Strip mine storm flood disaster early warning method and system

Abstract

The invention discloses a strip mine flood disaster early warning method and system, and relates to the field of strip mine disaster early warning, and the method comprises the steps: carrying out the three-dimensional scanning of a strip mine area, generating strip mine three-dimensional topographic data, calculating a water flow direction matrix based on a D8 algorithm, extracting a catchment boundary line through combining with a GIS hydrological analysis module, and obtaining the catchment area and topographic slope data; combining the catchment area and the topographic slope data with meteorological station forecast data and mining area local rain gauge real-time data, and calculating comprehensive predicted rainfall to obtain a rainfall spatial-temporal distribution thermodynamic diagram; according to the three-dimensional topographic data of the strip mine and the geological permeability coefficient of the mining area, calculating the total surface seepage amount; and comprehensively predicting the rainfall and the total surface seepage. According to the method, the ponding data and the three-dimensional terrain are input into the Bisshop slope stability model, the composite early warning signal is generated, the graded emergency response is triggered, and full-chain accurate prediction and quick response from data collection to disaster early warning are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of open-pit mine disaster early warning, in particular to an open-pit mine flood disaster early warning method and system. BACKGROUND

[0002] In the field of open-pit mine disaster early warning, flood disaster early warning technology has made significant progress in recent years. The traditional method mainly relies on the combination of meteorological forecast data and static hydrological model, through collecting rainfall forecast information of the weather station, combining with the preset mine area topographic parameters to predict flood risk, which improves the timeliness of early warning to a certain extent, but the core model still uses static topographic data and homogeneous hydrological parameters, which fails to fully consider the influence of dynamic changes of topography on hydrological process during open-pit mining.

[0003] Although the prior art realizes the basic early warning function, there are still key technical bottlenecks, and the traditional method has obvious shortcomings in multi-source data fusion and dynamic coupling analysis. Meteorological forecast data and local real-time monitoring data are usually fused with fixed weight, which is difficult to adapt to the influence of complex topography of open-pit mine on rainfall spatial redistribution. Seepage calculation is mostly based on saturated Darcy's law, ignoring the potential threat of unsaturated zone water migration to slope stability. This limitation directly leads to a high false alarm rate of the existing early warning system under the scenario of sudden heavy rainfall, and the system cannot accurately predict the cumulative effect of local water accumulation on slope stability. Although the existing technology attempts to introduce three-dimensional terrain modeling, it does not establish a real-time interaction mechanism between topographic data and hydro-mechanical coupling model, resulting in that the early warning response lags behind the disaster development speed. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides an open-pit mine flood disaster early warning method to solve the problems of insufficient early warning accuracy and response lag caused by fixed weight data fusion and saturated seepage assumption in the prior art.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides an open-pit mine flood disaster early warning method, which comprises: performing three-dimensional scanning on an open-pit mine area to generate three-dimensional topographic data of the open-pit mine, calculating a water flow direction matrix based on a D8 algorithm, extracting a catchment boundary line in combination with a GIS hydrological analysis module to obtain catchment area and topographic slope data;

[0008] Combining the catchment area and topographic slope data with weather station forecast data and local rain gauge real-time data, calculating the comprehensive predicted rainfall, and obtaining a rainfall spatio-temporal distribution heat map;

[0009] According to the three-dimensional terrain data of the open-pit mine and the geological permeability coefficient of the mining area, the total amount of surface seepage is calculated;

[0010] The total amount of surface seepage is calculated.

[0011] The water increment and the three-dimensional terrain data of the open-pit mine are input into the Bishop slope stability model, the safety factor is calculated, and the composite early warning signal is obtained by analysis.

[0012] Based on the composite early warning signal, multi-level emergency response measures are executed.

[0013] As a preferred scheme of the open-pit mine flood disaster early warning method, the three-dimensional scanning of the open-pit mine area is performed to generate three-dimensional terrain data of the open-pit mine, the water flow direction matrix is calculated based on the D8 algorithm, the catchment boundary line is extracted by combining the GIS hydrological analysis module, the catchment area and the terrain slope data are obtained, including the following steps:

[0014] The open-pit mine area is scanned by using an unmanned aerial LiDAR, high-precision point cloud data is obtained by GNSS / IMU combined positioning, and the three-dimensional terrain data of the open-pit mine is generated after denoising, classification and interpolation processing;

[0015] Based on the three-dimensional terrain data of the open-pit mine, the Jenson-Domingue depression filling method is used to fill the depression, the D8 single-flow direction algorithm is used to calculate the water flow direction of the grid unit, and the 8-direction coded water flow direction matrix is obtained.

[0016] Based on the 8-direction coded water flow direction matrix, the water flow accumulation is obtained by using the multi-flow direction accumulation method, the catchment threshold is set based on the statistical value of the minimum catchment area of the actual surface runoff formed in the historical rainstorm event, the catchment area is divided, the closed boundary is extracted by using the boundary tracking method, and the catchment boundary line is obtained.

[0017] Based on the catchment boundary line Gauss area algorithm, the grid units in the closed boundary are accumulated and calculated to obtain the catchment area, based on the three-dimensional terrain data of the open-pit mine, the three-order inverse distance square difference method is used to calculate the elevation change rate of the grid unit, and the terrain slope data is obtained.

[0018] As a preferred scheme of the open-pit mine flood disaster early warning method, the catchment area, the terrain slope data, the weather station forecast data and the local rain gauge real-time data are combined to calculate the comprehensive prediction rainfall, and the rainfall spatiotemporal distribution thermal map is obtained, including the following steps:

[0019] The Min-Max normalization method is used to standardize the catchment area, the terrain slope data, the weather station forecast data and the local rain gauge real-time data, and the multi-source data fused in the same dimension is obtained.

[0020] Based on the multi-source data, a dynamic weight distribution algorithm is used to calculate the grid-based comprehensive predicted rainfall;

[0021] The comprehensive predicted rainfall data is classified according to intensity to obtain a thermal map of spatiotemporal distribution of rainfall.

[0022] As a preferred scheme of the open-pit mine flood disaster early warning method, the total surface seepage is calculated according to the open-pit mine three-dimensional terrain data and the mine area geological permeability coefficient, including the following steps:

[0023] Based on the drilling sampling in the mine area geological exploration stage, the rock layer permeability coefficient is measured through the indoor variable water head permeability test to obtain the drilling data;

[0024] Based on the drilling data, the inverse distance weighted interpolation is used to generate the mine area geological permeability coefficient;

[0025] The relative permeability is calculated based on the Darcy law, and the grid unit seepage flow is obtained in combination with the hydraulic gradient and the grid area;

[0026] The grid seepage flow is grouped and summed according to the catchment boundary line, and the total surface seepage is obtained according to the real-time rainfall intensity;

[0027] As a preferred scheme of the open-pit mine flood disaster early warning method, the total surface seepage is calculated according to the open-pit mine three-dimensional terrain data and the mine area geological permeability coefficient, including the following steps:

[0028] The total surface seepage is calculated based on the total surface seepage and the water balance equation of the comprehensive predicted rainfall;

[0029] Based on the open-pit mine three-dimensional terrain data and the periodical water increment, the dynamic grid division technology is used to render the water level rise in real time through the CesiumJS engine to obtain the three-dimensional submergence simulation animation.

[0030] As a preferred scheme of the open-pit mine flood disaster early warning method, the total surface seepage is calculated according to the open-pit mine three-dimensional terrain data and the mine area geological permeability coefficient, including the following steps:

[0031] Based on the total surface seepage and the open-pit mine three-dimensional terrain data, the Bishop simplified method is used to calculate the slope safety factor by inputting the total surface seepage and the open-pit mine three-dimensional terrain data into the Bishop slope stability model,

[0032] Based on the rock-soil body shear strength test and the historical data of slope stability, the safety threshold is set;

[0033] Setting a water accumulation increment threshold value through the drainage unit and the historical flood event inundation depth inversion;

[0034] Setting a safety factor buffer tolerance based on the standard deviation of the safety factor, obtaining a water accumulation increment buffer tolerance through the maximum theoretical drainage rate of the drainage unit and the response time of starting to full load operation;

[0035] Triggering a red early warning when the safety factor is less than the safety threshold value and the water accumulation increment is greater than the water accumulation increment threshold value;

[0036] Triggering a yellow early warning when the safety factor is equal to the safety threshold value and the water accumulation increment is equal to the water accumulation increment threshold value;

[0037] Triggering a green early warning when the safety factor is greater than the safety threshold value and the safety factor is less than or equal to the safety threshold value plus the safety factor buffer tolerance, the water accumulation increment is less than the water accumulation increment threshold value, and the water accumulation increment buffer tolerance is greater than or equal to the water accumulation increment threshold value minus the water accumulation increment buffer tolerance;

[0038] Triggering a blue early warning when the safety factor is greater than the safety threshold value plus the safety factor buffer tolerance and the water accumulation increment buffer tolerance is less than the water accumulation increment threshold value minus the water accumulation increment buffer tolerance;

[0039] Combining the red early warning, the yellow early warning, the green early warning and the blue early warning to form a composite early warning signal.

[0040] As a preferred scheme of the open-pit mine flood disaster early warning method, based on the composite early warning signal, a multi-level emergency response measure is executed, including the following steps:

[0041] Triggering an emergency response process of a corresponding level based on the composite early warning signal;

[0042] Formulating a response scheme by establishing a mapping relationship table of early warning levels and specific emergency operations to obtain an instruction set to an execution terminal.

[0043] In a second aspect, the present application provides an open-pit mine flood disaster early warning system, which comprises a data acquisition module, a three-dimensional scanning of an open-pit mine area is performed to generate open-pit mine three-dimensional terrain data, a water flow direction matrix is calculated based on a D8 algorithm, a catchment boundary line is extracted in combination with a GIS hydrological analysis module to obtain catchment area and terrain slope data;

[0044] A multi-source rainfall prediction fusion module combines the catchment area, terrain slope data, weather station forecast data and local rain gauge real-time data to calculate a comprehensive predicted rainfall, and obtains a rainfall spatio-temporal distribution thermal map;

[0045] A surface seepage calculation module calculates the total amount of surface seepage according to the open-pit mine three-dimensional terrain data and the mine area geological permeability coefficient;

[0046] The three-dimensional visualization module simulates water accumulation increment in the open pit by a water balance equation based on the integrated predicted rainfall and total surface seepage, and generates a three-dimensional submergence simulation animation;

[0047] The early warning module inputs the water accumulation increment and the three-dimensional terrain data of the open pit into a Bishop slope stability model, calculates a safety factor, and analyzes to obtain a composite early warning signal;

[0048] The emergency response execution module executes multi-level emergency response measures based on the composite early warning signal.

[0049] In a third aspect, the present application provides a computer device comprising a memory and a processor, and the memory stores a computer program, wherein the computer program is executed by the processor to implement any step of the open pit flood disaster early warning method according to the first aspect of the present application.

[0050] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement any step of the open pit flood disaster early warning method according to the first aspect of the present application.

[0051] The present application has the beneficial effects that: high-precision three-dimensional terrain data is generated by UAV LiDAR scanning, catchment characteristics are extracted based on D8 algorithm and GIS hydrological analysis, integrated predicted rainfall is calculated by dynamically fusing weather forecast and real-time monitoring data, then unsaturated seepage theory and water balance equation are combined to accurately simulate water accumulation increment, water accumulation data and three-dimensional terrain are input into a Bishop slope stability model to generate a composite early warning signal and trigger a hierarchical emergency response, and the whole chain of accurate prediction and rapid response from data acquisition to disaster early warning is realized. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0053] Fig. 1 The flowchart of the open pit flood disaster early warning method.

[0054] Fig. 2 The schematic diagram of the open pit flood disaster early warning system.

[0055] Fig. 3 The flowchart of three-dimensional terrain scanning and hydrological analysis.

[0056] Fig. 4A schematic diagram for multi-source rainfall prediction data fusion. DETAILED DESCRIPTION

[0057] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0058] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the concept of the present application, so the present application is not limited to the specific embodiments disclosed below.

[0059] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is independent or alternative to other embodiments.

[0060] Reference Figs. 1-4 For one embodiment of the present application, the embodiment provides an open-pit mine flood disaster early warning method, comprising the following steps:

[0061] S1, three-dimensional scanning is performed on the open-pit mine area to generate open-pit mine three-dimensional terrain data, a water flow direction matrix is calculated based on the D8 algorithm, and a catchment boundary line is extracted in combination with a GIS hydrological analysis module to obtain catchment area and terrain slope data.

[0062] S1.1, the open-pit mine area is scanned by using an unmanned aerial LiDAR, high-precision point cloud data is obtained by GNSS / IMU combined positioning, and after denoising, classification and interpolation processing, open-pit mine three-dimensional terrain data is generated.

[0063] Further, the open-pit mine area is scanned by using an unmanned aerial LiDAR, the flight height is kept at one hundred meters, the heading overlap rate is set to eighty percent, the lateral overlap rate is set to sixty percent, high-precision point cloud data with a plane accuracy of zero point three meters and a height accuracy of zero point five meters is obtained by GNSS real-time dynamic positioning technology combined with IMU inertial measurement unit positioning, the point cloud data is denoised by using a statistical filtering method to remove outliers, the point cloud classification is performed by using a cloth simulation filtering algorithm to distinguish ground points and non-ground points, and the open-pit mine three-dimensional terrain data is generated by using linear interpolation of irregular triangle mesh.

[0064] S1.2, based on the open-pit mine three-dimensional terrain data, the Jenson-Domingue depression filling method is used to fill the depression, the D8 single-flow direction algorithm is used to calculate the water flow direction of the grid unit, and the 8-direction coded water flow direction matrix is obtained.

[0065] The expression for the water flow direction in a grid cell is:

[0066]

[0067] Where D represents the direction of water flow in the grid cell, z fill (i,j) represents the current grid cell elevation value, z neigh bor(k) Let d be the elevation value of the k-th neighborhood, where k is the neighborhood index number. k This represents the horizontal distance from the current grid cell to the k-th neighboring grid cell.

[0068] Furthermore, the Jenson-Domingue depression filling method is used to process the terrain depressions, with the filling depth set to 0.01 meters to ensure unobstructed natural drainage paths. The D8 single-flow direction algorithm is used to calculate the water flow direction of each grid cell. The algorithm traverses the eight neighboring grids of each grid cell, calculates the ratio of the elevation difference between the central grid and each neighboring grid to the horizontal distance, and selects the direction with the largest ratio as the water flow direction of that grid. The output result is integer raster data, where each grid value is encoded with integers from one to eight to represent the eight directions: east, southeast, south, southwest, west, northwest, north, and northeast.

[0069] S1.3. Based on the 8-direction encoded water flow direction matrix, the water flow accumulation is obtained by using the multi-direction accumulation method. The water catchment threshold is set based on the statistical value of the minimum catchment area of ​​the actual surface runoff formed in historical rainstorm events. The catchment area is divided, and the closed boundary is extracted by the boundary tracing method to obtain the catchment boundary line.

[0070] Furthermore, a multi-flow-direction accumulation method is used to obtain the water flow accumulation of each grid cell. The water flow accumulation represents the upstream catchment area. A catchment threshold is set based on the statistical value of the minimum catchment area that actually forms surface runoff in historical rainstorm events. Areas with water flow accumulation greater than this threshold are divided into independent catchment areas. The Moore neighborhood tracking method in the boundary tracing method is used to extract the closed polygon boundary of each catchment area.

[0071] S1.4. The Gaussian area algorithm based on the catchment boundary line is used to accumulate the grid cells within the closed boundary to obtain the catchment area. Based on the three-dimensional terrain data of the open-pit mine, the elevation change rate of the grid cells and the terrain slope data are calculated using the third-order inverse distance squared difference method.

[0072] Catchment area expression:

[0073]

[0074] Among them, S h x is the catchment area. iLet y be the x-coordinate of the i-th vertex on the catchment boundary, n be the total number of vertices on the catchment boundary, and i be the vertex index. i Let y be the ordinate of the i-th vertex on the catchment boundary line. i+1 Let x be the ordinate of the (i+1)th vertex on the catchment boundary line. i+1 Let x be the x-coordinate of the (i+1)th vertex on the water catchment boundary line.

[0075] The expression for terrain slope is:

[0076]

[0077] Where slope represents terrain slope data. For the vertical elevation difference of the grid cells, The grid spacing is the vertical axis. π represents the grid spacing of the horizontal axis, and π is the mathematical constant pi.

[0078] Furthermore, the Gaussian area algorithm is used to calculate the area of ​​the closed polygon region. The algorithm traverses all vertices on the catchment boundary line, calculates the vector area formed by each vertex and the next vertex in sequence, takes the absolute value, sums them, and divides by two to obtain the catchment area. Based on the three-dimensional terrain data of the open-pit mine, the third-order inverse distance squared difference method, namely the Horn algorithm, is used to calculate the elevation change rate of each grid cell. The algorithm uses a 3x3 window to calculate the partial derivatives of elevation in the east-west and north-south directions, and obtains the slope value in degrees through the arctangent function and radian conversion. The catchment area data and terrain slope data are output.

[0079] S2. Combine the data on catchment area and topographic slope with the forecast data from the meteorological station and the real-time data from the local rain gauge in the mining area to calculate the comprehensive predicted rainfall and obtain a heat map of the spatiotemporal distribution of rainfall.

[0080] S2.1. The Min-Max normalization method is used to standardize the catchment area, topographic slope data, meteorological forecast data and real-time data of local rain gauges in the mining area to obtain multi-source data fused under the same dimension.

[0081] Furthermore, the Min-Max normalization method was used to perform a unified dimension transformation on the catchment area, topographic slope data, meteorological forecast data, and real-time data from local rain gauges in the mining area. For the catchment area data, the maximum and minimum values ​​in all grid cells were extracted, and each grid value was subtracted from the minimum value and then divided by the difference between the maximum and minimum values. The topographic slope data was processed in the same way. Since the original units of the meteorological forecast data and the real-time data from local rain gauges in the mining area were consistent, only proportional scaling was performed. Finally, a standardized multi-source dataset with all data values ​​ranging from zero to one was obtained.

[0082] S2.2 Based on the multi-source data, a dynamic weight allocation algorithm is used to calculate the gridded comprehensive predicted rainfall.

[0083] The comprehensive forecast rainfall expression is as follows:

[0084]

[0085] Among them, R b To comprehensively predict rainfall, W f W represents the weighting coefficient of the meteorological forecast data. r W represents the weighting coefficient for real-time local rain gauge data in the mining area. s This is the weighting coefficient for the terrain slope correction term. R is the normalized grid slope value. forecast R provides gridded rainfall forecast data to the meteorological observatory. real The data is a gridded rainfall data generated from rain gauge data through Kriging interpolation.

[0086] Furthermore, based on the standardized multi-source data, a dynamic weight allocation algorithm is used to calculate the gridded comprehensive predicted rainfall. In the algorithm, the initial weight coefficient of the meteorological station forecast data is set to 0.6, the weight coefficient of the real-time data of the local rain gauge in the mining area is set to 0.3, and the weight coefficient of the terrain slope correction term is set to 0.1. When the terrain slope data is detected to exceed the threshold, the weight coefficient of the terrain slope correction term is automatically increased. At the same time, the weight allocation ratio between the meteorological station forecast data and the real-time data of the local rain gauge in the mining area is dynamically adjusted according to the degree of deviation between the two. Finally, the comprehensive predicted rainfall value of each grid unit is calculated by a weighted summation formula.

[0087] S2.3. The comprehensive predicted rainfall data is classified by intensity to obtain a heat map of the spatiotemporal distribution of rainfall.

[0088] Furthermore, the comprehensive predicted rainfall data is color-mapped according to a preset intensity level. The intensity level is divided into four intervals, each corresponding to a different color code: the lowest intensity interval is represented by blue, the medium intensity interval by green, the higher intensity interval by yellow, and the highest intensity interval by red. The color code is combined with spatial location using geographic information system software to generate a temporal and spatial distribution heat map of rainfall.

[0089] S3. Calculate the total surface seepage based on the three-dimensional topographic data of the open-pit mine and the geological permeability coefficient of the mining area.

[0090] S3.1 Based on borehole sampling during the geological exploration phase of the mining area, the permeability coefficient of the rock strata was determined through indoor variable head permeability tests to obtain borehole data.

[0091] Furthermore, based on the borehole sampling data obtained during the geological exploration phase of the mining area, the permeability coefficient of each rock layer was determined by a variable head permeability test. The test process was strictly carried out in accordance with standard testing methods, and the permeability coefficient values ​​of core samples at different depths were recorded. At the same time, the planar coordinates and elevation information of the boreholes were recorded, forming a borehole dataset containing location information and lithological parameters.

[0092] S3.2. Based on borehole data, inverse distance weighted interpolation is used to generate the geological permeability coefficient of the mining area.

[0093] Furthermore, an inverse distance weighted interpolation algorithm is used to generate the geological permeability coefficient field of the mining area. The distance weight index is set to two, and the search radius is 500 meters. For each grid cell to be interpolated, its horizontal distance to the surrounding boreholes is calculated. For fault zone areas, a higher permeability coefficient value is set separately to reflect the geological structure characteristics. The output is permeability coefficient raster data with the same spatial resolution as the three-dimensional topographic data of the open-pit mine.

[0094] S3.3. Calculate the relative permeability based on Darcy's law, and obtain the seepage flow rate of the grid cell by combining the hydraulic gradient and grid area.

[0095] The expression for relative permeability is:

[0096]

[0097] Among them, K r S represents the relative penetration rate. e The effective saturation is denoted by u, which is the shape parameter of the pore distribution.

[0098] Furthermore, the pore distribution shape parameters are determined based on lithological test data and calculated using soil volumetric water content and saturated water content. Then, the seepage flow rate is calculated by combining the hydraulic gradient and grid area. The hydraulic gradient is determined by the ratio of the elevation difference of the grid cells to the horizontal distance, and the grid area is the projected area of ​​the standard grid cells. The seepage flow rate value of each grid cell is then output.

[0099] S3.4. Group and sum the grid seepage flow according to the catchment boundary line, and obtain the total surface seepage based on the real-time rainfall intensity.

[0100] The expression for the total surface seepage is:

[0101]

[0102] Where G is the total surface infiltration, and R is the total surface infiltration. avg R represents the historical average rainfall intensity. d q represents the real-time rainfall intensity. vLet be the seepage flow rate of the v-th grid cell, m be the total number of catchment areas, O be the index of the total catchment area, n be the total number of grid cells in a single catchment area, v be the index of a single catchment area, and δ be the attribution indicator function.

[0103] Furthermore, the catchment area number of each grid cell is determined based on the catchment boundary line. The infiltration flow of all grid cells in each catchment area is accumulated to obtain the initial total infiltration flow of each catchment area. Then, it is dynamically adjusted according to the ratio of real-time rainfall intensity to historical average rainfall intensity, and the output is the total surface infiltration flow data of each catchment area.

[0104] S4. By comprehensively predicting rainfall and total surface infiltration, the increase in water accumulation in open pits is simulated through the water balance equation, generating a three-dimensional flood simulation animation.

[0105] S4.1 Calculate the water accumulation increment during the period using the water balance equation that combines the predicted rainfall and the total surface infiltration.

[0106] The expression for the increase in water accumulation over a period of time is:

[0107]

[0108] Where ΔW is the increase in water accumulation over a period of time, Δt is the time step, α is the correction coefficient for changes in rainfall intensity, t is the sign of the time derivative, and tR is the time derivative. b To comprehensively predict rainfall R s The amount of change over time.

[0109] Furthermore, the water balance equation is used to process the comprehensive predicted rainfall and total surface infiltration data, with a time step of fifteen minutes. The rainfall intensity change correction coefficient is determined by fitting historical data. The change in comprehensive predicted rainfall over time is calculated by dividing the difference between the rainfall in the current period and the previous period by the time step. The product of the catchment area and the comprehensive predicted rainfall is subtracted from the total surface infiltration, and then multiplied by the time step. Finally, the rainfall intensity change correction term is added, and the final output is the periodic water accumulation increment data.

[0110] S4.2 Based on the three-dimensional terrain data of the open-pit mine and the time-period water accumulation increment, dynamic mesh generation technology is adopted, and the water level rise is rendered in real time through the CesiumJS engine to obtain a three-dimensional flooding simulation animation.

[0111] Furthermore, the water level rise is calculated based on the incremental water accumulation data over a period of time. Dynamic mesh generation technology is used to refine the mesh in the waterlogged area, while the mesh in the non-waterlogged area is appropriately merged. The three-dimensional terrain data and water level data are loaded through the CesiumJS engine, and the dynamic rise of the water level is achieved by setting time axis control parameters. Color gradient effects are added to distinguish different water depth areas, and the final output is a three-dimensional flood simulation animation in WebGL format with a time dimension.

[0112] S5. Input the water accumulation increment and open-pit mine three-dimensional topographic data into the Bishop slope stability model, calculate the safety factor, and analyze it to obtain a composite early warning signal.

[0113] S5.1 Based on the water accumulation increment and open-pit mine three-dimensional topographic data, input them into the Bishop slope stability model, and use the Bishop simplified method to calculate the slope safety factor;

[0114] The expression for the slope safety factor is:

[0115]

[0116] Where F is the slope safety factor, c' is the effective cohesion of the soil, l is the length of the sliding surface segment, H is the weight of the soil strip, θ is the angle between the tangent of the sliding surface and the horizontal plane, and φ' is the effective internal friction angle of the soil.

[0117] Furthermore, based on the incremental water accumulation and the three-dimensional topographic data of the open-pit mine, the data are input into the Bishop slope stability model. When calculating the slope safety factor using the Bishop simplified method, the incremental water accumulation data is converted into pore water pressure and applied to the potential slip surface. The effective cohesion and effective internal friction angle of the soil are obtained through indoor direct shear tests. The effective cohesion of sand is taken as zero kPa, and the effective internal friction angle is taken as 30 degrees. The weight of the soil strip is calculated from the elevation value and the unit weight of the soil and rock mass in the three-dimensional topographic data of the open-pit mine. The segment length of the slip surface is determined by measuring the length of the slip arc after dividing the soil strip. The angle between the tangent of the slip surface and the horizontal plane is solved through geometric relationships. The most dangerous slip arc position is searched using an iterative method, and the ratio of the anti-slip moment to the sliding moment of each slip arc is calculated. The minimum value is taken as the slope safety factor output.

[0118] S5.2. Based on the shear strength test of soil and rock mass and historical data on slope stability, a safety threshold is set.

[0119] Furthermore, based on the effective cohesion and effective internal friction angle parameters of the soil obtained from the soil shear strength test, and combined with the critical safety factor value of landslide events recorded in the historical data of slope stability, the safety threshold is determined through statistical analysis.

[0120] S5.3 Set the threshold for water accumulation increment by inverting the inundation depth of drainage units with historical flash flood events.

[0121] Furthermore, by analyzing the design drainage capacity parameters of the drainage unit and the inundation depth data recorded in historical flash flood events, the critical values ​​of water accumulation increment corresponding to different intensity rainfall events are inverted, and the water accumulation increment threshold is set in combination with the actual operating efficiency of the drainage system.

[0122] S5.4. Based on the standard deviation of the slope safety factor, the safety factor buffer tolerance is obtained. The water accumulation increment buffer tolerance is obtained by taking the response time from the maximum theoretical drainage rate of the drainage unit to full load operation.

[0123] Furthermore, based on the uncertainty of soil and rock parameters in the slope safety factor calculation process, a large number of safety factor samples are generated through Monte Carlo simulation. Based on the standard deviation of the safety factor samples, the standard deviation is used as the safety factor buffer tolerance, which reflects the range of influence of soil and rock parameter variations on the stability assessment results.

[0124] The water accumulation increment buffer tolerance is obtained by multiplying the maximum theoretical drainage rate of the drainage unit by the response time required from startup to full-load operation. The response time of the drainage unit is obtained by measuring the time required for the drainage pump to output 90% of the design flow rate from receiving the command. The water accumulation increment buffer tolerance reflects the impact of the dynamic response capability of the drainage system on water accumulation control.

[0125] S5.5 When the safety factor is less than the safety threshold and the increase in water accumulation is greater than the increase in water accumulation threshold, a red warning is triggered.

[0126] Furthermore, during the red alert triggering phase, the slope safety factor and water accumulation increment data are monitored in real time. When the slope safety factor is less than or equal to the safety threshold and the water accumulation increment is greater than or equal to the water accumulation increment threshold, a red alert signal is immediately generated. The red alert signal includes the highest danger level indicator and emergency response instructions.

[0127] S5.6 When the safety factor equals the safety threshold and the water accumulation increment equals the water accumulation increment threshold, a yellow warning is triggered.

[0128] Furthermore, during the yellow alert triggering phase, a yellow alert signal is generated when the slope safety factor equals the safety threshold and the water accumulation increment equals the water accumulation increment threshold. The yellow alert signal includes a medium hazard level indicator and preventive action instructions.

[0129] S5.7 When the safety factor is greater than the safety threshold and the safety factor is less than or equal to the safety threshold plus the safety factor buffer tolerance, and the water accumulation increment is less than the water accumulation increment threshold and the water accumulation increment buffer tolerance is greater than or equal to the water accumulation increment threshold minus the water accumulation increment buffer tolerance, a green warning is triggered.

[0130] Furthermore, during the green warning triggering phase, when the slope safety factor is greater than the safety threshold but does not exceed the sum of the safety threshold and the safety factor buffer tolerance, and the water accumulation increment is less than the water accumulation increment threshold but not lower than the difference between the water accumulation increment threshold and the water accumulation increment buffer tolerance, a green warning signal is generated. The green warning signal indicates that a state of vigilance must be maintained.

[0131] S5.8 When the safety factor is greater than the safety threshold plus the safety factor buffer tolerance and the water accumulation increment buffer tolerance is less than the water accumulation increment threshold minus the water accumulation increment buffer tolerance, a blue warning is triggered.

[0132] Furthermore, during the blue alert triggering phase, when the slope safety factor is greater than the sum of the safety threshold and the safety factor buffer tolerance, and the water accumulation increment is less than the difference between the water accumulation increment threshold and the water accumulation increment buffer tolerance, a blue alert signal is generated, indicating that the current state is safe.

[0133] S5.9 Combine red, yellow, green and blue warnings into a composite warning signal.

[0134] Furthermore, in the stage of generating composite warning signals, red, yellow, green, and blue warning signals are integrated and processed according to warning level and spatial location to generate a standardized composite warning signal containing multi-level warning information. The composite warning signal is encapsulated and transmitted in JSON format.

[0135] S6. Implement multi-level emergency response measures based on composite early warning signals.

[0136] S6.1. Trigger the corresponding level of emergency response procedure based on composite early warning signals.

[0137] Furthermore, during the emergency response process triggering phase, based on the warning level parameters and spatial coordinate information carried in the composite warning signal, the predefined response level is automatically matched. Red warning corresponds to the highest response level, yellow warning corresponds to the intermediate response level, green warning corresponds to the primary response level, and blue warning corresponds to the basic response level. The corresponding emergency response program module is called according to the warning level parameters to complete the initialization of the response process.

[0138] S6.2 By establishing a mapping table between early warning levels and specific emergency operations, a response plan is formulated, and an instruction set is obtained and sent to the execution terminal.

[0139] Furthermore, during the response plan development phase, a pre-established mapping table between warning levels and emergency operations is used to convert the warning levels in the composite warning signals into a set of specific operational instructions. A red warning corresponds to cutting off the power supply to dangerous areas and evacuating all personnel; a yellow warning corresponds to suspending slope operations and strengthening monitoring; a green warning corresponds to restricting non-essential personnel from entering and conducting routine patrols; and a blue warning corresponds to checking equipment status. The instruction set is transmitted to the mining equipment controller and personnel positioning terminal through a standard communication protocol.

[0140] This embodiment also provides an early warning system for flash floods in open-pit mines, including: a data acquisition module that performs three-dimensional scanning of the open-pit mine area to generate three-dimensional topographic data of the open-pit mine, calculates the water flow direction matrix based on the D8 algorithm, and extracts the catchment boundary line in combination with the GIS hydrological analysis module to obtain the catchment area and topographic slope data;

[0141] The multi-source rainfall prediction fusion module combines catchment area and topographic slope data with meteorological forecast data and real-time data from local rain gauges in the mining area to calculate the comprehensive predicted rainfall and obtain a spatiotemporal distribution heat map of rainfall.

[0142] The surface seepage calculation module calculates the total surface seepage based on the three-dimensional topographic data of the open-pit mine and the geological permeability coefficient of the mining area;

[0143] The 3D visualization module uses comprehensive predicted rainfall and total surface infiltration to simulate the increase in water accumulation in open pits through the water balance equation, generating a 3D flood simulation animation.

[0144] The early warning module inputs the water accumulation increment and open-pit mine three-dimensional topographic data into the Bishop slope stability model, calculates the safety factor, and analyzes it to obtain a composite early warning signal;

[0145] The emergency response execution module executes multi-level emergency response measures based on composite early warning signals.

[0146] This embodiment also provides a computer device applicable to the open-pit mine flash flood disaster early warning method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the open-pit mine flash flood disaster early warning method proposed in the above embodiment.

[0147] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0148] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the method for early warning of flash floods in open-pit mines as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0149] In summary, this invention generates high-precision 3D terrain data through UAV LiDAR scanning, extracts water catchment characteristics based on the D8 algorithm and GIS hydrological analysis, calculates comprehensive predicted rainfall by dynamically weighting and fusing meteorological forecasts and real-time monitoring data, and then accurately simulates water accumulation increments by combining unsaturated seepage theory and water balance equations. The water accumulation data and 3D terrain are input into the Bishop slope stability model to generate a composite early warning signal and trigger a graded emergency response, thus realizing accurate prediction and rapid response across the entire chain from data acquisition to disaster early warning.

[0150] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for early warning of flood disaster in an open-pit mine, characterized in that: comprising, The open pit area is scanned in three dimensions, three-dimensional terrain data of the open pit is generated, the water flow direction matrix is calculated based on the D8 algorithm, the catchment boundary line is extracted by combining the GIS hydrological analysis module, the catchment area and the terrain slope data are obtained; The catchment area, terrain slope data, weather station forecast data and local rain gauge real-time data are combined to calculate the comprehensive predicted rainfall, and a rainfall spatiotemporal distribution heat map is obtained; According to the three-dimensional terrain data of the open pit and the geological permeability coefficient of the mine area, the total amount of surface seepage is calculated; Using the comprehensive predicted rainfall and the total amount of surface seepage, the water balance equation is used to simulate the water accumulation increment of the open pit, and a three-dimensional submergence simulation animation is generated; The water accumulation increment and the three-dimensional terrain data of the open pit are input into the Bishop slope stability model to calculate the safety factor, and the composite early warning signal is obtained by analysis; Based on the composite early warning signal, multi-level emergency response measures are executed.

2. The open-pit mine flood disaster early warning method according to claim 1, characterized in that: The open pit area is scanned in three dimensions, three-dimensional terrain data of the open pit is generated, the water flow direction matrix is calculated based on the D8 algorithm, the catchment boundary line is extracted by combining the GIS hydrological analysis module, the catchment area and the terrain slope data are obtained, including the following steps: The open pit area is scanned by an unmanned aerial LiDAR, high-precision point cloud data is obtained by GNSS / IMU combined positioning, and after denoising, classification and interpolation processing, three-dimensional terrain data of the open pit is generated; Based on the three-dimensional terrain data of the open pit, the Jenson-Domingue depression filling method is used to fill the depression, and the D8 single flow direction algorithm is used to calculate the water flow direction of the grid cell, and the 8-direction coded water flow direction matrix is obtained; Based on the 8-direction coded water flow direction matrix, the multi-flow direction accumulation method is used to obtain the water flow accumulation, the minimum catchment area statistical value of the actual surface runoff formed in the historical heavy rain event is set as the catchment threshold, the catchment area is divided, the closed boundary is extracted by the boundary tracking method, and the catchment boundary line is obtained; Based on the catchment boundary line Gauss area algorithm, the grid cells in the closed boundary are accumulated and calculated, the catchment area is obtained, and based on the three-dimensional terrain data of the open pit, the three-order inverse distance square difference method is used to calculate the elevation change rate of the grid cell, and the terrain slope data is obtained.

3. The open-pit mine flood disaster early warning method according to claim 2, characterized in that: The catchment area, terrain slope data, weather station forecast data and local rain gauge real-time data are combined to calculate the comprehensive predicted rainfall, and a rainfall spatiotemporal distribution heat map is obtained, including the following steps: The Min-Max normalization method is used to standardize the catchment area, terrain slope data, weather station forecast data and local rain gauge real-time data, and the multi-source data fused in the same dimension is obtained; Based on the multi-source data, a dynamic weight distribution algorithm is used to calculate the grid-based comprehensive predicted rainfall; The comprehensive predicted rainfall data is classified according to intensity to obtain a rainfall spatiotemporal distribution heat map.

4. The open-pit mine flood disaster early warning method according to claim 3, characterized in that: According to the three-dimensional terrain data of the open pit and the geological permeability coefficient of the mine area, the total amount of surface seepage is calculated, including the following steps: Based on the drilling sampling in the geological exploration stage of the mine area, the rock permeability coefficient is measured by indoor variable water head permeability test to obtain drilling data; Based on the drilling data, the inverse distance weighted interpolation is used to generate the geological permeability coefficient of the mine area; The relative permeability is calculated based on Darcy's law, and the grid area and the hydraulic gradient are combined to obtain the grid unit seepage flow.

5. The open-pit mine flood disaster early warning method according to claim 4, characterized in that: The total amount of surface seepage is predicted by using the comprehensive prediction rainfall, and the water balance equation is used to simulate the open pit ponding increment to generate a three-dimensional submergence simulation animation, including the following steps: The total amount of surface seepage is predicted by using the comprehensive prediction rainfall, and the water balance equation is used to simulate the open pit ponding increment to generate a three-dimensional submergence simulation animation, including the following steps: Based on the three-dimensional terrain data of the open pit mine and the time interval ponding increment, the dynamic grid division technology is adopted, and the water level rise is rendered in real time through the CesiumJS engine to obtain a three-dimensional submergence simulation animation.

6. The open-pit mine flood disaster early warning method according to claim 5, characterized in that: The ponding increment and the three-dimensional terrain data of the open pit mine are input into the Bishop slope stability model to calculate the safety factor and analyze the composite early warning signal, including the following steps: Based on the ponding increment and the three-dimensional terrain data of the open pit mine, the Bishop slope stability model is input into the Bishop slope stability model, and the Bishop simplified method is used to calculate the safety factor of the slope, Based on the rock-soil shear strength test and the historical data of slope stability, the safety threshold is set; Through the drainage unit and the historical flood event submergence depth inversion, the ponding increment threshold is set; Based on the standard deviation of the safety factor, the safety factor buffer tolerance is obtained, and through the maximum theoretical drainage rate of the drainage unit and the response time of starting to full load operation, the ponding increment buffer tolerance is obtained; When the safety factor is less than the safety threshold and the ponding increment is greater than the ponding increment threshold, a red warning is triggered; When the safety factor is equal to the safety threshold and the ponding increment is equal to the ponding increment threshold, a yellow warning is triggered; When the safety factor is greater than the safety threshold and the safety factor is less than or equal to the safety threshold plus the safety factor buffer tolerance, the ponding increment is less than the ponding increment threshold and the ponding increment buffer tolerance is greater than or equal to the ponding increment threshold minus the ponding increment buffer tolerance, a green warning is triggered; When the safety factor is greater than the safety threshold plus the safety factor buffer tolerance and the ponding increment buffer tolerance is less than the ponding increment threshold minus the ponding increment buffer tolerance, a blue warning is triggered; The red warning, yellow warning, green warning and blue warning form a composite early warning signal.

7. The open-pit mine flood disaster early warning method according to claim 6, characterized in that: Based on the composite early warning signal, multi-level emergency response measures are executed, including the following steps: Based on the composite early warning signal, the corresponding level of emergency response process is triggered; By establishing a mapping relationship table between the warning level and the specific emergency operation, a response scheme is developed to obtain an instruction set to an execution terminal.

8. A system for early warning of flood disaster in an open-pit mine, based on the method for early warning of flood disaster in an open-pit mine according to any one of claims 1 to 7, characterized in that: The data acquisition module, which includes three-dimensional scanning of the open pit mine area to generate three-dimensional terrain data of the open pit mine, calculates the water flow direction matrix based on the D8 algorithm, and extracts the catchment boundary line by combining the GIS hydrological analysis module to obtain the catchment area and terrain slope data; The multi-source rainfall prediction fusion module combines the catchment area, terrain slope data, weather station forecast data and local rain gauge real-time data to calculate the comprehensive prediction rainfall, and obtains the rainfall spatio-temporal distribution heat map; The surface seepage calculation module calculates the total amount of surface seepage according to the three-dimensional terrain data of the open pit mine and the geological permeability coefficient of the mine area; The three-dimensional visualization module uses the comprehensive prediction rainfall and the total amount of surface seepage to simulate the open pit ponding increment through the water balance equation to generate a three-dimensional submergence simulation animation; The early warning module inputs the water increment and the open-pit mine three-dimensional terrain data into a Bishop slope stability model, calculates a safety factor, and analyzes to obtain a composite early warning signal; The emergency response execution module executes multi-level emergency response measures based on the composite early warning signal. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to implement the steps of the open-pit mine flood disaster early warning method in any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the open-pit mine flood disaster early warning method in any one of claims 1-7.

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