Pipeline slope water damage meteorological early warning method and system based on slope unit

Abstract

The invention discloses a pipeline slope water damage meteorological early warning method and system based on a slope unit, and relates to the technical field of slope early warning, and the method comprises the steps: obtaining digital elevation model data, soil data and real-time rainfall data of a to-be-early-warned slope; re-sampling the digital elevation model data, and determining a division result of the slope to be pre-warned according to a re-sampling result in combination with the soil data and the real-time rainfall data; and through a pre-training model, according to the division result, carrying out susceptibility evaluation on the slope to be pre-warned to obtain a susceptibility score, and according to the susceptibility score, determining a pre-warning result of the slope to be pre-warned. According to the method, the physical relation formed by geological environment factors and geological disasters is clearly reflected, the prediction warning object is clear, the method accords with the actual emergency response work, and the water damage type ground disaster prevention and control work is reasonably transited from face to point.

Description

Background Art

[0002] In regional geological disaster risk assessment and rainfall research, the size of the grid unit is usually 10km×10km, 5km×5km, etc., and the specification of 1km×1km will be used in more precise cases. In addition, there are studies that use geomorphic units, watershed units, etc. as research units. However, as the research deepens, it is found that this large-scale research unit has many disadvantages. First, a grid unit may contain multiple slope bodies. The division of grid units destroys the integrity of natural mountain slopes and lacks physical connection with geological disasters. This method ignores the differences between the slope bodies it contains, resulting in a large deviation between the prediction results and the actual situation. Secondly, when the grid unit is applied to actual engineering operations, the warning object is unclear due to its large scope, which in turn increases the workload of engineering defense.

[0003] At present, there are many deterministic models for the study of water damage geological disasters, such as SHALSTAB, SINMAP, TRIGRS, etc. Selecting a suitable model is of great significance for the correct understanding and prediction of slopes. The SHALSTAB model mainly considers the influence of terrain factors and soil characteristics on shallow slopes; the SINMAP model focuses on combining rainfall intensity with soil cohesion to predict the stability of slopes under a single rainfall intensity; TRIGRS calculates the influence of rainfall intensity on slope stability under the premise of fully considering the duration of rainfall. In actual situations, the concept of rainfall intensity is relatively complex and its numerical value is difficult to obtain. Therefore, the models involving rainfall intensity factors have certain deviations from the actual situation in the analysis results of water damage geological disasters.

[0004] To achieve early warning and prediction of water-damaged geological disasters, it is also necessary to consider the inducing effect of rainfall on geological disasters. As a probabilistic event, rainfall-induced geological disasters interact with the geological environment to increase the possibility of geological disasters. Previous researchers used physical simulation models to simulate the slopes caused by rainfall under various rainfall intensities and developed a method for monitoring and warning slopes. However, the technical difficulty and cost are very high and difficult to implement. Some studies only combined rainfall thresholds and predicted rainfall for slope warnings in catchment areas, without coupling with slope susceptibility maps, which greatly increased the uncertainty of the warning. Summary of the invention

[0005] The technical problem to be solved by the present invention is to address the deficiencies of the prior art and specifically provide a pipeline slope water damage meteorological early warning method and system based on slope units, which are as follows:

[0006] 1) In the first aspect, the present invention provides a pipeline slope water damage meteorological early warning method based on slope units, and the specific technical scheme is as follows:

[0007] Obtain digital elevation model data, soil data and real-time rainfall data of the slope to be warned;

[0008] Resampling the digital elevation model data, and determining the division result of the slope to be warned according to the resampling result, in combination with the soil data and the real-time rainfall data;

[0009] Through the pre-training model, according to the division result, the susceptibility of the slope to be warned is evaluated to obtain a susceptibility score, and according to the susceptibility score, the warning result of the slope to be warned is determined.

[0010] The beneficial effects of the pipeline slope water damage meteorological early warning method based on slope units provided by the present invention are as follows:

[0011] The present invention not only clearly reflects the physical relationship between geological environmental factors and the formation of geological disasters, but also has clear prediction and warning objects, which is in line with the actual emergency response work, and makes a reasonable transition from the overall to the specific prevention and control of water-damaged geological disasters. It has a certain demonstration significance for improving the accuracy of geological disaster forecasts and scientific prevention.

[0012] Based on the above solution, the present invention can also be improved as follows.

[0013] Further, the process of determining the division result of the slope to be warned is as follows:

[0014] Performing fill-cut processing according to the resampling result to obtain depression-free digital elevation model data, determining a first water flow direction according to the depression-free digital elevation model data, taking the first water flow direction as a base map, determining first watershed raster data according to the river flow, and determining a watershed boundary based on the first watershed raster data;

[0015] Calculate the average curvature value according to the resampling result, determine the reverse curvature based on the average curvature value, calculate the second water flow direction according to the reverse curvature, determine the second watershed raster data with the second watershed raster data as the base map, and determine the valley line boundary according to the second watershed raster data;

[0016] The watershed boundary and the valley line boundary are subjected to vector merging processing to obtain the division result of the slope to be warned.

[0017] Furthermore, the pre-trained model is determined based on the Mohr-Coulomb failure criterion, combined with an infinite slope model and a steady-state hydrological model;

[0018] The infinite slope model is specifically:

[0019]

[0020] Where c is the cohesion of the soil, is the internal friction angle of the soil, ρ s is the density of wet soil, z is the soil thickness, θ is the slope gradient, ρ w is the density of water, h is the groundwater level, and g is the acceleration due to gravity;

[0021] The steady-state hydrological model is specifically:

[0022] q·a=b·k s ·h·sinθ·cosθ;

[0023] q is the effective rainfall intensity, a is the waterlogged area, b is the width of the drainage area, k s is the saturated permeability coefficient of soil;

[0024] The pre-training model is specifically:

[0025]

[0026] T is the hydraulic conductivity.

[0027] Further, the process of evaluating the susceptibility of the slope to be warned and obtaining a susceptibility score is as follows:

[0028] Calculating the probability of occurrence of geological disasters on the slope to be warned, and using the probability of occurrence of geological disasters as the susceptibility score;

[0029] The calculation process of the probability of occurrence of geological disasters is as follows:

[0030]

[0031] Among them, P 0 is the probability of geological disasters occurring in non-rainfall conditions; R c is the effective accumulated rainfall, L is the forecast rainfall adjustment coefficient, R f is the predicted rainfall, R' max It is the maximum value of the sum of the accumulated effective rainfall and the forecast rainfall in a forecast and warning period in the region, and K' is the effective rainfall influence coefficient.

[0032] 2) In the second aspect, the present invention also provides a pipeline slope water damage meteorological early warning system based on slope units, and the specific technical scheme is as follows:

[0033] The acquisition module is used to: obtain digital elevation model data, soil data and real-time rainfall data of the slope to be warned;

[0034] The division module is used to: resample the digital elevation model data, and determine the division result of the slope to be warned according to the resampling result, combined with the soil data and the real-time rainfall data;

[0035] The early warning module is used to: evaluate the susceptibility of the slope to be warned according to the division result through the pre-training model to obtain a susceptibility score, and determine the early warning result of the slope to be warned according to the susceptibility score.

[0036] Based on the above solution, the present invention can also be improved as follows.

[0037] Further, the process of determining the division result of the slope to be warned is as follows:

[0038] Performing fill-cut processing according to the resampling result to obtain depression-free digital elevation model data, determining a first water flow direction according to the depression-free digital elevation model data, taking the first water flow direction as a base map, determining first watershed raster data according to the river flow, and determining a watershed boundary based on the first watershed raster data;

[0039] Calculate the average curvature value according to the resampling result, determine the reverse curvature based on the average curvature value, calculate the second water flow direction according to the reverse curvature, determine the second watershed raster data with the second watershed raster data as the base map, and determine the valley line boundary according to the second watershed raster data;

[0040] The watershed boundary and the valley line boundary are subjected to vector merging processing to obtain the division result of the slope to be warned.

[0041] Furthermore, the pre-trained model is determined based on the Mohr-Coulomb failure criterion, combined with an infinite slope model and a steady-state hydrological model;

[0042] The infinite slope model is specifically:

[0043]

[0044] Where c is the cohesion of the soil, is the internal friction angle of the soil, ρ s is the density of wet soil, z is the soil thickness, θ is the slope gradient, ρ w is the density of water, h is the groundwater level, and g is the acceleration due to gravity;

[0045] The steady-state hydrological model is specifically:

[0046] q·a=b·k s ·h·sinθ·cosθ;

[0047] q is the effective rainfall intensity, a is the waterlogged area, b is the width of the drainage area, k s is the saturated permeability coefficient of soil;

[0048] The pre-training model is specifically:

[0049]

[0050] T is the hydraulic conductivity.

[0051] Further, the process of evaluating the susceptibility of the slope to be warned and obtaining a susceptibility score is as follows:

[0052] Calculating the probability of occurrence of geological disasters on the slope to be warned, and using the probability of occurrence of geological disasters as the susceptibility score;

[0053] The calculation process of the probability of occurrence of geological disasters is as follows:

[0054]

[0055] Among them, P 0 is the probability of geological disasters occurring in non-rainfall conditions; R c is the effective accumulated rainfall, L is the forecast rainfall adjustment coefficient, R f is the predicted rainfall, R' max It is the maximum value of the sum of the accumulated effective rainfall and the forecast rainfall in a forecast and warning period in the region, and K' is the effective rainfall influence coefficient.

[0056] 3) In a third aspect, the present invention further provides an electronic device, comprising a processor, wherein the processor is coupled to a memory, wherein at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements any of the above methods.

[0057] 4) In a fourth aspect, the present invention further provides a computer-readable storage medium, wherein at least one computer program is stored in the computer-readable storage medium, and the at least one computer program is loaded and executed by a processor so that a computer implements any of the above methods.

[0058] It should be noted that the beneficial effects achieved by the technical solutions of the second to fourth aspects of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0060] Figure 1 It is a flow chart of a pipeline slope water damage meteorological early warning method based on a slope unit according to an embodiment of the present invention;

[0061] Figure 2A schematic diagram of a flow chart of determining a division result of a pipeline slope surface water damage meteorological early warning method based on slope units according to an embodiment of the present invention;

[0062] Figure 3 The present invention is a structural framework diagram of an electronic device. DETAILED DESCRIPTION

[0063] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0064] like Figure 1 as well as Figure 2 As shown, a pipeline slope surface water damage meteorological early warning method based on a slope unit according to an embodiment of the present invention comprises the following steps:

[0065] S1, obtaining digital elevation model data, soil data and real-time rainfall data of the slope to be warned;

[0066] S2, resampling the digital elevation model data, and determining the division result of the slope to be warned according to the resampling result, in combination with the soil data and the real-time rainfall data;

[0067] S3, using the pre-trained model, based on the division result, performing a susceptibility evaluation on the slope to be warned to obtain a susceptibility score, and determining a warning result for the slope to be warned based on the susceptibility score.

[0068] The beneficial effects of the pipeline slope water damage meteorological early warning method based on slope units provided by the present invention are as follows:

[0069] The present invention not only clearly reflects the physical relationship between geological environmental factors and the formation of geological disasters, but also has clear prediction and warning objects, which is in line with the actual emergency response work, and makes a reasonable transition from the overall to the specific prevention and control of water-damaged geological disasters. It has a certain demonstration significance for improving the accuracy of geological disaster forecasts and scientific prevention.

[0070] S1, obtaining digital elevation model data, soil data and real-time rainfall data of the slope to be warned; wherein:

[0071] The digital elevation model data is obtained by scanning the vicinity of the oil and gas pipeline with an airborne laser radar carried by a drone (the vicinity refers to the area enclosed by a preset value, that is, the wall of the oil and gas pipeline as a reference and extending outward by a preset value) to obtain laser point cloud data, where each laser point cloud data contains three-dimensional coordinate information, three elements of X, Y, and Z, as well as color information, reflection intensity information, echo number information, etc.

[0072] By preprocessing the lidar point cloud data, ground filtering, and further fine classification of the laser point cloud combined with manual editing, the ground points are retained, and the remaining ground points are rasterized by constructing models such as irregular triangulated networks (TINs), and high-precision digital elevation model (DEM) data can be obtained. This data is used for slope unit division.

[0073] Soil data: Soil samples above the pipeline are collected multiple times at equal intervals along the length of the pipeline and sent to the laboratory to test their physical parameters, such as soil cohesion, internal friction angle, wet soil density, etc. This data is used to evaluate the susceptibility of slope surface water damage.

[0074] Real-time rainfall data: Get the real-time rainfall grid data per hour that intersects the pipeline. This data is used for meteorological warning of slope water damage.

[0075] S2, resampling the digital elevation model data, and determining the division result of the slope to be warned according to the resampling result, combined with the soil data and the real-time rainfall data; wherein:

[0076] Combining the advantages of the hydrological method and the curvature method in dividing slope units, a new forward hydrological-reverse curvature division method is formed. The division process is shown in Figure 2. The improved process is to resample the digital elevation model to the required resolution, calculate and fill the depression to generate a depression-free digital elevation model, and then extract the flow direction by comparing the elevation values ​​of adjacent pixels; accumulate the inflow of each pixel along the flow direction to obtain the river flow; river network extraction is to identify the river by setting a threshold for river flow, extract the grid cells with cumulative flow higher than the threshold, and construct a river network; calculate the connection relationship between each river through the flow direction data and the results of river network extraction, and generate a river link with a unique identifier. Then, starting from the river outlet, trace back along the flow direction through the flow direction data and river network data, mark all pixels belonging to the same basin, divide the entire study area into different basin units, and obtain basin raster data; finally, convert the basin represented by the raster into a surface vector format, extract the boundaries between polygons to generate watershed boundaries. The other part is to determine the optimal window size and statistical method for the resampled digital elevation model through multiple experiments and comparisons, and use focal statistics for processing to remove small changes in elevation data while retaining important terrain features. Then calculate the average curvature value of each grid, and multiply the curvature value of each grid by -1 through the grid calculator to obtain the reverse curvature, and use the reversed curvature value as the simulated elevation data. Compare the elevation values ​​of adjacent pixels to extract the flow direction, and then calculate the filling depression. Use the flow direction as the base map to divide the watershed units and generate the watershed raster data; then convert the raster to a surface vector format, extract the boundary to obtain the valley line boundary. Finally, merge the watershed and valley line vector data to form a new vector layer, which divides the original terrain area into multiple polygonal areas, each polygon is a slope unit; then combine the laser radar optical image to check whether the boundary of the slope unit is consistent with the actual terrain features, and manually delete or add the boundary of the slope unit to better reflect the actual terrain features.

[0077] S3, using the pre-trained model, based on the division results, evaluating the susceptibility of the slope to be warned, obtaining a susceptibility score, and determining the warning result of the slope to be warned based on the susceptibility score.

[0078] The SHALSTAB model is selected for the evaluation of slope water damage susceptibility. The model is based on the Mohr-Coulomb failure criterion and combines the infinite slope model and the steady-state hydrological model.

[0079] (1) Infinite slope model

[0080] The infinite slope stability model is very effective for slope disasters with small scale, shallow sliding surface depth, and the length of the sliding body is much greater than the thickness of the sliding body. This model mainly considers the sliding force and anti-sliding force of the rock and soil body, and ignores the edge effect. The infinite slope stability model is based on the Mohr-Coulomb failure criterion, and its correction formula is:

[0081]

[0082] Where: c is the cohesion of soil, Pa; is the internal friction angle of soil; ρ s is the density of wet soil, kg·m -3 ; z is the soil thickness, m; θ is the slope gradient, °; ρ w is the density of water, kg·m -3 ; h is the groundwater level, m; g is the acceleration due to gravity, m·s -2 .

[0083] (2) Steady-state hydrological model

[0084] The stability of the slope is directly related to the groundwater level. Therefore, it is necessary to establish a hydrological model to estimate the groundwater level. The TOPMODEL model is the most common hydrogeological conceptual model that describes the steady-state undercurrent of the slope. Its expression is a modified equation of Darcy's law equation:

[0085] q·a=b·k s ·h·sinθ·cosθ (2)

[0086] Where: q is the effective rainfall intensity, m·d -1 ; a is the water accumulation area, m 2 ; b is the width of the drainage area, m; k s is the saturated permeability coefficient of the soil (assuming it is uniform along the depth of the soil layer), m·d -1 ; h is the groundwater level, m; g is the acceleration due to gravity, m·s -2 .

[0087] (3) SHALSTAB model

[0088] According to formula (1) and formula (2), the unconditionally stable, unconditionally unstable and SHALSTAB equations based on the q / T function relationship can be established.

[0089] Unconditional instability means that when there is no groundwater in the soil, the slope still cannot remain stable.

[0090]

[0091] Another extreme condition occurs when h / z is 1 (fully saturated), the soil shear strength is large and the slope remains stable.

[0092]

[0093] The final SHALSTAB equation is:

[0094]

[0095] The parameters required for the SHALSTAB model are c, ρ s , z, a, b and θ, where the variables (a, b and θ) are extracted from the numerical elevation model (DEM). SHALSTAB mainly judges the slope stability based on the ratio (q / T) between the effective rainfall intensity q and the soil hydraulic conductivity T and the maximum saturation state that the soil strength can produce on the terrain. The slope is divided into 7 stability levels. Except for the two levels of unconditional stability and unconditional instability, the other levels are distinguished by the q / T ratio. Table 1 lists the discontinuity values ​​between each level.

[0096] Table 1 is the conversion table of q / T and log(q / T)

[0097]

[0098] Meteorological warning for slope water damage:

[0099] As a probabilistic event, the probability of occurrence of water-damaged geological disasters induced by rainfall is generally considered to consist of two parts: one is the geological conditions, which couple the internal conditions of the soil when there is no rainfall to obtain the base probability of induced disasters; the other is the rainfall conditions. The historical accumulated rainfall and forecast rainfall in the region will increase the possibility of inducing geological disasters, which is used as the incremental probability of induced disasters. In the present invention, the slope stability index q / T of the slope unit calculated by the SHALSTAB model under non-rainfall conditions is probabilized and converted into the probability under non-rainfall conditions as the base probability P 0 ; Then, the accumulated rainfall value, forecast rainfall value and other data are expressed in R, and the incremental probability ΔP of water damage geological disasters induced by rainfall is calculated. Finally, a meteorological early warning model for water damage geological disasters is constructed, and the early warning index of each slope unit is calculated. The model expression is shown in formula (6).

[0100] P = f(P 0 ,ΔP)=P 0 +f(G,R) (6)

[0101] Where: P is the probability of geological disasters, that is, the early warning index; P 0 is the probability of geological disasters occurring under non-rainfall conditions; ΔP is the incremental probability of geological disasters induced by rainfall; G is the environmental factor related to geological disasters; and R is a rainfall-related value.

[0102] In formula (5), the q / T value calculated by the SHALSTAB model is only used as an index to distinguish the stability level of slope units, and it cannot be directly used as a base probability value to be superimposed with the incremental probability of rainfall-induced disasters. Therefore, in order to make full use of the calculation results of the SHALSTAB model, the interpolation method is chosen to convert the slope stability level judgment index q / T into a probability value. The interpolation method calculation formula is as follows: Formula (7):

[0103]

[0104] Where: P 0 is the probability value obtained by converting the stability level, P 1 , P 2 are the starting and ending values ​​of the probability value interval, L 1 , L 2 It is the starting and ending values ​​of the interval corresponding to log(q / T).

[0105] The calculation of incremental probability needs to consider two parts: historical cumulative rainfall and future forecast rainfall. According to statistical reports, geological disasters during heavy rainfall are closely related to the cumulative rainfall data of the previous seven days in their location. The frequency of geological disasters is positively correlated with the cumulative rainfall, and the two are generally close to linear correlation.

[0106] The maximum cumulative rainfall or more in the history of the statistical study area is recorded as R max Compare it with the 7-day cumulative rainfall and remove the dimension R / R max , forming a value between 0 and 1. In order to express the basic understanding that the better the geological environment conditions, the greater the rainfall or rainfall intensity required to cause geological disasters, an adjustment coefficient k is set, and the probability of geological disasters under non-rainfall conditions is introduced, that is, the base probability P 0 , forming formula (8):

[0107] ΔP=k·P 0 ·R / R max (8)

[0108] Where: k is the adjustment coefficient; P 0 is the probability of geological disasters under non-rainfall conditions; R is the 7-day cumulative rainfall value; R max is the historical maximum accumulated rainfall or more in the study area. The above formula can be further simplified as:

[0109] ΔP=k / R max ·P 0 R = KP 0 R (9)

[0110] From the above formula, we can see that under the same rainfall, the probability of increment in the area where the possibility of disaster induced by internal factors is large (small); similarly, in the area where the internal factors of soil environment induce disasters with the same probability, the probability of increment in the area with large (small) rainfall is also large (small). Thus, the calculation formula of joint probability forecast and warning is obtained:

[0111] P=P 0 (1+KR) (10)

[0112] Where: P 0 is the base probability, K is the effective rainfall influence coefficient; R is the effective rainfall, which includes the historical accumulated rainfall and the future forecast rainfall.

[0113] Assume that the current time is t 0 , for the future Δt + First, t 0 The cumulative effective rainfall at the time can be expressed as:

[0114]

[0115] Where: Δt - is from the start of calculation to t 0 The duration of a moment is usually the period when the correlation between regional geological disasters and the previous continuous rainfall time is most significant, and is generally taken as an integer multiple of 24 hours, that is, several days; c (t) is the rainfall function of the rainfall process; k(t) is the effective rainfall attenuation function, which mainly considers factors such as the infiltration process of atmospheric precipitation on rock and soil and surface runoff. Generally, k(t) = kt, where k is an empirical coefficient between 0 and 1, which can also be called the attenuation coefficient.

[0116] Secondly, from t 0 Time to the future Δt + The period forecast rainfall can be expressed as:

[0117]

[0118] Where: Δt + It is the forecast period, which is determined by the meteorological rainfall forecast period, usually 24h; f (t) is the rainfall function of the rainfall prediction process. Therefore, the geological disaster forecast and warning model is established as follows:

[0119]

[0120] Where: is the effective rainfall adjustment coefficient; R c is the effective accumulated rainfall; R f is the forecast rainfall; L is the forecast rainfall adjustment factor; R' maxIt is the maximum value of the sum of the accumulated effective rainfall and the forecast rainfall in a forecast and warning period in the region.

[0121] Further, the process of determining the division result of the slope to be warned is as follows:

[0122] Performing fill-cut processing according to the resampling result to obtain depression-free digital elevation model data, determining a first water flow direction according to the depression-free digital elevation model data, taking the first water flow direction as a base map, determining first watershed raster data according to the river flow, and determining a watershed boundary based on the first watershed raster data;

[0123] Calculate the average curvature value according to the resampling result, determine the reverse curvature based on the average curvature value, calculate the second water flow direction according to the reverse curvature, determine the second watershed raster data with the second watershed raster data as the base map, and determine the valley line boundary according to the second watershed raster data;

[0124] The watershed boundary and the valley line boundary are subjected to vector merging processing to obtain the division result of the slope to be warned.

[0125] Furthermore, the pre-trained model is determined based on the Mohr-Coulomb failure criterion, combined with an infinite slope model and a steady-state hydrological model;

[0126] The infinite slope model is specifically:

[0127]

[0128] Where c is the cohesion of the soil, is the internal friction angle of the soil, ρ s is the density of wet soil, z is the soil thickness, θ is the slope gradient, ρ w is the density of water, h is the groundwater level, and g is the acceleration due to gravity;

[0129] The steady-state hydrological model is specifically:

[0130] q·a=b·k s ·h·sinθ·cosθ;

[0131] q is the effective rainfall intensity, a is the waterlogged area, b is the width of the drainage area, k s is the saturated permeability coefficient of soil;

[0132] The pre-training model is specifically:

[0133]

[0134] T is the hydraulic conductivity.

[0135] Further, the process of evaluating the susceptibility of the slope to be warned and obtaining a susceptibility score is as follows:

[0136] Calculating the probability of occurrence of geological disasters on the slope to be warned, and using the probability of occurrence of geological disasters as the susceptibility score;

[0137] The calculation process of the probability of occurrence of geological disasters is as follows:

[0138]

[0139] Among them, P 0 is the probability of geological disasters occurring in non-rainfall conditions; R c is the effective accumulated rainfall, L is the forecast rainfall adjustment coefficient, R f is the predicted rainfall, R' max It is the maximum value of the sum of the accumulated effective rainfall and the forecast rainfall in a forecast and warning period in the region, and K' is the effective rainfall influence coefficient.

[0140] The biggest difference between water damage geological disasters and conventional geological disasters is whether there is a role of precipitation. A certain period of precipitation will cause the water content of the soil to change, and the increase in water content will cause the inherent properties of the soil, such as the internal friction angle and cohesion, to decrease, making it easier for geological disasters such as slopes and debris flows to occur. Therefore, this type of geological disaster caused by a rapid increase in soil water content due to heavy rainfall is called a water damage geological disaster. This type of geological disaster has a great impact on the safe operation of the pipeline industry.

[0141] The sub-models mainly involved in the present invention are: 1) slope unit division model; 2) slope surface stability calculation model; 3) slope surface stability and rainfall factor coupling model.

[0142] Aiming at the shortcomings of traditional hydrology and curvature slope unit division, the advantages of both are combined to construct a slope unit division method of positive hydrology and reverse curvature. This method can overcome the shortcomings of hydrology division that cannot distinguish the boundary line between slope and plane, and also overcome the problem that curvature division does not reflect the details of slope surface in detail.

[0143] The invention uses slope units as the basic research object to construct a meteorological early warning model for water-damaged geological disasters. Compared with the commonly used statistical analysis model, the deterministic analysis model based on slope units avoids the difficulty in obtaining parameters for stability calculations in regional analysis, and also avoids the inaccuracy of conventional factor analysis and weighted summation. Compared with the early warning models constructed by grid units, geomorphological classification units, and watershed units, the meteorological early warning model for water-damaged geological disasters constructed using slope units as the basic research object can more clearly reflect the relationship between the formation of geological disasters and the intrinsic factors of the slope, and the prevention and control of the early warning results are more targeted.

[0144] The early warning model uses the SHALSTAB model to fit the slope soil environment to calculate the stability level and the rainfall data coupling to calculate the early warning index. By calling historical rainfall data, the early warning index of the slope around the disaster point when the historical disaster occurred was tested. It was found that the forecast success rate under heavy rainfall conditions was more consistent with the actual situation, while the forecast success rate was biased when the rainfall intensity was smaller.

[0145] The present invention not only clearly reflects the physical relationship between geological environmental factors and the formation of geological disasters, but also has clear prediction and warning objects, which is in line with the actual emergency response work, and makes a reasonable transition from the overall to the specific prevention and control of water-damaged geological disasters. It has a certain demonstration significance for improving the accuracy of geological disaster forecasts and scientific prevention.

[0146] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0147] The present invention also provides a pipeline slope water damage meteorological early warning system based on slope units, and the specific technical scheme is as follows:

[0148] The acquisition module is used to: obtain digital elevation model data, soil data and real-time rainfall data of the slope to be warned;

[0149] The division module is used to: resample the digital elevation model data, and determine the division result of the slope to be warned according to the resampling result, combined with the soil data and the real-time rainfall data;

[0150] The early warning module is used to: evaluate the susceptibility of the slope to be warned according to the division result through the pre-training model to obtain a susceptibility score, and determine the early warning result of the slope to be warned according to the susceptibility score.

[0151] Based on the above solution, the present invention can also be improved as follows.

[0152] Further, the process of determining the division result of the slope to be warned is as follows:

[0153] Performing fill-cut processing according to the resampling result to obtain depression-free digital elevation model data, determining a first water flow direction according to the depression-free digital elevation model data, taking the first water flow direction as a base map, determining first watershed raster data according to the river flow, and determining a watershed boundary based on the first watershed raster data;

[0154] Calculate the average curvature value according to the resampling result, determine the reverse curvature based on the average curvature value, calculate the second water flow direction according to the reverse curvature, determine the second watershed raster data with the second watershed raster data as the base map, and determine the valley line boundary according to the second watershed raster data;

[0155] The watershed boundary and the valley line boundary are subjected to vector merging processing to obtain the division result of the slope to be warned.

[0156] Furthermore, the pre-trained model is determined based on the Mohr-Coulomb failure criterion, combined with an infinite slope model and a steady-state hydrological model;

[0157] The infinite slope model is specifically:

[0158]

[0159] Where c is the cohesion of the soil, is the internal friction angle of the soil, ρ s is the density of wet soil, z is the soil thickness, θ is the slope gradient, ρ w is the density of water, h is the groundwater level, and g is the acceleration due to gravity;

[0160] The steady-state hydrological model is specifically:

[0161] q·a=b·k s ·h·sinθ·cosθ;

[0162] q is the effective rainfall intensity, a is the waterlogged area, b is the width of the drainage area, k s is the saturated permeability coefficient of soil;

[0163] The pre-training model is specifically:

[0164]

[0165] T is the hydraulic conductivity.

[0166] Further, the process of evaluating the susceptibility of the slope to be warned and obtaining a susceptibility score is as follows:

[0167] Calculating the probability of occurrence of geological disasters on the slope to be warned, and using the probability of occurrence of geological disasters as the susceptibility score;

[0168] The calculation process of the probability of occurrence of geological disasters is as follows:

[0169]

[0170] Among them, P 0 is the probability of geological disasters occurring in non-rainfall conditions; R cis the effective accumulated rainfall, L is the forecast rainfall adjustment coefficient, R f is the predicted rainfall, R' max It is the maximum value of the sum of the accumulated effective rainfall and the forecast rainfall in a forecast and warning period in the region, and K' is the effective rainfall influence coefficient.

[0171] The scope of use of this invention is applicable to mountainous areas that are greatly affected by rainfall, especially in heavy rainfall conditions such as typhoons and rainstorms. It can effectively predict potential areas of geological disasters and provide prediction information, thereby strengthening the monitoring and prediction of geological disasters in mountainous areas. In addition, traditional manual regular inspections have defects in the operation and maintenance of linear projects (such as water networks, power grids, oil and gas transportation, etc.) because they consume a lot of manpower and financial resources and cannot monitor the operation of the project in real time. In contrast, the present invention can not only make up for the shortcomings of traditional inspections, but also has the characteristics of real-time and visibility, which can provide guidance for the operation and maintenance of linear projects.

[0172] my country has many mountainous and hilly areas, which are prone to geological disasters caused by meteorological conditions. Monitoring meteorological changes and predicting geological disasters are basic needs to ensure people's livelihood and national economic development. At present, with the popularization of informatization, computer hardware technology is developing rapidly, network communication has entered the 5G era, and the application prospects of mobile Internet are broad, which has brought unprecedented opportunities for the development of public meteorological monitoring and geological disaster prediction services. The meteorological monitoring and geological disaster prediction service system can effectively meet the real-time and timely needs of the public, enterprises and governments for meteorological and geological disaster prediction, and has important practical value and broad research prospects.

[0173] It should be noted that the beneficial effects of the pipeline slope water damage meteorological warning system based on slope units provided in the above embodiment are the same as the beneficial effects of the pipeline slope water damage meteorological warning method based on slope units, which will not be repeated here. In addition, when the system provided in the above embodiment realizes its functions, it only uses the division of the above functional modules as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual conditions to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0174] like Figure 3 As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320, the processor 320 is coupled to a memory 310, and the memory 310 stores at least one computer program 330, and the at least one computer program 330 is loaded and executed by the processor 320, so that the electronic device 300 implements any of the above methods, specifically:

[0175] The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors 320 (Central Processing Units, CPU) and one or more memories 310, wherein the one or more memories 310 store at least one computer program 330, and the at least one computer program 330 is loaded and executed by the one or more processors 320, so that the electronic device 300 implements a pipeline slope water damage meteorological early warning method based on a slope unit provided in the above embodiment. Of course, the electronic device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the electronic device 300 may also include other components for realizing the functions of the device, which will not be repeated here.

[0176] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements any one of the above methods.

[0177] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0178] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs any of the above methods.

[0179] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0180] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0181] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0182] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A pipeline slope water damage meteorological early warning method based on slope units, characterized in that: include: Obtain digital elevation model data, soil data and real-time rainfall data of the slope to be warned; Resampling the digital elevation model data, and determining the division result of the slope to be warned according to the resampling result, in combination with the soil data and the real-time rainfall data; Through the pre-training model, according to the division result, the susceptibility of the slope to be warned is evaluated to obtain a susceptibility score, and according to the susceptibility score, the warning result of the slope to be warned is determined.

2. The method for early warning of pipeline slope water damage based on slope unit according to claim 1, characterized in that: The process of determining the division result of the slope to be warned is as follows: Performing fill-cut processing according to the resampling result to obtain depression-free digital elevation model data, determining a first water flow direction according to the depression-free digital elevation model data, taking the first water flow direction as a base map, determining first watershed raster data according to the river flow, and determining a watershed boundary based on the first watershed raster data; Calculate the average curvature value according to the resampling result, determine the reverse curvature based on the average curvature value, calculate the second water flow direction according to the reverse curvature, determine the second watershed raster data with the second watershed raster data as the base map, and determine the valley line boundary according to the second watershed raster data; The watershed boundary and the valley line boundary are subjected to vector merging processing to obtain the division result of the slope to be warned.

3. The method for early warning of pipeline slope water damage based on slope unit according to claim 1, characterized in that: The pre-trained model is determined based on the Mohr-Coulomb failure criterion, combined with an infinite slope model and a steady-state hydrological model; The infinite slope model is specifically: Where c is the cohesion of the soil, is the internal friction angle of the soil, ρ s is the density of wet soil, z is the soil thickness, θ is the slope gradient, ρ w is the density of water, h is the groundwater level, and g is the acceleration due to gravity; The steady-state hydrological model is specifically: q·a=b·k s ·h·sinθ·cosθ; q is the effective rainfall intensity, a is the waterlogged area, b is the width of the drainage area, k s is the saturated permeability coefficient of soil; The pre-training model is specifically: T is the hydraulic conductivity.

4. The method for early warning of pipeline slope water damage based on slope unit according to claim 1, characterized in that: The process of evaluating the susceptibility of the slope to be warned and obtaining the susceptibility score is as follows: Calculating the probability of occurrence of geological disasters on the slope to be warned, and using the probability of occurrence of geological disasters as the susceptibility score; The calculation process of the probability of occurrence of geological disasters is as follows: Among them, P0 is the probability of geological disasters occurring in non-rainfall conditions; R c is the effective accumulated rainfall, L is the forecast rainfall adjustment coefficient, R f is the predicted rainfall, R' max It is the maximum value of the sum of the accumulated effective rainfall and the forecast rainfall in a forecast and warning period in the region, and K' is the effective rainfall influence coefficient.

5. A pipeline slope water damage meteorological warning system based on slope units, characterized in that: include: The acquisition module is used to: obtain digital elevation model data, soil data and real-time rainfall data of the slope to be warned; The division module is used to: resample the digital elevation model data, and determine the division result of the slope to be warned according to the resampling result, combined with the soil data and the real-time rainfall data; The early warning module is used to: evaluate the susceptibility of the slope to be warned according to the division result through the pre-training model to obtain a susceptibility score, and determine the early warning result of the slope to be warned according to the susceptibility score.

6. The pipeline slope surface water damage meteorological early warning system based on slope unit according to claim 5 is characterized in that: The process of determining the division result of the slope to be warned is as follows: Performing fill-cut processing according to the resampling result to obtain depression-free digital elevation model data, determining a first water flow direction according to the depression-free digital elevation model data, taking the first water flow direction as a base map, determining first watershed raster data according to the river flow, and determining a watershed boundary based on the first watershed raster data; Calculate the average curvature value according to the resampling result, determine the reverse curvature based on the average curvature value, calculate the second water flow direction according to the reverse curvature, determine the second watershed raster data with the second watershed raster data as the base map, and determine the valley line boundary according to the second watershed raster data; The watershed boundary and the valley line boundary are subjected to vector merging processing to obtain the division result of the slope to be warned.

7. The pipeline slope surface water damage meteorological warning system based on slope unit according to claim 5 is characterized in that: The pre-trained model is determined based on the Mohr-Coulomb failure criterion, combined with an infinite slope model and a steady-state hydrological model; The infinite slope model is specifically: Where c is the cohesion of the soil, is the internal friction angle of the soil, ρ s is the density of wet soil, z is the soil thickness, θ is the slope gradient, ρ w is the density of water, h is the groundwater level, and g is the acceleration due to gravity; The steady-state hydrological model is specifically: q·a=b·k s ·h·sinθ·cosθ; q is the effective rainfall intensity, a is the waterlogged area, b is the width of the drainage area, k s is the saturated permeability coefficient of soil; The pre-training model is specifically: T is the hydraulic conductivity.

8. The pipeline slope surface water damage meteorological warning system based on slope unit according to claim 5 is characterized in that: The process of evaluating the susceptibility of the slope to be warned and obtaining the susceptibility score is as follows: Calculating the probability of occurrence of geological disasters on the slope to be warned, and using the probability of occurrence of geological disasters as the susceptibility score; The calculation process of the probability of occurrence of geological disasters is as follows: Among them, P0 is the probability of geological disasters occurring in non-rainfall conditions; R c is the effective accumulated rainfall, L is the forecast rainfall adjustment coefficient, R f is the predicted rainfall, R' max It is the maximum value of the sum of the accumulated effective rainfall and the forecast rainfall in a forecast and warning period in the region, and K' is the effective rainfall influence coefficient.

9. An electronic device, characterized in that: The electronic device comprises a processor, the processor is coupled to a memory, at least one computer program is stored in the memory, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements the method according to any one of claims 1 to 4.

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