Intelligent monitoring and early warning system for expansive red-bed geological slope

Abstract

This invention relates to the field of early warning device technology, specifically to an intelligent monitoring and early warning system for expansive red-bed geological slopes. The system divides the expansive red-bed geological slope area into several monitoring zones, determines hydrological data, strain data, acoustic velocity data, and environmental data for each monitoring zone within a specified time period, identifies hidden risk areas based on these data, and considers factors hindering water infiltration and deformation transmission. It also determines the degree of synergistic development of acoustic strain based on acoustic velocity and strain data, quantifying the synergistic effect of damage evolution and deformation accumulation. Furthermore, it determines the degree of correlation between water and force factors in each hidden risk area based on acoustic velocity, strain, and hydrological data, analyzes anomalies in water infiltration and stress transmission, and determines the degree of deformation approximation for each hidden risk area based on the degree of synergistic development and the degree of correlation transmission anomaly to determine the early warning result, thereby improving accuracy.

Description

Technical Field

[0001] This invention relates to the field of early warning device technology, specifically to an intelligent monitoring and early warning system for expansive red-bed geological slopes. Background Technology

[0002] Expansive red bed geological slopes are mainly composed of red bed rock masses containing expansive clay minerals such as montmorillonite and illite. Their significant characteristic is that when exposed to water, the clay minerals absorb water and expand, leading to an increase in the volume of the slope rock mass and a sharp decrease in mechanical strength. When water is lost, shrinkage and cracking occur, destroying the integrity of the rock mass. Under the influence of long-term wet-dry cycles, rainfall infiltration, seismic loads, and engineering disturbances, they are prone to continuous progressive deformation, eventually leading to geological disasters such as slope collapse and landslides. This poses a serious threat to the structural safety, operational stability, and safety of surrounding personnel and property of infrastructure such as highways, railways, water conservancy projects, and building foundation pits. Therefore, monitoring expansive red bed geological slopes is of paramount importance.

[0003] Currently, the monitoring of expansive red-bed geological slopes mostly relies on a combination of regular manual inspections and traditional single-point sensors (such as displacement gauges, inclinometers, and piezometers). This approach suffers from low inspection efficiency, limited coverage, and difficulty in capturing the dynamic deformation details of the slope. Single-point sensors also have limited monitoring parameters, making it difficult to achieve efficient fusion and collaborative analysis of multi-source monitoring data. Early warnings are based on fixed parameter thresholds and lack the ability to intelligently identify the nonlinear deformation patterns of expansive red-bed geological slopes, thus affecting the accuracy of early warning judgments. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide an intelligent monitoring and early warning system for expansive red-bed geological slopes. The specific technical solution adopted is as follows:

[0005] This application provides an intelligent monitoring and early warning system for expansive red-bed geological slopes, including:

[0006] The segmentation module is used to divide the expansive red-bed geological slope area into several monitoring areas and determine the hydrological data, strain data, acoustic velocity data and environmental data of the monitoring areas within a specified time period.

[0007] The first determining module is used to determine the hidden risk area based on the hydrological data, the strain data and the environmental data. The hidden risk area is a monitoring area where water infiltration and deformation transmission are obstructed.

[0008] The second determining module is used to determine the degree of coordinated development of acoustic strain in each of the hidden risk areas based on the acoustic velocity data and the strain data, and to determine the degree of correlation transmission anomaly of water and force factors in each of the hidden risk areas based on the acoustic velocity data, the strain data and the hydrological data.

[0009] The early warning module is used to determine the degree of deformation approximation of each hidden risk area based on the degree of coordinated development and the degree of related transmission anomaly, and to determine the early warning result based on the degree of deformation approximation.

[0010] In one embodiment, the first determining module includes a first unit, a second unit, a third unit, and a fourth unit, the environmental data includes precipitation prediction data and temperature and humidity prediction data, and the hydrological data includes water content;

[0011] The first unit is used to determine the rainwater infiltration replenishment and water evaporation based on the precipitation prediction data and the temperature and humidity prediction data, and to determine the water content prediction value of each monitoring area based on the first difference between the rainwater infiltration replenishment and the water evaporation and the water content.

[0012] The second unit is used to determine the moisture content gradient sequence and the corresponding deformation factor gradient sequence of two adjacent monitoring areas based on the moisture content and the predicted moisture content value. The deformation factor is the product of cohesion and internal friction angle.

[0013] The third unit is used to determine the lag time of each monitoring area based on the moisture content gradient sequence and the deformation factor gradient sequence.

[0014] The fourth unit is used to designate monitoring areas with lag times greater than those of other monitoring areas within a preset range as hidden risk areas.

[0015] In one embodiment, the third unit is specifically used for:

[0016] Extract the first peak point of the moisture content gradient sequence and the second peak point of the deformation factor gradient sequence respectively; and refit the first peak point sequence based on the first peak point to obtain the second peak point sequence based on the second peak point.

[0017] The lag time of the first peak point sequence and the second peak point sequence in each monitoring region is calculated using a cross-correlation function.

[0018] In one implementation, the second determining module includes a fifth unit, a sixth unit, and a seventh unit, and the specified time period includes several cycles;

[0019] The fifth unit is used to determine the acoustic integrity attenuation rate of each of the hidden risk areas based on the acoustic velocity data.

[0020] The sixth unit is used to determine, based on the strain data, the corresponding total strain accumulation, the strain increment of the strain increment decreasing segment and the strain increment increasing segment in a first number of cycles, and to determine the strain plasticity ratio of each of the hidden risk areas based on the total strain accumulation, the strain increment of the strain increment decreasing segment and the strain increment increasing segment.

[0021] The seventh unit is used to determine the degree of synergistic development of acoustic strain in each of the hidden risk areas based on the acoustic integrity attenuation rate of each hidden risk area and the strain-plastic ratio, wherein the degree of synergistic development characterizes the degree of synchronous aggravation of damage and plastic deformation.

[0022] In one embodiment, the fifth unit is specifically used for:

[0023] Based on the acoustic velocity data of a first number of cycles, the temporal fluctuation of acoustic velocity in each of the hidden risk areas is determined, and the temporal fluctuation of acoustic velocity characterizes the degree of volatility of velocity over time.

[0024] Perform a Fourier transform on the sound wave velocity data for a first number of cycles, identify the dominant frequency component with the highest energy proportion, obtain the initial dominant frequency and the current dominant frequency of the sound wave corresponding to each hidden risk area, and determine the second difference between the initial dominant frequency and the current dominant frequency of the sound wave respectively.

[0025] The acoustic integrity attenuation rate of each hidden risk area is determined based on the ratio of the second difference to the initial acoustic wave dominant frequency and the acoustic wave velocity temporal fluctuation.

[0026] In one embodiment, the second determining module includes an eighth unit, a ninth unit, a tenth unit, and an eleventh unit. The specified time period includes several cycles. The hydrological data includes pore water pressure and water content at different depths. The correlation anomaly of water and force factors includes the anomaly of water-pressure transmission and the anomaly of stress transmission.

[0027] The eighth unit is used to determine, based on the water content at different depths in the second number of cycles, the spatial variation coefficient of water content in each of the hidden risk areas, the rise time from the start of water content increase to the peak water content at different depths, and the deviation of rise time between different depths; to determine the pressure rise rate gradient of each of the hidden risk areas based on the pore water pressure in the second number of cycles; and to determine the time difference between the occurrence time of the peak water content and the occurrence time of the peak pore water pressure in each of the hidden risk areas based on the water content at different depths in the second number of cycles and the pore water pressure. The spatial variation coefficient of water content characterizes the uniformity of water distribution in the depth direction.

[0028] The ninth unit is used to determine the degree of transmission anomaly of water and pressure transmission in each of the hidden risk areas based on the spatial variation coefficient of water content, the rise time duration deviation between different depths, the pressure rise rate gradient, and the time difference.

[0029] The tenth unit is used to determine the effective stress and the average effective stress for each cycle based on the pore water pressure of the first number of cycles, the monitoring depth of the strain data, and the rock density of the monitoring area. The effective stress that is greater than the average effective stress by a first preset multiple is taken as the effective stress peak value. The average strain increment is determined based on the absolute value of the strain increment of the strain data of the first number of cycles. The number of strain increments whose absolute values ​​are greater than the average strain increment by a second preset multiple is taken as the strain local mutation rate. The stress-strain synchronization is determined based on the consistency of the sign of the strain increment and the effective stress increment of the first number of cycles.

[0030] The eleventh unit is used to determine the acoustic integrity attenuation rate of each of the hidden risk areas based on a first number of acoustic velocity data, and to determine the stress transmission anomaly degree of each of the hidden risk areas based on each of the acoustic integrity attenuation rates, the frequency of occurrence of the effective stress peak, the local strain mutation rate, and the stress-strain synchronicity.

[0031] In one embodiment, the ninth unit is specifically used for:

[0032] The permeability uniformity index of each hidden risk area is determined based on the spatial variation coefficient of water content, the maximum rise time deviation among the rise time deviations between different depths, and the preset historical normal rise time.

[0033] The pressure transmission efficiency of each hidden risk area is determined based on the pressure rise rate gradient, the preset historical normal pressure rise rate gradient, and the time difference.

[0034] The degree of water and pressure transmission anomaly in each of the hidden risk areas is determined based on the permeability uniformity index and the pressure transmission efficiency, respectively.

[0035] In one embodiment, determining the degree of water and pressure transmission anomaly in each of the hidden risk areas based on the permeability uniformity index and the pressure transmission efficiency includes:

[0036] The permeability uniformity index of each of the hidden risk areas is determined by a first product of a first weight, and the pressure transmission efficiency of each of the hidden risk areas is determined by a second product of a second weight.

[0037] The sum of the first product and the second product is determined, and the degree of transmission anomaly of water and pressure transmission in each of the hidden risk areas is determined based on the third difference between the preset value and the sum.

[0038] In one implementation, the eleventh unit is specifically used for:

[0039] The effective stress temporal variation amplitude of each hidden risk area is determined based on the difference between the maximum and minimum effective stress values ​​of the first number of cycles.

[0040] The effective stress transmission disorder of each hidden risk area is determined based on the frequency of occurrence of the effective stress peak, the preset historical effective stress peak frequency, and the effective stress temporal variation amplitude.

[0041] Based on the stress-strain synchronicity and the strain local abrupt change rate, the strain response anomaly coefficient of each of the hidden risk regions is determined.

[0042] The stress transmission anomaly degree of each of the hidden risk areas is determined by the third product of the strain response anomaly coefficient, the effective stress transmission disorder degree, and the acoustic integrity attenuation rate.

[0043] In one embodiment, the early warning module includes a first early warning unit, a second early warning unit, and a third early warning unit;

[0044] The first early warning unit is used to determine the fourth product of the water and pressure transmission anomaly degree and the stress transmission anomaly degree in each of the hidden risk areas, determine the fourth difference between the preset value and the degree of coordinated development of each of the hidden risk areas, and determine the deformation approximation degree of each of the hidden risk areas based on the fourth product and the fourth difference.

[0045] The second early warning unit is used to determine the overall slope time-series displacement data of the expansive red layer geological slope area, and to determine the time-series fitting sequence of deformation approximation degree based on the deformation approximation degree of each of the hidden risk areas.

[0046] The third early warning unit is used to determine, based on the overall slope time-series displacement data and the time-series fitting sequence, the Pearson correlation coefficient between the overall slope time-series displacement data and the time-series fitting sequence, the time of occurrence of the first peak in the overall slope time-series displacement data and the time-series fitting sequence, and the time of occurrence of the second peak in the time-series fitting sequence. If the Pearson correlation coefficient is greater than the correlation threshold and the time of occurrence of the second peak is earlier than the time of occurrence of the first peak, an early warning result is determined to trigger an early warning.

[0047] The present invention has the following beneficial effects:

[0048] By dividing the expansive red-bed geological slope area into several monitoring zones and determining the hydrological, strain, acoustic velocity, and environmental data for each zone within a specified time period, hidden risk areas are identified based on these data, taking into account factors hindering water infiltration and deformation transmission. The degree of synergistic development of acoustic strain in each hidden risk area is determined based on acoustic velocity and strain data, quantifying the synergistic effect of damage evolution and deformation accumulation. Furthermore, the correlation anomaly degree of water and force factors in each hidden risk area is determined based on acoustic velocity, strain, and hydrological data, analyzing anomalies in water infiltration and stress transmission. This multi-source data fusion and collaborative analysis facilitates a more accurate analysis of the deformation patterns of expansive red-bed geological slopes. Finally, the deformation approximation degree of each hidden risk area is determined based on the degree of synergistic development and correlation approximation degree, and the early warning result is determined based on this deformation approximation degree, improving the accuracy of the early warning results. Attached Figure Description

[0049] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a structural block diagram of an intelligent monitoring and early warning system for expansive red-bed geological slopes provided in one embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the expansive red-bed geological slope area monitored by this invention. Detailed Implementation

[0052] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an intelligent monitoring and early warning system for expansive red-bed geological slopes proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0054] It should be noted that "exemplary" in the embodiments of this application refers to examples listed for ease of explanation, and other embodiments are not limited to the listed examples.

[0055] The specific scheme of the intelligent monitoring and early warning system for expansive red-bed geological slopes provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0056] Please see Figure 1 This diagram illustrates the structural block diagram of an intelligent monitoring and early warning system for expansive red-bed geological slopes according to an embodiment of the present invention. The intelligent monitoring and early warning system for expansive red-bed geological slopes may include at least:

[0057] The segmentation module is used to divide the expansive red-bed geological slope area into several monitoring areas and determine the hydrological data, strain data, acoustic velocity data and environmental data of the monitoring areas within a specified time period.

[0058] The first determination module is used to determine the hidden risk area based on hydrological data, strain data and environmental data. The hidden risk area is the monitoring area where water infiltration and deformation transmission are obstructed.

[0059] The second determining module is used to determine the degree of coordinated development of acoustic strain in each hidden risk area based on acoustic velocity data and strain data, and to determine the degree of correlation transmission anomaly of water and force factors in each hidden risk area based on acoustic velocity data, strain data and hydrological data.

[0060] The early warning module is used to determine the degree of deformation approach for each hidden risk area based on the degree of coordinated development and the degree of related transmission anomalies, and to determine the early warning result based on the degree of deformation approach.

[0061] In this embodiment, based on the physical characteristics of expansive red-bed geological slopes that swell when exposed to water and shrink when dehydrated, as well as the cumulative deformation pattern under long-term wet-dry cycles and rainfall infiltration, a multi-dimensional (multi-source) data monitoring system is constructed. Around the core coupling relationship of "water-force-deformation-damage", an intelligent monitoring and early warning system for expansive red-bed geological slopes is designed.

[0062] The technical solution of this application divides the expansive red-bed geological slope area into several monitoring areas and determines the hydrological data, strain data, acoustic velocity data, and environmental data of the monitoring areas within a specified time period. Based on the hydrological data, strain data, and environmental data, hidden risk areas are identified, taking into account factors that hinder water infiltration and deformation transmission. Based on the acoustic velocity data and strain data, the degree of synergistic development of acoustic strain in each hidden risk area is determined, quantifying the synergistic effect of damage evolution and deformation accumulation. Based on the acoustic velocity data, strain data, and hydrological data, the correlation anomaly degree of water and force factors in each hidden risk area is determined, and the anomalies of water infiltration and stress transmission are analyzed, realizing multi-source data fusion and collaborative analysis, which is conducive to more accurate analysis of the deformation law of expansive red-bed geological slopes. Based on the degree of synergistic development and the correlation anomaly degree, the deformation approximation degree of each hidden risk area is determined, and the early warning result is determined based on the deformation approximation degree, improving the accuracy of the early warning result.

[0063] In one implementation, the monitored expansive red-bed geological slope area is as follows: Figure 2 As shown, the partitioning module divides the expansive red-bed geological slope area into several grids according to a preset grid size (e.g., 1m×1m in plane and 0.5m in depth), with each grid being a monitoring area. In this embodiment, the partitioning module is equipped with monitoring instruments, including but not limited to GNSS receivers, fiber optic strain sensors, moisture content sensors, strain sensors, environmental sensors, ultrasonic flaw detection sensors, etc.

[0064] It should be noted that the monitoring instruments can monitor relevant data within a specified time period (based on actual needs). These data collectively cover the entire process of water driving the expansion and contraction of the rock mass, the decay of mechanical strength, the accumulation of deformation, and the development of internal damage. The specified time period can include multiple cycles (the cycle length is adjustable, and the cycles of different types of monitoring instruments can be different). When deploying monitoring instruments, a certain number of monitoring areas can be selected for deployment, and other undeployed monitoring areas can be supplemented by interpolation, so that each monitoring area has corresponding and continuous data within the specified time period.

[0065] GNSS receivers are deployed at key points selected at the top and bottom of the slope to monitor overall displacement and acquire temporal displacement data of the entire slope. Fiber optic strain sensors are deployed at different depths to monitor local strain and acquire strain data of the deep and surface layers of the local rock mass. The strain data refers to the axial strain along the slope's sliding direction, specifically the axial strain values ​​collected by fiber optic strain sensors at different depths along the potential sliding direction (or main deformation direction) of the slope. This data characterizes the tensile or compressive deformation state of the rock mass at a specific depth.

[0066] Moisture content sensors and strain sensors are deployed simultaneously at each depth to monitor hydrological data, including the moisture content of the rock mass at different depths and the pore water pressure at the potential sliding surface. Environmental sensors monitor the environmental data of the slope, which may include rainfall and temperature and humidity. This may include rainfall and temperature and humidity predictions obtained from meteorological departments, either based on predictions of rainfall and temperature and humidity using predictive neural networks. Ultrasonic flaw detection sensors monitor slope damage and acquire sound wave velocity data of the rock mass, with one hour as a cycle, and one sound wave velocity data (sound wave propagation speed value) obtained in each cycle.

[0067] It is understandable that when dealing with various types of data acquired at different depths, subsequent fusion processing of multiple types will utilize data from the same depth for processing, and data from different depths will be processed multiple times based on data from the same depth.

[0068] In one implementation, the first determining module includes a first unit, a second unit, a third unit, and a fourth unit:

[0069] The first unit is used to determine the amount of rainwater infiltration and water evaporation based on the precipitation forecast data and the temperature and humidity forecast data, and to determine the predicted water content value of each monitoring area based on the first difference between the amount of rainwater infiltration and water evaporation and the water content.

[0070] Optionally, based on precipitation forecast data (e.g., precipitation forecast for the next 24 hours), and combined with the infiltration coefficient of the red bed rock mass (usually expressed as permeability coefficient), taking 0.7-0.9 for sandy red beds and 0.3-0.5 for clayey red beds, the rainwater infiltration recharge is calculated using existing methods; based on temperature and humidity forecast data (e.g., temperature and humidity forecast for the next 24 hours), the water evaporation is calculated using existing methods. Then, the water content is determined by adding the first difference between the rainwater infiltration recharge and the water evaporation to the water content.

[0071] The second unit is used to determine the moisture content gradient sequence and the corresponding deformation factor gradient sequence of two adjacent monitoring areas based on the moisture content and the predicted moisture content.

[0072] Optionally, after determining the predicted water content, combining it with the original water content can extend the continuous spatial distribution map of water content. Therefore, based on the existing water content gradient calculation method, the water content gradient sequence and the corresponding deformation factor gradient sequence of each pair of adjacent monitoring areas can be determined. It should be noted that a large water content gradient indicates that the monitoring area is prone to uneven expansion and contraction of the rock mass due to differences in water content, which is a potential source of hidden risks. In this embodiment, since the water content change of the red bed rock mass is related to the attenuation of cohesion and internal friction angle, the mapping relationship between water content gradient and cohesion, and between water content gradient and internal friction angle is established in advance through local rock mass mechanical strength derivation and indoor tests. The former is the horizontal axis and the latter is the vertical axis, obtaining a preset fitting curve. Based on the water content gradient of each pair of adjacent monitoring areas, the values ​​of cohesion and internal friction angle can be determined. Finally, based on the product of the determined cohesion and internal friction angle, the deformation factor gradient corresponding to each pair of adjacent monitoring areas can be determined, forming a deformation factor gradient sequence. The larger the gradient of the deformation factor, the greater the decrease in mechanical strength, the lower the regional anti-skid capacity, the higher the level of hidden risk, and the more likely the slope deformation will occur.

[0073] The third unit is used to determine the lag time for each monitoring area based on the moisture content gradient sequence and the deformation factor gradient sequence. Specifically:

[0074] First, the first peak point of the water content gradient sequence and the second peak point of the deformation factor gradient sequence are extracted respectively. The first peak point sequence is obtained by refitting based on the first peak point, and the second peak point sequence is obtained by refitting based on the second peak point.

[0075] Secondly, by using the cross-correlation function, the lag time of the first peak point sequence and the second peak point sequence in each monitoring area is calculated, that is, the time difference between the first peak point sequence and the second peak point sequence. For example, the time difference between each first peak point and the second peak point can be determined and then summed.

[0076] The fourth unit is used to identify monitoring areas with lag times greater than those of other monitoring areas within a preset range as hidden risk areas.

[0077] Optionally, the preset range can be set according to actual needs. For example, with a certain monitoring area as the center, the other monitoring areas within the preset range can be the remaining eight adjacent monitoring areas. Monitoring areas with a lag time greater than that of other monitoring areas within the preset range are designated as hidden risk areas. It should be noted that hidden risk areas are monitoring areas where water infiltration and deformation transmission are obstructed, indicating that there is hidden damage (such as microcracks) inside the rock mass in these hidden risk areas, and water infiltration and deformation transmission are obstructed, resulting in an accumulation of risk.

[0078] In one implementation, the second determining module includes a fifth unit, a sixth unit, and a seventh unit;

[0079] The fifth unit is used to determine the acoustic integrity attenuation rate for each hidden risk area based on acoustic velocity data. Specifically:

[0080] First, based on the acoustic velocity data of the first number of cycles, the temporal fluctuation of acoustic velocity in each hidden risk area is determined. The temporal fluctuation of acoustic velocity characterizes the degree of volatility of velocity over time.

[0081] For example, the first quantity is 10, one cycle is 1 hour, and 10 cycles can obtain 10 sound wave velocity data (sound wave propagation speed values). The sound wave velocity data mean is calculated using the sound wave velocity data of 10 cycles for each hidden risk area, and the absolute value of the deviation between the sound wave velocity data of each cycle and the sound wave velocity data mean is calculated. The absolute values ​​of the 10 deviations are summed and divided by the number of cycles, 10, to obtain the sound wave velocity temporal fluctuation of each hidden risk area.

[0082] Secondly, Fourier transform is performed on the sound wave velocity data of the first number of cycles to identify the dominant frequency component with the highest energy proportion, obtain the initial sound wave dominant frequency and the current sound wave dominant frequency corresponding to each hidden risk area, and determine the second difference between the initial sound wave dominant frequency and the current sound wave dominant frequency respectively.

[0083] Specifically, since the dominant frequency of intact rock mass is stable in the low-frequency range, after cracking, due to the increase in fracture interfaces, the dominant frequency will shift from the low-frequency range to the high-frequency range (the frequency ranges of the low-frequency and high-frequency ranges can be defined based on actual conditions). Therefore, Fourier transform is performed on the acoustic velocity data of 10 cycles to identify the dominant frequency component with the highest energy proportion, obtaining the initial acoustic dominant frequency and the current acoustic dominant frequency corresponding to each hidden risk area, and determining the second difference between the initial acoustic dominant frequency and the current acoustic dominant frequency. The initial acoustic dominant frequency can be the dominant frequency component that first appears in the low-frequency range, and the current acoustic dominant frequency can be the dominant frequency component of the last cycle.

[0084] Then, based on the ratio of the second difference to the initial dominant frequency of the acoustic wave and the temporal fluctuation of the acoustic velocity, the acoustic integrity attenuation rate of each hidden risk area is determined, comprehensively reflecting the degree of attenuation of acoustic characteristics with damage. The specific formula is as follows:

[0085] ;

[0086] in, This represents the acoustic wave integrity attenuation rate; a larger value indicates a greater degree of attenuation. The initial dominant frequency of the sound wave. The current dominant frequency of the sound wave. This represents the temporal fluctuation of the sound wave velocity.

[0087] The sixth unit is used to determine, based on strain data, the total cumulative strain in the first number of cycles, the strain increment in the decreasing strain increment segment and the strain increment in the increasing strain increment segment corresponding to several adjacent cycles, and to determine the strain plasticity ratio of each hidden risk area based on the total cumulative strain, the strain increment in the decreasing strain increment segment and the strain increment in the increasing strain increment segment.

[0088] It should be noted that, in order to ensure that the calculation results are meaningful, when performing fractional operations, if the denominator is 0, a parameter adjustment factor greater than 0 needs to be added to the denominator to prevent the denominator from being 0. The value of the parameter adjustment factor shall be set by the implementer according to the actual situation, and this application does not impose any special restrictions.

[0089] For example, the first quantity is 10. Based on the difference in strain data at adjacent times, the strain increment at adjacent times can be determined. Typically, the strain increment corresponding to tensile strain is positive, and the strain increment corresponding to compressive strain is negative, reflecting the cumulative degree of rock mass deformation. Therefore, based on the strain data, all strain increments in the 10 cycles can be added together to obtain the total cumulative strain in the 10 cycles. Furthermore, among several adjacent cycles (for example, 3 adjacent cycles), the adjacent cycles with increasing strain increments are denoted as the strain increment increase segment (also known as the stress loading stage), and the adjacent cycles with decreasing strain increments are denoted as the strain increment decrease segment (also known as the stress unloading stage). The strain rebound rate is determined based on the ratio of the strain increment in the strain increment decrease segment to the strain increment in the strain increment increase segment. Finally, the strain plasticity ratio of each hidden risk area is determined based on a formula. In the formula, the cumulative strain in the initial intact state for the same period is the cumulative strain recorded in the same period under the normal state of the expansive red-bed geological slope. The specific formula is as follows:

[0090] ;

[0091] in, This represents the proportion of strain to plasticity; a larger value indicates a greater influence from stress. For strain rebound rate, For the total amount of strain accumulated, This represents the cumulative strain during the same period in the initial, intact state.

[0092] The seventh unit is used to determine the degree of coordinated development of acoustic strain in each hidden risk area based on the acoustic integrity attenuation rate and the ratio of strain to plasticity. The degree of coordinated development characterizes the degree of synchronous aggravation of damage and plastic deformation. The larger the value of the degree of coordinated development, the more severe the damage and plastic deformation, the more obvious the synchronous aggravation, and the greater the intensity of synchronous aggravation.

[0093] Optionally, the acoustic integrity attenuation rate and strain-plastic ratio of each hidden risk area are normalized, and then the degree of synergistic development of acoustic strain (also known as coupling coordination degree) of each hidden risk area is calculated based on the following formula:

[0094] ;

[0095] in, To the degree of coordinated development, The sound wave integrity attenuation rate, For strain plasticity ratio, degree of coordinated development This reflects the degree of synergistic development between acoustic wave attenuation (damage) and plastic deformation; the degree of synergistic development. The larger the value, the more severe the damage and plastic deformation, and the more prominent the risk of latent cracking.

[0096] In one implementation, the second determining module includes an eighth unit, a ninth unit, a tenth unit, and an eleventh unit, wherein the correlation anomaly of water and force factors includes the anomaly of water and pressure transmission and the anomaly of stress transmission.

[0097] Unit 8 determines the spatial variation coefficient of water content in each hidden risk area, the rise time from the start of water content increase to the peak water content at different depths for the second number of cycles, and the deviation of rise time between different depths, based on the water content at different depths for the second number of cycles. It also determines the pressure rise rate gradient of each hidden risk area based on the pore water pressure for the second number of cycles. Furthermore, it determines the time difference between the occurrence of the peak water content and the occurrence of the peak pore water pressure in each hidden risk area based on the water content at different depths for the second number of cycles and the pore water pressure for the second number of cycles. The spatial variation coefficient of water content characterizes the uniformity of water distribution in the depth direction.

[0098] For example, the second quantity is 12, and the different depths include 2m, 4m, and 6m; based on the water content of three different depths in 12 cycles of each hidden risk area, the standard deviation and mean of the water content are determined, and then the standard deviation is divided by the mean to determine the spatial variation coefficient of water content corresponding to each hidden risk area, which reflects the uniformity of water distribution in the depth direction.

[0099] Then, based on the water content at three different (monitoring) depths for 12 cycles in each hidden risk area, the rise time from the start of the rise to the peak water content at the three different depths is determined, as well as the rise time deviation between different depths. For example, based on the difference in rise time between 2m and 4m, and between 4m and 6m, the rise time deviation between different depths is obtained, also known as the water content depth response difference, which reflects the difference in the infiltration rate of water at different depths.

[0100] Furthermore, based on the pore water pressure over 12 cycles, the pressure rise rate gradient for each hidden risk area is determined. For example, the pore water pressure rise rate is determined every three cycles, and then the rate difference between adjacent pore water pressure rise rates is taken as the pressure rise rate gradient, reflecting the acceleration / deceleration trend of pressure transmission.

[0101] Furthermore, based on the water content and pore water pressure at different depths during the 12 cycles, the time difference between the peak water content and the peak pore water pressure in each hidden risk area was determined, also known as the lag time. After cracking, leakage from the cracks will increase the lag time. It should be noted that, similarly, the time differences between the peak water content and the peak pore water pressure in each hidden risk area at depths of 2m, 4m, and 6m can be determined separately, and the largest time difference can be used as the final determined time difference.

[0102] Unit 9 is used to determine the transmission anomalies of water and pressure in each hidden risk area based on the spatial variability coefficient of water content, the deviation of rise time between different depths, the pressure rise rate gradient, and the time difference. Specifically, Unit 9 is used for:

[0103] First, based on the spatial variation coefficient of water content, the maximum rise time deviation (also known as the maximum water content depth response difference) among the rise time deviations between different depths, and the preset historical normal rise time (also known as the preset historical normal water content response difference, i.e., the rise time from the start of water content rise to the peak water content recorded in historical time measurements under normal conditions), the permeability uniformity index of each hidden risk area is determined. The specific formula is as follows:

[0104] ;

[0105] in, The permeability uniformity index, The spatial variation coefficient of moisture content. The difference in response at the maximum moisture content depth, The response was poor under pre-set historical moisture content; permeability uniformity index This is equivalent to quantifying the uniformity of water penetration; the smaller the value, the more abnormal the penetration path.

[0106] Secondly, the pressure transmission efficiency of each hidden risk area is determined based on the pressure rise rate gradient, the preset historical normal pressure rise rate gradient (i.e., the pressure rise rate gradient under normal conditions recorded in historical time), and the time difference. The specific formula is as follows:

[0107] ;

[0108] in, For pressure transmission efficiency, This represents the historical normal rate of pressure rise gradient. The pressure rise rate gradient; For time difference; pressure transmission efficiency This value reflects the decrease in pressure transmission efficiency; the smaller the value, the more significant the decrease in transmission efficiency.

[0109] Then, based on the permeability uniformity index and pressure transmission efficiency, the degree of anomaly in water and pressure transmission in each hidden risk area is determined.

[0110] For example, the preset value is 1, the first weight is 0.6, and the second weight is 0.4. The first and second weights can be set according to the water sensitivity of the red layer. The permeability uniformity index of each hidden risk area is determined by multiplying it by the first weight of 0.6, and the pressure transmission efficiency of each hidden risk area is determined by multiplying it by the second weight of 0.4. The sum of the first and second products is determined, and based on the third difference between the preset value and the sum, the degree of water and pressure transmission anomaly in each hidden risk area is determined. The specific formula for the degree of water and pressure transmission anomaly (also known as water-pressure transmission anomaly) is as follows:

[0111] ;

[0112] in, The degree of anomaly in the transmission of water and pressure. For pressure transmission efficiency The permeability uniformity index; the degree of anomaly in water and pressure transmission. The larger the value, the more significant the anomaly in water and pressure transmission due to the fissure.

[0113] The tenth unit is used to determine the effective stress and the average effective stress for each cycle based on the pore water pressure, strain data monitoring depth, and rock mass density of the monitoring area for the first number of cycles. The effective stress that is greater than the average effective stress by a first preset multiple is taken as the effective stress peak value. The average strain increment is determined based on the absolute value of the strain increment of the strain data for the first number of cycles. The number of strain increments whose absolute values ​​are greater than the average strain increment by a second preset multiple is taken as the local strain mutation rate. The stress-strain synchronization is determined based on the consistency of the signs of the strain increment and the effective stress increment for the first number of cycles.

[0114] For example, the first quantity is 10. Based on the pore water pressure, the monitoring depth h of the strain data and the rock density ρ of the monitoring area for 10 cycles, the effective stress for each cycle is determined as ρ×g×h - pore water pressure, where g=9.8N / kg. Then, based on the effective stress for each cycle, the average effective stress can be calculated.

[0115] Optionally, the effective stress exceeding a first preset multiple (e.g., 1.2 times) of the average effective stress is defined as the effective stress peak. The average strain increment is determined based on the absolute value of the strain increments over 10 cycles of strain data, and the number of strain increments whose absolute values ​​exceed a second preset multiple (e.g., 2 times) of the average strain increment is defined as the local strain mutation rate. It should be noted that the frequency of effective stress peak occurrences increases when cracking occurs.

[0116] In addition, stress-strain synchronicity is determined based on the sign consistency between the strain increment and the effective stress increment over 10 cycles. For example, if both the strain increment and the effective stress increment have signs of "+" or "-", then the sign consistency is 1; otherwise, the sign consistency is 0. The sum of the sign consistency over 10 cycles is determined, and the sum of the sign consistency is divided by the number of cycles, 10, to obtain the stress-strain synchronicity, which reflects the degree of synchronous response between the two. The smaller the synchronicity, the more abnormal the mechanical response.

[0117] The eleventh unit is used to determine the acoustic integrity attenuation rate of each hidden risk area based on a first number of acoustic velocity data, and to determine the stress transmission anomaly degree of each hidden risk area based on each acoustic integrity attenuation rate, the frequency of occurrence of effective stress peaks, the local strain mutation rate, and the stress-strain synchronicity.

[0118] Specifically, Unit 11 is used for:

[0119] First, the temporal variation range of effective stress in each hidden risk area is determined based on the difference between the maximum and minimum effective stress values ​​for the first number of cycles.

[0120] Optionally, the effective stress temporal variation amplitude of each hidden risk area is determined based on the difference between the maximum and minimum effective stress values ​​over 10 cycles.

[0121] Secondly, based on the frequency of occurrence of effective stress peaks, the preset historical effective stress peak frequency (i.e., the effective stress peak frequency under normal conditions in historical time), and the temporal variation amplitude of effective stress, the effective stress transmission disorder degree of each hidden risk area is determined, using the following formula:

[0122] ;

[0123] in, For effective stress transmission disorder, The frequency of occurrence of effective stress peak, To preset the frequency of historical effective stress peaks; Effective stress temporal variation amplitude; effective stress transmission disorder. It reflects the degree of disorder in stress transmission. The greater the disorder, the more uneven the stress distribution. norm represents linear normalization.

[0124] Furthermore, based on the stress-strain synchronicity and the local strain mutation rate, the strain response anomaly coefficient for each hidden risk region is determined, using the following formula:

[0125] ;

[0126] in, The strain response anomaly coefficient, For stress-strain synchronization, Local abrupt change rate of strain; strain response anomaly coefficient It is used to quantify the degree of deviation between strain response and stress change; the larger the coefficient, the more abnormal the strain response.

[0127] Then, the stress transmission anomaly degree of each hidden risk area is determined based on the third product of the strain response anomaly coefficient, the effective stress transmission disorder degree, and the acoustic integrity attenuation rate. (Also known as mechanical distortion risk), norm represents linear normalization.

[0128] Among them, the higher the acoustic integrity attenuation rate, the larger the correction coefficient of the rock mass elastic modulus, and the greater the final stress transmission anomaly, indicating that the hidden cracks have destroyed the mechanical transmission path and the greater the probability of mechanical distortion.

[0129] In one embodiment, the early warning module includes a first early warning unit, a second early warning unit, and a third early warning unit:

[0130] The first early warning unit is used to determine the fourth product of the water and pressure transmission anomaly degree and the stress transmission anomaly degree in each hidden risk area, respectively, to determine the fourth difference between the preset value and the degree of coordinated development of each hidden risk area, and to determine the deformation approximation degree of each hidden risk area based on the fourth product and the fourth difference.

[0131] For example, with a preset value of 1, before calculating the deformation approximation degree of each hidden risk area using the following formula, the maximum values ​​of the water and pressure transmission anomaly degree, stress transmission anomaly degree, and synergistic development degree can be normalized. Specifically, the formula for calculating the deformation approximation degree of each hidden risk area is as follows:

[0132] ;

[0133] in, The larger the value, the stronger and more unstable the interaction between water, pressure, and deformation, indicating that it is "approaching" a critical or unstable state. The degree of anomaly in the transmission of water and pressure. For stress transmission anomaly degree, To ensure the degree of coordinated development, when F is the same as the preset value, the denominator can be adjusted to 0.1 to ensure the result is meaningful. norm represents linear normalization.

[0134] The second early warning unit is used to determine the overall temporal displacement data of the expansive red-bed geological slope area, and to determine the temporal fitting sequence of the deformation approximation degree based on the deformation approximation degree of each hidden risk area.

[0135] Optionally, the second early warning unit can receive the overall slope time-series displacement data acquired by the GNSS receiver. Since the data used to calculate the degree of deformation approximation are time-series data within a specified time period, the degree of deformation approximation of each hidden risk area can be fitted in time (achieved through existing methods) to determine the time-series fitting sequence of the degree of deformation approximation.

[0136] The third early warning unit is used to determine the Pearson correlation coefficient between the overall slope time-series displacement data and the time-series fitted sequence, the time of occurrence of the first peak in the overall slope time-series displacement data, and the time of occurrence of the second peak in the time-series fitted sequence based on the overall slope time-series displacement data and the time-series fitted sequence. If the Pearson correlation coefficient is greater than the correlation threshold and the time of occurrence of the second peak is earlier than the time of occurrence of the first peak, the early warning result that triggers the early warning is determined.

[0137] Optionally, based on the overall slope time-series displacement data and the time-series fitted sequence, the Pearson correlation coefficient between the overall slope time-series displacement data and the time-series fitted sequence, the time of occurrence of the first peak in the overall slope time-series displacement data, and the time of occurrence of the second peak in the time-series fitted sequence are determined. If the Pearson correlation coefficient is greater than a correlation threshold, it indicates the existence of a locally high-risk hidden risk area. Then, the time of occurrence of the second peak is compared with the time of occurrence of the first peak to determine whether the locally high-risk hidden risk area is related to the occurrence of the overall slope displacement. For example, if the Pearson correlation coefficient is greater than the correlation threshold and the time of occurrence of the second peak is earlier than the time of occurrence of the first peak, it indicates that the locally hidden risk has a tendency to spread to the whole slope, and the entire slope should be included in the warning scope to determine the warning result that triggers the warning. It is understood that if the Pearson correlation coefficient is less than or equal to the correlation threshold, or if the time of occurrence of the second peak is later than the time of occurrence of the first peak, then the warning result that does not trigger the warning is determined.

[0138] This application's embodiments integrate displacement data, hydrological data, strain data, acoustic velocity data, and environmental data to construct a multi-dimensional monitoring system covering all elements of water, force, deformation, and damage. This overcomes the limitations of single-point sensor monitoring parameters, which make it difficult to achieve efficient fusion and collaborative analysis of multi-source monitoring data. Simultaneously, it proposes a method for identifying hidden risk areas where water infiltration and deformation transmission are obstructed, quantifying the degree of synergistic development of damage and plastic deformation. Through the analysis and evaluation of the anomalies in water and pressure transmission and stress transmission, it accurately captures anomalies in seepage paths and stress transmission distortions caused by crack propagation. Furthermore, the early warning system abandons the fixed threshold model and establishes a dynamic evaluation model based on the degree of deformation approximation and overall slope temporal displacement data. This reveals the spatiotemporal traction effect of local deformation on overall stability, forming an intelligent decision-making chain of hidden damage identification, multi-field coupled analysis, and risk cascade early warning, improving the ability to capture precursors of slope instability and the timeliness of early warnings under complex geological conditions.

[0139] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0140] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. An intelligent monitoring and early warning system for expansive red-bed geological slopes, characterized in that, include: The segmentation module is used to divide the expansive red-bed geological slope area into several monitoring areas and determine the hydrological data, strain data, acoustic velocity data and environmental data of the monitoring areas within a specified time period. Among them, the strain data is the axial strain along the slope sliding direction. The first determining module is used to determine the hidden risk area based on the hydrological data, the strain data and the environmental data. The hidden risk area is a monitoring area where water infiltration and deformation transmission are obstructed. The second determining module is used to determine the degree of coordinated development of acoustic strain in each of the hidden risk areas based on the acoustic velocity data and the strain data, and to determine the degree of correlation transmission anomaly of water and force factors in each of the hidden risk areas based on the acoustic velocity data, the strain data and the hydrological data. The early warning module is used to determine the degree of deformation approximation of each hidden risk area based on the degree of coordinated development and the degree of related transmission anomaly, and to determine the early warning result based on the degree of deformation approximation.

2. The intelligent monitoring and early warning system for expansive red-bed geological slopes according to claim 1, characterized in that: The first determining module includes a first unit, a second unit, a third unit, and a fourth unit. The environmental data includes precipitation prediction data and temperature and humidity prediction data. The hydrological data includes water content. The first unit is used to determine the rainwater infiltration replenishment and water evaporation based on the precipitation prediction data and the temperature and humidity prediction data, and to determine the water content prediction value of each monitoring area based on the first difference between the rainwater infiltration replenishment and the water evaporation and the water content. The second unit is used to determine the moisture content gradient sequence and the corresponding deformation factor gradient sequence of two adjacent monitoring areas based on the moisture content and the predicted moisture content value. The deformation factor is the product of cohesion and internal friction angle. The third unit is used to determine the lag time of each monitoring area based on the moisture content gradient sequence and the deformation factor gradient sequence. The fourth unit is used to designate monitoring areas with lag times greater than those of other monitoring areas within a preset range as hidden risk areas.

3. The intelligent monitoring and early warning system for expansive red-bed geological slopes according to claim 2, characterized in that: The third unit is specifically used for: Extract the first peak point of the moisture content gradient sequence and the second peak point of the deformation factor gradient sequence respectively; and refit the first peak point sequence based on the first peak point to obtain the second peak point sequence based on the second peak point. The lag time of the first peak point sequence and the second peak point sequence in each monitoring region is calculated using a cross-correlation function.

4. The intelligent monitoring and early warning system for expansive red-bed geological slopes according to claim 1, characterized in that: The second determining module includes a fifth unit, a sixth unit, and a seventh unit, and the specified time period includes several cycles; The fifth unit is used to determine the acoustic integrity attenuation rate of each of the hidden risk areas based on the acoustic velocity data. The sixth unit is used to determine, based on the strain data, the corresponding total strain accumulation, the strain increment of the strain increment decreasing segment and the strain increment increasing segment in a first number of cycles, and to determine the strain plasticity ratio of each of the hidden risk areas based on the total strain accumulation, the strain increment of the strain increment decreasing segment and the strain increment increasing segment. The seventh unit is used to determine the degree of synergistic development of acoustic strain in each of the hidden risk areas based on the acoustic integrity attenuation rate of each hidden risk area and the strain-plastic ratio, wherein the degree of synergistic development characterizes the degree of synchronous aggravation of damage and plastic deformation.

5. The intelligent monitoring and early warning system for expansive red-bed geological slopes according to claim 4, characterized in that: The fifth unit is specifically used for: Based on the acoustic velocity data of a first number of cycles, the temporal fluctuation of acoustic velocity in each of the hidden risk areas is determined, and the temporal fluctuation of acoustic velocity characterizes the degree of volatility of velocity over time. Perform a Fourier transform on the sound wave velocity data for a first number of cycles, identify the dominant frequency component with the highest energy proportion, obtain the initial dominant frequency and the current dominant frequency of the sound wave corresponding to each hidden risk area, and determine the second difference between the initial dominant frequency and the current dominant frequency of the sound wave respectively. The acoustic integrity attenuation rate of each hidden risk area is determined based on the ratio of the second difference to the initial acoustic wave dominant frequency and the acoustic wave velocity temporal fluctuation.

6. The intelligent monitoring and early warning system for expansive red-bed geological slopes according to claim 1, characterized in that: The second determining module includes units eight, nine, ten, and eleven. The specified time period includes several cycles. The hydrological data includes pore water pressure and water content at different depths. The correlation anomaly of water and force factors includes the anomaly of water and pressure transmission and the anomaly of stress transmission. The eighth unit is used to determine the spatial variation coefficient of water content in each of the hidden risk areas, the rise time from the start of water content increase to the peak water content at different depths for the second number of cycles, and the rise time deviation between different depths, respectively. It is also used to determine the pressure rise rate gradient of each of the hidden risk areas based on the pore water pressure for the second number of cycles, and to determine the time difference between the occurrence time of the peak water content and the occurrence time of the peak pore water pressure in each of the hidden risk areas based on the water content and pore water pressure at different depths for the second number of cycles. The spatial variation coefficient of moisture content characterizes the uniformity of moisture distribution in the depth direction; The ninth unit is used to determine the degree of transmission anomaly of water and pressure transmission in each of the hidden risk areas based on the spatial variation coefficient of water content, the rise time duration deviation between different depths, the pressure rise rate gradient, and the time difference. The tenth unit is used to determine the effective stress and the average effective stress for each cycle based on the pore water pressure of the first number of cycles, the monitoring depth of the strain data, and the rock density of the monitoring area. The effective stress that is greater than the average effective stress by a first preset multiple is taken as the effective stress peak value. The average strain increment is determined based on the absolute value of the strain increment of the strain data of the first number of cycles. The number of strain increments whose absolute values ​​are greater than the average strain increment by a second preset multiple is taken as the strain local mutation rate. The stress-strain synchronization is determined based on the consistency of the sign of the strain increment and the effective stress increment of the first number of cycles. The eleventh unit is used to determine the acoustic integrity attenuation rate of each of the hidden risk areas based on a first number of acoustic velocity data, and to determine the stress transmission anomaly degree of each of the hidden risk areas based on each of the acoustic integrity attenuation rates, the frequency of occurrence of the effective stress peak, the local strain mutation rate, and the stress-strain synchronicity.

7. The intelligent monitoring and early warning system for expansive red-bed geological slopes according to claim 6, characterized in that: The ninth unit is specifically used for: The permeability uniformity index of each hidden risk area is determined based on the spatial variation coefficient of water content, the maximum rise time deviation among the rise time deviations between different depths, and the preset historical normal rise time. The pressure transmission efficiency of each hidden risk area is determined based on the pressure rise rate gradient, the preset historical normal pressure rise rate gradient, and the time difference. The degree of water and pressure transmission anomaly in each of the hidden risk areas is determined based on the permeability uniformity index and the pressure transmission efficiency, respectively.

8. The intelligent monitoring and early warning system for expansive red-bed geological slopes according to claim 7, characterized in that: The determination of the degree of water and pressure transmission anomaly in each of the hidden risk areas based on the permeability uniformity index and the pressure transmission efficiency includes: The permeability uniformity index of each of the hidden risk areas is determined by a first product of a first weight, and the pressure transmission efficiency of each of the hidden risk areas is determined by a second product of a second weight. The sum of the first product and the second product is determined, and the degree of transmission anomaly of water and pressure transmission in each of the hidden risk areas is determined based on the third difference between the preset value and the sum.

9. The intelligent monitoring and early warning system for expansive red-bed geological slopes according to claim 6, characterized in that: The eleventh unit is specifically used for: The effective stress temporal variation amplitude of each hidden risk area is determined based on the difference between the maximum and minimum effective stress values ​​of the first number of cycles. The effective stress transmission disorder of each hidden risk area is determined based on the frequency of occurrence of the effective stress peak, the preset historical effective stress peak frequency, and the effective stress temporal variation amplitude. Based on the stress-strain synchronicity and the strain local abrupt change rate, the strain response anomaly coefficient of each of the hidden risk regions is determined. The stress transmission anomaly degree of each of the hidden risk areas is determined by the third product of the strain response anomaly coefficient, the effective stress transmission disorder degree, and the acoustic integrity attenuation rate.

10. The intelligent monitoring and early warning system for expansive red-bed geological slopes according to claim 6, characterized in that: The early warning module includes a first early warning unit, a second early warning unit, and a third early warning unit; The first early warning unit is used to determine the fourth product of the water and pressure transmission anomaly degree and the stress transmission anomaly degree in each of the hidden risk areas, determine the fourth difference between the preset value and the degree of coordinated development of each of the hidden risk areas, and determine the deformation approximation degree of each of the hidden risk areas based on the fourth product and the fourth difference. The second early warning unit is used to determine the overall slope time-series displacement data of the expansive red layer geological slope area, and to determine the time-series fitting sequence of deformation approximation degree based on the deformation approximation degree of each of the hidden risk areas. The third early warning unit is used to determine, based on the overall slope time-series displacement data and the time-series fitting sequence, the Pearson correlation coefficient between the overall slope time-series displacement data and the time-series fitting sequence, the time of occurrence of the first peak in the overall slope time-series displacement data and the time-series fitting sequence, and the time of occurrence of the second peak in the time-series fitting sequence. If the Pearson correlation coefficient is greater than the correlation threshold and the time of occurrence of the second peak is earlier than the time of occurrence of the first peak, an early warning result is determined to trigger an early warning.

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