Drainage basin high-pressure tower geological disaster risk assessment method and system constructed based on complex network

Abstract

The invention relates to the technical field of natural disaster prevention and evaluation, in particular to a drainage basin high-pressure tower geological disaster risk evaluation method based on complex network construction. The method has the beneficial effects that the disaster risk of a plurality of specific facilities in an area can be quickly analyzed, and the simulation calculation result is closer to real data.

Description

Technical Field

[0001] The present invention relates to the technical field of natural disaster prevention and control assessment, and in particular to a method and system for assessing the risk of geological disasters of high-voltage towers in a watershed based on complex networks. Background Art

[0002] High-voltage lines, towers and other power transmission equipment are important components of my country's power system. Due to my country's large land area and complex terrain, the risk of geological disasters varies greatly from place to place. This brings huge challenges to the equipment construction and maintenance process.

[0003] How to use computational models to evaluate the geological hazard risk of high-voltage towers in a river basin is an issue that technical personnel in this field urgently need to solve. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for assessing the geological hazards of high-voltage towers in a watershed based on a complex network, thereby solving the problems existing in the prior art.

[0005] The present invention discloses a method for assessing the risk of geological disasters of high-voltage towers in a watershed based on a complex network, comprising the following steps:

[0006] S1. Based on the high-precision terrain data of the target area, the slope units and water network structures are divided and extracted; and the key node parameters are identified;

[0007] S2. Construct a regional geological disaster complex network feature dataset;

[0008] S3. Combined with the precipitation and geological stability analysis of the target area, a geological disaster risk assessment model for high-voltage towers in the basin is constructed;

[0009] S4. Based on the assessment model constructed in step S3, dynamically and rapidly assess the geological hazard risk of specific high-voltage towers in the target area.

[0010] Furthermore, the S1 step includes:

[0011] S11. Collect digital elevation (DEM) data of the study area;

[0012] S12, using the positive and negative terrain without depressions to extract the ridge line (corresponding to the watershed line), taking the flow value as 60, and generating the sub-basin unit;

[0013] S13. Taking the sub-basin unit as the unit, use the positive and negative terrain without depressions to extract the ridge line again, take the watershed generated by the flow value of 0, take the watershed as the valley line, merge the ridge line and the valley line, and after the later revision of the cells with smaller areas, the area finally divided by the watershed line and the watershed line is the slope unit.

[0014] Furthermore, in the step S1, the key node parameters include the slope, area, saturated soil density, soil porosity, internal friction angle, rock and soil cohesion, soil root cohesion, soil moisture content, and soil depth of each slope unit.

[0015] Furthermore, step S4 includes:

[0016] S41. Based on the positional relationship between slope units and depressions in the study area, the water flow transfer relationship between slope units in the study area was extracted, and a complex network describing the water flow transfer between slope units was established;

[0017] The complex network model includes: precipitation falls on each slope unit in the basin. If the area bordering the slope unit and the adjacent slope unit is a depression (i.e., a valley area), the uninfiltrated rainfall will form surface runoff on the surface of the slope unit and flow into the depression. The water flow in the low-lying area infiltrates into the adjacent slope units through groundwater. Considering the relationship between slope units in water flow transmission, we extracted all the depressions in the study area (i.e., river networks with a flow rate of 0), cut the river network with slope units to obtain the water confluence points of the slope units on the river network, and connect the slope units that cut the water confluence points to the water confluence points to obtain the actual slope unit water confluence network;

[0018] S42. Considering the transmission of precipitation on a complex network, the FLSAM (Fast Shallow Landslide Assessment Model) model is improved. Taking into account surface runoff, inflow and infiltration, and whether the slope unit is saturated, the spread of water generated by rainfall on the network and its impact on the risk of geological disasters are quantitatively calculated to obtain the model in this paper (NLSAM, the full name of which is Network-based Landslide).

[0019] Furthermore, the present invention also discloses a system for constructing a wind zone distribution map of high-voltage lines and tower facilities based on a CFD typhoon wind field, which is implemented using the above method.

[0020] The beneficial effects of the present invention are:

[0021] It can quickly analyze the disaster risks of multiple specific facilities in the area, and the simulation results are closer to the real data. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Model specific flow chart;

[0023] Figure 2 NLSAM prediction results diagram;

[0024] Figure 3 Unstable slope node diagram;

[0025] Figure 4 (A) Box plot of safety factor of slope units in the basin under different rainfall intensities under the landslide hazard calculation methods with and without considering the network structure; (B) Proportion of unstable landslides under different rainfall intensities under the two methods;

[0026] Figure 5 Figure 2. Landslide hazard assessment results, where (A) the original method was used to assess the landslide hazard when the rainfall intensity was 10 mm / h for 24 hours; (B) the method used in this paper was used to assess the landslide hazard when the rainfall intensity was 10 mm / h for 24 hours; (C) the original method was used to assess the landslide hazard when the rainfall intensity was 20 mm / h for 24 hours; (D) the method used in this paper was used to assess the landslide hazard when the rainfall intensity was 20 mm / h for 24 hours. DETAILED DESCRIPTION

[0027] The specific implementation modes of the present invention are further described below in conjunction with examples. The following examples are only used to more clearly illustrate the technical embodiments of the present invention and are not intended to limit the protection scope of the present invention.

[0028] On July 22, 2023, Fuyang District, Zhejiang Province, suffered an extreme rainstorm. More than 145 shallow landslides occurred in a 20 square kilometer mountainous area in Fuyang District, of which 60 occurred in the study area of ​​this paper. In the week before the landslide, the rainfall in the area exceeded 404 mm. In the two days from July 21 to 22, the rainfall reached 320 mm, and the rainfall was mainly concentrated from 17:40 to 20:40 on July 22. Based on the landslide events caused by this event, this paper uses the NLSAM and FSLAM models to evaluate the landslide susceptibility of the area, and verifies the reliability of the model through the landslide inventory.

[0029] Example 1

[0030] A method for assessing the risk of geological hazards of high-voltage towers in a watershed based on complex networks includes the following steps: specific implementation methods and simulation calculation results.

[0031] 1. Single-point precipitation results considering network structure

[0032] In order to understand the diffusion of water flow in the network and analyze the cascade process between nodes caused by the diffusion of water flow, we attacked a single node in the network and observed the changes in the scale and safety factor of the affected nodes after the rainfall attack. This paper randomly selected the No. 104 slope unit in the basin, extracted the parameters required for the model through the data set of the study area, and subjected the node to a 48-hour rainfall attack with a rainfall intensity of 50mm / h and 100mm / h, respectively, and analyzed the water flow changes and landslide hazards of all nodes under this scenario. The impact of attacking the No. 104 node on the basin is as follows: Figure 2 As shown in the figure, the blue area is the diffusion range of rainfall in the network, and the red area is the unstable slope with a safety factor less than 1 as the rainfall on node 104 continues. First of all, it can be seen that with the increase of rainfall intensity, the influence range of rainfall flow in the network continues to expand. When the rainfall intensity is 50mm / h ( Figure 3 (A)), the water flow spreads from the initial node 104 to the eight nodes 102, 106, 101, 89, 92, 91, 95, and 107. When the rainfall intensity is 100 mm / h ( Figure 3 (B)), the water flow spreads from the initial node 104 to 16 nodes 102, 106, 101, 89, 92, 91, 95, 107, 103, 75, 77, 81, 85, 86, 88, and 96. The number of unstable slopes increases with the increase of rainfall intensity. The unstable slopes are composed of 4 nodes 104, 106, 95, and 107 ( Figure 3 (A)) becomes 5 nodes: 104, 106, 95, 107, 91 ( Figure 3 (B)). In addition, it can be seen from the legend that the safety factor of the affected slope gradually decreases. In order to explore the changes in the safety factor of the slope with the rainfall time and rainfall intensity, the specific changes in the safety factor of the attacked node 104 and the node 107 affected by the rainfall at node 104 are analyzed in detail below.

[0033] 2. Global precipitation results considering network structure

[0034] In the real world, precipitation is not limited to a specific slope unit, but occurs in a wider area. In order to explore the cluster landslide phenomenon caused by precipitation attacking the entire basin and obtain the results of the basin landslide hazard assessment, this paper conducts global rainfall on the basin. First, the area is subjected to three rainfall attacks with 24-hour rainfall intensities of 10mm / h, 20mm / h, and 50mm / h. The node evaluation model (original FLSAM model) without considering the network structure and the scheme provided in this paper are used to calculate the landslide hazard assessment results and distribution of each node in the basin unit. It can be seen that when the rainfall intensity is 10mm / h, the two methods of considering the network structure and not considering the network structure have little effect on the regional landslide hazard results. When the rainfall intensity is 20mm / h and above, the safety factor of the slope unit considering the network structure is significantly smaller than the safety factor of the slope unit not considering the network structure. This shows that when the rainfall time is fixed, the rainfall intensity needs to reach a certain threshold, and the water flow between the slope units will have a significant impact on the safety factor of the node. At this time, it is necessary to consider the network structure.

[0035] In order to explore the critical value of daily rainfall that requires consideration of the network structure, this paper calculates the safety factor of each slope unit in the study area under 24-hour rainfall attacks of different rainfall intensities by using two methods, one considering the network structure and the other not. The proportion of slope units with a safety factor less than 1 using the above two methods to the total number of nodes in the basin is statistically analyzed as the rainfall intensity changes. Figure 4 (A) It can be seen that the threshold of rainfall intensity is between 10mm / h-20mm / h, so we select rainfall intensity of 0mm / h-20mm / h for experiment. The calculation results are as follows: Figure 4 (B). It can be seen that when the rainfall intensity is less than 15 mm, the safety factor calculation results of the overall slope unit are not significantly affected by considering the network structure or not. When the rainfall intensity is greater than 15 mm, the impact of the proposed method on the landslide hazard in the basin is significantly greater than that of the FSLAM method, and the cascade effect of the network structure on the safety factor of the slope unit is reflected. Therefore, for the study area provided in this paper, when the hourly rainfall intensity of the day is greater than 15 mm, the network relationship becomes non-negligible, and the connection between slope units needs to be considered to re-evaluate the landslide hazard. It also shows that the disaster transmission effect between nodes is more obvious under extreme rainfall.

[0036] Application Example 1

[0037] As a comparison of traditional calculation or simulation methods, the rainfall intensity of 10 mm / h and 20 mm / h was selected to attack the basin for 24 hours, and the FSLAM model and the method in this paper were selected to evaluate the landslide hazard of the study area respectively. Figure 5 ).contrast Figure 5(A) and Figure 5 (C) The FSLAM method was used to conduct 24-hour rainfall attacks with rainfall intensities of 10 mm / h and 20 mm / h in the basin, and the number of unstable slopes increased from 36 to 40. Figure 5 (A) and Figure 5 (C) The method proposed in this paper was used to conduct 24-hour rainfall attacks on the basin with rainfall intensities of 10 mm / h and 20 mm / h, and the number of unstable slopes increased from 36 to 98. It can be seen that the number of unstable slopes increases with the increase of rainfall intensity, and considering the water flow transfer between slope units will reduce the slope safety factor, resulting in a significant increase in the number of unstable slopes. Figure 5 (A) and Figure 5 (B) 10 mm / h rainfall intensity attack for 24 hours. Since the rainfall intensity threshold is not reached, there is no difference in the classification results between the two methods considering the network structure and not considering the network structure. The number of unstable slopes is 36. Figure 5 (C) and Figure 5 (D), 20mm / h rainfall intensity attacks for 24h. Since the rainfall intensity is greater than the rainfall threshold, the effect of the network is reflected in the results. The classification results considering the network structure include the classification results without considering the network structure, and the number of unstable slopes has increased greatly, from 40 to 98. From the changes in the number of unstable slopes, we can see that the cascade relationship between nodes increases the danger of the nodes, making the slope units more vulnerable. At the same time, it also shows that under extreme rainfall, we need to consider the cascade relationship between nodes to predict the danger of landslides in the basin, so as to more accurately identify group landslides and avoid the risks brought by disasters.

[0038] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing the risk of geological hazards of high-voltage towers in a watershed based on a complex network, characterized in that: The steps include: S1. Based on the high-precision terrain data of the target area, the slope units and water network structures are divided and extracted; and the key node parameters are identified; S2. Construct a regional geological disaster complex network feature dataset; S3. Combined with the precipitation and geological stability analysis of the target area, a geological disaster risk assessment model for high-voltage towers in the basin is constructed; S4. Based on the assessment model constructed in step S3, dynamically and rapidly assess the geological hazard risk of specific high-voltage towers in the target area.

2. The method for assessing the risk of geological hazards of high-voltage towers in a watershed based on complex networks according to claim 1 is characterized in that: The S1 step includes: S11. Collect digital elevation (DEM) data of the study area; S12, using the positive and negative terrain without depressions to extract the ridge line (corresponding to the watershed line), taking the flow value as 60, and generating the sub-basin unit; S13. Taking the sub-basin unit as the unit, use the positive and negative terrain without depressions to extract the ridge line again, take the watershed generated by the flow value of 0, take the watershed as the valley line, merge the ridge line and the valley line, and after the later revision of the cells with smaller areas, the area finally divided by the watershed line and the watershed line is the slope unit.

3. The method for assessing the risk of geological hazards of high-voltage towers in a watershed based on complex networks according to claim 1 is characterized in that: In the step S1, the key node parameters include the slope, area, saturated soil density, soil porosity, internal friction angle, rock and soil cohesion, soil root cohesion, soil moisture content, and soil depth of each slope unit.

4. The method for assessing the risk of geological hazards of high-voltage towers in a watershed based on complex networks according to claim 1 is characterized in that: The S4 step includes: S41. Based on the positional relationship between slope units and depressions in the study area, the water flow transfer relationship between slope units in the study area was extracted, and a complex network describing the water flow transfer between slope units was established; The complex network model includes: extracting all the depressions in the study area, cutting the river network with slope units to obtain the water confluence points of the slope units on the river network, and connecting the slope units that cut the water confluence points with the water confluence points to obtain the actual slope unit water confluence network; S42. Improve the FLSAM model by considering the transmission of precipitation on complex networks, taking into account surface runoff, inflow and infiltration, and whether the slope units are saturated, and quantitatively calculate the spread of water generated by rainfall on the network and its impact on the risk of geological disasters.

5. A device for assessing the risk of geological disasters of high-voltage towers in a watershed based on a complex network, characterized in that: The method for assessing the geological hazards of high-voltage towers in a watershed based on complex networks is adopted according to any one of claims 1 to 4.

Images