm range of rockslide, such as the particle size distribution, topographic slope and potential source quality of rockslide. At the same time, the environmental information of the area where the rockslide is located also needs to be comprehensively obtained, including but not limited to geological structure, climate condition, vegetation coverage and human activity influence, etc. The related information is helpful to deeply understand the harm range of rockslide disaster and optimize the prediction model. Therefore, the comprehensive collection and analysis of the rockslide disaster geological environment information can lay an information foundation for the subsequent disaster harm range prediction method.

[0040] The first key influence index, the second key influence index, the third key influence index and the fourth key influence index of the landslide hazard range are selected according to the landslide geological environment information.

[0041] Four key influence indexes are selected based on the obtained landslide geological environment information to predict the hazard range of the landslide.

[0042] The first key influence index is mainly derived from the particle size distribution characteristics of the landslide, and the first key influence index mainly includes: the content of particle size of 10-40mm in the particle size distribution of the landslide, the content ratio of particle size of 10-20mm and 20-40mm in the particle size distribution of the landslide, and the content ratio of particle size less than 10mm and greater than 40mm in the particle size distribution of the landslide. Through the particle analysis test, the percentage of particle content in the range of 10 to 40 millimeters, the content ratio of 10 to 20 millimeter and 20 to 40 millimeter particle size, and the content ratio of less than 10 millimeter and greater than 40 millimeter particle size are selected, and the above particle size distribution index provides an important physical basis for the prediction of the hazard range of the landslide.

[0043] The second key influence index mainly includes the topographic slope of the landslide sliding area. The second key influence index combines field investigation data and remote sensing image technology, and takes the topographic slope of the landslide sliding area as the second key influence index, which is accurately measured in degrees. The index is directly related to the movement speed and energy release when the landslide occurs, and is a key element for evaluating the influence range.

[0044] The third key influence index includes the topographic slope of the landslide accumulation area. The third key influence index mainly relies on remote sensing image technology and field investigation method, and takes the topographic slope of the landslide accumulation area as the third key influence index, which is helpful to predict the potential accumulation area of the landslide disaster and the damage degree it may cause.

[0045] The fourth key influence index includes the potential source quality of the landslide source area causing the landslide disaster. The fourth key influence index is mainly derived from remote sensing image data and theoretical analysis, and quantifies the potential source quality of the landslide source area that may cause disaster. The index directly reflects the potential scale and destructive power of the landslide disaster, and is an indispensable key factor for formulating disaster prevention and mitigation measures.

[0046] Through the selection and setting of the above key influence indexes, the hazard range of the landslide can be more comprehensively and accurately evaluated, and scientific basis can be provided for its early warning and response.

[0047] Further, the above-mentioned key influence indicators are respectively marked, and the specific content is as follows.

[0048] In the first key influence indicator, represents the content of the particle size of 10-40mm in the particle size distribution of the stone slide slope, represents the content ratio of the particle size of 10-20mm to 20-40mm in the particle size distribution of the stone slide slope, represents the content ratio of the particle size less than 10mm to more than 40mm in the particle size distribution of the stone slide slope.

[0049] In the second key influence indicator, represents the terrain slope of the sliding area of the stone slide slope.

[0050] In the third key influence indicator, represents the terrain slope of the accumulation area of the stone slide slope.

[0051] In the fourth key influence indicator, represents the potential source quality of the stone slide slope disaster caused by the source area of the stone slide slope.

[0052] The different key influence indicators are marked, so that the key influence indicators are more clear, easy to understand and remember, and in the subsequent data analysis, model construction and result interpretation process, the different marks can quickly identify the specific meaning represented by each indicator, and avoid confusion.

[0053] Further, the selection method of the key influence indicator of the stone slide slope disaster range in the embodiment is only one optional condition of the present application, and in other one or some embodiments, the selection method of the key influence indicator can be optimized according to the prediction needs of the harm situation and harm range of the stone slide slope disaster. Different stone slide slope disasters have different characteristics and influence factors, and the selection method of the key influence indicator is adjusted according to the actual situation, so that the accuracy and applicability of the stone slide slope disaster harm range prediction method can be ensured.

[0054] S2, a quantitative determination method of the key influence indicator is established, and the quantitative results of different key influence indicators are obtained according to the above-mentioned quantitative determination method, and the specific steps and implementation content are as follows:

[0055] The quantitative determination method of the key influence indicator of the stone slide slope disaster harm range in the embodiment mainly includes the first key influence indicator calculation method, the second key influence indicator calculation method, the third key influence indicator calculation method and the fourth key influence indicator calculation method.

[0056] The first key influence indicator calculation method satisfies the following relationship:

[0057]

[0058] wherein, represents the content of the i-th particle size distribution of the talus slope, represents the mass of the i-th particle size distribution of the talus slope, represents the total mass of the talus slope sample in the particle analysis test;

[0059] The content of different particle size distributions in the talus slope reflects the proportion of particles in a specific particle size range in the talus slope sample.

[0060] wherein i is an index variable for indicating different particle size distributions, in this embodiment, i specifically refers to the content of particle size distribution of 10-40 mm, 10-20 mm, 20-40 mm, less than 10 mm and greater than 40 mm in the talus slope.

[0061] In order to obtain the mass of different particle size distributions in the talus slope, the particles in a specific particle size range can be separated from the talus slope sample by sieving or other particle analysis methods, so as to measure the mass of different particle size distributions. The above mass value reflects the actual number of particles in different particle size ranges in the sample.

[0062] Before performing the particle analysis, a certain amount of talus slope sample is weighed for the particle analysis test. The total mass of the talus slope sample is the sample mass used for the particle analysis test, which is the basis for calculating the content of various particle size distributions.

[0063] In calculating the first key influence index, the mass of each particle size distribution needs to be measured or calculated respectively and the total mass of the sample Then the content of each particle size distribution is obtained by the first key influence index calculation method The related content values are beneficial to evaluate the particle size distribution characteristics of the talus slope sample, and further provide a reference basis for predicting the damage range of the talus slope disaster.

[0064] The second key influence index calculation method satisfies the following relationship:

[0065]

[0066] wherein, represents the terrain slope of the sliding area of the talus slope, represents the vertical height of the sliding area of the talus slope, represents the horizontal distance of the sliding area of the talus slope;

[0067] The terrain slope of the sliding area of the rockslide is usually expressed in degrees (°) or radians (rad). The terrain slope is an important parameter for describing the degree of inclination of the terrain, reflecting the degree of inclination of the ground in a certain direction. In the prediction of the damage range of the rockslide disaster, the terrain slope is one of the key factors affecting the movement speed and energy release of the rockslide.

[0068] The vertical height of the sliding area of the rockslide is usually expressed in meters (m) or other length units. The vertical height refers to the vertical distance from the starting point to the ending point of the sliding area, reflecting the size of the gravitational potential energy overcome by the rockslide in the process of sliding. The greater the above-mentioned vertical height, the greater the energy released by the rockslide in the process of sliding, and the greater the degree of damage of the disaster.

[0069] The horizontal distance of the sliding area of the rockslide is also expressed in meters (m) or other length units. The horizontal distance refers to the horizontal projection distance from the starting point to the ending point of the sliding area, reflecting the distance moved by the rockslide in the process of sliding along the horizontal direction. The ratio of the above-mentioned horizontal distance to the vertical height (i.e. the slope) determines the sliding path and speed of the rockslide, thereby affecting the damage range of the disaster.

[0070] In calculating the second key influence index, the vertical height and the horizontal distance of the sliding area of the rockslide need to be measured or calculated respectively, and then the terrain slope is calculated by the second key influence index calculation method. The above-mentioned slope value can be used to evaluate the inclination of the sliding area of the rockslide, thereby providing a reference basis for predicting the damage range of the rockslide disaster.

[0071] The third key influence index calculation method satisfies the following relationship:

[0072]

[0073] wherein, represents the terrain slope of the accumulation area of the rockslide, represents the vertical height of the accumulation area of the rockslide, represents the horizontal distance of the accumulation area of the rockslide.

[0074] The terrain slope of the accumulation area of the rockslide is expressed in degrees (°) or radians (rad) similar to the second key influence index. The terrain slope is a key influence index for predicting the damage range of the rockslide disaster, which affects the accumulation form, accumulation thickness and possible impact force of the rockslide in the accumulation area. The greater the above-mentioned terrain slope of the accumulation area, the higher the speed and energy of the rockslide in the process of accumulation, thereby increasing the degree of damage of the disaster.

[0075] The vertical height of the accumulation area of the rockslide slope, i.e. the vertical distance from the starting point (or a certain point) of the accumulation area to the bottom (or a reference plane) of the accumulation area, reflects the size of the gravitational potential energy that the rockslide slope overcomes during the accumulation process, and the maximum height that the accumulation body can reach. The greater the vertical height, the greater the gravitational potential energy of the accumulation body, and the more serious the damage to the accumulation area and its surrounding environment.

[0076] The horizontal distance of the accumulation area of the rockslide slope, i.e. the horizontal projection distance between the starting point and the ending point (or two points) of the accumulation area, reflects the horizontal movement distance of the rockslide slope during the accumulation process. The ratio of the horizontal distance to the vertical height (i.e. the slope) determines the shape, stability and impact force of the accumulation body, so changes in the horizontal distance will have a significant impact on the scope of the disaster.

[0077] In calculating the third key influence index, the vertical height and horizontal distance of the accumulation area of the rockslide slope need to be measured or calculated respectively, and then the terrain slope is calculated by the third key influence index calculation method. The slope value can be used to evaluate the inclination of the accumulation area of the rockslide slope, and to provide information support for predicting the scope of the rockslide disaster, the accumulation shape and the impact force that may be generated, etc.

[0078] The fourth key influence index calculation method satisfies the following relationship:

[0079]

[0080] wherein, represents the potential source quality of the rockslide slope source area leading to rockslide disasters, and 2900 represents the average density of the source of the rockslide slope source area, represents the area of the strong medium weathering region of the rockslide slope source area, represents the stratum thickness of the strong medium weathering region of the rockslide slope source area.

[0081] The potential source quality of the rockslide slope source area leading to rockslide disasters is a key index that affects the scope of the rockslide disaster, which can evaluate the total amount of substances that may be involved in the rockslide disaster. The greater the potential source quality, the more sources will participate in the movement when the disaster occurs, thereby increasing the degree of harm and the scope of influence of the disaster.

[0082] The strong medium weathering region refers to a region where rocks or soils become loose and fragile due to weathering. The rocks or soils in this region are prone to sliding or collapsing under the action of gravity, water flow or earthquake, etc. external force, becoming a potential source of rockslide disasters. Therefore, the size of the strong medium weathering region directly affects the amount of potential source quality.

[0083] The stratum thickness refers to the vertical distance from the ground surface to the interface of a specific stratum. In areas of strong weathering, a greater stratum thickness means that there is more loose material in the area, which is prone to being carried and participating in movement during disasters, thereby increasing the potential source quality.

[0084] In calculating the fourth key influence index, the area and stratum thickness of the strong weathering area in the rock avalanche source area are measured or calculated respectively, and then the potential source quality is calculated by the fourth key influence index calculation method. On the other hand, the above-mentioned 2900 is the average density of the rock avalanche source area.

[0085] Then, the quantitative results of different key influence indexes are obtained based on the key influence index quantitative determination method.

[0086] The first key influence index calculation method is used to obtain the different particle size distribution contents of the rock avalanche, which mainly includes the content of the particle size of 10-40mm in the rock avalanche particle size distribution, the content ratio of the particle size of 10-20mm and 20-40mm in the rock avalanche particle size distribution, and the content ratio of the particle size less than 10mm and greater than 40mm in the rock avalanche particle size distribution.

[0087] The particle size distribution content is analyzed and quantified, and the first key influence index calculation method is used to obtain the particle size distribution content of different particle size ranges in the rock avalanche, which includes the particle content in the particle size range of 10-40mm, the content ratio between the particle size of 10-20mm and 20-40mm, and the content ratio between the particle size less than 10mm and greater than 40mm. The above data provides an intuitive understanding of the composition of the rock avalanche.

[0088] According to the second key influence index calculation method, the topographic slope of the rock avalanche sliding area is obtained. For the determination of the topographic slope of the sliding area, the second key influence index calculation method is used to calculate the topographic slope of the rock avalanche sliding area. The above index is crucial for evaluating the potential energy of the rock avalanche and the damage range it may cause.

[0089] According to the third key influence index calculation method, the topographic slope of the rock avalanche accumulation area is obtained. For the measurement of the topographic slope of the accumulation area, the third key influence index calculation method is used to further determine the topographic slope of the rock avalanche accumulation area. The relevant data is helpful for predicting the distribution characteristics of the rock avalanche in the accumulation area and its potential impact on the surrounding environment.

[0090] The potential source quality of the rockslide source area causing the rockslide disaster is obtained based on the fourth key influence index calculation method. The estimation of the potential source quality is a scientific estimation of the potential source quality of the rockslide source area that may cause disasters by the fourth key influence index calculation method. The above index is directly related to the scale of the disaster and the damage it may cause.

[0091] The quantitative results of different key influence indexes can be obtained by combining the content of particle size of 10-40mm in the rockslide particle size distribution, the content ratio of particle size of 10-20mm and 20-40mm in the rockslide particle size distribution, the content ratio of particle size less than 10mm and greater than 40mm in the rockslide particle size distribution, the topographic slope of the rockslide sliding area, the topographic slope of the rockslide accumulation area, and the potential source quality of the rockslide source area causing the rockslide disaster.

[0092] In summary, the key data about the different particle size distribution contents of the rockslide, the topographic slopes of the sliding area and the accumulation area, and the potential source quality are obtained by a series of key influence index quantitative determination methods. The above data provide a data basis for subsequent rockslide disaster range prediction.

[0093] Further, the quantitative determination method of the key influence index in the embodiment is only an optional condition of the present application. In other one or some embodiments, the quantitative determination method of the key influence index can be optimized and upgraded according to the actual situation of the rockslide and the characteristic properties of the key influence index. Different rockslides have different geological, geomorphic and climatic conditions, and therefore their disaster characteristics may also differ. The optimized calculation method can better adapt to environmental differences, provide more personalized prediction methods for different types of rockslides, and enhance the accuracy of the calculation results.

[0094] S3, based on the test data and the quantitative results, a quantitative change relationship formula between the rockslide disaster damage range and the key influence index is constructed, and the specific steps and implementation contents are as follows:

[0095] In order to construct the quantitative change relationship formula between the rockslide disaster damage range and the key influence index, the mutual relationship between the rockslide disaster damage range and the key influence index is further understood.

[0096] The mutual change relationship between the rockslide disaster damage range and the key influence index is established by combining the quantitative results of the key influence indexes such as the content of particle size of 10-40mm in the rockslide particle size distribution, the content ratio of particle size of 10-20mm and 20-40mm in the rockslide particle size distribution, the content ratio of particle size less than 10mm and greater than 40mm in the rockslide particle size distribution, the topographic slope of the rockslide sliding area, the topographic slope of the rockslide accumulation area, and the potential source quality of the rockslide source area causing the rockslide disaster.

[0097] Based on the key influence indicators, including the content of different particle size ranges in the particle size distribution of the sliding slope (10-40 mm, 10-20 mm and 20-40 mm content ratio, content ratio of less than 10 mm and greater than 40 mm), the terrain slope of the sliding area and the accumulation area, and the potential source quality, in order to further analyze the mutual change relationship between the key influence indicators and the disaster damage range of the sliding slope, the embodiment expresses the internal relationship between the key influence indicators and the disaster damage range in the form of a mathematical function, that is, a quantitative change relationship is established.

[0098] The above quantitative change relationship satisfies the following relationship:

[0099] ,

[0100] wherein, represents the damage range of the sliding slope disaster, represents the content of particle size 10-40 mm in the particle size distribution of the sliding slope, represents the content ratio of particle size 10-20 mm and 20-40 mm in the particle size distribution of the sliding slope, represents the content ratio of particle size less than 10 mm and greater than 40 mm in the particle size distribution of the sliding slope, represents the terrain slope of the sliding area of the sliding slope, represents the terrain slope of the accumulation area of the sliding slope, represents the potential source quality of the source area of the sliding slope leading to the sliding slope disaster.

[0101] In this embodiment, linear model, logarithmic model, exponential model and power function model are introduced based on the theoretical analysis model; and the quantitative change relationship between the key influence indicators and the disaster damage range is determined by combining the quantitative change relationship, the linear model, the logarithmic model, the exponential model and the power function model.

[0102] In order to further explore the quantitative change relationship between the key influence indicators and the disaster damage range, thereby effectively guiding the prediction and evaluation of the disaster damage range, a variety of theoretical analysis models are introduced in the embodiment, and the related models cover multiple dimensions of linear and nonlinear relationships. In addition to reflecting the proportional change between variables by using the linear model, logarithmic model, exponential model and power function model are also included. The above nonlinear models provide more flexible and complex ways to capture and explain the possible nonlinear change relationship between the key influence indicators and the disaster damage range of the sliding slope based on different mathematical assumptions and perspectives. Based on this, the mechanism of disaster occurrence can be more comprehensively understood, and the accuracy of disaster prediction and evaluation can be improved.

[0103] Linear model, logarithmic model, exponential model and power function model are introduced for theoretical analysis, and the related information of the above various theoretical analysis models is collected in Table 1.

[0104] Table 1 Information table of theoretical analysis model of harm range of rockslide

[0105]

[0106] Based on the information in Table 1, the linear model assumes that there is a direct linear relationship between the key influencing indicators and the harm range of rockslide; the logarithmic model considers the possible nonlinear influence of the indicators on the harm range of rockslide, especially when the indicator value is large; the exponential model assumes that the harm range of rockslide increases exponentially with the increase of the indicator value; and the power function model provides another mathematical form to describe the complex relationship.

[0107] The constants a, b, c, d, e, f, and g in Table 1 can be obtained by fitting the experimental data. In order to determine the constant parameters in the above models, the example uses the experimental data of the harm range of rockslide and the quantitative results of the key influencing indicators to obtain the constant parameter values through data fitting method, and then the harm range of rockslide under different conditions can be more accurately predicted.

[0108] In the example, a comprehensive prediction and evaluation tool for the harm range of rockslide is provided by combining the above theoretical analysis models such as linear model, logarithmic model, exponential model and power function model. The harm range of rockslide can be dynamically adjusted and corrected according to the actual observation data and prediction results, so as to improve the accuracy and reliability of the prediction results, and at the same time provide support for in-depth understanding of the relationship between the harm range of rockslide and the key influencing indicators.

[0109] In order to explore the quantitative change relationship between the harm range of rockslide and the key influencing indicators, a series of rockslide harm range test schemes are designed and implemented in the example. The above test schemes comprehensively consider the key influencing indicators of the harm range of rockslide, i.e., the content of 10~40mm particle size, the content ratio of 10~20mm and 20~40mm particle size, the content ratio of less than 10mm and greater than 40mm particle size, the topographic slope of sliding area, the topographic slope of accumulation area, and the potential source quality of source area.

[0110] The test process records the harm range of disaster under each test condition in detail, and the related test results are arranged and summarized in Table 2. The table clearly shows the specific values of different key influencing indicators and the corresponding harm range of rockslide under different test numbers.

[0111] Table 2 Test scheme and test result table of harm range of rockslide

[0112]

[0113] Subsequently, the test data in Table 2 is fitted and analyzed based on the theoretical analysis model in Table 1. Through the above steps, the quantitative change relationship between the damage range of the rockslide slope disaster and the key influence index can be revealed. The theoretical analysis model includes a linear model, a logarithmic model, an exponential model, and a power function model, which respectively provide different perspectives to analyze and understand the quantitative change relationship between the damage range of the disaster and the key influence index.

[0114] Further fitting of the test data obtains the quantitative change relationship formula corresponding to each theoretical analysis model, and the above relationship formula and the corresponding fitting degree value are arranged and summarized in Table 3.

[0115] Table 3 Quantitative change relationship formula between damage range of rockslide slope disaster and key influence index

[0116]

[0117] As can be seen from Table 3, the linear model and the exponential model show relatively good fitting effects in describing the relationship between the damage range of the rockslide slope disaster and the key influence index (the fitting degree values are 77.90% and 79.87% respectively), while the fitting effects of the logarithmic model and the power function model are relatively poor.

[0118] The quantitative change relationship formula established in the embodiment based on the test data and the quantitative results of the key influence index can more accurately predict the damage range of the rockslide slope disaster, which helps to take preventive measures before the disaster occurs and reduce losses. On the other hand, the establishment and analysis of the quantitative change relationship formula help to deeply understand the occurrence mechanism of the rockslide slope disaster, and analyze the internal relationship between the key influence index and the damage range of the disaster through the quantitative change relationship formula, which provides a basis for subsequent disaster prediction.

[0119] Furthermore, the quantitative change relationship formula between the damage range of the rockslide slope disaster and the key influence index in the embodiment is only an optional condition of the present application. In other one or some embodiments, the quantitative change relationship formula can be optimized and changed according to the actual prediction needs of the damage range of the rockslide slope disaster and the actual situation of the key influence index. By adjusting and optimizing the quantitative change relationship formula, the mutual change relationship between the damage range of the rockslide slope disaster and various key influence indexes can be more accurately captured, thereby improving the accuracy and reliability of the prediction results, which plays an important role in formulating effective disaster prevention and mitigation strategies.

[0120] S4, determining a rockslide slope disaster damage range prediction model according to the above quantitative change relationship formula, and analyzing and predicting the damage range of the rockslide slope disaster by using the rockslide slope disaster damage range prediction model, the specific steps and related contents are as follows: ​

[0121] In the present embodiment, the prediction model of the damage range of the rockslide is determined according to the quantitative change relationship formula, and the specific implementation content is as follows:

[0122] Based on the information in Table 3, among the quantitative change relationship formulas corresponding to the four theoretical analysis models, the exponential model has the highest fitting degree in fitting the quantitative change relationship between the damage range of the rockslide and the key influence indicators (the content of the particle size of 10-40 mm, the content ratio of the particle size of 10-20 mm and 20-40 mm, the content ratio of the particle size less than 10 mm and greater than 40 mm, the terrain slope of the sliding area, the terrain slope of the accumulation area, and the potential source quality of the source area) , which means that the exponential model can more accurately describe the mutual relationship between the key influence indicators and the damage range of the rockslide.

[0123] Therefore, the quantitative change relationship formula obtained by the exponential model is taken as the optimal model of the quantitative change relationship between the damage range of the rockslide and the key influence indicators, that is, the prediction model of the damage range of the rockslide is obtained. The prediction model of the damage range of the rockslide satisfies the following relationship:

[0124] ,

[0125] wherein, represents the damage range of the rockslide, represents the content of the particle size of 10-40 mm in the particle size distribution of the rockslide, represents the content ratio of the particle size of 10-20 mm and 20-40 mm in the particle size distribution of the rockslide, represents the content ratio of the particle size less than 10 mm and greater than 40 mm in the particle size distribution of the rockslide, represents the terrain slope of the sliding area of the rockslide, represents the terrain slope of the accumulation area of the rockslide, represents the potential source quality of the source area of the rockslide.

[0126] In an optional embodiment, the prediction model of the damage range of the rockslide can accurately predict the damage range of different rockslide disasters. The damage range of the rockslide calculated by the prediction model is 1.28 m 2 , 0.71 m 2 and 0.61 m 2 , respectively. At the same time, the corresponding values obtained by experimental measurement are 1.17 m 2 , 0.65 m 2 and 0.57 m 2In comparison, the error rates between the calculated values and the experimentally measured values are 8.59%, 8.45%, and 6.56%, respectively, and all errors do not exceed 10%. Based on the above comparison results, it is further illustrated that the present application can quickly and accurately predict the damage range of the rockslide.

[0127] In the embodiment, the prediction result of the damage range of the rockslide is obtained based on the rockslide damage range prediction model; and the damage range of the rockslide is comprehensively analyzed in combination with the prediction result, the key influence indexes, and the geological environment information of the rockslide.

[0128] After successfully constructing and verifying the rockslide damage range prediction model, the model is used to predict the damage range of the rockslide under different conditions. The related prediction results not only provide important information about the possible impact area range of the rockslide, but also provide a data basis for comprehensive analysis and risk assessment of the rockslide.

[0129] In the comprehensive analysis process, not only the prediction result is considered, but also the key influence indexes such as the particle size distribution of the rockslide, the terrain slope, and the potential source quality of the source area are fully combined. Comprehensive analysis based on the above information can more comprehensively understand the background, causes, and future development trend of the rockslide disaster.

[0130] Through the comprehensive analysis method, the damage degree of the rockslide disaster can be more accurately evaluated, and the potential high-risk areas can be identified. Accordingly, corresponding disaster prevention and mitigation measures can be developed, including but not limited to strengthening monitoring and early warning, optimizing emergency plans, implementing engineering governance, etc., aiming to minimize the negative impact of disasters on human society and the natural environment.

[0131] In addition, the comprehensive analysis results can also provide scientific basis for relevant departments and decision-makers, better develop rockslide disaster prevention and control planning and policies, and promote the scientific and standardized development of disaster prevention and control work.

[0132] In summary, based on the prediction result obtained from the rockslide damage range prediction model, and in combination with the prediction result, the key influence indexes, and the geological environment information, the damage range of the rockslide is comprehensively analyzed, which is an important technical means to understand and cope with the rockslide disaster. The rockslide damage range prediction method of the present embodiment not only improves the disaster recognition level, but also provides strong technical support and decision-making basis for disaster prevention and mitigation work.

[0133] The rockslide slope disaster hazard range prediction method of the embodiment effectively fills the technical blank in the field of rockslide slope disaster hazard range, initiatively proposes an effective prediction method for the rockslide slope disaster hazard range, can quickly and accurately define the potential influence range of the rockslide slope disaster, provides a solid theoretical cornerstone for the rockslide slope disaster prevention and control work, and provides a scientific basis for the formulation of the emergency response and long-term prevention and control strategy of the rockslide slope disaster.

[0134] In the method implementation aspect, the parameter setting process of the rockslide slope disaster hazard range prediction method of the embodiment is simple and clear, easy to operate, greatly reduces the engineering cost and improves the work efficiency, so that the method shows high practical value in the field of geological disaster prevention and mitigation and environmental protection. In addition, the method is easy to popularize, can provide effective prediction and control means for rockslide slope disasters in a wider area, and further enhances the application potential in practice.

[0135] In summary, the above rockslide slope disaster hazard range prediction method not only promotes the development of the rockslide slope disaster hazard range prediction technology, but also provides strong technical support for the prevention and control of geological disasters, and has far-reaching social and environmental benefits.

[0136] See Figure 2 In an optional embodiment, the present application further provides a rockslide slope disaster hazard range prediction system, which comprises a processor, an input device, an output device and a memory, and the processor, the input device, the output device and the memory are connected with each other, wherein the memory is used for storing a computer program, the computer program comprises program instructions, the processor is configured to call the program instructions, and the specific steps of the rockslide slope disaster hazard range prediction method and related embodiments provided by the present application are executed. The rockslide slope disaster hazard range prediction system of the present application has a complete structure and is objective and stable.

[0137] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should be covered in the scope of the claims and the specification of the present application.

Claims

1. A method for predicting a range of harm of a landslide disaster, characterized by, The method comprises the following steps: obtaining landslide slope disaster geological environment information, and selecting key influence indexes of landslide slope disaster hazard range according to the landslide slope disaster geological environment information; establishing a quantitative determination method of the key influence indexes, and obtaining quantitative results of different key influence indexes according to the quantitative determination method; constructing a quantitative change relationship between the landslide slope disaster hazard range and the key influence indexes based on test data and the quantitative results; determining a landslide slope disaster hazard range prediction model according to the quantitative change relationship, and analyzing and predicting the hazard range of the landslide slope disaster by using the landslide slope disaster hazard range prediction model; the step of constructing the quantitative change relationship between the landslide slope disaster hazard range and the key influence indexes based on test data and the quantitative results comprises: combining the content of particle size of 10-40 mm in the landslide slope particle size distribution, the content ratio of particle size of 10-20 mm and 20-40 mm in the landslide slope particle size distribution, the content ratio of particle size less than 10 mm and greater than 40 mm in the landslide slope particle size distribution, the topographic slope of the landslide slope sliding area, the topographic slope of the landslide slope accumulation area, the potential source quality of the landslide slope source area causing the landslide slope disaster, and the landslide slope disaster hazard range test data to construct the quantitative change relationship between the landslide slope disaster hazard range and the key influence indexes; the quantitative change relationship satisfies the following relationship: ， wherein, represents the hazard range of the rock avalanche, represents the content of the particle size of 10-40 mm in the particle size distribution of the rock avalanche, represents the content ratio of the particle size of 10-20 mm to 20-40 mm in the particle size distribution of the rock avalanche, represents the content ratio of the particle size less than 10 mm to more than 40 mm in the particle size distribution of the rock avalanche, represents the terrain slope of the sliding area of the rock avalanche, represents the terrain slope of the accumulation area of the rock avalanche, represents the potential source quality of the source area of the rock avalanche leading to the rock avalanche disaster; introducing a linear model, a logarithmic model, an exponential model and a power function model based on a theoretical analysis model; combining the quantitative change relationship, the linear model, the logarithmic model, the exponential model and the power function model to determine the landslide slope disaster hazard range prediction model; the step of determining the landslide slope disaster hazard range prediction model according to the quantitative change relationship comprises: the landslide slope disaster hazard range prediction model satisfies the following relationship: ， wherein, represents the hazard range of the rock avalanche, represents the content of the particle size of 10-40 mm in the particle size distribution of the rock avalanche, represents the content ratio of the particle size of 10-20 mm to 20-40 mm in the particle size distribution of the rock avalanche, represents the content ratio of the particle size less than 10 mm to more than 40 mm in the particle size distribution of the rock avalanche, represents the terrain slope of the sliding area of the rock avalanche, represents the terrain slope of the accumulation area of the rock avalanche, represents the potential source quality of the source area of the rock avalanche leading to the rock avalanche disaster; obtaining a prediction result of the landslide slope disaster hazard range based on the landslide slope disaster hazard range prediction model; combining the prediction result, the key influence indexes and the landslide slope geological environment information to comprehensively analyze the landslide slope disaster hazard range.

2. The method according to claim 1, wherein the step of selecting the key influence indexes of the landslide slope disaster hazard range according to the landslide slope disaster geological environment information comprises: selecting a first key influence index, a second key influence index, a third key influence index and a fourth key influence index of the landslide slope disaster hazard range according to the landslide slope disaster geological environment information; the first key influence index comprises the content of particle size of 10-40 mm in the landslide slope particle size distribution, the content ratio of particle size of 10-20 mm and 20-40 mm in the landslide slope particle size distribution, and the content ratio of particle size less than 10 mm and greater than 40 mm in the landslide slope particle size distribution; the second key influence index comprises the topographic slope of the landslide slope sliding area; the third key influence index comprises the topographic slope of the landslide slope accumulation area; and the fourth key influence index comprises the potential source quality of the landslide slope source area causing the landslide slope disaster.

3. The method according to claim 1, wherein the step of establishing the quantitative determination method of the key influence indexes comprises: The first key influence index calculation method, the second key influence index calculation method, the third key influence index calculation method and the fourth key influence index calculation method are established based on the key influence index.

4. The rockslide hazard reach prediction method of claim 3, wherein, The first key influence index calculation method satisfies the following relationship: ， wherein, represents the content of the i-th particle size fraction of the talus slope, represents the mass of the i-th particle size fraction of the talus slope, represents the total mass of the talus slope sample of the particle analysis test; The second key influence index calculation method satisfies the following relationship: ， wherein, represents a topographic slope of a rockslide slope slip zone, represents a vertical height of a rockslide slope slip zone, represents a horizontal distance of a rockslide slope slip zone; The third key influence index calculation method satisfies the following relationship: ， wherein, represents a topographical slope of the rock slide slope accumulation area, represents a vertical height of the rock slide slope accumulation area, represents a horizontal distance of the rock slide slope accumulation area; The fourth key influence index calculation method satisfies the following relationship: ， wherein, represents the potential source quality of the landslide source area causing the landslide disaster, 2900 represents the average density of the source of the landslide source area, represents the area of the strong medium weathering region of the landslide source area, represents the stratum thickness of the strong medium weathering region of the landslide source area.

5. The rockslide hazard reach prediction method of claim 4, wherein, The quantitative results of different key influence indexes obtained according to the quantitative determination method include: The first key influence index calculation method is used to obtain different particle size distribution contents of the stone slide slope, including the content of the particle size distribution of 10-40 mm in the stone slide slope, the content ratio of the particle size distribution of 10-20 mm and 20-40 mm in the stone slide slope, and the content ratio of the particle size distribution of less than 10 mm and more than 40 mm in the stone slide slope; The second key influence index calculation method is used to obtain the terrain slope of the sliding area of the stone slide slope; The third key influence index calculation method is used to obtain the terrain slope of the accumulation area of the stone slide slope; The fourth key influence index calculation method is used to obtain the potential source quality of the stone slide slope source area causing the stone slide slope disaster; The quantitative results of different key influence indexes are obtained by combining the content of the particle size distribution of 10-40 mm in the stone slide slope, the content ratio of the particle size distribution of 10-20 mm and 20-40 mm in the stone slide slope, the content ratio of the particle size distribution of less than 10 mm and more than 40 mm in the stone slide slope, the terrain slope of the sliding area of the stone slide slope, the terrain slope of the accumulation area of the stone slide slope, and the potential source quality of the stone slide slope source area causing the stone slide slope disaster.

6. A system for predicting the hazard range of a rockfall slope disaster, characterized in that, The system comprises a processor, an input device, an output device and a memory, which are connected to each other, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the stone slide slope disaster hazard range prediction method according to any one of claims 1-5.

Images