Intelligent monitoring and early warning system and method for highway slope based on multi-dimensional parameters

Abstract

The present application relates to the technical field of safety early warning monitoring, and more particularly to a highway slope intelligent monitoring and early warning system and method based on multi-dimensional parameters, the system of the present application frames the monitoring area at the junction position of different engineering category slopes through a region screening module; the soil moisture content of each monitoring point is obtained through a data acquisition module; the seepage risk characteristic points of each height layer are determined through a risk identification module; the spatial risk characteristic point set corresponding to different engineering category slopes is constructed through a feature aggregation module; the safety early warning module determines whether there is a safety risk in the monitoring area and determines the risk early warning area according to the spatial distribution state of the slope seepage trajectory of different engineering category slopes; the method of the present application quantitatively analyzes the precipitation seepage trend of the slope. Furthermore, the precipitation seepage trend of the slope is quantitatively analyzed, the risk early warning area is accurately positioned, and the fine and intelligent monitoring and operation requirements of the highway slope are met.

Description

Technical Field

[0001] This invention relates to the field of safety early warning and monitoring technology, and in particular to a highway slope intelligent monitoring and early warning system and method based on multi-dimensional parameters. Background Technology

[0002] The stability of highway slopes is the core of road engineering operation and maintenance. The boundary area between artificially excavated and filled slopes and natural slopes has significant differences in rock and soil structure and seepage characteristics, making it a high-risk area for slope instability. Changes in soil moisture content caused by precipitation will further aggravate the seepage hazards in this area, which can easily induce safety accidents such as slope collapse and landslide.

[0003] Existing highway slope monitoring technologies mostly adopt a uniform distribution of monitoring points across the entire area to monitor soil moisture content, without conducting differentiated and refined monitoring of special areas with high seepage risk at the junction of artificially excavated and filled slopes and non-artificially excavated and filled slopes.

[0004] For example, Chinese invention patent CN115050163A discloses a slope monitoring and early warning system, which includes a monitoring subsystem, a data platform, and an early warning subsystem. The monitoring subsystem is used to monitor the slope to obtain monitoring data, including stress data obtained by monitoring the stress of the slope reinforcement structure. The data platform is used to obtain the monitoring data and generate analysis results based on the monitoring data. The early warning subsystem is used to obtain the analysis results and provide early warning feedback based on the analysis results.

[0005] Existing technologies cannot quantify the trend of precipitation seepage on slopes, cannot accurately locate risk warning areas, and are insufficient to meet the needs of refined and intelligent monitoring and maintenance of highway slopes. Summary of the Invention

[0006] To address this, the present invention provides a multi-dimensional parameter-based intelligent monitoring and early warning system and method for highway slopes, which overcomes the problems of existing technologies being unable to quantitatively analyze the precipitation seepage trend of slopes, unable to accurately locate risk warning areas, and unable to meet the needs of refined and intelligent monitoring and maintenance of highway slopes.

[0007] To achieve the above objectives, the present invention provides a highway slope intelligent monitoring and early warning system based on multi-dimensional parameters, comprising: The area filtering module is used to distinguish the engineering categories of highway slopes and to select monitoring areas at the boundaries of slopes of different engineering categories. The data acquisition module is connected to the area screening module and is used to set up several monitoring points at preset spatial height intervals within the monitoring area and collect the soil moisture content of each monitoring point within the precipitation monitoring time window. The risk identification module is connected to the data acquisition module and is used to draw seepage change curves based on the soil moisture content of each monitoring point at several times. Based on the curve characteristic information of the seepage change curves, seepage risk characteristic points of each height layer are determined in different engineering category slope sub-regions. The feature aggregation module, which is connected to the risk identification module, is used to construct spatial risk feature point sets corresponding to slopes of different engineering categories by using the coordinates of seepage risk feature points at each height layer as sub-elements. The safety early warning module, which is connected to the feature aggregation module, is used to determine the slope seepage trajectory based on the spatial risk feature point set, and to determine whether there is a safety risk in the monitoring area and to determine the risk early warning area based on the spatial distribution of slope seepage trajectories of slopes of different engineering categories.

[0008] Furthermore, the engineering categories of the highway slopes include artificially excavated and filled slopes and non-artificially excavated and filled slopes; The area filtering module is used to select the monitoring area at the boundary between artificially cut and filled slopes and non-artificially cut and filled slopes.

[0009] Furthermore, the risk identification module is used to determine the curve characteristic information of the seepage change curve, wherein, The risk identification module is used to determine a first feature factor based on the maximum difference in the axis coordinates of the seepage change curves corresponding to each monitoring point within the precipitation monitoring time window, and to determine a second feature factor based on the maximum slope value of the seepage change curves. The risk identification module calculates a seepage risk characterization value by weighted summation of the first feature factor and the second feature factor. The horizontal axis of the seepage change curve represents the time window of precipitation monitoring, and the vertical axis represents the soil moisture content at each monitoring point.

[0010] Furthermore, the risk identification module is used to compare the seepage risk characterization values ​​of each monitoring point at the same height layer within the monitoring area of ​​the artificially excavated and filled slope, and to determine the monitoring point with the largest seepage risk characterization value as the seepage risk characteristic point of each height layer within the monitoring area of ​​the artificially excavated and filled slope. In addition, the seepage risk characterization values ​​of each monitoring point at the same height layer are compared within the monitoring area of ​​the non-artificially excavated and filled slope, and the monitoring point with the largest seepage risk characterization value is determined as the seepage risk characteristic point of each height layer within the monitoring area of ​​the non-artificially excavated and filled slope.

[0011] Furthermore, the feature aggregation module is used to construct a set of spatial risk feature points corresponding to slopes of different engineering categories, wherein, The feature aggregation module is used to construct a first spatial risk feature point set within the monitoring area of ​​artificially excavated and filled slopes, and a second spatial risk feature point set within the monitoring area of ​​non-artificially excavated and filled slopes.

[0012] Furthermore, the first spatial risk feature point set P a =[P a,1 P a,2 P a,3 , ..., P a,n The second spatial risk feature point set P b =[P b,1 P b,2 P b,3 , ..., P b,n ]; Among them, P a,1 P is the seepage risk characteristic point at the first height layer within the monitoring area of ​​the artificially excavated and filled slope. a,n P represents the seepage risk characteristic point at the nth height layer within the monitoring area of ​​the artificially excavated and filled slope; b,1 P is the seepage risk characteristic point at the first height layer within the monitoring area of ​​the non-manually excavated and filled slope. b,n This refers to the seepage risk characteristic point at the nth height layer within the monitoring area of ​​a non-manually excavated and filled slope.

[0013] Furthermore, the safety early warning module is used to construct the slope seepage trajectory, wherein, The safety early warning module connects each seepage risk feature point in the first spatial risk feature point set in the order of height layers to construct the first slope seepage trajectory. And connect the seepage risk feature points in the second spatial risk feature point set in order of height layer to construct the seepage trajectory of the second slope.

[0014] Furthermore, the safety early warning module is used to determine whether there is a safety risk in the monitoring area based on the spatial distribution of the slope seepage trajectory. If the distance values ​​between the first slope seepage trajectory and the second slope seepage trajectory, determined by the order of height layers, form a decreasing sequence, or if the minimum spatial distance between the first slope seepage trajectory and the second slope seepage trajectory is less than a preset distance reference value, then the safety early warning module determines that there is a safety risk in the monitoring area.

[0015] Furthermore, the safety early warning module is used to determine the risk warning area based on the spatial distribution of the slope seepage trajectory, wherein, If the distance values ​​between the first slope seepage trajectory and the second slope seepage trajectory, determined in order of height layers, form a decreasing sequence, then the safety warning module determines a risk warning area of ​​a preset area at the bottom of the slope between the first slope seepage trajectory and the second slope seepage trajectory. If the minimum spatial distance between the first slope seepage trajectory and the second slope seepage trajectory is less than a preset distance reference value, the safety early warning module determines a risk warning area of ​​a preset area at a height position where the spatial distance between the first slope seepage trajectory and the second slope seepage trajectory is less than the preset distance reference value.

[0016] Furthermore, the present invention also provides a method for intelligent monitoring and early warning of highway slopes based on multi-dimensional parameters, comprising: Select the monitoring area at the boundary of slopes of different engineering types; Several monitoring points are set at preset spatial height intervals within the monitoring area, and the soil moisture content of each monitoring point is collected within the precipitation monitoring time window; Based on the soil moisture content at various monitoring points at several times, seepage change curves are plotted. Based on the curve characteristics of the seepage change curves, seepage risk characteristic points at each height layer are determined in different engineering category slope sub-regions. The coordinates of seepage risk characteristic points at each height level are used as sub-elements to construct spatial risk characteristic point sets corresponding to slopes of different engineering categories; Based on the spatial risk feature point set, the slope seepage trajectory is determined, and based on the spatial distribution of slope seepage trajectories of different engineering categories, the presence of safety risks in the monitoring area is determined, and risk warning areas are identified.

[0017] The beneficial effects of the technical solution presented in this application include: selecting monitoring areas at the boundary locations of slopes of different engineering categories through a regional screening module; setting up several monitoring points to acquire soil moisture content at each monitoring point through a data acquisition module; determining seepage risk characteristic points at each height layer through a risk identification module based on the curve characteristics of the seepage change curve; constructing a spatial risk characteristic point set corresponding to slopes of different engineering categories through a feature aggregation module; and determining whether there is a safety risk in the monitoring area and identifying risk warning areas through a safety early warning module based on the spatial distribution of slope seepage trajectories of slopes of different engineering categories. Furthermore, this enables quantitative analysis of the precipitation seepage trend of slopes and precise location of risk warning areas, meeting the needs of refined and intelligent monitoring and maintenance of highway slopes.

[0018] Furthermore, the regional screening module of this invention specifically selects the boundary between artificially cut and filled slopes and non-artificially cut and filled slopes as the monitoring area. Essentially, it is based on the engineering geological and hydrogeological characteristics of highway slopes to identify highly sensitive areas of seepage risk, so that the risk characteristic analysis focuses on the key areas affecting slope instability.

[0019] Furthermore, in this invention, the maximum difference in the axis coordinates of the seepage change curve within the monitoring window reflects the fluctuation range of soil moisture content during the rainfall period; the larger the difference, the more significant the cumulative effect of pore water pressure in the soil and rock. The maximum slope of the seepage change curve reflects the instantaneous rate of change of soil moisture content; the larger the slope, the faster the infiltration or migration of water. The degree of slope seepage instability is quantified by weighted summation of the first and second characteristic factors.

[0020] Furthermore, in this invention, the seepage risk characteristic points at each height level serve as the core risk points for that corresponding height level. The lines connecting these points accurately reflect the spatial distribution and correlation characteristics of seepage risks at different height levels of the slope, allowing discrete risk points at different height levels to form a continuous seepage risk evolution path. This clearly presents the spatial relationship of seepage trajectories in the boundary areas of different types of slopes, avoiding the limitations of point-based risk analysis. By transforming the spatial distribution of slope seepage risks into a concrete trajectory form, an intuitive and scientific spatial analysis carrier is provided, meeting the needs of refined and intelligent monitoring and maintenance of highway slopes.

[0021] Furthermore, when the distance values ​​determined by the height layer in this invention form a decreasing sequence, it indicates that the core areas of seepage risk for slopes of different engineering types are continuously converging downwards along the slope height layer. This means that the seepage fields formed by precipitation infiltration are constantly converging and superimposing at the lower part of the slope, which can easily cause rapid accumulation of pore water pressure at the slope bottom in the interface area and a sharp decrease in the effective stress of the soil and rock, thereby inducing instability accidents such as slope bottom sliding and collapse. When the minimum spatial distance between the seepage trajectories of different engineering types is less than the preset distance reference value, it indicates that the seepage risk areas of different engineering types at a certain height layer of the slope have formed close contact, and this location will become the core area of ​​seepage convergence and the source point of local slope instability. Thus, this invention takes into account both the overall evolution trend of seepage trajectory along the slope height layer and the superposition state of seepage risk at local height layers, which meets the engineering practice and refined requirements of highway slope seepage risk assessment. Attached Figure Description

[0022] Figure 1 This is a system block diagram of a highway slope intelligent monitoring and early warning system based on multi-dimensional parameters, according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the logic of the safety warning module in this embodiment of the invention for determining whether a monitoring area has a safety risk. Figure 3This is a flowchart illustrating the steps of a multi-dimensional parameter-based intelligent monitoring and early warning method for highway slopes according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] It should be noted that in the description of this invention, the terms "upper," "lower," "inner," "outer," etc., which indicate the direction or positional relationship, are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0026] It should be understood that although the present invention may use terms such as "first," "second," etc., to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of the present invention, first information may also be referred to as second information, and similarly, second information may also be referred to as first information.

[0027] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0028] Please see Figure 1 The diagram shown is a system block diagram of a multi-dimensional parameter-based intelligent monitoring and early warning system for highway slopes according to an embodiment of the present invention. The multi-dimensional parameter-based intelligent monitoring and early warning system for highway slopes of the present invention includes: The area filtering module is used to distinguish the engineering categories of highway slopes and to select monitoring areas at the boundaries of slopes of different engineering categories. This invention does not limit the area screening module, which can be a data processor that distinguishes between artificially excavated and filled slopes and non-artificially excavated and filled slopes by reading data such as highway slope engineering survey reports and construction records; and delineates the boundary range of the monitoring area based on the boundary coordinate data of slopes of different engineering categories.

[0029] The data acquisition module is connected to the area screening module and is used to set up several monitoring points at preset spatial height intervals within the monitoring area and collect the soil moisture content of each monitoring point within the precipitation monitoring time window. Optionally, in the implementation of this invention, the preset spatial height interval is determined based on the total slope height. The total slope height refers to the vertical height from the ground elevation at the bottom of the slope to the elevation at the top of the slope. When the total slope height is ≤10m, the preset spatial height interval is set to 0.5m; when the slope height is >10m, the preset spatial height interval is set to 1m. The lateral layout of the monitoring points is based on the boundary line between the artificially excavated and filled slope and the non-artificially excavated and filled slope, and is arranged perpendicular to the boundary line. The lateral distance between adjacent monitoring points is 1m. The number of monitoring points in each height layer within the monitoring area of ​​each type of slope is not less than 3.

[0030] In this invention, the precipitation event trigger time of the precipitation monitoring time window is the starting time of the rainfall ≥ 0.5 mm / h in the meteorological monitoring data; the duration of the precipitation monitoring time window is 48 hours.

[0031] The present invention does not limit the specific structure of the data acquisition module, which can be a time domain reflectometer-type soil moisture sensor with an acquisition frequency of once every 15 minutes.

[0032] The risk identification module is connected to the data acquisition module and is used to draw seepage change curves based on the soil moisture content of each monitoring point at several times. Based on the curve characteristic information of the seepage change curves, seepage risk characteristic points of each height layer are determined in different engineering category slope sub-regions. The present invention does not limit the risk identification module, which can be a microprocessor. It can draw the seepage change curve and determine the seepage risk characteristic points by calling the soil moisture content time series data stored in the data acquisition module.

[0033] The feature aggregation module, which is connected to the risk identification module, is used to construct spatial risk feature point sets corresponding to slopes of different engineering categories by using the coordinates of seepage risk feature points at each height layer as sub-elements. This invention does not limit the feature aggregation module, which can use a data storage device to store information such as the three-dimensional spatial coordinates, slope category, and height layer number corresponding to each associated feature point; and serialize the data.

[0034] The safety early warning module, which is connected to the feature aggregation module, is used to determine the slope seepage trajectory based on the spatial risk feature point set, and to determine whether there is a safety risk in the monitoring area and to determine the risk early warning area based on the spatial distribution of slope seepage trajectories of slopes of different engineering categories.

[0035] This invention does not limit the safety warning module. The module itself or its units can be constructed using logic components. The logic components can be field-programmable logic components or processors used in computers, etc., which will not be elaborated here.

[0036] In this invention, the data transmission protocol between the functional modules adopts Modbus-RTU.

[0037] Specifically, the engineering categories of the highway slopes include artificially excavated and filled slopes and non-artificially excavated and filled slopes; The area filtering module is used to select the monitoring area at the boundary between artificially cut and filled slopes and non-artificially cut and filled slopes.

[0038] In the implementation of this invention, the specific process of distinguishing between artificially excavated and backfilled slopes and non-artificially excavated and backfilled slopes can be determined through engineering construction records. In this invention, artificially excavated and backfilled slopes refer to slopes formed by artificial excavation and backfilling operations, while non-artificially excavated and backfilled slopes refer to slopes formed by natural geological processes.

[0039] During implementation, the monitoring area is centered on the boundary line between slopes of different engineering categories, extending 10m towards the artificially cut and filled slope and 10m towards the non-artificially cut and filled slope, longitudinally covering the complete height range from the top to the bottom of the slope. The boundary line between the artificially cut and filled slope and the non-artificially cut and filled slope refers to the connection line between the toe line of the artificially cut and filled operation and the natural slope, based on the cut and fill boundary line in the highway engineering construction drawings.

[0040] It is understandable that there are differences in the occurrence state, structural characteristics, and hydrogeological properties of soil and rock masses between artificially excavated and filled slopes and non-artificially excavated and filled slopes. The boundary between slopes of different engineering types is abruptly different from the interface of soil and rock properties. The stress distribution and seepage field transmission in this area show obvious boundary effects. During the precipitation infiltration process, the transport rate and accumulation capacity of water in soil and rock masses of different engineering types vary significantly, which easily leads to the formation of seepage accumulation zones. This, in turn, causes the soil and rock mass strength to decrease and the pore water pressure to increase sharply, becoming a risk source for highway slope instability. The regional screening module specifically selects this boundary location as the monitoring area. Essentially, it is based on the engineering geological and hydrogeological characteristics of highway slopes to identify highly sensitive areas of seepage risk, so that the risk characteristic analysis focuses on the key areas affecting slope instability.

[0041] Specifically, the risk identification module is used to determine the curve characteristic information of the seepage change curve, wherein, The risk identification module is used to determine a first feature factor based on the maximum difference in the axis coordinates of the seepage change curves corresponding to each monitoring point within the precipitation monitoring time window, and to determine a second feature factor based on the maximum slope value of the seepage change curves. For example, the first characteristic factor M1 = (maximum soil moisture content value of the seepage change curve within the precipitation monitoring time window - minimum soil moisture content value) / maximum soil moisture content value of the seepage change curve within the precipitation monitoring time window; The maximum slope value of the seepage change curve is obtained by dividing the difference in soil moisture content between two adjacent collection times by the time interval, and the maximum absolute value of all slope values ​​within the window is taken as the second characteristic factor M2.

[0042] The risk identification module calculates a seepage risk characterization value by weighted summation of the first feature factor and the second feature factor. The seepage risk characterization value = α×M1+β×M2, where α is the weight coefficient of the first characteristic factor and β is the weight coefficient of the second characteristic factor. α+β=1, and optionally, α=0.5 and β=0.5.

[0043] The horizontal axis of the seepage change curve represents the time window of precipitation monitoring, and the vertical axis represents the soil moisture content at each monitoring point.

[0044] Understandably, the seepage change curve, with the precipitation monitoring time window as the horizontal axis and soil moisture content as the vertical axis, reflects the water accumulation and transport dynamics of the soil at each monitoring point during precipitation. The maximum difference in the axis coordinates of the seepage change curve within the monitoring window reflects the fluctuation range of soil moisture content during the precipitation period. The larger the difference, the more significant the cumulative effect of pore water pressure in the soil and rock, and the higher the potential risk of slope seepage instability. The maximum slope of the seepage change curve reflects the instantaneous rate of change of soil moisture content. The larger the slope, the faster the water infiltration or transport, and the higher the potential risk of slope seepage instability. The degree of slope seepage instability was quantified by weighted summation of the first and second characteristic factors.

[0045] Specifically, the risk identification module is used to compare the seepage risk characterization values ​​of each monitoring point at the same height layer within the monitoring area of ​​the artificially excavated and filled slope, and to determine the monitoring point with the largest seepage risk characterization value as the seepage risk characteristic point of each height layer within the monitoring area of ​​the artificially excavated and filled slope. In addition, the seepage risk characterization values ​​of each monitoring point at the same height layer are compared within the monitoring area of ​​the non-artificially excavated and filled slope, and the monitoring point with the largest seepage risk characterization value is determined as the seepage risk characteristic point of each height layer within the monitoring area of ​​the non-artificially excavated and filled slope.

[0046] Understandably, within the monitoring areas of artificially excavated and non-artificially excavated slopes, although different monitoring points at the same height layer are located at the same height layer, due to differences in local geological properties such as soil particle size distribution, porosity, and permeability coefficient, as well as the influence of micro-topography and the degree of fissure development, the water accumulation and transport states of each monitoring point during the precipitation infiltration process are different, and the corresponding seepage risk characterization values ​​also show differentiated distributions. The monitoring point with the highest seepage risk characterization value is the location within that height layer that is most significantly affected by precipitation seepage and has the highest potential risk of soil instability.

[0047] Specifically, the feature aggregation module is used to construct a set of spatial risk feature points corresponding to slopes of different engineering categories, wherein, The feature aggregation module is used to construct a first spatial risk feature point set within the monitoring area of ​​artificially excavated and filled slopes, and a second spatial risk feature point set within the monitoring area of ​​non-artificially excavated and filled slopes.

[0048] Specifically, the first spatial risk feature point set P a =[P a,1 P a,2 P a,3 , ..., P a,n The second spatial risk feature point set P b =[P b,1 P b,2 P b,3 , ..., P b,n ]; Among them, P a,1 P is the seepage risk characteristic point at the first height layer within the monitoring area of ​​the artificially excavated and filled slope. a,n P represents the seepage risk characteristic point at the nth height layer within the monitoring area of ​​the artificially excavated and filled slope; b,1 P is the seepage risk characteristic point at the first height layer within the monitoring area of ​​the non-manually excavated and filled slope. b,n This refers to the seepage risk characteristic point at the nth height layer within the monitoring area of ​​a non-manually excavated and filled slope.

[0049] In this invention, the first spatial risk feature point set P a Second spatial risk feature point set P b All are sorted by height from highest to lowest, P a,1 P is the seepage risk characteristic point at the first height layer at the top of the slope within the monitoring area corresponding to the artificially excavated and filled slope. a,n P is the seepage risk characteristic point at the nth height layer at the bottom of the slope within the monitoring area corresponding to the artificially excavated and filled slope. b,1 P is the seepage risk characteristic point at the first height layer of the slope crest within the monitoring area corresponding to non-manually excavated and filled slopes. b,nThe seepage risk characteristic point at the nth height layer at the bottom of the slope within the monitoring area corresponding to a non-manually excavated and filled slope.

[0050] This invention addresses the differences in engineering geological properties of slopes of different engineering categories by constructing first and second spatial risk feature point sets, allowing each type of slope to have an independent quantitative characterization system for seepage risk. Simultaneously, it sequentially arranges each seepage risk feature point according to height layer, ensuring that each element in the point set corresponds to the core seepage risk point at a specific height layer of the slope, thus achieving the quantification of spatial risk characteristics across the entire area.

[0051] Specifically, the safety early warning module is used to construct the slope seepage trajectory, wherein, The safety early warning module connects each seepage risk feature point in the first spatial risk feature point set in the order of height layers to construct the first slope seepage trajectory. And connect the seepage risk feature points in the second spatial risk feature point set in order of height layer to construct the seepage trajectory of the second slope.

[0052] In this invention, linear interpolation is used to connect the seepage risk feature points of adjacent height layers within the same feature point set in sequence to form a continuous broken line trajectory; the trajectory smoothing process uses the moving average method with a window size of 3 adjacent feature points.

[0053] The first spatial risk feature point set P in this invention a Second spatial risk feature point set P b They are all arranged in order of height from the top of the slope to the bottom of the slope.

[0054] During implementation, for each height layer k (k=1,2,...,n), P on the first seepage trajectory is calculated. a,k Point P on the second seepage trajectory b,k The straight-line distances between points in three-dimensional space form a sequence of distance values ​​{D1, D2, ..., D...} n}, where the spatial distance is calculated using the Euclidean distance formula: D k =[(X a,k -X b,k )²+(Y a,k -Y b,k )²+(Z a,k -Z b,k )²] 1 / 2 X a,k Y a,k Z a,k P on the first seepage trajectory a,k The three-dimensional spatial coordinates of a point, X b,k Y b,k Z b,k P on the second seepage trajectoryb,k The three-dimensional spatial coordinates of point P on the first seepage trajectory a,k Point P on the second seepage trajectory b,k The coordinate system in which the point's coordinates lie is the geodetic coordinate system.

[0055] In this invention, a GNSS receiver can be selected to receive satellite signals, and the position of each monitoring point can be measured using the GNSS receiver to obtain the three-dimensional spatial coordinates of each point on the first seepage trajectory and each point on the second seepage trajectory.

[0056] Understandably, the seepage risk characteristic points at each height level serve as the core risk points for that corresponding height level. Connecting these points accurately reflects the spatial distribution and correlation characteristics of seepage risks at different height levels of the slope, allowing discrete risk points at different height levels to form a continuous seepage risk evolution path. This clearly presents the spatial relationship of seepage trajectories in the boundary areas of different types of slopes, avoiding the limitations of point-based risk analysis. By transforming the spatial distribution of slope seepage risks into a concrete trajectory form, it provides an intuitive and scientific spatial analysis framework.

[0057] Specifically, please refer to Figure 2 As shown, this is a flowchart illustrating the logic of the safety early warning module in this embodiment of the invention for determining whether a monitoring area has a safety risk. The safety early warning module is used to determine whether a monitoring area has a safety risk based on the spatial distribution of the slope seepage trajectory. If the distance values ​​between the first slope seepage trajectory and the second slope seepage trajectory, determined according to the order of height layers, form a decreasing sequence, or if the minimum spatial distance between the first slope seepage trajectory and the second slope seepage trajectory is less than a preset distance reference value, then the safety early warning module determines that there is a safety risk in the monitoring area. If the distance values ​​determined by the first slope seepage trajectory and the second slope seepage trajectory according to the order of height layers do not form a decreasing sequence, and the minimum spatial distance between the first slope seepage trajectory and the second slope seepage trajectory is greater than or equal to a preset distance reference value, then the safety early warning module determines that there is no safety risk in the monitoring area.

[0058] In this invention, the preset distance reference value is 0.5m.

[0059] Those skilled in the art will understand that when the distance values ​​determined by the height layer form a decreasing sequence, it indicates that the core areas of seepage risk for slopes of different engineering types are continuously converging downwards along the slope height layer. This means that the seepage fields formed by precipitation infiltration are constantly converging and superimposing at the lower part of the slope, which can easily cause rapid accumulation of pore water pressure at the slope bottom in the interface area and a sharp decrease in the effective stress of the soil and rock, thereby inducing instability accidents such as slope bottom sliding and collapse. When the minimum spatial distance between the seepage trajectories of different engineering types is less than the preset distance reference value, it indicates that the seepage risk areas of different engineering types at a certain height layer of the slope have formed close contact, and this location will become the core area of ​​seepage convergence and the source point of local slope instability. Therefore, this approach takes into account both the overall evolution trend of seepage trajectories along the slope height layer and the superposition state of seepage risk at local height layers, which meets the engineering practice and refined requirements for the assessment of seepage risk on highway slopes.

[0060] Specifically, the safety early warning module is used to determine the risk warning area based on the spatial distribution of the slope seepage trajectory, wherein... If the distance values ​​between the first slope seepage trajectory and the second slope seepage trajectory, determined in order of height layers, form a decreasing sequence, then the safety warning module determines a risk warning area of ​​a preset area at the bottom of the slope between the first slope seepage trajectory and the second slope seepage trajectory. If the minimum spatial distance between the first slope seepage trajectory and the second slope seepage trajectory is less than a preset distance reference value, the safety early warning module determines a risk warning area of ​​a preset area at a height position where the spatial distance between the first slope seepage trajectory and the second slope seepage trajectory is less than the preset distance reference value.

[0061] For example, in the implementation of the present invention, the risk warning area determined at the bottom of the slope between the first slope seepage trajectory and the second slope seepage trajectory is a rectangular area. The length of the risk warning area along the slope bottom extension direction is the minimum value of the distance determined by the first slope seepage trajectory and the second slope seepage trajectory in the order of height layers. The width in the direction perpendicular to the slope bottom is 1m. The rectangular risk warning area at the bottom of the slope has its length direction parallel to the slope direction, its width direction perpendicular to the slope direction and extends to the inside of the boundary area between the artificially excavated and filled slope and the non-artificially excavated and filled slope.

[0062] The risk warning area determined at a height position where the spatial distance is less than the preset distance reference value is a circular area with a radius of 0.5m. The center of the circle is the midpoint of the spatial distance between the seepage trajectory of the first slope and the seepage trajectory of the second slope where the spatial distance is less than the preset distance reference value.

[0063] It is understandable that when seepage trajectories of different engineering types form a decreasing sequence according to the distance between height layers, the pore water pressure generated by seepage will converge and superimpose at the slope bottom, causing the soil and rock mass at the slope bottom to be in a state of high water content for a long time, significantly reducing the effective stress and becoming the area with the most concentrated instability risk. Therefore, delineating a pre-set warning zone at the slope bottom between different engineering type trajectories can accurately cover the range of instability risk caused by seepage accumulation. When the minimum spatial distance between different engineering type trajectories is less than the preset reference value, it indicates that the seepage risk area at a specific height position has come into close contact. The local soil and rock mass at this height layer will quickly become saturated due to seepage accumulation, resulting in strength decay and potential local instability. Therefore, delineating a risk warning zone at this height position can specifically control the sudden instability risk caused by the superposition of local seepage. Furthermore, this avoids the waste of resources for full-area warning, enables quantitative analysis of the precipitation seepage trend of the slope, accurately locates the risk warning area, and meets the needs of refined and intelligent monitoring and maintenance of highway slopes.

[0064] Specifically, please refer to Figure 3 The diagram illustrates the steps of a multi-dimensional parameter-based intelligent monitoring and early warning method for highway slopes according to an embodiment of the present invention. The present invention also provides a multi-dimensional parameter-based intelligent monitoring and early warning method for highway slopes, comprising: Step S1: Select the monitoring area at the boundary of slopes of different engineering categories; Step S2: Set up several monitoring points at preset spatial height intervals within the monitoring area, and collect soil moisture content at each monitoring point within the precipitation monitoring time window; Step S3: Based on the soil moisture content of each monitoring point at several times, draw the seepage change curve, and determine the seepage risk characteristic points of each height layer in different engineering category slope sub-regions according to the curve characteristic information of the seepage change curve. Step S4: Use the coordinates of seepage risk characteristic points at each height level as sub-elements to construct spatial risk characteristic point sets corresponding to slopes of different engineering categories. Step S5: Determine the slope seepage trajectory based on the spatial risk feature point set, and determine whether there is a safety risk in the monitoring area and identify the risk warning area based on the spatial distribution of slope seepage trajectories for different engineering categories of slopes.

[0065] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A highway slope intelligent monitoring and early warning system based on multi-dimensional parameters, characterized in that, include: The area filtering module is used to distinguish the engineering categories of highway slopes and to select monitoring areas at the boundaries of slopes of different engineering categories. The data acquisition module, which is connected to the area screening module, is used to set up a number of monitoring points at preset spatial height intervals within the monitoring area, and to collect the soil moisture content of each monitoring point within the precipitation monitoring time window. The monitoring points are arranged in different height layers along the direction perpendicular to the boundary line of slopes of different engineering categories. The risk identification module is connected to the data acquisition module and is used to draw seepage change curves based on the soil moisture content of each monitoring point at several times. Based on the curve characteristic information of the seepage change curves, seepage risk characteristic points of each height layer are determined in different engineering category slope sub-regions. The feature aggregation module, which is connected to the risk identification module, is used to construct spatial risk feature point sets corresponding to slopes of different engineering categories by using the coordinates of seepage risk feature points at each height layer as sub-elements. A safety early warning module, which is connected to the feature aggregation module, is used to determine the slope seepage trajectory based on the spatial risk feature point set, and to determine whether there is a safety risk in the monitoring area and to determine the risk early warning area based on the spatial distribution of slope seepage trajectories of slopes of different engineering categories. The safety early warning module connects each seepage risk feature point in the first spatial risk feature point set in the order of height layers to construct the first slope seepage trajectory; and connects each seepage risk feature point in the second spatial risk feature point set in the order of height layers to construct the second slope seepage trajectory. If the distance values ​​between the first slope seepage trajectory and the second slope seepage trajectory, determined according to the order of height layers, form a decreasing sequence, or if the minimum spatial distance between the first slope seepage trajectory and the second slope seepage trajectory is less than a preset distance reference value, then the safety early warning module determines that there is a safety risk in the monitoring area. If the distance values ​​between the first slope seepage trajectory and the second slope seepage trajectory, determined in order of height layers, form a decreasing sequence, then the safety warning module determines a risk warning area of ​​a preset area at the bottom of the slope between the first slope seepage trajectory and the second slope seepage trajectory. If the minimum spatial distance between the first slope seepage trajectory and the second slope seepage trajectory is less than a preset distance reference value, the safety early warning module determines a risk warning area of ​​a preset area at a height position where the spatial distance between the first slope seepage trajectory and the second slope seepage trajectory is less than the preset distance reference value.

2. The intelligent monitoring and early warning system for highway slopes based on multi-dimensional parameters according to claim 1, characterized in that, The engineering categories of the highway slopes include artificially excavated and filled slopes and non-artificially excavated and filled slopes; The area filtering module is used to select the monitoring area at the boundary between artificially cut and filled slopes and non-artificially cut and filled slopes.

3. The intelligent monitoring and early warning system for highway slopes based on multi-dimensional parameters according to claim 2, characterized in that, The risk identification module is used to determine the curve characteristic information of the seepage change curve, wherein, The risk identification module is used to determine a first feature factor based on the maximum difference in the axis coordinates of the seepage change curves corresponding to each monitoring point within the precipitation monitoring time window, and to determine a second feature factor based on the maximum slope value of the seepage change curves. The risk identification module calculates a seepage risk characterization value by weighted summation of the first feature factor and the second feature factor. The horizontal axis of the seepage change curve represents the time window of precipitation monitoring, and the vertical axis represents the soil moisture content at each monitoring point.

4. The intelligent monitoring and early warning system for highway slopes based on multi-dimensional parameters according to claim 3, characterized in that, The risk identification module is used to compare the seepage risk characterization values ​​of each monitoring point at the same height layer in the monitoring area of ​​the artificially excavated and filled slope, and to determine the monitoring point with the largest seepage risk characterization value as the seepage risk characteristic point of each height layer in the monitoring area of ​​the artificially excavated and filled slope. In addition, the seepage risk characterization values ​​of each monitoring point at the same height layer are compared within the monitoring area of ​​the non-artificially excavated and filled slope, and the monitoring point with the largest seepage risk characterization value is determined as the seepage risk characteristic point of each height layer within the monitoring area of ​​the non-artificially excavated and filled slope.

5. The intelligent monitoring and early warning system for highway slopes based on multi-dimensional parameters according to claim 4, characterized in that, The feature aggregation module is used to construct a set of spatial risk feature points corresponding to slopes of different engineering categories, wherein... The feature aggregation module is used to construct a first spatial risk feature point set within the monitoring area of ​​artificially excavated and filled slopes, and a second spatial risk feature point set within the monitoring area of ​​non-artificially excavated and filled slopes.

6. The intelligent monitoring and early warning system for highway slopes based on multi-dimensional parameters according to claim 5, characterized in that, The first spatial risk feature point set P a =[P a,1 P a,2 P a,3 , ..., P a,n The second spatial risk feature point set P b =[P b,1 P b,2 P b,3 , ..., P b,n ]; Among them, P a,1 P is the seepage risk characteristic point at the first height layer within the monitoring area of ​​the artificially excavated and filled slope. a,n P represents the seepage risk characteristic point at the nth height layer within the monitoring area of ​​the artificially excavated and filled slope; b,1 P is the seepage risk characteristic point at the first height layer within the monitoring area of ​​the non-manually excavated and filled slope. b,n This refers to the seepage risk characteristic point at the nth height layer within the monitoring area of ​​a non-manually excavated and filled slope.

7. A method for intelligent monitoring and early warning of highway slopes based on multidimensional parameters, used in the intelligent monitoring and early warning system for highway slopes based on multidimensional parameters as described in any one of claims 1-6, characterized in that, include: Select the monitoring area at the boundary of slopes of different engineering types; Several monitoring points are set at preset spatial height intervals within the monitoring area, and the soil moisture content of each monitoring point is collected within the precipitation monitoring time window; Based on the soil moisture content at various monitoring points at several times, seepage change curves are plotted. Based on the curve characteristics of the seepage change curves, seepage risk characteristic points at each height layer are determined in different engineering category slope sub-regions. The coordinates of seepage risk characteristic points at each height level are used as sub-elements to construct spatial risk characteristic point sets corresponding to slopes of different engineering categories; Based on the spatial risk feature point set, the slope seepage trajectory is determined, and based on the spatial distribution of slope seepage trajectories of different engineering categories, the presence of safety risks in the monitoring area is determined, and risk warning areas are identified.

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