Mountain road deep foundation pit and high slope multi-source heterogeneous data monitoring and early warning method

Abstract

The invention discloses a mountainous road deep foundation pit and high slope multi-source heterogeneous data monitoring and early warning method, and relates to the technical field of mountainous road construction, and the method comprises the steps: obtaining an electronic distribution diagram of a mountainous road deep foundation pit and high slope, dividing the electronic distribution diagram into a plurality of monitoring regions, collecting related geological data, meteorological environment data and structural data, and carrying out the early warning of the mountainous road deep foundation pit and high slope multi-source heterogeneous data. And establishing a corresponding data set. Then, a geological stability coefficient is calculated, the geological instability risk is evaluated, and if the geological instability risk exists, a first risk signal is sent out; after the first signal is received, the underground water inrush index is further calculated in combination with the meteorological environment data, and the permeation channel risk is evaluated. And then, a deep foundation pit collapse risk coefficient and a high slope inclination deformation risk coefficient are calculated, and an emergency strategy is generated according to a preset threshold value. And finally, constructing a risk dynamic early warning model by using a convolutional neural network, performing training and testing, and providing real-time risk prediction.

Description

Technical Field

[0001] The invention relates to the technical field of mountain highway construction, and in particular to a multi-source heterogeneous data monitoring and early warning method for deep foundation pits and high slopes of mountain highways. Background Art

[0002] With the acceleration of urbanization and the continuous development of infrastructure construction, the safety issues of mountain roads, tunnels, deep foundation pits, high slopes and other engineering projects have attracted more and more attention from all walks of life. Especially in complex terrain areas such as mountain roads, risk management of deep foundation pits and high slopes has become particularly important. These engineering constructions not only face uncertainties during the construction process, but are also affected by factors such as climate change, geological conditions and external environment. Especially under extreme weather conditions such as rainfall and temperature fluctuations, the stability of deep foundation pits and high slopes may be seriously threatened.

[0003] Traditional risk monitoring methods for deep foundation pits and high slopes usually focus on monitoring a single factor, such as soil pressure, water level changes, or slope displacement. However, these single monitoring data cannot comprehensively assess and predict the overall risk of deep foundation pits and high slopes, resulting in the possibility of missing important risk signals in complex environments. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a multi-source heterogeneous data monitoring and early warning method for deep foundation pits and high slopes of mountain roads to solve the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A multi-source heterogeneous data monitoring and early warning method for deep foundation pits and high slopes on mountain roads comprises the following steps:

[0006] Preferably, in step 1, an electronic distribution map of deep foundation pits and high slopes of mountain roads is obtained in advance, and the deep foundation pits and high slopes of mountain roads are divided into several monitoring areas, and real-time geological data of deep foundation pits and high slopes of mountain roads in the monitoring areas are collected, and a first data set is established; meteorological environment data of the monitoring areas are collected, and a second data set is established; structural data of deep foundation pits and high slopes of mountain roads in the monitoring areas are collected, and a third data set is established;

[0007] Step 2: Analyze the first data set, monitor the geological conditions of the deep foundation pit and high slope in the ith monitoring area, and calculate to obtain the geological stability coefficient DZxs of the ith monitoring area. i , pre-set the first standard threshold P and the geological stability coefficient DZxs of the i-th monitoring area i Conduct comparative analysis to assess the geological risk of the ith monitoring area and issue the first risk signal if there is a risk of geological instability;

[0008] Step 3: After receiving the first risk signal, the real-time rainfall Prain, real-time groundwater level Dxw, groundwater pressure T, groundwater flow Q, flow path length L, groundwater flow cross-sectional area Ad, head difference Δh and groundwater infiltration value Flz of the ith monitoring area are further monitored in combination with the data of the second data set, and the groundwater inrush index QXxs of the ith monitoring area is calculated. i , pre-set the second standard threshold Q, conduct comparative analysis to evaluate whether there is a permeable channel formed in the geological area in the monitoring area with geological instability risk, inducing the risk of water inrush. If there is a geological instability risk and a permeable channel is formed in the geological area, inducing the risk of water inrush, and issuing a second risk signal;

[0009] Step 4: After receiving the second risk signal, combine the geological stability coefficient DZxs of the i-th monitoring area i , groundwater inrush index QXxs in the ith monitoring area i and the data of the third data set, respectively calculate and obtain the deep foundation pit collapse risk factor Sfx of the i-th monitoring area i , and the tilt deformation risk factor Gfx of the high slope in the i-th monitoring area i , and by presetting the third standard threshold value Z1 and the fourth standard threshold value Z2, comparative analysis is performed to generate corresponding strategies;

[0010] Step 5. Use the convolutional neural network to build an initial convolutional neural network model, train and test the risk dynamic early warning model through the acquired feature information, use the trained risk dynamic early warning model as data for running prediction, and conduct real-time risk prediction of deep foundation pits and high slopes of mountain roads.

[0011] Preferably, step one comprises:

[0012] S11. Use GIS technology and related surveying and mapping data to obtain electronic distribution maps of deep foundation pits and high slopes on mountain roads;

[0013] The deep foundation pit and high slope area of ​​mountain highway are divided into several monitoring areas, and several monitoring areas are marked on the electronic distribution map of deep foundation pit and high slope of mountain highway as: Jcq1, Jcq2, ..., Jcq n ; n represents the number of monitoring areas;

[0014] S12, collect real-time geological data of deep foundation pits and high slopes of mountain roads in the monitoring area, and establish the first data set including: compressive strength σ of rock and soil layers in the monitoring area c, the hardness H of the rock and soil layer, the thickness d of the rock and soil layer and the water content w of the rock and soil layer; collect meteorological environmental data in the monitoring area, and establish a second data set including: real-time rainfall Prain in the monitoring area, real-time groundwater level Dxw in the monitoring area, groundwater pressure T and groundwater penetration value FLz; collect structural data of deep foundation pits and high slopes of mountain roads in the monitoring area, and establish a third data set including: bearing capacity value P of deep foundation pit support structure in the monitoring area support , Load value W of deep foundation pit buildings build , the area of ​​the deep foundation pit A, the shear strength of the high slope soil in the monitoring area σ soil , the height of the high slope Hg, the angle of the high slope θ, the load value of the high slope W load and the bottom area of ​​the high slope Ag.

[0015] Preferably, step 2 includes:

[0016] S21. Using the data from the first data set, monitor the geological conditions of the deep foundation pit height and slope. After dimensionless processing, calculate and obtain the geological stability coefficient DZxs of the i-th monitoring area. i , the formula is as follows:

[0017]

[0018] Where n i represents the number of rock and soil layers in the i-th monitoring area, σ cj,i represents the compressive strength of the jth rock and soil layer in the i-th monitoring area, σ max Indicates the maximum compressive strength of the rock and soil layer, H j,i represents the hardness of the j-th rock layer in the i-th monitoring area, d j,i represents the thickness of the jth rock and soil layer in the ith monitoring area, w j,i represents the moisture content of the soil in the jth rock and soil layer in the ith monitoring area, k1 represents the influence coefficient of soil moisture content, k2 represents the influence coefficient of soil pressure, and k1 and k2 are obtained through experimental data.

[0019] Preferably, step 2 further comprises:

[0020] S22, pre-set the first standard threshold value P, and set the geological stability coefficient DZxs of the i-th monitoring area i Comparing and analyzing with the first standard threshold value P to obtain a first evaluation result includes:

[0021] When the geological stability coefficient DZxs of the i-th monitoring area i When it is ≥ the first standard threshold value P, it means that there is no geological instability risk in the current monitoring area;

[0022] When the geological stability coefficient DZxs of the i-th monitoring areai When < the first standard threshold value P, it indicates that there is a risk of geological instability in the current monitoring area, triggering the first early warning instruction and generating the first risk signal.

[0023] Preferably, step three includes:

[0024] S31. After receiving the first risk signal, the precipitation, groundwater level, temperature and wind speed data of the ith monitoring area are combined with the data of the second data set. For areas with geological instability risks, further monitoring of meteorological environmental interference is performed. After dimensionless processing, the groundwater inrush index QXxs of the ith monitoring area is calculated. i , the formula is as follows:

[0025]

[0026] In the formula, Flz i Indicates the groundwater permeability value of the i-th monitoring area, K is the permeability coefficient, including: Sandy soil: K = 10 -4 ~10 -3 cm / s; Clay: K = 10 -8 ~10 -8 cm / s;

[0027] Q is the groundwater flow rate, L is the length of the water flow path, that is, the distance from the inflow point to the outflow point; Ad is the cross-sectional area of ​​the groundwater flow, which is measured by the vertical penetration test to measure the cross-sectional area of ​​the water flow in the medium, and Δh is the head difference;

[0028] n represents the number of monitoring areas, i represents the ith monitoring area, Prain i represents the real-time rainfall in the ith monitoring area, Prain max Indicates the maximum rainfall that the geology can withstand, Dxw i represents the real-time groundwater level value of the i-th monitoring area, Dxw max Indicates the highest groundwater level that the geology can bear, T i represents the groundwater pressure value of the ith monitoring area, T max Indicates the maximum groundwater pressure that the geology can withstand, Flz i represents the groundwater infiltration value of the ith monitoring area, Flz max It represents the maximum groundwater infiltration value that the geology can bear, and w1, w2, w3 and w4 represent weight coefficients.

[0029] Preferably, step three also includes:

[0030] S32, pre-set the second standard threshold K, and calculate the groundwater inrush index QXxs of the i-th monitoring area. iComparing and evaluating with the second standard threshold value K to obtain a second evaluation result, including:

[0031] When the groundwater inrush index QXxs in the i-th monitoring area i When it is less than or equal to the second standard threshold value K, it means that there is no risk of geological instability in the current monitoring area. There is no risk of formation of infiltration channels in the geological area, which may induce water inrush risk, and continuous monitoring is required;

[0032] When the groundwater inrush index QXxs in the i-th monitoring area i >the second standard threshold value K, it indicates that there is a risk of geological instability in the current monitoring area and there is a risk of formation of a seepage channel in the geological area, inducing a risk of water inrush, triggering the second early warning instruction and generating a second risk signal.

[0033] Preferably, step four includes:

[0034] S41, after receiving the second risk signal, combined with the geological stability coefficient DZxs of the i-th monitoring area i , groundwater inrush index QXxs in the ith monitoring area i The data of the third data set are dimensionless processed to calculate the deep foundation pit collapse risk factor Sfx of the i-th monitoring area. i , and the tilt deformation risk factor Gfx of the high slope in the i-th monitoring area i , the formula is as follows:

[0035]

[0036] Where n represents the number of monitoring areas, P supporti represents the bearing capacity of the deep foundation pit support structure in the ith monitoring area, DZxs i represents the geological stability coefficient of the ith monitoring area, QXxs i represents the groundwater inrush index of the ith monitoring area, PS loadi represents the load factor of the deep foundation pit in the ith monitoring area, W buildi represents the load value of the deep foundation pit building in the i-th monitoring area, A i represents the area of ​​the deep foundation pit in the ith monitoring area;

[0037]

[0038] In the formula, σ soili represents the shear strength of the high slope soil in the ith monitoring area, Hg i represents the height of the high slope in the ith monitoring area, θ i represents the angle of the high slope in the ith monitoring area, PG loadi represents the load factor of the high slope in the ith monitoring area, W loadirepresents the load value of the ith monitoring area and the high slope, Ag i Represents the bottom area of ​​the high slope in the i-th monitoring area.

[0039] Preferably, step 4 further comprises:

[0040] S42, pre-set the third standard threshold Z1, and calculate the deep foundation pit collapse risk coefficient Sfx of the i-th monitoring area. i A comparison and evaluation is performed with the third standard threshold value Z1 to obtain a third evaluation result, including:

[0041] When the deep foundation pit collapse risk factor Sfx in the i-th monitoring area i When the value is less than the third standard threshold Z1, it means that there is no risk of collapse of the deep foundation pit and continuous monitoring is required;

[0042] When the deep foundation pit collapse risk factor Sfx in the i-th monitoring area i When it is ≥ the third standard threshold Z1, it indicates that the deep foundation pit is at risk of collapse, triggering the third warning instruction and generating the first strategy, including: increasing the compressive strength of the soil around the foundation pit by 10% and the soil layer stability by 20% through deep mixing method; reinforcing the loose soil layer through grouting technology and increasing the soil bearing capacity by 30%; building temporary shielding facilities and setting up 2 drainage points to reduce the impact of precipitation on the foundation pit by 70%.

[0043] Preferably, step 4 further comprises:

[0044] S43, pre-set the fourth standard threshold Z2, and calculate the inclination deformation risk coefficient Gfx of the high slope in the i-th monitoring area. i A comparison and evaluation is performed with the fourth standard threshold value Z2 to obtain a fourth evaluation result, including:

[0045] When the high slope deformation risk factor Gfx in the i-th monitoring area i < the fourth standard threshold Z2, indicating that the high slope has no risk of tilt deformation and continuous monitoring is required;

[0046] When the high slope deformation risk factor Gfx in the i-th monitoring area i When it is ≥ the fourth standard threshold value Z2, it indicates that the high slope is at risk of tilting and deformation, triggering the fourth warning instruction and generating the second strategy, including: reinforcing and improving the bearing capacity of the deep foundation pit retaining wall by 20%, increasing the anchor density by 25% and improving the strength of the reinforced concrete retaining wall by 10%; reducing water accumulation by 40% and reducing the water penetration pressure on the soil by 25% by setting 2 drainage points; strengthening the bearing capacity of the loose soil through shotcrete technology and increasing the tensile strength of the soil by 35%.

[0047] Preferably, step five includes:

[0048] S51. Use the convolutional neural network to build the initial model of the convolutional neural network, and use the deep foundation pit collapse risk coefficient Sfx of the i-th monitoring area i and the tilt deformation risk factor Gfx of the high slope in the i-th monitoring area i , train and test the initial model of the convolutional neural network, and use the trained initial model of the convolutional neural network as a risk dynamic early warning model, while using the deep foundation pit collapse risk factor Sfx of the i-th monitoring area i and the tilt deformation risk factor Gfx of the high slope in the i-th monitoring area i The output of the middle layer is used as a feature vector to identify feature information, and the risk dynamic early warning model is trained and tested through the acquired feature information. The trained risk dynamic early warning model is used as data for running prediction to perform real-time risk prediction of deep foundation pits and high slopes of mountain roads.

[0049] The present invention provides a multi-source heterogeneous data monitoring and early warning method for deep foundation pits and high slopes on mountain roads. It has the following beneficial effects:

[0050] (1) This method of monitoring and early warning of deep foundation pits and high slopes on mountain roads with multi-source heterogeneous data, using multi-source heterogeneous data including geological data, meteorological environment data and structural data, comprehensively evaluates the multiple risks of deep foundation pits and high slopes on mountain roads, and can accurately judge the risk level of different monitoring areas, thereby achieving more efficient and accurate early warning and avoiding missing potential safety hazards.

[0051] (2) The multi-source heterogeneous data monitoring and early warning method for deep foundation pits and high slopes of mountain roads dynamically adjusts risk assessment parameters according to different geological environments and meteorological conditions. It combines multiple factors such as precipitation, groundwater pressure, and soil moisture content to calculate key indicators such as groundwater inrush index and geological stability coefficient, thereby comprehensively assessing the risks of high slopes and deep foundation pits. Especially in extreme weather or complex geological conditions, it can timely identify potential water inrush risks or geological instability risks and take preventive measures in advance.

[0052] (3) The multi-source heterogeneous data monitoring and early warning method for deep foundation pits and high slopes on mountain roads comprehensively analyzes the real-time data in the monitoring area, calculates the risk coefficient of deep foundation pit collapse and the risk coefficient of high slope tilt deformation, analyzes and evaluates them, and automatically generates targeted early warning strategies. When the deep foundation pit is at risk of collapse, the system can automatically recommend improving the stability of the foundation pit through deep mixing, grouting reinforcement technology, and temporary shielding facilities, and can give specific reinforcement values. This intelligent strategy generation not only improves the efficiency of the implementation of protective measures, but also allows flexible adjustments based on actual conditions.

[0053] (4) The multi-source heterogeneous data monitoring and early warning method for deep foundation pits and high slopes on mountain roads can dynamically monitor and predict risks in real time by establishing a convolutional neural network model. The early warning model is continuously optimized by combining real-time acquisition of feature information and training, improving prediction accuracy, reducing reliance on manual intervention, and ensuring automation and efficiency of risk management. Through systematic risk assessment and early warning measures, the safety management level of the project is improved, and data support and reference basis are provided for subsequent project maintenance and upgrades, which has strong sustainability. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of the steps of the multi-source heterogeneous data monitoring and early warning method for deep foundation pits and high slopes on mountain roads of the present invention.

[0055] Figure 2 A block diagram of a computing device capable of implementing various embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] Example 1

[0058] See also Figure 1 The present invention provides a method for monitoring and early warning of multi-source heterogeneous data of deep foundation pits and high slopes on mountain roads, comprising the following steps:

[0059] Step 1: Obtain the electronic distribution map of deep foundation pits and high slopes of mountain roads in advance, divide the deep foundation pits and high slopes of mountain roads into several monitoring areas, collect real-time geological data of deep foundation pits and high slopes of mountain roads in the monitoring areas, and establish a first data set; collect meteorological environment data of the monitoring areas, and establish a second data set; collect structural data of deep foundation pits and high slopes of mountain roads in the monitoring areas, and establish a third data set;

[0060] Step 2: Analyze the first data set, monitor the geological conditions of the deep foundation pit and high slope in the ith monitoring area, and calculate to obtain the geological stability coefficient DZxs of the ith monitoring area. i , pre-set the first standard threshold P and the geological stability coefficient DZxs of the i-th monitoring area i Conduct comparative analysis to assess the geological risk of the ith monitoring area and issue the first risk signal if there is a risk of geological instability;

[0061] Step 3: After receiving the first risk signal, the real-time rainfall Prain, real-time groundwater level Dxw, groundwater pressure T, groundwater flow Q, flow path length L, groundwater flow cross-sectional area Ad, head difference Δh and groundwater infiltration value Flz of the ith monitoring area are further monitored in combination with the data of the second data set, and the groundwater inrush index QXxs of the ith monitoring area is calculated. i , pre-set the second standard threshold Q, conduct comparative analysis to evaluate whether there is a permeable channel formed in the geological area in the monitoring area with geological instability risk, inducing the risk of water inrush. If there is a geological instability risk and a permeable channel is formed in the geological area, inducing the risk of water inrush, and issuing a second risk signal;

[0062] Step 4: After receiving the second risk signal, combine the geological stability coefficient DZxs of the i-th monitoring area i , groundwater inrush index QXxs in the ith monitoring area i and the data of the third data set, respectively calculate and obtain the deep foundation pit collapse risk factor Sfx of the i-th monitoring area i , and the tilt deformation risk factor Gfx of the high slope in the i-th monitoring area i , and by presetting the third standard threshold value Z1 and the fourth standard threshold value Z2, comparative analysis is performed to generate corresponding strategies;

[0063] Step 5. Use the convolutional neural network to build an initial convolutional neural network model, train and test the risk dynamic early warning model through the acquired feature information, use the trained risk dynamic early warning model as data for running prediction, and conduct real-time risk prediction of deep foundation pits and high slopes of mountain roads.

[0064] In this embodiment, through geological data, meteorological environment data and structural data, a comprehensive assessment of the multiple risks of deep foundation pits and high slopes of mountain roads is carried out, which can accurately judge the risk level of different monitoring areas, thereby achieving more efficient and accurate early warning and avoiding missing potential safety hazards. According to different geological environments and meteorological conditions, the risk assessment parameters are dynamically adjusted. Combined with various factors such as precipitation, groundwater pressure and soil moisture content, key indicators such as groundwater inrush index and geological stability coefficient are calculated to comprehensively assess the risks of high slopes and deep foundation pits. The collapse risk coefficient of the deep foundation pit and the tilt deformation risk coefficient of the high slope are calculated separately, and analyzed and evaluated to automatically generate targeted early warning strategies. When the deep foundation pit is at risk of collapse, the system can automatically suggest improving the stability of the foundation pit through deep mixing method, grouting reinforcement technology and temporary shielding facilities, and can give specific reinforcement values. A convolutional neural network model is established to dynamically monitor and predict risks in real time. Combined with real-time acquisition of feature information and training, the early warning model is continuously optimized to ensure that timely alarms can be issued and effective response strategies can be provided when sudden risk events occur, thereby reducing the occurrence and losses of disasters.

[0065] Example 2

[0066] This example is an explanation of Example 1. Figure 1 ,Specifically, step one includes:

[0067] S11. Use GIS technology and related surveying and mapping data to obtain electronic distribution maps of deep foundation pits and high slopes on mountain roads;

[0068] The deep foundation pit and high slope area of ​​mountain highway are divided into several monitoring areas, and several monitoring areas are marked on the electronic distribution map of deep foundation pit and high slope of mountain highway as: Jcq1, Jcq2, ..., Jcq n ; n represents the number of monitoring areas;

[0069] S12, collecting real-time geological data of deep foundation pits and high slopes of mountain roads in the monitoring area, and establishing the first data set, including: installing a soil mechanics tester in the rock and soil layer in the monitoring area to monitor the compressive strength σ cand the hardness H of the rock and soil layer, measure the thickness d of the rock and soil layer by installing an electromagnetic wave radar, and measure the moisture content w of the rock and soil layer by installing a soil moisture tester; collect meteorological environmental data in the monitoring area, and establish a second data set including: monitor the rainfall Prain by installing a tipping bucket rain gauge in the monitoring area, monitor the real-time groundwater level value Dxw, groundwater flow value Q, water flow path length L, groundwater flow cross-sectional area Ad, head difference Δh, groundwater pressure T value and groundwater penetration value FLz by installing a groundwater pressure sensor; collect structural data of deep foundation pits and high slopes of mountain roads in the monitoring area, and establish a third data set including: monitor the bearing capacity value P of the deep foundation pit support structure by installing a load sensor in the deep foundation pit in the monitoring area support and the load value W of the deep foundation pit building build The area A of the deep foundation pit is collected through the electronic distribution map, and the shear strength σ of the high slope soil is monitored by installing a shear strength measuring instrument on the high slope. soil , through the electronic distribution map collection, the height Hg of the high slope, the angle θ of the high slope and the bottom area Ag of the high slope, and by installing a load sensor at the bottom of the high slope, the load value W of the high slope is monitored load .

[0070] In this embodiment, by using GIS geographic information system technology to construct an electronic distribution map of deep foundation pits and high slopes of mountain roads, accurate division of complex terrain can be achieved. Dividing the monitoring area into multiple specific areas can make the risk data of each monitoring area more refined and independent, avoiding the problem of inaccurate or missed monitoring data due to the wide range of areas in traditional monitoring methods, thereby improving the overall monitoring efficiency and accuracy. This method not only collects geological data on the compressive strength of rock and soil layers, soil hardness, thickness and water content, but also combines meteorological data on rainfall, groundwater pressure, permeability and structural data on the bearing capacity and load value of supporting structures. This multi-dimensional data collection can fully understand the environment, structure and meteorological conditions of deep foundation pits and high slopes of mountain roads, provide more accurate input for subsequent risk assessment, and effectively improve the comprehensiveness of monitoring and the integrity of data.

[0071] Example 3

[0072] This example is explained in Example 2. Figure 1 ,Specifically, step 2 includes:

[0073] S21. Using the data from the first data set, monitor the geological conditions of the deep foundation pit height and slope. After dimensionless processing, calculate and obtain the geological stability coefficient DZxs of the i-th monitoring area. i , the formula is as follows:

[0074]

[0075] Where n i represents the number of rock and soil layers in the i-th monitoring area, σ cj,i represents the compressive strength of the jth rock and soil layer in the i-th monitoring area, σ max Indicates the maximum compressive strength of the rock and soil layer, H j,i represents the hardness of the j-th rock layer in the i-th monitoring area, d j,i represents the thickness of the jth rock and soil layer in the ith monitoring area, w j,i It represents the moisture content of the jth rock and soil layer in the i-th monitoring area, k1 represents the influence coefficient of soil moisture content, k2 represents the influence coefficient of soil pressure, k1 and k2 are obtained through experimental data, and the commonly used values ​​are as follows:

[0076]

[0077]

[0078] In this embodiment, by comprehensively considering multiple geological indicators such as the compressive strength, hardness, thickness, and soil moisture content of the rock and soil layer, the calculated geological stability coefficient can fully reflect the geological stability of each monitoring area. This method can effectively identify potential risk areas and predict possible slope landslides or foundation pit collapses in advance. Accurately calculating the influence coefficients of soil moisture content and soil pressure based on experimental data can enable the monitoring system to take into account complex changes in soil conditions. The influence coefficients for different moisture content intervals and pressure ranges can ensure that the monitoring results under different environments are more accurate, providing a reliable scientific basis for engineering decision-making.

[0079] Example 4

[0080] This example is an explanation of Example 1. Figure 1 Specifically, step 2 also includes:

[0081] S22, pre-set the first standard threshold value P, and set the geological stability coefficient DZxs of the i-th monitoring area i Comparing and analyzing with the first standard threshold value P to obtain a first evaluation result includes:

[0082] When the geological stability coefficient DZxs of the i-th monitoring area i When it is ≥ the first standard threshold value P, it means that there is no geological instability risk in the current monitoring area;

[0083] When the geological stability coefficient DZxs of the i-th monitoring area i When < the first standard threshold value P, it indicates that there is a risk of geological instability in the current monitoring area, triggering the first early warning instruction and generating the first risk signal.

[0084] In this embodiment, based on the calculation of the geological stability coefficient and the comparative analysis with the pre-set threshold, the stability of the monitoring area can be evaluated, thereby providing scientific guidance for resource allocation and protective measures during the construction process. For areas with poor stability, early warning signals can be issued, and reinforcement support or precipitation measures can be increased to ensure the safety of personnel and equipment during the construction process.

[0085] Example 5

[0086] This example is an explanation of Example 1. Figure 1 ,Specifically, step three includes:

[0087] S31. After receiving the first risk signal, the precipitation, groundwater level, temperature and wind speed data of the ith monitoring area are combined with the data of the second data set. For areas with geological instability risks, further monitoring of meteorological environmental interference is performed. After dimensionless processing, the groundwater inrush index QXxs of the ith monitoring area is calculated. i , the formula is as follows:

[0088]

[0089] In the formula, Flz i Indicates the groundwater permeability value of the i-th monitoring area, K is the permeability coefficient, including: Sandy soil: K = 10 -4 ~10 -3 cm / s; Clay: K = 10 -8 ~10 -8 cm / s;

[0090] Q is the groundwater flow rate, L is the length of the water flow path, that is, the distance from the inflow point to the outflow point; Ad is the cross-sectional area of ​​the groundwater flow, which is measured by the vertical penetration test to measure the cross-sectional area of ​​the water flow in the medium, and Δh is the head difference;

[0091] n represents the number of monitoring areas, i represents the ith monitoring area, Prain i represents the real-time rainfall in the ith monitoring area, Prain max Indicates the maximum rainfall that the geology can withstand, Dxw i represents the real-time groundwater level value of the i-th monitoring area, Dxw max Indicates the highest groundwater level that the geology can bear, T i represents the groundwater pressure value of the ith monitoring area, T max Indicates the maximum groundwater pressure that the geology can withstand, Flz i represents the groundwater infiltration value of the ith monitoring area, Flz maxIt represents the maximum groundwater infiltration value that the geology can withstand, w1, w2, w3 and w4 represent weight coefficients, 0<w1<1, 0<w2<1, 0<w3<1 and 0<w4<1 and w1+w2+w3+w4=1.

[0092] In this embodiment, the real-time meteorological environment data can reflect the impact of the current meteorological environment on geological stability in real time. When it is found that abnormal weather causes the groundwater level to rise or the groundwater flow to increase, the groundwater inrush index is calculated, which can accurately reflect the impact of groundwater on the stability of deep foundation pits and high slope areas. The system can issue warnings in time and identify potential risk areas of geological instability in advance, so as to take necessary protective measures to avoid accidents.

[0093] Example 6

[0094] This example is an explanation of Example 1. Figure 1 Specifically, step three also includes:

[0095] S32, pre-set the second standard threshold K, and calculate the groundwater inrush index QXxs of the i-th monitoring area. i Comparing and evaluating with the second standard threshold value K to obtain a second evaluation result, including:

[0096] When the groundwater inrush index QXxs in the i-th monitoring area i When it is less than or equal to the second standard threshold value K, it means that there is no risk of geological instability in the current monitoring area. There is no risk of formation of infiltration channels in the geological area, which may induce water inrush risk, and continuous monitoring is required;

[0097] When the groundwater inrush index QXxs in the i-th monitoring area i >the second standard threshold value K, it indicates that there is a risk of geological instability in the current monitoring area and there is a risk of formation of a seepage channel in the geological area, inducing a risk of water inrush, triggering the second early warning instruction and generating a second risk signal.

[0098] In this embodiment, by comparing and analyzing the threshold value set in advance with the groundwater inrush index, specific risk values ​​can be provided to on-site engineers and decision makers, which can directly affect the formulation of emergency measures. When the groundwater pressure or permeability value reaches a critical value, an early warning signal is issued. Support structures can be reinforced, drainage facilities can be enhanced, or temporary plugging measures can be taken in a timely manner to prevent sudden groundwater flow from threatening foundation pits and slopes.

[0099] Example 7

[0100] This example is an explanation of Example 1. Figure 1 ,Specifically, step four includes:

[0101] S41, after receiving the second risk signal, combined with the geological stability coefficient DZxs of the i-th monitoring area i , groundwater inrush index QXxs in the ith monitoring area i The data of the third data set are dimensionless processed to calculate the deep foundation pit collapse risk factor Sfx of the i-th monitoring area. i , and the tilt deformation risk factor Gfx of the high slope in the i-th monitoring area i , the formula is as follows:

[0102]

[0103] In the formula, n represents the number of monitoring areas, represents the bearing capacity of the deep foundation pit support structure in the ith monitoring area, DZxs i represents the geological stability coefficient of the ith monitoring area, QXxs i represents the groundwater inrush index of the ith monitoring area, represents the load factor of the deep foundation pit in the ith monitoring area, represents the load value of the deep foundation pit building in the i-th monitoring area, A i represents the area of ​​the deep foundation pit in the ith monitoring area;

[0104]

[0105] In the formula, represents the shear strength of the high slope soil in the ith monitoring area, Hg i represents the height of the high slope in the ith monitoring area, θ i represents the angle of the high slope in the ith monitoring area, represents the load factor of the high slope in the ith monitoring area, represents the load value of the ith monitoring area and the high slope, Ag i Represents the bottom area of ​​the high slope in the i-th monitoring area.

[0106] In this embodiment, the real-time data of the monitoring area is comprehensively analyzed and dimensionlessly processed to calculate the risk coefficient of deep foundation pit collapse and the risk coefficient of inclination deformation of high slopes. This method is more comprehensive than traditional single factor analysis and can comprehensively evaluate potential risks under complex geological and environmental conditions, thereby improving the accuracy of risk prediction.

[0107] Example 8

[0108] This example is an explanation of Example 1. Figure 1 Specifically, step four also includes:

[0109] S42, pre-set the third standard threshold Z1, and calculate the deep foundation pit collapse risk coefficient Sfx of the i-th monitoring area. i A comparison and evaluation is performed with the third standard threshold value Z1 to obtain a third evaluation result, including:

[0110] When the deep foundation pit collapse risk factor Sfx in the i-th monitoring area i When the value is less than the third standard threshold Z1, it means that there is no risk of collapse of the deep foundation pit and continuous monitoring is required;

[0111] When the deep foundation pit collapse risk factor Sfx in the i-th monitoring area i When it is ≥ the third standard threshold Z1, it indicates that the deep foundation pit is at risk of collapse, triggering the third warning instruction and generating the first strategy, including: increasing the compressive strength of the soil around the foundation pit by 10% and the soil layer stability by 20% through deep mixing method; reinforcing the loose soil layer through grouting technology and increasing the soil bearing capacity by 30%; building temporary shielding facilities and setting up 2 drainage points to reduce the impact of precipitation on the foundation pit by 70%.

[0112] In this embodiment, by setting a standard threshold Z1 and comparing and evaluating it with the risk factor, the system can issue an early warning in time when the risk of a deep foundation pit exceeds the set threshold. This early warning mechanism has an efficient response capability, can identify potential risks in advance, avoid sudden disasters, and ensure the safety of construction personnel and projects. When the risk factor reaches the early warning standard, the system can automatically generate the first strategy and propose targeted reinforcement and protection measures, such as deep mixing method, grouting technology, and the construction of temporary shielding facilities. These measures help to directly improve the compressive strength, bearing capacity and stability of the soil, reduce the probability of foundation pit collapse, and effectively reduce safety hazards in construction.

[0113] Example 9

[0114] This example is an explanation of Example 1. Figure 1 Specifically, step four also includes:

[0115] S43, pre-set the fourth standard threshold Z2, and calculate the inclination deformation risk coefficient Gfx of the high slope in the i-th monitoring area. i A comparison and evaluation is performed with the fourth standard threshold value Z2 to obtain a fourth evaluation result, including:

[0116] When the high slope deformation risk factor Gfx in the i-th monitoring area i < the fourth standard threshold Z2, indicating that the high slope has no risk of tilt deformation and continuous monitoring is required;

[0117] When the high slope deformation risk factor Gfx in the i-th monitoring area iWhen it is ≥ the fourth standard threshold value Z2, it indicates that the high slope is at risk of tilting and deformation, triggering the fourth warning instruction and generating the second strategy, including: reinforcing and improving the bearing capacity of the deep foundation pit retaining wall by 20%, increasing the anchor density by 25% and improving the strength of the reinforced concrete retaining wall by 10%; reducing water accumulation by 40% and reducing the water penetration pressure on the soil by 25% by setting 2 drainage points; strengthening the bearing capacity of the loose soil through shotcrete technology and increasing the tensile strength of the soil by 35%.

[0118] In this embodiment, by presetting the standard threshold value Z2 and comparing it with the tilt deformation risk coefficient, the system can promptly identify the tilt deformation risk of high slopes. Once the risk coefficient exceeds the set threshold, the system will automatically trigger the early warning mechanism to effectively prevent potential landslides or tilt deformation accidents, and provide advance warnings for construction and the surrounding environment. When the risk coefficient exceeds the standard threshold, the system will generate a second strategy, including targeted protective measures such as strengthening the deep foundation pit support wall, increasing the density of anchor rods, and improving the strength of retaining walls. These measures can effectively enhance the compressive resistance of the soil, improve the bearing capacity of the support structure, and reduce the accumulation and seepage pressure of water, thereby reducing the possibility of slope tilt deformation.

[0119] Example 10

[0120] This example is an explanation of Example 1. Figure 1 ,Specifically, step five includes:

[0121] S51. Use the convolutional neural network to build the initial model of the convolutional neural network, and use the deep foundation pit collapse risk coefficient Sfx of the i-th monitoring area i and the tilt deformation risk factor Gfx of the high slope in the i-th monitoring area i , train and test the initial model of the convolutional neural network, and use the trained initial model of the convolutional neural network as a risk dynamic early warning model, while using the deep foundation pit collapse risk factor Sfx of the i-th monitoring area i and the tilt deformation risk factor Gfx of the high slope in the i-th monitoring area i The output of the middle layer is used as a feature vector to identify feature information, and the risk dynamic early warning model is trained and tested through the acquired feature information. The trained risk dynamic early warning model is used as data for running prediction to perform real-time risk prediction of deep foundation pits and high slopes of mountain roads.

[0122] In this embodiment, by establishing a convolutional neural network model, risks can be dynamically monitored and predicted in real time. Combined with real-time acquisition of feature information and training, the early warning model is continuously optimized to improve prediction accuracy, reduce reliance on manual intervention, and ensure the automation and efficiency of risk management. Through systematic risk assessment and early warning measures, the safety management level of engineering projects is improved, and data support and reference basis are provided for subsequent engineering maintenance and upgrades, which has strong sustainability.

[0123] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0124] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

[0125] Figure 2 2 is a schematic block diagram of an example device 200 that can be used to implement an embodiment of the present disclosure. The device 200 can be used to implement Figure 1 One or more operations in the method. As shown in the figure, the device 200 includes a central processing unit (CPU) 201, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory (ROM) 202 or computer program instructions loaded from a storage unit 208 to a random access memory (RAM) 203. In RAM 203, various programs and data required for the operation of the device 200 can also be stored. CPU 201, ROM 202 and RAM 203 are connected to each other via a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0126] A number of components in the device 200 are connected to the I / O interface 205, including: an input unit 206, such as a keyboard, a mouse, etc.; an output unit 207, such as various types of displays, speakers, etc.; a storage unit 208, such as a disk, an optical disk, etc.; and a communication unit 209, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 209 allows the device 200 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0127] The processing unit 201 executes the various methods and processes described above, such as Figure 1 For example, in some embodiments, Figure 1 The various operations in the above can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 208. In some embodiments, part or all of the computer program can be loaded and / or installed on device 400 via ROM 202 and / or communication unit 209. When the computer program is loaded into RAM 203 and executed by CPU 201, the above-described Figure 1 Alternatively, in other embodiments, the CPU 201 may be configured to perform the operations in any other appropriate manner (eg, by means of firmware). Figure 1 Each operation in .

[0128] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip systems (SOCs), load programmable logic devices (CPLDs), and the like.

[0129] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0130] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0131] In addition, although each operation is described in a specific order, this should be understood as requiring such operation to be performed in the specific order shown or in a sequential order, or requiring that all illustrated operations should be performed to obtain desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination mode.

[0132] Although the subject matter has been described in language specific to structural features and / or methodological logical actions, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely example forms of implementing the claims.

Claims

1. A multi-source heterogeneous data monitoring and early warning method for deep foundation pits and high slopes on mountain roads, characterized in that: The following steps are involved: Step 1: Obtain the electronic distribution map of deep foundation pits and high slopes of mountain roads in advance, divide the deep foundation pits and high slopes of mountain roads into several monitoring areas, collect real-time geological data of deep foundation pits and high slopes of mountain roads in the monitoring areas, and establish a first data set; collect meteorological environment data of the monitoring areas, and establish a second data set; collect structural data of deep foundation pits and high slopes of mountain roads in the monitoring areas, and establish a third data set; Step 2: Analyze the first data set, monitor the geological conditions of the deep foundation pit and high slope in the ith monitoring area, and calculate to obtain the geological stability coefficient DZxs of the ith monitoring area. i , pre-set the first standard threshold P and the geological stability coefficient DZxs of the i-th monitoring area i Conduct comparative analysis to assess the geological risk of the ith monitoring area and issue the first risk signal if there is a risk of geological instability; Step 3: After receiving the first risk signal, the real-time rainfall Prain, real-time groundwater level Dxw, groundwater pressure T, groundwater flow Q, flow path length L, groundwater flow cross-sectional area Ad, head difference Δh and groundwater infiltration value Flz of the ith monitoring area are further monitored in combination with the data of the second data set, and the groundwater inrush index QXxs of the ith monitoring area is calculated. i , pre-set the second standard threshold Q, conduct comparative analysis to evaluate whether there is a permeable channel formed in the geological area in the monitoring area with geological instability risk, inducing the risk of water inrush. If there is a geological instability risk and a permeable channel is formed in the geological area, inducing the risk of water inrush, and issuing a second risk signal; Step 4: After receiving the second risk signal, combine the geological stability coefficient DZxs of the i-th monitoring area i , groundwater inrush index QXxs in the ith monitoring area i and the data of the third data set, respectively calculate and obtain the deep foundation pit collapse risk factor Sfx of the i-th monitoring area i and the tilt deformation risk factor Gfx of the high slope in the i-th monitoring area i , and by presetting the third standard threshold value Z1 and the fourth standard threshold value Z2, comparative analysis is performed to generate corresponding strategies; Step 5. Use the convolutional neural network to build an initial convolutional neural network model, train and test the risk dynamic early warning model through the acquired feature information, use the trained risk dynamic early warning model as data for running prediction, and conduct real-time risk prediction of deep foundation pits and high slopes of mountain roads.

2. The method for monitoring and early warning of multi-source heterogeneous data of deep foundation pits and high slopes on mountain roads according to claim 1 is characterized in that: Step one includes: S11. Use GIS technology and related surveying and mapping data to obtain electronic distribution maps of deep foundation pits and high slopes on mountain roads; The deep foundation pit and high slope area of ​​mountain highway are divided into several monitoring areas, and several monitoring areas are marked on the electronic distribution map of deep foundation pit and high slope of mountain highway as: Jcq1, Jcq2, ..., Jcq n ; n represents the number of monitoring areas; S12, collect real-time geological data of deep foundation pits and high slopes of mountain roads in the monitoring area, and establish the first data set including: compressive strength σ of rock and soil layers in the monitoring area c , the hardness H of the rock and soil layer, the thickness d of the rock and soil layer and the water content w of the rock and soil layer; collect meteorological environmental data in the monitoring area, and establish a second data set including: real-time rainfall Prain in the monitoring area, real-time groundwater level Dxw in the monitoring area, groundwater pressure T and groundwater penetration value FLz; collect structural data of deep foundation pits and high slopes of mountain roads in the monitoring area, and establish a third data set including: bearing capacity value P of deep foundation pit support structure in the monitoring area support , Load value W of deep foundation pit buildings build , the area of ​​the deep foundation pit A, the shear strength of the high slope soil in the monitoring area σ soil , the height of the high slope Hg, the angle of the high slope θ, the load value of the high slope W load and the bottom area of ​​the high slope Ag.

3. The method for monitoring and early warning of multi-source heterogeneous data of deep foundation pits and high slopes on mountain roads according to claim 2 is characterized in that: Step 2 includes: S21. Using the data from the first data set, monitor the geological conditions of the deep foundation pit height and slope. After dimensionless processing, calculate and obtain the geological stability coefficient DZxs of the i-th monitoring area. i , the formula is as follows: Where n i represents the number of rock and soil layers in the i-th monitoring area, σ cj,i represents the compressive strength of the jth rock and soil layer in the i-th monitoring area, σ max Indicates the maximum compressive strength of the rock and soil layer, H j,i represents the hardness of the j-th rock layer in the i-th monitoring area, d j,i represents the thickness of the jth rock and soil layer in the ith monitoring area, w j,i represents the moisture content of the soil in the jth rock and soil layer in the ith monitoring area, k1 represents the influence coefficient of soil moisture content, k2 represents the influence coefficient of soil pressure, and k1 and k2 are obtained through experimental data.

4. The method for monitoring and early warning of multi-source heterogeneous data of deep foundation pits and high slopes on mountain roads according to claim 3 is characterized in that: Step 2 also includes: S22, pre-set the first standard threshold value P, and set the geological stability coefficient DZxs of the i-th monitoring area i Comparing and analyzing with the first standard threshold value P to obtain a first evaluation result includes: When the geological stability coefficient DZxs of the i-th monitoring area i When it is ≥ the first standard threshold value P, it means that there is no geological instability risk in the current monitoring area; When the geological stability coefficient DZxs of the i-th monitoring area i When < the first standard threshold value P, it indicates that there is a risk of geological instability in the current monitoring area, triggering the first early warning instruction and generating the first risk signal.

5. The method for monitoring and early warning of multi-source heterogeneous data of deep foundation pits and high slopes on mountain roads according to claim 4 is characterized in that: Step three includes: S31. After receiving the first risk signal, the precipitation, groundwater level, temperature and wind speed data of the ith monitoring area are combined with the data of the second data set. For areas with geological instability risks, further monitoring of meteorological environmental interference is performed. After dimensionless processing, the groundwater inrush index QXxs of the ith monitoring area is calculated. i , the formula is as follows: In the formula, Flz i Indicates the groundwater permeability value of the i-th monitoring area, K is the permeability coefficient, including: Sandy soil: K = 10 -4 ~10 -3 cm / s; Clay: K = 10 -8 ~10 -8 cm / s; Q is the groundwater flow rate, L is the length of the water flow path, that is, the distance from the inflow point to the outflow point; Ad is the cross-sectional area of ​​the groundwater flow, which is measured by the vertical penetration test to measure the cross-sectional area of ​​the water flow in the medium, and Δh is the head difference; n represents the number of monitoring areas, i represents the ith monitoring area, Prain i represents the real-time rainfall in the ith monitoring area, Prain max Indicates the maximum rainfall that the geology can withstand, Dxw i represents the real-time groundwater level value of the i-th monitoring area, Dxw max Indicates the highest groundwater level that the geology can bear, T i represents the groundwater pressure value of the ith monitoring area, T max Indicates the maximum groundwater pressure that the geology can withstand, Flz i represents the groundwater infiltration value of the ith monitoring area, Flz max It represents the maximum groundwater infiltration value that the geology can bear, and w1, w2, w3 and w4 represent weight coefficients.

6. The method for monitoring and early warning of multi-source heterogeneous data of deep foundation pits and high slopes on mountain roads according to claim 5 is characterized in that: Step three also includes: S32, pre-set the second standard threshold K, and calculate the groundwater inrush index QXxs of the i-th monitoring area. i Comparing and evaluating with the second standard threshold value K to obtain a second evaluation result, including: When the groundwater inrush index QXxs in the i-th monitoring area i When it is less than or equal to the second standard threshold value K, it means that there is no risk of geological instability in the current monitoring area. There is no risk of formation of infiltration channels in the geological area, which may induce water inrush risk, and continuous monitoring is required; When the groundwater inrush index QXxs in the i-th monitoring area i >the second standard threshold value K, it indicates that there is a risk of geological instability in the current monitoring area and there is a risk of formation of a seepage channel in the geological area, inducing a risk of water inrush, triggering the second early warning instruction and generating a second risk signal.

7. The method for monitoring and early warning of multi-source heterogeneous data of deep foundation pits and high slopes on mountain roads according to claim 6 is characterized in that: Step 4 includes: S41, after receiving the second risk signal, combined with the geological stability coefficient DZxs of the i-th monitoring area i , groundwater inrush index QXxs in the ith monitoring area i The data of the third data set are dimensionless processed to calculate the deep foundation pit collapse risk factor Sfx of the i-th monitoring area. i , and the tilt deformation risk factor Gfx of the high slope in the i-th monitoring area i , the formula is as follows: In the formula, n represents the number of monitoring areas, represents the bearing capacity of the deep foundation pit support structure in the ith monitoring area, DZxs i represents the geological stability coefficient of the ith monitoring area, QXxs i represents the groundwater inrush index of the ith monitoring area, represents the load factor of the deep foundation pit in the ith monitoring area, represents the load value of the deep foundation pit building in the i-th monitoring area, A i represents the area of ​​the deep foundation pit in the ith monitoring area; In the formula, represents the shear strength of the high slope soil in the ith monitoring area, Hg i represents the height of the high slope in the ith monitoring area, θ i represents the angle of the high slope in the ith monitoring area, represents the load factor of the high slope in the ith monitoring area, represents the load value of the ith monitoring area and the high slope, Ag i Represents the bottom area of ​​the high slope in the i-th monitoring area.

8. The method for monitoring and early warning of multi-source heterogeneous data of deep foundation pits and high slopes on mountain roads according to claim 7 is characterized in that: Step 4 also includes: S42, pre-set the third standard threshold Z1, and calculate the deep foundation pit collapse risk coefficient Sfx of the i-th monitoring area. i A comparison and evaluation is performed with the third standard threshold value Z1 to obtain a third evaluation result, including: When the deep foundation pit collapse risk factor Sfx in the i-th monitoring area i When the value is less than the third standard threshold value Z1, it indicates that there is no risk of collapse of the deep foundation pit and continuous monitoring is required; When the deep foundation pit collapse risk factor Sfx in the i-th monitoring area i When it is ≥ the third standard threshold Z1, it indicates that the deep foundation pit is at risk of collapse, triggering the third warning instruction and generating the first strategy, including: increasing the compressive strength of the soil around the foundation pit by 10% and the soil layer stability by 20% through deep mixing method; reinforcing the loose soil layer through grouting technology and increasing the soil bearing capacity by 30%; building temporary shielding facilities and setting up 2 drainage points to reduce the impact of precipitation on the foundation pit by 70%.

9. The method for monitoring and early warning of multi-source heterogeneous data of deep foundation pits and high slopes on mountain roads according to claim 8 is characterized in that: Step 4 also includes: S43, pre-set the fourth standard threshold Z2, and calculate the inclination deformation risk coefficient Gfx of the high slope in the i-th monitoring area. i A comparison and evaluation is performed with the fourth standard threshold value Z2 to obtain a fourth evaluation result, including: When the high slope deformation risk factor Gfx in the i-th monitoring area i < the fourth standard threshold Z2, indicating that the high slope has no risk of tilt deformation and continuous monitoring is required; When the high slope deformation risk factor Gfx in the i-th monitoring area i When it is ≥ the fourth standard threshold value Z2, it indicates that the high slope is at risk of tilting and deformation, triggering the fourth warning instruction and generating the second strategy, including: reinforcing and improving the bearing capacity of the deep foundation pit retaining wall by 20%, increasing the anchor density by 25% and improving the strength of the reinforced concrete retaining wall by 10%; reducing water accumulation by 40% and reducing the water penetration pressure on the soil by 25% by setting 2 drainage points; strengthening the bearing capacity of the loose soil through shotcrete technology and increasing the tensile strength of the soil by 35%.

10. The method for monitoring and early warning of multi-source heterogeneous data of deep foundation pits and high slopes on mountain roads according to claim 9 is characterized in that: Step five includes: S51. Use the convolutional neural network to build the initial model of the convolutional neural network, and use the deep foundation pit collapse risk coefficient Sfx of the i-th monitoring area i and the tilt deformation risk factor Gfx of the high slope in the i-th monitoring area i , train and test the initial model of the convolutional neural network, and use the trained initial model of the convolutional neural network as a risk dynamic early warning model, while using the deep foundation pit collapse risk factor Sfx of the i-th monitoring area i and the tilt deformation risk factor Gfx of the high slope in the i-th monitoring area i The output of the middle layer is used as a feature vector to identify feature information, and the risk dynamic early warning model is trained and tested through the acquired feature information. The trained risk dynamic early warning model is used as data for running prediction to perform real-time risk prediction of deep foundation pits and high slopes of mountain roads.

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